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All articles that needed to be used for reference
please if you have any question you may ask
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the topic is memory
References of six articles
Beaulieu-Prévost, D., & Zadra, A. (2015). When people remember dreams they never experienced: A study of the malleability of dream recall over time.
(1), 18-31. DOI: 10.1037/a0038788
Bowden, V. K., Visser, T. A. W., & Loft, S. (2017). Forgetting induced speeding: Can prospective memory failure account for drivers exceeding the speed limit?
Journal of Experimental Psychology: Applied
(2), 180–190. DOI:
Horgan, T., Stein, J. M., Southworth, J., & Swarbrick, M. (2012). Gender differences in memory for what others say about themselves and their family members.
Journal of Individual Differences
(3), 169-174. DOI: 10.1027/1614-0001/a000087
Hyman, I. E., Burland, N. K., Duskin, H. M., Cook, M. C., Roy, C. M., McGrath, J. C., & Roundhill, R. F. (2013). Going Gaga: Investigating, creating, and manipulating the song stuck in my head.
Applied Cognitive Psychology
, 204–215. DOI: 10.1002/acp.2897
Marsh, J. E., Patel, K., Labonté, K., Threadgold, E., Skelton, F. C., Fodarella, C., . . . Vachon, F. (2017). Chatting in the face of the eyewitness: The impact of extraneous cell-phone conversation on memory for a perpetrator.
Canadian Journal of Experimental Psychology, 71
(3), 183-190. DOI: 10.1037/cep000101
Summerfelt, H., Lippman, L., & Hyman, I. E. Jr., (2010). The effect of humor on memory Constrained by the pun.
Journal of General Psychology
I have aploaded 6 articles and one instruction file All articles that needed to be used for reference please if you have any question you may ask thank you
Christian 1 Final paper guidelines Spring 2019 Now that you have your six articles, it is time to write the final paper. The paper is to be three to five pages in length and is to cover the topic of memory – since that is what the six articles cover, right? The type of paper I want you to write is a research report. That is what the articles you have read are. They are meant to report information – they are not persuasive, informative, or opinion papers. You are presenting data found by research that has been completed on the topic. More specifically, you will be completing is the literature review found in the Introduction section of a paper (since you will not actually b e conducting independent research ). That stated, one way to approach the paper is like this: What do my articles have in common? Where are there differences in the ideas? How do they all relate to memory? What you should not do is write about e ach individual article and connect them with transitional phrases. That is basically what you did with the summaries . It is now time to integrate the topics. Focus on the topic of memory and show me how information from the articles tie together or how they disagree with each other. There are three components to the paper and they should be in this order: cover page, body of the paper, and references. Each one will have a specific set of instructions to go along with it. Remember, everything is to b e double spaced and in Times New Roman size 12 . That includes the header and cover page . All of the other requirements that I have listed on the previous assignments are also in place. Cover Page: Look over the cover page you did for assignment 6 and make any corrections necessary . Your cover page will include the following information: • In the header: your last name and the page number. The easiest way to do this in Word is to go to Insert Page Number and place it in the upper right hand corner; then type in your last name with a space between your name and the page number . DO NOT FORGET TO CHANGE THE FONT! • Centered on the page left to right and top to bottom : the title and name blocks. This is two separate blocks of information and sep arated by an extra double space. o The title is your chance to be a bit creative – you could just call it something like “Memory” (without the quotation marks), or you can jazz it up a bit. o The name block will include your name (first and last), your sect ion, Introduction to Psychology, and Spring 2019 . o The cover page is page 1. o Notice that there is no mention of your instructor’s name on the cover page. Body: this is where you write about what you learned. Since you have your personal information on t he cover page, you should start your writing on the top line of the page. You do not need to include the title for the paper or a name block here they are included on the cover page. Christian 2 You will need to cite the information as well – tell me where your id eas come from and give those authors their due. You should include all six of the articles, and if you have found others as well, feel free to use them. Again, write about the topic of memory, not about the individual articles. Combine the ideas. References: the References are on the final page and must be started on a new page. Most likely, you can include them on a single page. Look over the References page that you completed before and follow that sample. If you are still unsure of how to do it, come see me. General guidelines: Again, look over all of the prior assignments. Pay attention to details. Proof read and edit your assignment . You are not allowed to use quotes in your final paper. I want to see your writing. Finally, while you are proof -reading and editing, look for the things you know I’ll be looking for – second person voice, contractions, sentences beginning with a conjunction, stating that something has been proven, etc. If you have any combination of these things three (3) times on your final paper, I will quit reading at the third error a nd your final score will be a 20 out of 35 point s. This works out to a 59 % for the final paper and can seriously hurt your final portfolio score. And remember – your papers are to be YOUR work. If you have questions – come see me. Due Dates: For the MWF /MW classes, the paper is due May 8 th; for the TTh class , the paper is due May 7 th. For each day late, you will be docked 5 points. Christian 3 Final Paper and Portfolio Check Sheet — Before turning in your paper, check that you have: Check Item How it should be Font Everything in the document is Times New Roman Size 12 – including everything on the title page, the text, and the header Stapled Is everything you submit stapled? Name Is your name on everything being submitted – including your articles? Header Does your header include just your last name and the page number? Are the page numbers in sequence? NOTE: If you type your last name and 1 on the first page, that is how it will show up throughout. Instead – insert the page number and then, write your last name to the left side with a space between your name and the number Body The text body should be: Double spaced No extra double spacing between paragraphs Times New Roman size 12 After spacing = 0 Left justification First line of the paragraph indented one tab 3-5 pages of text 1 inch margins all around Citations Only cite the articles you have actually read – do not cite the authors within the text. Use the appropriate APA citation form Remember the rules for et al. (only with 3+ authors, what to do with 7+ authors, etc.) Use proper punctuation References The word References needs to be centered on the top line of the page The References page needs a header/page number Make sure to follow the APA format All items cited must be on the References page Folder Name/section on the front Includes all previous assignments, the two articles you found, and the final paper. Everything in the folder has your name/section included Numbers Numbers under 10 should be written as a word unless it is an age or has a decimal. Numbers over 10 should be written as a numeral. If the number is the first word of a sentence, it is always written as a word. Pages 54 -56 in Perrin cover this as well. Editing Make sure you do not have contractions, second person voice, or have started a sentence with a conjunction Look for misspellings and improper word use (to, two, too; whether, weather, etc.) No quotes – it should be all your own writing The introduction and conclusion are well written – remember, if you write a strong enough introduction, you won’t need to say “this paper is about…” If you write a strong enough conclusion, you do not need to tell me that it is the conclusion.
I have aploaded 6 articles and one instruction file All articles that needed to be used for reference please if you have any question you may ask thank you
Chatting in the Face of the Eyewitness: The Impact of Extraneous Cell-Phone Conversation on Memory for a Perpetrator John E. Marsh University of Central Lancashire and University of Gävle Krupali Patel University of Central Lancashire Katherine Labonté Université Laval Emma Threadgold University of Central Lancashire Faye C. Skelton Edinburgh Napier University Cristina Fodarella, Rachel Thorley, Kirsty L. Battersby, Charlie D. Frowd, and Linden J. Ball University of Central Lancashire François VachonUniversité Laval Cell-phone conversation is ubiquitous within public spaces. The current study investigates whether ignored cell-phone conversation impairs eyewitness memory for a perpetrator. Participants viewed a video of a staged crime in the presence of 1 side of a comprehensible cell-phone conversation (meaningful halfalogue), 2 sides of a comprehensible cell-phone conversation (meaningful dialogue), 1 side of an incomprehensible cell-phone conversation (meaningless halfalogue), or quiet. Between 24 and 28 hr later, participants freely described the perpetrator’s face, constructed a single composite image of the perpetrator from memory, and attempted to identify the perpetrator from a sequential lineup. Further, participants rated the likeness of the composites to the perpetrator. Face recall and lineup identification were impaired when participants witnessed the staged crime in the presence of a meaningful halfalogue compared to a meaningless halfalogue, meaningful dialogue, or quiet. Moreover, likeness ratings showed that the composites constructed after ignoring the meaningful halfalogue resembled the perpetrator less than did those constructed after experiencing quiet or ignoring a meaningless halfalogue or a meaningful dialogue. The unpredictability of the meaningful content of the halfalogue, rather than its acoustic unexpectedness, produces distraction. The results are novel in that they suggest that an everyday distraction, even when presented in a different modality to target information, can impair the long-term memory of an eyewitness. Keywords:distraction, cell phones, eyewitness memory, dialogue, halfalogue Personal accounts and perceptions of how an event under in- vestigation unfolds is a vital element in police investigations. Indeed, the apprehension ofcriminal suspects is often aided bydescriptions of crimes and their perpetrators (Cutler & Kovera, 2010). Accounts provided from eyewitness memory offer valuable informa- tion that can contribute to the arrest and conviction of offenders (Samaha, 2005), especially in cases wherein the “hard evidence” needed for a conviction is lacking (Ainsworth, 2002). Eyewitness memory is therefore a domain in which accuracy is crucial, and given its importance, investigations of the various factors that may moderate eyewitness error are vital. The auditory environment is just one component of a myriad of complex facets of information that one may experience when witnessing an event such as a crime. Little is known, however, about the influence of the auditory scene on what is perceived or encoded from complex visual scenes that one would experience when witnessing a crime. In this study we investigate the potential impact of extraneous cell-phone con- versations—an omnipresent facet of the auditory environment in public areas— on the capability of an eyewitness (a) to recall detailed and accurate information about a perpetrator’s face, (b) to construct a composite accurate likeness of that face, and (c) to identify the perpetrator from a sequential lineup of visually similar identities. This article was published Online First June 12, 2017. John E. Marsh, School of Psychology, University of Central Lancashire, and Department of Building, Energy and Environmental Engineering, University of Gävle; Krupali Patel, School of Psychology, University of Central Lan- cashire; Katherine Labonté, School of Psychology, Université Laval; Emma Threadgold, School of Psychology, University of Central Lancashire; Faye C. Skelton, School of Applied Sciences, Edinburgh Napier University; Cristina Fodarella, Rachel Thorley, Kirsty L. Battersby, Charlie D. Frowd, and Linden J. Ball, School of Psychology, University of Central Lancashire; François Vachon, School of Psychology, Université Laval. The research reported in this article was financially supported by British Academy Grant SG122309, awarded to John E. Marsh, Faye C. Skelton, and Charlie D. Frowd. Correspondence concerning this article should be addressed to John E. Marsh, School of Psychology, University of Central Lancashire, Darwin Building, Preston, Lancashire, United Kingdom PR1 2HE. E-mail: [email protected] Canadian Journal of Experimental Psychology / Revue canadienne de psychologie expérimentale © 2017 Canadian Psychological Association 2017, Vol. 71, No. 3, 183–1901196-1961/17/$12.00http://dx.doi.org/10.1037/cep0000101 183 Within modern society, engaging in cell-phone conversation is known to have adverse consequences on cognition, particularly in relation to driver accuracy (Strayer & Johnston, 2001) and pedes- trian behavior (Stavrinos, Byington, & Schwebel, 2011). For a passive bystander, others’ halfalogues (halves of conversations such as a cell-phone conversation whereby only one speaker can be heard) are rated as more noticeable and intrusive than are dialogues (i.e., complete conversations, as in when one can hear both sides of the conversation;Monk, Fellas, & Ley, 2004). Moreover, cognitive performance can be differentially affected by halfalogues and dialogues. For example,Emberson, Lupyan, Goldstein, and Spivey (2010; see alsoGalvan, Vessal, & Golley, 2013) found that ignoring a halfalogue compared with a dialogue produced disruption to performance on a visual monitoring (track- ing) task and a choice reaction task. Although the existing evi- dence has suggested that overhearing half of a cell-phone conver- sation is enough to reduce performance on a concurrent, attentionally demanding task, there has been no attempt to inves- tigate the potential impact of ignoring cell-phone conversations on the recall of complex visual information in more applied tasks such as following the witnessing of a (staged) crime. Typically, existing work on distraction via background sound has found impairment of short-term memory (STM) for sequences of visually presented items (e.g.,Hughes, Vachon, & Jones, 2005), but no study has shown impairment of long-term memory (LTM) when sound is presented during the encoding of visual material. Certainly, from what is known about auditory distraction, it should be the case that background sounds that cause attention to be withdrawn from the prevailing task will impair encoding of visual events and therefore the later ability to recall those events from LTM. One type of auditory distraction has been attributed to atten- tional diversion and occurs when the sound draws the attentional focus away from the prevailing mental activity (such as when an unexpected acoustic deviation is detected; e.g., the “m” in the irrelevant sequence “kkkkkkkmkk”;Hughes, Vachon, & Jones, 2007). Another type of auditory distraction is attributable to interference-by-process (Jones & Tremblay, 2000). Essentially, performance impairment ensues when there is a conflict between processes engaged to perform the focal task and processes applied involuntarily to the sound. According to the attentional diversion standpoint, overhearing half of a conversation during study could impair encoding and therefore later recall from LTM at test because attention is directed involuntarily toward the sound due to a “need-to-listen.” This need-to-listen is driven by the tendency to predict the semantic content of the inaudible half of the conversation (Monk et al., 2004;Norman & Bennett, 2014). Attentional diversion can also occur due to rudimentary processing of the acoustic features of the ignored speech (Hughes et al., 2007): The unexpected onset and offset of the voice within one side of a phone conversation could produce a violation of the expectancy of auditory events within the sound stream, causing a disengagement of attention away from the focal task and impoverished recall of visual events. This “atten- tional capture” produced by the unpredictable onsets and offsets of a cell-phone conversation would be synonymous with the finding that unexpected changes in the pattern of auditory stimulation (e.g., the “m” in the irrelevant sequence “kkkkkkkmkk”) impairs STM for a sequence of visually presented items (e.g.,Hughes et al., 2005,2007;Vachon, Hughes, & Jones, 2012). Therefore, both the need-to-listen and attentional capture accounts suggest that distraction is produced via attentional diversion. According to the interference-by-process view, only tasks that require retention of serial order information should be vulnerable to distraction via changing-state sound (i.e., sound sequences that demonstrate abrupt changes in their acoustic properties; e.g., “c t g u”;Beaman & Jones, 1997). However, in contrast to the distrac- tion produced by interference-by-process, attentional diversion effects occur regardless of the task processes involved (Hughes et al., 2007;Vachon, Labonté, & Marsh, 2016). Therefore, if a half-conversation produces an attentional diversion effect, then disruption should manifest in complex cognitive tasks regardless of whether it involves serial STM. Witnessing and remembering an event is an example of such a task: Witnesses encode complex visual and/or auditory information that must be maintained so that it may later be recalled. Any distraction during the event may prevent eyewitnesses from encoding details that would later help to retrieve information from LTM, impacting negatively on their memory for event and person details. The Current Study The current study’s primary aim was to determine whether a to-be-ignored halfalogue negatively impacts on the LTM of an eyewitness to a staged crime. Attention was manipulated during the encoding of the crime event. Participants witnessed a video of a staged crime, prior to which they were told to ignore one of the following that occurred during the video: a full conversation (meaningful dialogue), a cell-phone conversation (meaningful hal- falogue) in a language they spoke, a spectrally rotated cell-phone conversation (incomprehensible to the participant and hence a “meaningless halfalogue”), or no sound (quiet). Between 24 and 28 hr later, the same participants described the perpetrator’s face from the staged-crime video in as much detail as possible and constructed a computer-generated likeness of the perpetrator (a composite). Finally, the participants were presented with a sequen- tial lineup (cf.Steblay, Dysart, Fulero, & Lindsay, 2001) of nine static facial photographs that included the perpetrator and eight distractor faces that were similar to that of the perpetrator in overall visual appearance. For each facial photograph, the partic- ipants were required to rate on a scale of 1–7 how certain they were that the identity depicted was the person they witnessed in the staged-crime video they viewed the previous day. These tasks were selected due to their ready use within police investigation (Frowd et al., 2013). Following this initial wave of experimentation, a set of independent judges rated the similarity of composites generated in each of the conditions (meaningful dialogue, meaningful halfa- logue, meaningless halfalogue, and quiet) to the perpetrator. Given the demonstrable effect that unexpected auditory stimu- lation can have on simple attentional tasks (Emberson et al., 2010) regardless of the processes that underpin performance of the pri- mary task (Hughes et al., 2007), it was expected that ignoring a halfalogue would result in greater distraction than would ignoring a dialogue (and witnessing the staged crime in quiet; e.g., Emberson et al., 2010). Within this setting, distraction could manifest via recall of fewer correct facial details about the perpe- trator, impaired ability to identify the perpetrator from the sequen- tial lineup, and the production of composites that bear weak 184 MARSH ET AL. resemblance to the perpetrator. It is important to note that our inclusion of a meaningless halfalogue offered an opportunity to tease out whether any unique distraction produced by the halfa- logue could be attributable to a need-to-listen, whereby the seman- tic properties of the task-irrelevant speech draws attention from the primary task (Monk et al., 2004;Norman & Bennett, 2014), or to attentional capture, whereby an unexpected physical change in the auditory environment (such as the sudden onset of speech) is responsible for the withdrawal of attention from the focal task (e.g.,Hughes et al., 2005,2007). Method Participants Ninety-six students at the University of Central Lancashire (71 female) between the ages of 20 and 31 years (M 23.5,SD 3.21) took part in the main empirical study. Participants were recruited via opportunity sample. All participants spoke English as their first language and reported normal (or corrected-to-normal) vision and normal hearing. Twenty-four participants were allo- cated to each of the four sound conditions in the experiment. Nine participants did not return for the second part of the study and were replaced. A further 20 participants (14 female) between 21 and 37 years of age (M 25.9,SD 4.9) were recruited for the rating phase. Apparatus and Materials Four versions of the same video of a staged crime that differed only with regard to the auditory background were used. The versions consisted of the following: quiet, a meaningful halfalogue (one side of a cell-phone conversation between two female speak- ers presented in the participants’ native language), a meaningless halfalogue (the sound presented for the meaningless halfalogue but spectrally rotated to render it incomprehensible), and a meaningful dialogue (two sides of the same cell-phone conversation presented as meaningful halfalogue). The same cell-phone conversation was therefore used for both the meaningful halfalogue and the mean- ingful dialogue conditions, with the former being created by de- leting one of the speaker’s voices. In the halfalogue version, there were nine pauses that ranged between 1.4 and 7.7 s (M 3.14, SD 2.08). The video and the cell-phone conversation lasted for 1 minute, and the onset of this conversation coincided with the onset of the video. The video depicted a man in his early 20s entering a corner shop and attempting to steal money from an unoccupied cash register—which could not be forced open— before making good his escape with several packets of cigarettes. The topic of the phone conversation was based on a BBC news article about the nation’s favorite children’s book and was digitally recorded and sampled with a 16-bit resolution at a sampling rate of 44.1 kHz using a broadcast quality Dictaphone in an anechoic chamber. Halfalogues were created by silencing the voice of one of the speakers within the auditory file. The spectrally rotated halfa- logue was created by spectrally inverting the speech recording around 2 kHz (as inScott, Rosen, Beaman, Davis, & Wise, 2009). Spectrally rotating speech involves transforming the high- frequency energy into low-frequency energy and vice versa. Spec- trally rotated speech is almost identical to normal speech (Scott etal., 2009). For example, variations in sound pressure level (SPL) across time and the duration of pauses between words and sen- tences are fairly equal. However, rotated speech is meaningless because it is incomprehensible. The four versions of the same video (with different audio backgrounds) were created by embedding the audio onto the video using Windows Live Movie Maker (Microsoft Corporation, Red- mond, WA). Both normal speech and rotated speech were pre- sented over stereo headphones at approximately 69 dB (LAeq) as measured with an artificial ear. The computer program PRO-fit (Version 3.5; ABM Limited, Nottingham, UK) was used to generate the facial composites. PRO-fit is a feature-based system that involves presenting the witness with facial features (e.g., hair, eyes, nose, mouth) that match the face that the witness has previously described (for an overview, seeFrowd et al., 2014). This stage is described in more detail in the Procedure section. Procedure In the first session, participants viewed a staged-crime video in the context of one of the four sound conditions that they were randomly allocated to with equal sampling. They were seated at a distance of approximately 60 cm from the PC monitor in a testing cubicle and wore headphones. They were instructed to ignore any background sound, to know that they would not be asked anything about the sounds during the experiment, and to focus on studying the video. Participants were asked to return between 24 and 28 hr later, but the nature of the second visit was not revealed at this time. In the second session, we revealed that a composite of the perpetrator witnessed in the staged-crime video would be required. Participants were told that the goal of creating the composite was to produce an accurate portrayal of the perpetrator’s face so that another person could recognize the face as such. Participants were told that they would first describe the appearance of the face and then construct a composite of it. They were also told that there was no time limit to complete the face composite construction proce- dure (for existing articles explaining the detailed procedure for undertaking the face-recall interview and PRO-fit construction, see, e.g.,Frowd et al., 2013). In brief, participants were asked to think back to the time when the perpetrator had been seen, visu- alize the face, and then to try to recall as much detail about it as possible without guessing. The experimenter wrote down informa- tion that the participants recalled in relation to the face in this free-recall format. Participants were then informed that a compos- ite would be constructed of the face using PRO-fit. The experi- menter entered details from the face-recall phase into the descrip- tion details of PRO-fit. This generated the different features for the described face. If participants were not satisfied with a feature, then its size or location was adjusted or it was exchanged for another feature. Once participants reported that the best likeness had been achieved, the face was saved to a disk as the composite. Following completion of the composite, participants undertook the sequential lineup task. They were given a sequential presenta- tion of facial photographs of nine identities that comprised the target (perpetrator), eight of which were foils that resembled the target to some extent in overall appearance. Using a 7-point Likert scale ranging from 1 (guess)to7(certain), participants were asked 185 IMPACT OF CELL-PHONE CONVERSATION ON MEMORY to indicate the certainty with which they considered that each facial photograph was the same identity as the person they wit- nessed in the staged-crime video they had viewed. The order in which the facial photographs were presented was pseudorandom: Although the foils were presented in a random order for each participant, the target was presented in either Position 4 or Position 5 within the sequence. Participants were reminded that there was no time limit to complete the sequential lineup task. The time taken to complete the face composite construction and sequential lineup task varied between 25 and 45 min. Once all of the composites had been constructed, other partici- pants were asked to rate the likeness of each of the composites compared to a frontal shot of the target (perpetrator) using a 7-point Likert scale ranging from 1 (very-poor likeness)to7(very-good likeness). Participants provided ratings for 96 composites (the 24 composites generated from within each sound condition). Composites were presented individually, each one next to the photograph of the target on a page in an A4 booklet. The presentation order of the composites was random for each participant. Design The main empirical study (compared to the composite rating task) employed a between-subjects design whereby the indepen- dent variable was sound condition, with four levels: quiet, mean- ingless halfalogue, meaningful halfalogue, and meaningful dia- logue. For the face-recall part of the study (usually undertaken as part of a cognitive interview), the dependent variable was facial descriptor type, which had three levels: correct details, incorrect details, and subjective details; see further explanation later). For the sequential lineup component of the task, the independent variable was identity and had two levels: target (i.e., perpetrator) or foil, and the dependent variable was the confidence rating given to the target face and the mean rating given to the eight foils (col- lapsed). Finally, for the set of participants who independently rated the similarity of the composites to the target, the design was fully repeated measures, whereby the within-subject factor was sound condition (again quiet, meaningless halfalogue, meaningful halfa- logue, and meaningful dialogue) and the dependent variable was the similarity of each composite to the target rated on a scale of 1–7 (described earlier). Results Verbal Recall The quality of the face descriptions given by the participants within each sound condition was analyzed by two individuals. Following the procedure used byMeissner, Brigham, and Kelley (2001), a correct description was generated by the two raters for the perpetrator’s face, and a decision was reached between the two raters as to which details would be classed as correct. Details in the descriptions were coded as correct, incorrect, or subjective. Sub- jective details were those that could not be verified directly (e.g., inferences about personality, or similarity to a well-known celeb- rity or family member). Interrater agreement was high, Cohen’s (72) .87,p .001 (Cohen, 1988). Contradictory scorings were resolved through discussion. The mean number of correct and incorrect features listed per condition can be seen inFigure 1. Themean number of correct descriptors provided was lower in the meaningful halfalogue condition compared to the meaningless halfalogue, meaningful dialogue, and quiet conditions. No differ- ence between means was apparent for incorrect descriptors. Only five details were classified as subjective descriptors across all four conditions, and because of this, subjective descriptors were ex- cluded in the further analysis. A 4 (sound condition: meaningful dialogue vs. meaningful halfalogue vs. meaningless halfalogue vs. quiet) 2 (facial descriptor type: correct response vs. incorrect response) mixed factor analysis of variance (ANOVA) carried out on the mean number of face descriptors recalled revealed a main effect of facial descriptor type,F(1, 92) 47.70,MSE 6.61,p .001, with more correct than incorrect descriptors recalled ( p2 .34), but no main effect of sound condition,F(3, 92) 2.09,MSE 2.62,p .11, p2 .06. The Facial Descriptor Type Sound Condition interaction was significant,F(3, 92) 2.80,MSE 6.61,p .043, p2 .084. A simple-effects analysis (least significant difference [LSD]) revealed that correct facial de- scriptors were more frequent than incorrect facial descriptors for the quiet condition (p .001), meaningful dialogue condi- tion (p .001), and meaningless halfalogue condition (p .001) but not for the meaningful halfalogue condition (p .35). Moreover, correct descriptors were less frequent in the mean- ingful halfalogue condition compared with the quiet condition (p .004), meaningful dialogue condition (p .012), and meaningless halfalogue condition (p .005). There was no difference between the means for the quiet and meaningless halfalogue conditions (p .95), quiet and meaningful dialogue conditions (p .70), and meaningless halfalogue and mean- ingful dialogue conditions (p .75). Moreover, there was no difference between conditions with respect to the frequency of incorrect information provided (p .1 for all comparisons). Therefore, a to-be-ignored halfalogue, provided it is meaning- ful, presented during the witnessing of the staged-crime video diminished the quality of face description given the next day. Figure 1.Mean number of face descriptors recalled as a function of descriptor type and sound condition. Error bars represent the standard error of the mean. p .05. p .01. p .001. 186 MARSH ET AL. Sequential Lineup Task For the lineup task, the ratings reflecting the certainty that the identity was the same as the target in the video previously were addressed by comparing the mean rating given to the foil faces with the rating given to the target.Figure 2shows the mean certainty ratings for the foil identities (collapsed across identities) and the target for each of the four sound conditions. The confi- dence ratings were clearly greater for the target in the quiet, meaningful dialogue, and meaningless halfalogue conditions com- pared to the meaningful halfalogue condition. However, confi- dence ratings assigned to foil identities appears to differ little between conditions. A 4 (sound condition) 2 (identity: target or foil) mixed- factorial ANOVA performed on mean confidence ratings revealed a main effect of identity, with higher confidence ratings for the target than for foils,F(1, 92) 250.12,MSE 1.91,p .001, p2 .73, but no main effect of sound condition,F(3, 92) 1.90, MSE 1.70,p .14, p2 .06. However, there was a significant Sound Condition Identity interaction,F(3, 92) 3.50,MSE 1.91,p .019, p2 .10. A simple-effects analysis (LSD) revealed that the mean confidence rating given to the target was lower in the meaningful halfalogue condition compared to the quiet condition (p .010), the meaningful dialogue condition (p .042), and the meaningfulness halfalogue condition (p .019). There was no significant difference between the quiet and meaningful dialogue conditions (p .58), quiet and meaningless halfalogue conditions (p .81), or the meaningful dialogue condition and the meaning- less halfalogue conditions (p .75). Therefore, a meaningful to-be-ignored halfalogue presented concurrently with the mock- crime video reduced the confidence with which the target was chosen from a lineup the next day. Composite Likeness Ratings Figure 3shows the means for the likeness scores given by the raters for the 24 composites within each of the four sound condi- tions. The mean values indicate that the raters considered that the composites generated in the quiet, meaningful dialogue condition, and meaningless halfalogue conditions looked more similar to the target face than did the composites generated in the meaningful halfalogue condition. A one-way repeated-measures ANOVA demonstrated a signif- icant effect of sound condition on composite likeness,F(3, 57) 5.31,MSE .132,p .003, p2 .22. Planned repeated contrasts revealed that composites in the meaningful halfalogue condition bore less resemblance to the perpetrator than did those for the quiet condition (p .001), meaningless halfalogue condition (p .029), and meaningful dialogue condition (p .001). Additionally, those created in the meaningful dialogue condition were rated as better likenesses of the target face than were those created in the quiet condition (p .023; no other comparisons were significant). Therefore, a meaningful to-be-ignored halfalogue presented con- currently with the mock-crime video resulted in facial composites that were rated poorer likenesses to the target.Figure 4show examples of the male target constructed in each of the sound conditions. Discussion To summarize, ignoring half of a cell-phone conversation, pro- viding it is meaningful, was shown to impair the long-term mem- ory (LTM) of the participant eyewitnesses. That the accuracy of eyewitness LTM—as measured through recall of facial descrip- tors, identification from a lineup, and composite accuracy—is susceptible to disruption via the presence of intermittent conver- sational background speech is important to acknowledge given the prominent role that eyewitnesses play in many criminal cases. Composite images serve two purposes. On presentation within the media, they can generate leads from the general public to aid criminal investigations. They are also used as a reference from Figure 2.Mean confidence ratings as a function of sound condition in the context of the lineup task. These relate to whether the target or one of the foils was viewed earlier in the context of the mock-crime video. The mean ratings given to the eight foils are collapsed (1 guessand 7 certainthat the identity was seen earlier). Note therefore that a rating of 7 given to the target would essentially be a “hit,” whereas a rating of 1 given to the target would be a “miss.” Similarly, a rating of 1 given to a foil would be a “correct rejection,” whereas a rating of 7 to a foil would be a “false alarm.” Error bars represent the standard error of the mean. p .05. p .001. Figure 3.Mean likeness ratings awarded to the composites in the pres- ence of a photograph of the target as a function of sound condition (1 very poor likeness,7 very good likeness). Error bars represent the standard error of the mean. p .05. p .001. 187 IMPACT OF CELL-PHONE CONVERSATION ON MEMORY which criminal investigators can narrow likely suspects that may already be on file. Therefore, inaccuracies in eyewitnesses’ mem- ory—and subsequent composite quality— can potentially lead to false identifications (and arrests) and the pursuit of erroneous leads. It is emerging that extraneous background speech can impair face memory in several ways. One way, for example, is through disruption of subvocal verbalization. It has become reasonably well accepted that spontaneous verbal codes are created for faces (Schooler, 2002). Indirect evidence that participants verbally re- hearse descriptions of faces within STM, and that such rehearsal ordinarily facilitates face recognition performance, comes from studies preventing subvocal verbalization by the use of articulatory suppression, a technique that requires participants to utter some repeated sounds (e.g., “ba ba ba ba”). Articulatory suppression impairs face recognition (Nakabayashi & Burton, 2008, Experi- ment 1;Nakabayashi, Burton, Brandimonte, & Lloyd-Jones, 2012; Wickham & Swift, 2006), whereas manual tapping—assumed to be as attentionally demanding as articulatory suppression without preventing verbalization— does not (e.g.,Nakabayashi & Burton, 2008, Experiment 3;Wickham & Swift, 2006). Whereas articula- tory suppression potentially eliminates the use of subvocal re- hearsal, extraneous changing-state speech (sound sequences that are acoustically changing [e.g., “c t g u”] compared to unchanging, steady-state speech [e.g., “c c c c”]) disrupts subvocal rehearsal due to processing conflict (seeJones, Madden, & Miles, 1992). Consistent with the view that changing-state speech disrupts sub- vocal rehearsal and that subvocal rehearsal is used spontaneously to facilitate unfamiliar face learning,Marsh et al. (2017)have found that extraneous changing-state speech (randomly presented strings of letters), compared to steady-state speech (a string of the same letter repeated), presented during a 6-s exposure to a target face impairs recognition of that face from a lineup. However, that such interference is entirely independent of the semantic content of the speech suggests that the disruption is consistent with an interference-by-process view of distraction (Jones et al., 1992). Here, the preattentive processing of the serial order of changes within sound interferes with the similar, deliberate process of subvocally rehearsing information derived from the visual modal- ity in serial order. In the context of the current study, however, we favor an attentional diversion account (Hughes et al., 2007;Monk, Fellas, & Ley, 2004) over the disruption of subvocal rehearsal account for three reasons. First, participants did not know in advance that facerecall, composite construction, and lineup identification would be required subsequently. Therefore, the participants may not have rehearsed facial details explicitly. Second, perhaps counterintui- tively, the subvocal rehearsal process appears to utilize configural as opposed to featural information (Nakabayashi, Lloyd-Jones, Butcher, & Liu, 2012), which, according toSchooler (2002), involves information concerning the face’s global percept, includ- ing the spatial layout among its facial features. If disruption of subvocal rehearsal were the cause of face memory impairment, then it would appear quite counterintuitive that PRO-fit, a feature- based system (due to its requirement for recall of individual, isolated features and recognition of features in the context of the whole face) could capture the distraction effect. Third, since to- be-ignored meaningful dialogue speech—which presumably con- tains sufficient changing-state information to disrupt serial re- hearsal (Jones et al., 1992) and, in fact, more change than within halfalogues—failed to produce disruption, it is unlikely that the action of the meaningful to-be-ignored halfalogue speech is attrib- utable to the disruption of subvocal rehearsal. Moreover, in the context of attentional diversion accounts (e.g., Hughes et al., 2007;Monk et al., 2004) the results of the experi- ment were unequivocal in providing support for the need-to-listen account of the halfalogue effect (Monk et al., 2004;Norman & Bennett, 2014) over an attentional capture account (cf.Hughes et al., 2005,2007). The halfalogue effect appeared only when the background speech material was meaningful. Because both the meaningful and meaningless (rotated) halfalogue speech were equated in terms of their acoustic complexity and temporal unpre- dictability, that only the meaningful halfalogue produces impair- ment refutes the idea that the halfalogue produces disruption due to the acoustic unexpectedness (and hence attentional capture) attrib- utable to the physical characteristics of sound (cf.Hughes et al., 2005). That the halfalogue effect is dependent upon the presence of semantic properties within the sound demonstrates that it is a form of distraction that differs from that attributable to acoustic unex- pectedness (Hughes et al., 2005,2007;Vachon, Hughes, & Jones, 2012). In the context of the current study, it appears that the meaningful halfalogue produces attentional diversion whereby the need-to-listen engendered by the tendency to want to predict or complete the missing part of the conversation causes an impover- ished encoding of details about the perpetrator, thereby impairing face recall and recognition. Although the task of face description, face construction, and target identification from a lineup are usu- ally carried out in this sequence in the real world, it is possible that Figure 4.Examples of the male target constructed in the four conditions of the experiment (displayed are those composites that have received the highest ratings for each sound condition). For copyright reasons, we are unable to reproduce the target photograph or stills from the video used in the experiment. 188 MARSH ET AL. these tasks may influence each other. For example, describing the target could have influenced the composite construction, and the composite construction may have influenced target identification in the lineup. Therefore, impoverished memory for the target produced by the meaningful halfalogue could have knock on effects at several loci within the procedures undertaken with the eyewitness. Although it is perhaps intuitive that masking or otherwise in- terfering effects of additional environmental sounds such as voices may impede recognition and recall of a perpetrator’s voice (cf. Stevenage et al., 2013), it is perhaps less intuitive that stimulation from a specific modality (auditory) should impair processing of information that is derived from another modality (visual). How- ever, the present findings unequivocally demonstrate that cell- phone conversation (meaningful halfalogue) breaks through selec- tive attention and impairs LTM even if participants know that the sounds contain no information that is relevant to the prevailing task (cf.Marsh et al., 2015) and therefore should be ignored. To our knowledge the current results are novel in demonstrating that extraneous speech presented during encoding can produce adverse effects on LTM for complex visual information: the ap- pearance of a human face. Therefore, the findings illustrate the importance of considering the auditory environment when assess- ing the reliability of eyewitness memory. Moreover, these findings have implications far beyond the forensic context. Exposure to half of a conversation is a common occurrence that can impact nega- tively on one’s memory for complex visual information. Our results show that this irrelevant auditory information cannot sim- ply be ignored and as such has the potential to interfere with one’s processing of information in a wide range of daily activities. Résumé Les conversations par téléphone cellulaire sont omniprésentes dans les espaces publics. La présente étude examine si le fait d’ignorer une conversation par téléphone cellulaire altère la mémoire d’un témoin oculaire par rapport au visage d’un malfaiteur. Les participants ont visionné une mise en scène de crime, sur vidéo, où l’on y présentait un côté d’une conversation par téléphone cellulaire compréhensible (milogue sensé), les deux côtés d’une conversation par téléphone cellulaire compréhensible (dialogue sensé), un côté d’une conversa- tion par téléphone cellulaire incompréhensible (milogue insensé), ou le silence. Entre 24 et 48 heures plus tard, les participants décrivaient librement le visage du malfaiteur, construisaient une image composite du malfaiteur a `partir de leur mémoire et tentaient d’identifier le malfaiteur dans une séance d’identification. Par la suite, les partici- pants devaient évaluer dans quelle mesure leur image composite ressemblait au malfaiteur. Le rappel du visage et l’identification lors de la séance d’identification étaient altérés chez les participants qui avaient été témoins de la scène de crime en présence d’un milogue sensé comparativement a `ceux qui étaient en présence d’un milogue insensé, d’un dialogue sensé ou du silence. De plus, les évaluations en termes de similarité ont montré que les composites construits après avoir ignoré les milogues sensés ressemblaient moins au malfaiteur que les composites construits par les participants en présence de silence, de milogue insensé ou de dialogue sensé. Le caractère im- prévisible du contenu sensé du milogue, plutôt que son imprévisibilité acoustique, engendre de la distraction. Les résultats sont innovateurs en ce sens qu’ils suggèrent qu’une distraction de tous les jours, mêmesi présentée sous une modalité différente pour cibler l’information, peut altérer la mémoire a `long terme d’un témoin oculaire. Mots-clés: Distraction, téléphones cellulaires, mémoire d’un témoin oculaire, dialogue, milogue. References Ainsworth, P. B. (2002).Psychology and policing. Portland, OR: Willan. Beaman, C. P., & Jones, D. M. (1997). The role of serial order in the irrelevant speech effect: Tests of the changing state hypothesis.Journal of Experimental Psychology: Learning, Memory, and Cognition, 23, 459 – 471.http://dx.doi.org/10.1037/0278-73220.127.116.119 Cohen, J. (1988).Statistical power analysis for the behavioral sciences. Hillsdale, NJ: Erlbaum. Cutler, B. L., & Kovera, M. B. (2010).Evaluating eyewitness identifica- tion. New York, NY: Oxford University Press.http://dx.doi.org/10 .1093/med:psych/9780195372687.001.0001 Emberson, L. L., Lupyan, G., Goldstein, M. H., & Spivey, M. J. (2010). Overheard cell-phone conversations: When less speech is more distract- ing.Psychological Science, 21,1383–1388.http://dx.doi.org/10.1177/ 0956797610382126 Frowd, C. D., Jones, S., Fodarella, C., Skelton, F., Fields, S., Williams, A., . . . Hancock, P. J. B. (2014). Configural and featural information in facial-composite images.Science & Justice, 54,215–227.http://dx.doi .org/10.1016/j.scijus.2013.11.001 Frowd, C. D., Skelton, F., Hepton, G., Holden, L., Minahil, S., Pitchford, M.,…Hancock, P. J. B. (2013). Whole-face procedures for recovering facial images from memory.Science & Justice, 53,89 –97.http://dx.doi .org/10.1016/j.scijus.2012.12.004 Galván, V. V., Vessal, R. S., & Golley, M. T. (2013). The effects of cell phone conversations on the attention and memory of bystanders.PLoS One, 8,e58579.http://dx.doi.org/10.1371/journal.pone.0058579 Hughes, R. W., Vachon, F., & Jones, D. M. (2005). Auditory attentional capture during serial recall: Violations at encoding of an algorithm- based neural model?Journal of Experimental Psychology: Learning, Memory, and Cognition, 31,736 –749.http://dx.doi.org/10.1037/0278- 7318.104.22.1686 Hughes, R. W., Vachon, F., & Jones, D. M. (2007). Disruption of short- term memory by changing and deviant sounds: Support for a duplex- mechanism account of auditory distraction.Journal of Experimental Psychology: Learning, Memory, and Cognition, 33,1050 –1061.http:// dx.doi.org/10.1037/0278-7322.214.171.1240 Jones, D., Madden, C., & Miles, C. (1992). Privileged access by irrelevant speech to short-term memory: The role of changing state.Quarterly Journal of Experimental Psychology A: Human Experimental Psychol- ogy, 44,645– 669.http://dx.doi.org/10.1080/14640749208401304 Jones, D. M., & Tremblay, S. (2000). Interference in memory by process or content? A reply to Neath (2000).Psychonomic Bulletin & Review, 7, 550 –558.http://dx.doi.org/10.3758/BF03214370 Marsh, J. E., Demaine, J., Bell, R., Skelton, F. C., Frowd, C. D., Röer, J. P., & Buchner, A. (2015). The impact of irrelevant auditory facial descrip- tions on memory for target faces: Implications for eyewitness memory. Journal of Forensic Practice, 17,271–280.http://dx.doi.org/10.1108/ JFP-08-2014-0029 Marsh, J. E., Skelton, F. C., Vachon, F., Thorley, R., Frowd, C. D., & Ball, L. J. (2017). In the face of distraction:Irrelevant speech impairs person identification. Manuscript submitted for publication. Meissner, C. A., Brigham, J. C., & Kelley, C. M. (2001). The influence of retrieval processes in verbal overshadowing.Memory & Cognition, 29, 176 –186.http://dx.doi.org/10.3758/BF03195751 Monk, A., Fellas, E., & Ley, E. (2004). Hearing only one side of normal and mobile phone conversations.Behaviour & Information Technology, 23,301–305.http://dx.doi.org/10.1080/01449290410001712744 189 IMPACT OF CELL-PHONE CONVERSATION ON MEMORY Nakabayashi, K., & Burton, A. M. (2008). The role of verbal processing at different stages of recognition memory for faces.European Journal of Cognitive Psychology, 20,478 – 496. Nakabayashi, K., Burton, A. M., Brandimonte, M. A., & Lloyd-Jones, T. J. (2012). Dissociating positive and negative influences of verbal process- ing on the recognition of pictures of faces and objects.Journal of Experimental Psychology: Learning, Memory, and Cognition, 38,376 – 390.http://dx.doi.org/10.1037/a0025782 Nakabayashi, K., Lloyd-Jones, T. J., Butcher, N., & Liu, C. H. (2012). Independent influences of verbalization and race on the configural and featural processing of faces: A behavioral and eye movement study. Journal of Experimental Psychology: Learning, Memory, and Cogni- tion, 38,61–77.http://dx.doi.org/10.1037/a0024853 Norman, B., & Bennett, D. (2014). Are mobile phone conversations always so annoying? The “need-to-listen” effect revisited.Behaviour & Infor- mation Technology, 33,1294 –1305.http://dx.doi.org/10.1080/ 0144929X.2013.876098 Samaha, J. (2005).Criminal justice. Belmont, CA: Wadsworth. Schooler, J. W. (2002). Verbalization produces a transfer inappropriate processing shift.Applied Cognitive Psychology, 16,989 –997.http://dx .doi.org/10.1002/acp.930 Scott, S. K., Rosen, S., Beaman, C. P., Davis, J. P., & Wise, R. J. S. (2009). The neural processing of masked speech: Evidence for different mech- anisms in the left and right temporal lobes.Journal of the Acoustical Society of America, 125,1737–1743.http://dx.doi.org/10.1121/1 .3050255 Stavrinos, D., Byington, K. W., & Schwebel, D. C. (2011). Distracted walking: Cell phones increase injury risk for college pedestrians.Jour-nal of Safety Research, 42,101–107.http://dx.doi.org/10.1016/j.jsr.2011 .01.004 Steblay, N., Dysart, J., Fulero, S., & Lindsay, R. C. L. (2001). Eyewitness accuracy rates in sequential and simultaneous lineup presentations: A meta-analytic comparison.Law and Human Behavior, 25,459 – 473. http://dx.doi.org/10.1023/A:1012888715007 Stevenage, S. V., Neil, G. J., Barlow, J., Dyson, A., Eaton-Brown, C., & Parsons, B. (2013). The effect of distraction on face and voice recogni- tion.Psychological Research, 77,167–175.http://dx.doi.org/10.1007/ s00426-012-0450-z Strayer, D. L., & Johnston, W. A. (2001). Driven to distraction: Dual-task studies of simulated driving and conversing on a cellular telephone. Psychological Science, 12,462– 466.http://dx.doi.org/10.1111/1467- 9280.00386 Vachon, F., Hughes, R. W., & Jones, D. M. (2012). Broken expectations: Violation of expectancies, not novelty, captures auditory attention.Jour- nal of Experimental Psychology: Learning, Memory, and Cognition, 38, 164 –177.http://dx.doi.org/10.1037/a0025054 Vachon, F., Labonté, K., & Marsh, J. E. (2016). Attentional capture by deviant sounds: A noncontingent form of auditory distraction?Journal of Experimental Psychology: Learning, Memory, and Cognition. Ad- vance online publication.http://dx.doi.org/10.1037/xlm0000330 Wickham, L. H. V., & Swift, H. (2006). Articulatory suppression attenu- ates the verbal overshadowing effect: A role for verbal encoding in face identification.Applied Cognitive Psychology, 20,157–169.http://dx.doi .org/10.1002/acp.1176 Received May 26, 2016 Accepted August 24, 2016 E-Mail Notification of Your Latest CPA Issue Online! Would you like to know when the next issue of your favorite APA journal will be available online? This service is now available to you. Sign up at https://my.apa.org/portal/alerts/ and you will be notified by e-mail when issues of interest to you become available! Avis par courriel de la disponibilité des revues de la SCP en ligne! Vous voulez savoir quand sera accessible en ligne le prochain numéro de votre revue de la Société canadienne de psychologie préférée? Il est désormais possible de le faire. Inscrivez-vous a `https://my.apa.org/portal/alerts/ et vous serez avisé par courriel de la date de parution en ligne des numéros qui vous intéressent! 190 MARSH ET AL.
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Forgetting Induced Speeding: Can Prospective Memory Failure Account for Drivers Exceeding the Speed Limit? Vanessa K. Bowden, Troy A. W. Visser, and Shayne Loft University of Western Australia It is generally assumed that drivers speed intentionally because of factors such as frustration with the speed limit or general impatience. The current study examined whether speeding following an interrup- tion could be better explained by unintentional prospective memory (PM) failure. In these situations, interrupting drivers may create a PM task, with speeding the result of drivers forgetting their newly encoded intention to travel at a lower speed after interruption. Across 3 simulated driving experiments, corrected or uncorrected speeding in recently reduced speed zones (from 70 km/h to 40 km/h) increased on average from 8% when uninterrupted to 33% when interrupted. Conversely, the probability that participants traveled under their new speed limit in recently increased speed zones (from 40 km/h to 70 km/h) increased from 1% when uninterrupted to 23% when interrupted. Consistent with a PM explana- tion, this indicates that interruptions lead to a general failure to follow changed speed limits, not just to increased speeding. Further testing a PM explanation, Experiments 2 and 3 manipulated variables expected to influence the probability of PM failures and subsequent speeding after interruptions. Experiment 2 showed that performing a cognitively demanding task during the interruption, when compared with unfilled interruptions, increased the probability of initially speeding from 1% to 11%, but that participants were able to correct (reduce) their speed. In Experiment 3, providing participants with 10s longer to encode the new speed limit before interruption decreased the probability of uncorrected speeding after an unfilled interruption from 30% to 20%. Theoretical implications and implications for road design interventions are discussed. Keywords:driver safety, interruptions, speeding, prospective memory Approximately 1.25 million people worldwide die in road traffic accidents each year, with excessive speed identified as a major contributor (World Health Organization, 2015). It is generally assumed that drivers who speed are doing so intentionally as a result of factors such as frustration with the speed limit or general impatience (Fleiter, Lennon, & Watson, 2010;Kanellaidis, Golias, & Zarifopoulos, 1995). As such, existing programs to reduce speeding have focused on enforcement and punitive measures to discourage drivers from choosing to exceed the speed limit (De- laney, Ward, Cameron, & Williams, 2005;Pilkington & Kinra, 2005). Critically however, 20% to 50% of drivers fined for speed- ing report that they were unaware of their actions until ticketed for the offense (Blincoe, Jones, Sauerzapf, & Haynes, 2006;Corbett, 2001). If a significant proportion of speeding is unintentional, then it is crucial to understand the causes of this behavior and to identifyways to reduce it. In a recent real-world study,Gregory, Irwin, Faulks, and Chekaluk (2014)examined the speeding behavior of drivers who were interrupted shortly after they encountered a new lower speed limit. They found that 100 m after an interruption (caused by a red traffic light) drivers exceeded the new 40 km/h speed limit in a school zone by an average of 8 km/h. Uninter- rupted drivers, on the other hand, exceeded the speed limit by less than 2 km/h. To explain this, Gregory et al. suggested that the speeding resulted from drivers forgetting to travel at the new lower speed limit following interruption—a type of memory error known in the psychological science literature as a prospective memory (PM) failure (Kliegel, McDaniel, & Einstein, 2008). A PM task requires individuals to remember to perform a deferred task at an appropriate time in the future (Einstein & McDaniel, 1990). PM failures occur when individuals forget to enact their deferred intention at the appropriate time. PM tasks also include a retrospective memory component since individuals need to remember what their PM intention is and when it will be required. However, a defining feature of PM tasks is that, unlike retrospective memory tasks, there are no external agents directing individuals to engage in a memory search at the point that the PM action should be performed, and therefore individuals need to self-initiate the retrieval of their intended action (Einstein, Smith, McDaniel, & Shaw, 1997). A PM intention can be formed when one task is interrupted by another since the interruption makes it necessary to remember to resume the original task after finishing the interrupting task (Dodhia & Dismukes, 2009). The conscious recollection of the PM intention at the appropriate time after the This article was published Online First March 6, 2017. Vanessa K. Bowden, Troy A. W. Visser, and Shayne Loft, School of Psychology, University of Western Australia. The authors acknowledge Dale Long and Drew Butson for their assis- tance with data collection. This research was supported by Grant G06176 from the Australian Neurotrauma Research Program awarded to the au- thors. Correspondence concerning this article should be addressed to Vanessa K. Bowden, School of Psychology, University of Western Australia, Crawley, WA 6009, Australia. E-mail:[email protected] This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. Journal of Experimental Psychology: Applied© 2017 American Psychological Association 2017, Vol. 23, No. 2, 180 –1901076-898X/17/$12.00http://dx.doi.org/10.1037/xap0000118 180 interruption constitutes the prospective component of the PM task (Smith & Bayen, 2004). Relating this to theGregory et al. (2014) study, drivers needed to form a PM intention to remember to travel at the new reduced speed of 40 km/h after the traffic lights turned green. As initial evidence in favor of this PM explanation, Gregory et al. were able to reduce speeding by placing a flashing LED sign immediately after the interruption to remind drivers to check their speed. This is consistent with studies showing that PM accuracy can be improved by providing memory aids to assist with appro- priate recall (Grundgeiger et al., 2013;Loft, Smith, & Bhaskara, 2011). However, although effective, there are several potential issues with applying this solution more widely. First, drivers are likely to habituate to the signs over time (Loft et al., 2011;Shiffrin & Schneider, 1977;Yanko & Spalek, 2013), thus reducing their effectiveness as an intervention. Second, it is not always practical or safe to introduce additional visual clutter to the driving envi- ronment since directing attention away from the road can be dangerously distracting (Bendak & Al-Saleh, 2010;Bowden, Loft, Tatasciore, & Visser, 2017;Chan & Singhal, 2013). From a theoretical standpoint, the evidence for the PM expla- nation provided byGregory et al. (2014)is equivocal. For one, their experimental design confounded frustration and memory failure as possible explanations for speeding. It cannot be ruled out that drivers were frustrated by interruptions and intentionally chose to speed afterward to make up lost time (Shinar, 1998), rather than unintentionally forgetting. The “check-speed” sign used in Gregory et al. may have reduced speeding by raising concern that enforcement might be present nearby (Oei, 1996), rather than by promoting the retrieval of the intention to travel at the new lower speed limit. It is also possible that the presence of the sign had other effects not directly tied to improving PM. For example, the flashing sign may have served as a general alerting signal, improving the ability of drivers to effectively monitor their speed. In sum then, though intuitively persuasive, there is insuf- ficient evidence that the speeding observed by Gregory et al. was due to PM failures. Conducting the current experiments in a controlled simulator environment allowed for the removal of the external pressures that could have contributed to frustration likely experienced by drivers in Gregory et al. study (e.g., real-life time constraints, social pressure from other drivers etc.). Should similar rates of speeding still occur in the current study following inter- ruptions, this would provide stronger evidence for the role of PM failure in speeding after interruption. Another real-world constraint inGregory et al. (2014)’s study was that vehicle speed could only be measured at a single point 100 m after the interruption using a speed camera. As a result, any variation in speeding behavior during the postinterruption period was not available for analysis. Capturing this information is im- portant because continuous assessment of driver behavior would enable discrimination between drivers who completely forgot the revised speed limit (i.e., those who sped through the whole postin- terruption period), and those who initially forgot but then corrected their speeding. Measuring the extent to which interruptions cause drivers to forget the speed limit completely, or whether drivers are later able to remember to travel at the new speed limit without prompting, will allow us to better understand the role of PM failures by establishing the probability of uncorrected and cor- rected speeding behaviors arising from PM failures under differentdriving conditions. This enhanced understanding should further assist with the development of effective road design interventions. In the current study, we examine speeding after interruptions in a controlled driving simulator environment. This study seeks to (a) provide evidence supporting a PM explanation for speeding in a driving environment where potential frustrating factors are con- trolled and (b) use PM theory to identify driving conditions which will further influence the probability of PM failures and subse- quent speeding after interruptions, thereby further bolstering a PM-based explanation for speeding following interruption. In Ex- periment 1A, we examine whether speeding occurs after all inter- ruptions or only those in recently reduced speed limit zones. Conversely, Experiment 1B investigates whether participants travel under their new speed limit when interruptions occur in recently increased speed limit zones. That is, we test whether participants are as likely to forget their new speed limit and travel too fast as they are to forget and travel too slow. The remaining two experiments draw on theory to identify two driving conditions that should influence the probability of PM failures after interruption, and thus the probability of speeding. In Experiment 2, we investigate whether the probability of speeding is increased when participants perform a cognitively demanding task during the interruption when compared to unfilled interruptions. In Experiment 3, we investigate whether providing participants with 10 s longer to encode a new speed limit before interruption can significantly decrease the probability of speeding. Experiment 1A: Driving Too Fast After an Interruption The aim of Experiment 1A was to determine whether partici- pants speed after all interruptions or only after those where the speed limit has recently been reduced. In Experiment 1A, the probability of speeding after interruption was examined within recently reduced speed zones (from 70 km/h limit to 40 km/h limit) and in unchanged speed zones (70 km/h limit). Interruptions oc- curred 5 s after a change to the reduced speed or at the equivalent time in the driving scenario for unchanged speed zones. All experiments reported here investigated speeding in a simu- lator environment where the real-world factors contributing to frustration (e.g., time pressure, social pressure) were largely re- moved. If PM failures contribute significantly to speeding follow- ing interruption, we would expect to replicate the findings of Gregory et al. (2014)with regards to maximum speeds reached after an interruption. However as discussed earlier, we argue that it is more informative from a theoretical perspective to examine the probability of uncorrected and corrected speeding following interruption, and in this way we extend the analysis of Gregory et al. We predicted that the probability of postinterruption speeding should increase when the interruption occurs shortly after a speed limit reduction but not when drivers are interrupted in unchanged speed zones in which no new speed limit intention has to be remembered. Method Participants.Sixteen undergraduate student participants (M age 19.8 years; 10 males) from the University of Western Australia participated in exchange for course credit. Participants This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. 181 PROSPECTIVE MEMORY FAILURE INDUCED SPEEDING were required to have at least a probationary driver’s license. On average they had been licensed for 30.4 months. A sample of younger, more inexperienced drivers was used because they are disproportionately represented in accidents where speeding is in- volved (Palamara, Kaura, & Fraser, 2013). The effect size of the difference in speed between interrupted and uninterrupted 40 km/hr zones reported inGregory et al. (2014;d 1.08) informed the target sample size. A power analysis based on this effect size suggested thatN 16 would yield 0.98 power at a .05 alpha level. No participants were replaced for failing to understand the task instructions. Stimuli.The driving simulator used Oktal’s SCANeR Studio software (France, Paris), housed in a cockpit rig supporting a 135° wide-field video driving display. Data was recorded at 1,000 Hz and down-sampled to 100 Hz for analysis. The display comprised three parallel monitors, with the central monitor representing the front windscreen view and a digital speedometer (seeFigure 1). The display also simulated two side mirrors and a central rear- vision mirror. Participants were seated approximately 85 cm from the central monitor and controlled their simulated automatic trans- mission vehicle using a modified Logitech computer steering wheel and pedal set (China, Beijing). The simulated vehicle and environment were configured for left-hand drive vehicle and road conditions. All participants drove on a continuous 15 km road and were instructed not to turn off the road. Participants kept to the far left lane of the four-lane road, and while no other vehicles ap- peared in the participants’ lane, there was light density traffic ( 5 vehicles per min) across the other three lanes. Participants drove at 70 km/h (70-zone) but encountered 10 zones where the speed limit was reduced to 40 km/h (40-zone) for 300 m. The distance between 40-zones varied between 800 m and 1,400 m. Thus, participants spent approximately 70% of their time traveling in the 70-zones. When the speed limit changed, signs indicating the new limit appeared in the middle of the central display and remained on screen for a maximum of 10 s, or until a response was made. Participants were instructed to respond by pressing a button on the steering wheel to acknowledge the sign. Participants were interrupted by a red traffic light in five of the 40-zones and in five of the 70-zones. Traffic lights were presentedon the left side on the central monitor. Red traffic lights in 40-zones appeared 5 s after the start of the 40-zone. Red traffic lights in 70-zones appeared 400 m before the start of the next 40-zone, when participants had been traveling at 70 km/h for 20 s to 50 s. A red light was preceded by an amber light that lasted for 3 s and signaled that the participant should begin slowing. The red light then appeared and remained on screen for 42 s until being replaced by a green traffic light. Together, the yellow and red light created a 45 s interruption. The green traffic light disappeared once participants began accelerating. Procedure.Participants first completed a 10 min training scenario where they were instructed to drive safely and obey any traffic signals. Participants were told that although the speed limit would usually be 70 km/h, they would also encounter short sec- tions of road where it would be reduced to 40 km/h. After training was complete, participants were then informed that they would start the experiment with a $3.85 bonus that would be reduced if they drove either too slowly or too quickly. The aim of the bonus was to incentivize participants to travel close to the speed limit, as they would in real-world driving. The order of conditions was counterbalanced across participants and the experiment took ap- proximately 35 min to complete. After the experiment, participants completed a short demographics questionnaire. In this question- naire they were also asked what speed they should have returned to after an interruption. Participants who answered this question incorrectly were considered not to have understood the task in- structions and were excluded and replaced. Results We report both the maximum speed reached in the postinter- ruption period and the proportion of trials that involved speeding. The postinterruption period in a 40-zone began at the end of the interruption (offset of red traffic light) and covered the next 130 m traveled, whereas the postinterruption period in a 70-zone covered the next 250 m traveled. Note that the longer postinterruption period in 70-zones ensured that participants had sufficient time to reach, and potentially exceed, the speed limit upon resumption of driving. Two kinds of speeding were assessed:uncorrected speeding (speed limit exceeded by at least 5 km/h with no attempt to return below the limit) andcorrected speeding(speed limit initially exceeded by at least 5 km/h, but speed was then reduced by at least 10 km/h). Exceeding the limit by at least 5 km/h is equivalent to the threshold where our current participants indicated on a post- experiment questionnaire that they believed they would be fined for speeding in the real-world (M 5.06 km/h). A reduction of 10 km/h was considered sufficient evidence of a participant attempt- ing to return to the appropriate speed limit given the distance available in the postinterruption period. We present point (effect size) and interval (within-subjects, 95% confidence) estimates, where within-subjects confidence intervals were determined by the method recommended byMorey (2008). Because we have made clear a priori hypotheses concerning the relationship between interruption and speed limit changes, in all cases we followed up the within-subjects analyses of variance (ANOVAs) with planned contrasts that directly evaluated our specific hypotheses (Rosenthal & Rosnow, 1985). In Experiment 1A, this involved comparing speeding in the interrupted 40-zone to Figure 1.Central monitor view of the driving environment. Digital speedometer displayed at the bottom. See the online article for the color version of this figure. This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. 182 BOWDEN, VISSER, AND LOFT the uninterrupted 40-zone and in the interrupted 70-zone to the uninterrupted 70-zone. Maximum speed.A 2 (zone: 40, 70) 2 (interruption: in- terrupted, uninterrupted) within-subjects ANOVA yielded main effects of zone,F(1, 15) 707.9,p .001, p2 .98, and interruption,F(1, 15) 7.45,p .016, p2 .33, and an inter- action between zone and interruption,F(1, 15) 10.96,p .005, p2 .42. The maximum postinterruption speed reached during the interrupted 40-zones (M 47.8 km/h, 95% CI [45.7, 49.9]) was significantly higher than in the uninterrupted 40-zones (M 42.6 km/h, [40.5, 44.7]),t(15) 3.19,p .006,d .96. This postinterruption speed increase of 5.2 km/h in the simulator is very similar toGregory et al.’s (2014)6.5 km/h increase for real-world driving. There was no difference in the maximum speed reached between the interrupted 70-zones (M 71.4 km/h, [70.6, 72.1]) and the uninterrupted 70-zones (M 71.8 km/h, [71.1, 72.6];t 1). This indicates that participants’ maximum speed was only increased following interruptions in the recently reduced 40-zones. Uncorrected speeding proportions.A 2 (zone: 40, 70) 2 (interruption: interrupted, uninterrupted) within-subjects ANOVA revealed no effect of zone,F(1, 15) 3.30,p .089, p2 .18, a main effect of interruption,F(1, 15) 11.81,p .004, p2 .44, and an interaction between zone and interruption,F(1, 15) 6.51, p .022, p2 .30. The proportion of 40-zones where uncorrected speeding occurred was significantly higher for interrupted zones (M .26, 95% CI [.20, .32]) compared with uninterrupted 40- zones (M .09, [.03, .15]),t(15) 3.96,p .001,d .71. Participants were therefore nearly three times more likely to speed throughout the entire 40-zone after an interruption (seeFigure 2). When speeding occurred in an interrupted 40-zone the average maximum speed reached was 59.7 km/h (95% CI [55.3, 64.1]), which suggests that participants were attempting to return to the previous but no longer relevant speed limit of 70 km/h. Conversely when speeding occurred in uninterrupted 40-zones, the average maximum speed reached was only 46.8 km/h (95% CI [45.8, 47.8]). There was no difference in uncorrected speeding between the interrupted (M .08, 95% CI [.03, .12]) and uninterrupted 70-zones (M .04, [–.01, .09];t 1). Corrected speeding proportions.A 2 (zone: 40, 70) 2 (interruption: interrupted, uninterrupted) within-subjects ANOVArevealed no main effects of zone,F(1, 15) 1.31,p .27, or interruption (F 1), and no interaction between zone and inter- ruption,F(1, 15) 3.46,p .083, p2 .19 (seeFigure 2). There was no increase in corrected speeding after interruptions in 40- zones, with no difference between the interrupted (M .04, 95% CI [.02, .06]) and uninterrupted 40-zones (M .01, 95% CI [–.01, .03]),t(15) 1.46,p .164. There was also no difference in corrected speeding between the interrupted (M .00, 95% CI [–.02, .02]) and uninterrupted 70-zones (M .01, 95% CI [.00, .03];t 1). In summary, interruptions increased the probability of speeding under conditions in which the speed limit had recently been reduced from 70 km/h to 40 km/h. Furthermore, this speeding was uncorrected and therefore persisted throughout the postinterruption period with no evidence of self-correction. In contrast, interrup- tions had no effect on speeding probability if they occurred during unchanged 70 km/h speed zones. Overall, the findings indicate that interruptions increased the probability of speeding only after a recent change in the speed limit, where drivers were required to form a PM intention to reduce their speed. Experiment 1B: Driving Too Slowly After an Interruption If speeding after an interruption can be due to PM failure, then drivers should not only forget and drive too fast following a recent reduction in the speed limit, but should also forget and drive too slowly following a recent increase in the speed limit. To test this in Experiment 1B, the speed zones used in Experiment 1A were reversed such that participants traveled at 40 km/h and encoun- tered 10 zones where the speed limit was increased to 70 km/h. If speeding following an interruption is the result of PM failure then participants should attempt to return to their no longer relevant, preinterruption speed (in this case 40 km/h). That is, a significantly higher proportion of interrupted drivers compared with uninter- rupted drivers should travel more slowly than the new 70 km/h speed limit. An attempt to return to 40 km/h would indicate that interruptions can lead to a general failure to follow a recently changed speed limit, rather than only leading to increased speed- ing. Method Participants.Sixteen new participants (M age 20.9 years; 10 males) were recruited for Experiment 2. On average they had been licensed for 36.6 months. Two participants were replaced for failing to understand the task instructions. The sample size was the same as for Experiment 1A. Stimuli and procedure.Participants drove at 40 km/h (40- zone), and they encountered 10 zones where the speed limit was increased to 70 km/h for 525m (70-zone). The distance between subsequent 40-zones varied between 500 m and 800 m. Note that the distances used in Experiment 1B have been scaled from Ex- periment 1A to ensure participants spent the same amount of time (approximately 70%) traveling at 40 km/h as participants in Ex- periment 1A spent at 70 km/h, and vice versa. Participants were interrupted by a red traffic light in five of the 70-zones and in five of the 40-zones. Red traffic lights in 70-zones appeared 5 s after the start of the 70-zone. Red traffic lights in 40-zones appeared 229 Figure 2.Proportion of interrupted (Int) and uninterrupted (No Int) 40-zones (left panel) and 70-zones (right panel) where speeding occurred in Experiment 1A. Uncorrected speeding ( 5 km/h over limit, no attempt to return below the limit) and corrected speeding ( 5 km/h over limit, then reduced by at least 10 km/h) is shown. 95% within-subjects CIs determined by the method recommended byMorey (2008). This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. 183 PROSPECTIVE MEMORY FAILURE INDUCED SPEEDING m before the start of the next 70-zone. All other details were the same as Experiment 1A. Results Maximum speed.A 2 (zone: 40, 70) 2 (interruption: in- terrupted, uninterrupted) within-subjects ANOVA yielded main effects of zone,F(1, 15) 1020.6,p .001, p2 .99, and interruption,F(1, 15) 12.24,p .003, p2 .45, and an interaction between zone and interruption,F(1, 15) 28.68,p .001, p2 .66. The maximum speed reached during the inter- rupted 70-zones (M 66.3 km/h, 95% CI [65.0, 67.7]) was significantly lower than in the uninterrupted 70-zones (M 71.2 km/h, 95% CI [69.8, 72.6]),t(15) 4.60,p .001,d 1.18, indicating that interruptions in recently increased speed zones caused participants to travel under their new speed limit. The maximum speed reached during the interrupted 40-zones (M 42.5 km/h, 95% CI [42.1, 42.9]) was significantly higher than in the uninterrupted 40-zones (M 41.7 km/h, 95% CI [41.3, 42.1]) t(15) 2.33,p .034,d .39, although this difference (0.8 km/h) was much smaller than seen in Experiment 1A (5.2 km/h). While this small difference in Experiment 1B could indicate that drivers increased their speeding after interruptions in the un- changed 40-zone, we suggest that it is more likely a result of participants initially exceeding the intended speed of 40 km/h slightly when accelerating following an interruption. Uncorrected speeding proportions.To demonstrate that for- getting leads to participants traveling substantially under the speed limit, the proportion of 70-zones where participants reached a maximum speed that was less than 65 km/h in the postinterruption period was determined (referred to here asunderspeeding). Un- derspeeding in 40-zones was not evident, as no participants trav- eled less than 35 km/h. Also, corrected underspeeding could not be determined due to the absence of a turning point in the data (e.g., from increasing speed to decreasing speed), therefore proportions reported below reflect overall speeding (combination of uncor- rected and corrected speeding). The proportion of 70-zones where underspeeding occurred (see Figure 3) was significantly higher for interrupted zones (M .23, 95% CI [.14, .31]) compared with uninterrupted 70-zones (M .01, 95% CI [–.07, .09]),t(15) 3.44,p .004,d 1.09. When underspeeding occurred after an interruption, the average maxi- mum speed reached in the 70-zone was 51.0 km/h (95% CI [46.1,56.0]). The fact that participants were traveling nearly 20 km/h under the limit strongly suggests that they had forgotten to travel at the new 70 km/h speed limit and had reverted to traveling closer to the previous limit of 40 km/h. In summary, Experiment 1B shows that underspeeding after an interruption increased to a similar extent as speeding increased in Experiment 1A. We have therefore demonstrated across Experi- ments 1A and 1B that interruptions lead to a general failure to follow changed speed limits, rather than only leading to increased speeding. These findings are clearly consistent with a PM expla- nation. Experiment 2: Adding Cognitive Distraction to the Interruption A prominent concept in cognitive psychology is that memory items vary along a continuum of activation and that item accessi- bility varies as a function of activation. Thus, the more frequently an item is rehearsed or otherwise strengthened, the greater its activation level and probability of retrieval (e.g.,Altmann & Trafton, 2002;Anderson & Lebiere, 2014;Nowinski & Dismukes, 2005). It follows that more cognitively demanding interruptions should be more disruptive to PM because they would prevent rehearsal by introducing dual task interference, thus reducing item activation. In line with this, previous research indicates that intro- ducing a cognitively demanding secondary task during the interval between PM encoding and retrieval can increase PM failures (Marsh & Hicks, 1998;Stone, Dismukes, & Remington, 2001). In Experiment 2, a similar experimental manipulation was used to provide further evidence for the role of PM failure in speeding following interruption. This manipulation involved introducing a cognitively demanding task for drivers to complete during the red traffic light interruption. If failures of PM contribute significantly to the probability of speeding following interruption, then we would expect a higher probability of speeding following a cogni- tively demanding interruption than following an unfilled interrup- tion. Inaddition to being theoretically informative, this manipulation is practically relevant because drivers seldom focus exclusively on the driving environment when waiting at traffic lights. Rather they often engage in other tasks such as conversing with passengers or using in-vehicle entertainment/communication systems (Huth, Sanchez, & Brusque, 2015;Strayer, Drews, & Johnston, 2003). As such, the levels of speeding seen here may more faithfully reflect the magnitude of speeding encountered in real-life driving situations. Method Participants.Thirty-two new participants (M age 19.8 years; 17 males) were recruited. On average they had been licensed for 26.0 months. Two participants were replaced for failing to under- stand the task instructions. The sample size was twice that of Experiments 1A and 1B to maintain statistical power, because the addition of the cognitively demanding secondary task reduced the number of observations per condition from five to three. Stimuli and procedure.In Experiment 2, we returned to the design used in Experiment 1A where participants drove at 70 km/h, except in 12 zones where the speed limit was reduced to 40 km/h. The number of 40-zones was increased from 10 in Experi- ment 1A to 12 in Experiment 2 to ensure participants experienced Figure 3.Proportion of 70-zones where underspeeding (maximum speed 65 km/h) occurred in Experiment 1B. 95% within-subjects confi- dence intervals determined by the method recommended byMorey (2008). This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. 184 BOWDEN, VISSER, AND LOFT the same number of each interruption type. Participants drove at 70 km/h (70-zone), and they encountered 12 zones where the speed limit was reduced to 40 km/h (40-zone) for 300 m. Participants spent approximately 70% of their time traveling in the 70-zones. The distance between 40-zones varied between 800 m and 1,400 m. Participants were interrupted by a red traffic light in six of the 40-zones and in six of the 70-zones. Half of these interruptions (three 40-zones, three 70-zones) included a cognitively demanding secondary task, and half included no task. The secondary task was an auditory N-back task in which single letters were presented serially over headphones and participants were required to indicate verbally whether each letter came before, after, or was identical to the immediately preceding item (Monk, Trafton, & Boehm-Davis, 2008). For example, if the letter sequence wasFfollowed byR then the correct response was to say “after.” Letters were presented at a rate of one letter every 1.6 s. Training was amended to include N-back task instructions, but all other details were the same as in Experiment 1A. Results The experimental design did not allow us to perform a fully factorial 2 (zone: 40, 70) 2 (interruption: interrupted, uninter- rupted) 2 (task: N-back, No-task) within-subjects ANOVA. This is because the N-back task manipulation only occurred during interruptions (i.e., task type was not manipulated under uninter- rupted conditions). Instead, wefirst conducted 2 (zone: 40, 70) 2 (interruption: interrupted, uninterrupted) within-subjects ANOVAs to assess the impact of interruption presence and followed these up with planned contrasts that directly evaluated our specific hypoth- eses by comparing the interrupted 40-zone to the uninterrupted 40-zone and the interrupted 70-zone to the uninterrupted 70-zone (thereby replicating Experiment 1A). We then conducted 2 (zone: 40, 70) 2 (task: N-back, No-task) within-subjects ANOVAs to assess the impact of interruption type, and followed these up with planned contrasts comparing interrupted 40-zones with No-task to interrupted 40-zones with the N-back task and comparing inter- rupted 70-zones with No-task to interrupted 70-zones with the N-back task. The impact of interruption. Maximum speed.A 2 (zone: 40, 70) 2 (interruption: inter- rupted, uninterrupted) within-subjects ANOVA yielded main ef- fects of zone,F(1, 31) 1501.3,p .001, p2 .98, and interruption,F(1, 31) 25.93,p .001, p2 .46, and an interaction between zone and interruption,F(1, 31) 54.55,p .001, p2 .64. Replicating Experiment 1A results, the maximum speed reached during the interrupted 40-zones (M 50.3 km/h, 95% CI [48.9, 51.7]) was significantly higher than during the uninterrupted 40-zones (M 42.2 km/h, 95% CI [40.8, 43.6]), t(31) 6.52,p .001,d 1.54. However unlike Experiment 1A, the maximum speed reached during the interrupted 70-zones (M 70.3 km/h, 95% CI [69.8, 70.7]) was significantly lower than in the uninterrupted 70-zones (M 71.5 km/h, 95% CI [71.1, 72.0]), t(31) 3.05,p .005,d .68. Practically this is a small effect (only a 1.2 km/h decrease), but at the same time this was not an expected finding. It is however consistent withGregory et al. (2014)who found that maximum speeds reached in 60 km/h or 70 km/h unchanged speed zones were slower following interruption. As suggested by Gregory et al., these slightly decreased speedsmay reflect the speed at which drivers feel generally comfortable traveling following interruption. Uncorrected speeding.A 2 (zone: 40, 70) 2 (interruption: interrupted, uninterrupted) within-subjects ANOVA yielded main effects of zone,F(1, 31) 38.93,p .001, p2 .56, and interruption,F(1, 31) 30.40,p .001, p2 .50, and an interaction between zone and interruption,F(1, 31) 27.50,p .001, p2 .47 (seeFigure 4). The proportion of 40-zones where uncorrected speeding occurred was significantly higher for inter- rupted zones (M .33, 95% CI [.28, .39]) compared with unin- terrupted 40-zones (M .05, [.00, .11]),t(31) 5.61,p .001, d 1.26. There was no difference in uncorrectedspeeding be- tween the interrupted (M .02, 95% CI [.00, .03]) and unin- terrupted 70-zones (M .03, 95% CI [.01, .05];t 1). These findings replicate Experiment 1A. Corrected speeding.A 2 (zone: 40, 70) 2 (interruption: interrupted, uninterrupted) within-subjects ANOVA yielded main effects of zone,F(1, 31) 4.73,p .037, p2 .13, and interruption,F(1, 31) 7.15,p .012, p2 .19, and an inter- action between zone and interruption,F(1, 31) 4.73,p .037, p2 .13 (seeFigure 4). The proportion of 40-zones where cor- rected speeding occurred was significantly higher for interrupted zones (M .06, 95% CI [.04, .09]) compared with uninter- rupted 40-zones (M .01, 95% CI [–.02, .03]),t(31) 2.78, p .009,d .71. Similar to Experiment 1A, there was no difference in corrected speeding between the interrupted (M .01, 95% CI [.00, .02]) and uninterrupted 70-zones (M .01, 95% CI [–.01, .02];t 1). The impact of the cognitive demand of the interruption. Maximum speed.A 2 (zone: 40, 70) 2 (task: N-back, No-task) within-subjects ANOVA yielded a main effect of zone, F(1, 31) 252.6,p .001, p2 .89, no main effect of task,F(1, 31) 2.45,p .13, p2 .07, and an interaction between zone and task that approached significance,F(1, 31) 3.62,p .066, p2 .11. The increase in the maximum speed reached when the inter- rupted 40-zones included the N-back task (M 51.8 km/h, 95% CI [50.1, 53.5]) compared with No-task (M 48.9 km/h, [47.2, 50.6]) approached significance,t(31) 1.90,p .067,d .34. There was no difference in the maximum speed reached between Figure 4.Proportion of interrupted (Int) and uninterrupted (No Int) 40-zones (left panel) and 70-zones (right panel) where speeding occurred in Experiment 2. Interruptions either had no additional task (No task) or an N-back task included. Uncorrected speeding ( 5 km/h over limit, no attempt to return below the limit) and corrected speeding ( 5 km/h over limit, then reduced by at least 10 km/h) is shown. 95% within-subjects confidence intervals determined by the method recommended byMorey (2008). This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. 185 PROSPECTIVE MEMORY FAILURE INDUCED SPEEDING the interrupted 70-zones that included the N-back task (M 70.1 km/h, [69.3, 70.8]) compared with No-task (M 70.5 km/h, 95% CI [69.7, 71.2];t 1). Uncorrected speeding.A 2 (zone: 40, 70) 2 (task: N-back, No-task) within-subjects ANOVA yielded a main effect of zone, F(1, 31) 37.80,p .001, p2 .55, no main effect of task,F(1, 31) 1.39,p .25, and no interaction between zone and task (F 1; seeFigure 4). The proportion of 40-zones where uncor- rected speeding occurred was not significantly different when the interruption included the N-back task (M .35, 95% CI [.28, .42]) compared with No-task (M .31, 95% CI [.24, .38];t 1). The proportion of 70-zones where uncorrected speeding occurred was not significantly different when the interruption included the N-back task (M .03, 95% CI [.01, .05]) compared with No-task (M .00, 95% CI [–.02, .02])t(31) 1.79,p .083,d .45. Corrected speeding.A 2 (zone: 40, 70) 2 (task: N-back, No-task) within-subjects ANOVA yielded a main effect of zone, F(1, 31) 5.07,p .032, p2 .14, an effect of task that approached significance,F(1, 31) 3.82,p .060, p2 .11, and an interaction between zone and task,F(1, 31) 10.26,p .003, p2 .25 (seeFigure 4). The proportion of 40-zones where cor- rected speeding occurred was significantly higher when the inter- ruption included the N-back task (M .11, 95% CI [.08, .15]) compared with No-task (M .01, 95% CI [–.03, .05]),t(31) 2.98,p .006,d .70. The proportion of 70-zones where corrected speeding occurred was no different when the interruption included the N-back task (M .00, 95% CI [–.02, .02]) compared with No-task (M .02, 95% CI [.00, .04];t 1). These results indicate that performing a cognitively demanding task during the interruption increased the likelihood of initially speeding after driving resumption from 1% to 11%, but that participants remembered to correct (reduce) their speed before the end of the postinterruption period (seeFigure 4). This finding supports the conclusion that PM failure is involved, since partic- ipants initially sped when they had fewer cognitive resources available to retrieve the new reduced speed limit. In contrast, we did not find evidence that performing a cognitively demanding task during the interruption increased uncorrected speeding. In the context of driving, an increase in the probability of initial speeding from 1% to 11% is definitely of practical concern, because it would potentially result in a significantly increased likelihood of injury from collisions or other speed-related accidents. Experiment 3: Increasing the Interruption Lag The terminterruption lagis used in the cognitive psychology literature to refer to the duration between being alerted about an upcoming interruption and the beginning of that interruption (Traf- ton & Monk, 2007). Increasing the interruption lag improves primary task resumption, presumably because stronger encoding strengthens the representation in memory of the primary task goal (Trafton, Altmann, Brock, & Mintz, 2003). More importantly, there is also evidence in the PM literature to suggest that longer encoding times can provide an advantage when it comes to re- membering to perform a deferred task action (Brandimonte, Ein- stein, & McDaniel, 2014;Clark-Foos & Marsh, 2008;Smith & Bayen, 2004). In the case of driving, we operationalized interruption lag as the duration that the new, reduced speed limit was active (and pre-sumably being adhered to) prior to the interruption. We did not think it was ecologically valid to warn participants of the upcom- ing interruption, butinstead assumed that spending more time trav- eling at the new speed limit before interruption would strengthen the intention to return to that speed after the interruption. To evaluate this prediction, in Experiment 3 we compared speeding when the inter- ruption lag was 5 s (as in Experiments 1A and 2) compared with 15 s. To the extent that PM failure can contribute to increased speeding after an interruption, we expected to observe a reduced probability of speeding after a 15-s interruption lag compared with a 5-s interruption lag. Method Participants.Thirty-two new participants (M age 22.4 years; 13 males) were recruited. On average they had been licensed for 56.5 months. Three participants were replaced for failing to un- derstand the task instructions. As in Experiment 2, the sample size was twice that of Experiments 1A and 1B to maintain statistical power, because the addition of the interruption lag reduced the number of observations per condition from five to three. Stimuli and procedure.Participants drove at 70 km/h (70- zone) and they encountered 12 zones where the speed limit was reduced to 40 km/h (40-zone) for 400 m. Note that compared with earlier experiments, we increased the length of both the 40-zones and the 70-zones to accommodate the longer 15-s lag condition. The distance between 40-zones varied between 1,100 m and 1,800 m. Participants spent approximately 70% of their time traveling in the 70-zones. Participants were interrupted by a red traffic light in six of the 40-zones and in six of the 70-zones. Half of these interrup- tions (three 40-zones, three 70-zones) included a 5-s lag between the 40 sign and the red light interruption, and half included a 15-s lag. All other details were the same as Experiment 2. Results The design did not allow us to perform a fully factorial 2 (zone: 40, 70) 2 (interruption: interrupted, uninterrupted) 2 (inter- ruption lag: 5 s, 15 s) within-subjects ANOVA because the lag manipulation only occurred during interruptions (i.e., lag was not manipulated under uninterrupted conditions). We therefore used the same analytical approach as outlined for Experiment 2. The impact of interruption. Maximum speed.A 2 (zone: 40, 70) 2 (interruption: inter- rupted, uninterrupted) within-subjects ANOVA yielded main ef- fects of zone,F(1, 31) 1784.7,p .001, p2 .98, and interruption,F(1, 31) 7.76,p .009, p2 .20, and an inter- action between zone and interruption,F(1, 31) 48.1,p .001, p2 .61. The maximum speed reached during the interrupted 40-zones (M 47.8 km/h, 95% CI [46.5, 49.0]) was significantly higher than in the uninterrupted 40-zones (M 42.1 km/h, 95% CI [40.9, 43.3]),t(31) 5.18,p .001,d 1.15. Similar to Experiment 2, the maximum speed reached during the interrupted 70-zones (M 69.2 km/h, 95% CI [68.7, 69.7]) was significantly lower than in the uninterrupted 70-zones (M 71.4 km/h, 95% CI [70.8, 71.9]),t(31) 4.63,p .001,d .88. Uncorrected speeding.A 2 (zone: 40, 70) 2 (interruption: interrupted, uninterrupted) within-subjects ANOVA yielded main effects of zone,F(1, 31) 29.52,p .001, p2 .49, and This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. 186 BOWDEN, VISSER, AND LOFT interruption,F(1, 31) 12.15,p .001, p2 .28, and an interaction between zone and interruption,F(1, 31) 15.21,p .001, p2 .33 (seeFigure 5). The proportion of 40-zones where uncorrected speeding occurred was significantly higher for inter- rupted zones (M .25, 95% CI [.20, .30]) compared with unin- terrupted 40-zones (M .07, 95% CI [.02, .12]),t(31) 3.91,p .001,d .85. There was no difference in uncorrected speeding between the interrupted (M .02, 95% CI [.00, .04]) and unin- terrupted 70-zones (M .02, 95% CI [.00, .04];t 1). Corrected speeding.A 2 (zone: 40, 70) 2 (interruption: interrupted, uninterrupted) within-subjects ANOVA yielded a main effect of zone,F(1, 31) 7.55,p .010, p2 .20, no effect of interruption,F(1, 31) 3.21,p .083, p2 .09, and an interaction between zone and interruption,F(1, 31) 4.43,p .044, p2 .13 (seeFigure 5). The proportion of 40-zones where corrected speeding occurred was higher for interrupted zones (M .05, 95% CI [.03, .07]) compared with uninterrupted 40- zones (M .02, 95% CI [0.00, .04]),t(31) 2.04,p .050,d .51. There was no difference in corrected speeding between the interrupted (M .01, 95% CI [.00, .01]) and uninterrupted 70- zones (M .01, 95% CI [.00, .02];t 1). The impact of interruption lag length. Maximum speed.A 2 (zone: 40, 70) 2 (interruption lag: 5 s, 15 s) within-subjects ANOVA yielded a main effect of zone,F(1, 31) 352.9,p .001, p2 .92, no effect of lag,F(1, 31) 2.95, p .096, p2 .09, and no interaction between zone and lag,F(1, 31) 2.91,p .098, p2 .09. However, the planned contrast revealed that the maximum speed reached during the interrupted 40-zones was significantly higher when the interruption lag was 5 s(M 49.0, 95% CI [47.8, 50.3]) compared with 15 s (M 46.5, 95% CI [45.2, 47.8]),t(31) 2.24,p .032,d .34. There was no difference in the maximum speed reached during the inter- rupted 70-zones when the interruption lag was5s(M 69.0, 95% CI [67.9, 70.1]) compared with 15 s (M 69.4, 95% CI [68.3, 70.5];t 1). Uncorrected speeding.A 2 (zone: 40, 70) 2 (interruption lag: 5 s, 15 s) within-subjects ANOVA yielded a main effect of zone,F(1, 31) 27.59,p .001, p2 .47, an effect of lag that approached significance,F(1, 31) 3.92,p .057, p2 .11, and an interaction between zone and lag that also approached signifi-cance,F(1, 31) 3.93,p .056, p2 .11 (seeFigure 5). The proportion of 40-zones where uncorrected speeding occurred was significantly higher when the interruption lag was5s(M .30, 95% CI [.25, .36]) compared with 15 s (M .20, 95% CI [.14, .25]),t(31) 2.06,p .047,d .33. The proportion of 70-zones where uncorrected speeding occurred was no different when the interruption lag was5s(M .02, 95% CI [.00, .04]) compared with 15 s (M .02, 95% CI [.00, .04];t 1). Corrected speeding.A 2 (zone: 40, 70) 2 (interruption lag: 5 s, 15 s) within-subjects ANOVA yielded a main effect of zone, F(1, 31) 9.21,p .005, p2 .23,) no effect of lag (F 1), and no interaction between zone and lag,F(1, 31) 1.71,p .20 (see Figure 5). The proportion of 40-zones where corrected speeding occurred was no different when the interruption lag was5s(M .03, 95% CI [–.01, .07]) compared with 15 s (M .07, 95% CI [.03, .11]),t(31) 1.16,p .25. The proportion of 70-zones where corrected speeding occurred was no different when the interruption lag was5s(M .01, 95% CI [.00, .02]) compared with 15 s (M .00, 95% CI [–.01, .01];t 1). These results indicate that providing participants with 10 s longer to encode the new speed limit reduced the likelihood of uncorrected speeding after driving resumption from 30% to 20%. In contrast, we did not find evidence that delaying the onset of an interruption decreased corrected speeding. These results are con- sistent with the notion that PM failure is linked to speeding. They also suggest that increasing the interval between speed limit changes and interruptions could significantly decrease uncorrected speeding and thus the likelihood of serious accidents and accident severity. General Discussion Road safety campaigns targeting speeding typically rely on changing drivers’ attitudes toward speeding, often by encouraging them to consider the risks involved (Lewis, Watson, Tay, & White, 2007). Such campaigns are based on the premise that speeding is primarily an intentional behavior. The current study sought to determine whether a significant amount of the speeding that occurs after interruptions might be accounted for by unintentional PM failures. Consistent withGregory et al. (2014), we demonstrated that drivers’ speed in a recently reduced speed zone (40 km/h limit school zone) was higher when they were stopped by a red traffic light, compared with when they were uninterrupted. Gregory et al. suggested that the red light was creating a PM task requiring drivers to remember the newly reduced speed when they resumed traveling (Dodhia & Dismukes, 2009). However, as outlined ear- lier, from a theoretical standpoint the evidence for a PM explana- tion provided by Gregory et al. was equivocal since their real- world study potentially covaried frustration with forgetting. To address this, we investigated speeding in a simulator environment where many of the real-world factors contributing to frustration were minimized. We could also more closely examine the nature of speeding resulting from PM failure following interruption by partitioning the data into uncorrected and corrected speeding prob- abilities. In Experiment 1A, we demonstrated that the probability of speeding increased when participants were interrupted in a re- cently reduced speed zone (40 km/h limit), but not when they were interrupted at an unchanged speed (70 km/h limit)—an effect that Figure 5.Proportion of interrupted (Int) and uninterrupted (No Int) 40-zones (left panel) and 70-zones (right panel) where speeding occurred in Experiment 3. Interruptions either had a 5-s lag or a 15-s lag. Uncor- rected speeding ( 5 km/h over limit, no attempt to return below the limit) and corrected speeding ( 5 km/h over limit, then reduced by at least 10 km/h) is shown. 95% within-subjects CIs determined by the method recommended byMorey (2008). This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. 187 PROSPECTIVE MEMORY FAILURE INDUCED SPEEDING was also very closely replicated in Experiments 2 and 3. We also manipulated driving conditions directly linked to PM storage and retrieval that should increase speeding after interruptions. Exper- iment 2 showed that performing a cognitively demanding task during the interruption, when compared with unfilled interrup- tions, increased the probability of initially speeding, but that par- ticipants were able to subsequently correct (reduce) their speed. In Experiment 3, providing participants with 10 s longer to encode the new speed limit before interruption decreased the probability of uncorrected speeding after an unfilled interruption. A significant advantage of a controlled simulator study is that we can determine the probability of speeding after an interruption, rather than just examining the speed at a single time-point after interruption. Thus, in Experiment 1A we not only replicatedGreg- ory et al.’s (2014)average speed increase of 6 km/h after interruption, but were additionally able to show an increase in the probability of uncorrected speeding (speed limit exceeded by at least 5 km/h with no attempt to return below the limit) from 9% when uninterrupted to 26% when interrupted. To confirm that participants were forgetting the newly encoded speed limit, we conducted Experiment 1B to show that forgetting was not biased toward traveling more quickly. In other words, we know that drivers will forget and go too fast, but will they also forget and go too slow? Our results showed that this was the case, and forgetting was just as likely to cause underspeeding as it was overspeeding— with 23% of interrupted drivers forgetting and traveling too slowly compared to 1% of uninterrupted drivers. Therefore, even in an environment where potential frustrating factors are controlled, participants were still forgetting to resume driving at the new speed following an interruption. Forgetting is a relatively binary outcome— drivers either remember to drive at the new speed limit, or they forget. However, forgetting could also lead to two different kinds of speeding behavior where drivers either return to the previous speed limit or, alternatively, are uncertain and choose a speed somewhere between the new and previous speed limit (e.g., the mean of the two speeds). The current results provide more support for the former explanation, with speeders on average in Experiment 1A traveling nearly 20 km/h over their limit of 40 km/h, and underspeeders on average in Experiment 1B traveling nearly 20 km/h under their limit of 70 km/h. Therefore, not only is speeding after interruptions relatively common, but when it occurs drivers are likely to try and revert to their previous speed. As such, we expect speeding to be a particular problem when real-world drivers are interrupted shortly after the speed limit has been substantially reduced (e.g., 70 km/h down to 40 km/h). These findings have implications for the careful selec- tion of routine speeds (e.g., 50 km/h in residential areas) be- cause this speed is likely to be what drivers default to when they are uncertain or have been interrupted. Practical Implications Unfortunately, many of the techniques for reducing the detri- mental effects of interruptions (e.g., introducing attention captur- ing reminder signs) have the unintended consequence of increasing demand on drivers’ limited cognitive and visual resources (Boehm-Davis & Remington, 2009;Bowden et al., 2017;Logan & Gordon, 2001). It is therefore vital that nonintrusive interventionsare also used to counter the PM-induced speeding reported here. In addition to providing support for the PM explanation of postint- erruption speeding, the current research allowed us to evaluate the benefits of two different nonintrusive interventions: reducing driver distraction during interruptions and increasing the time between speed limit changes and interruptions. Using a mobile phone while driving is still a very common practice, particularly among young adults (Nelson, Atchley, & Little, 2009), with many drivers believing it is safe to do so when stopped in traffic (Atchley, Atwood, & Boulton, 2011). Our find- ings suggest that performing a cognitively demanding task during interruptions, such as texting while stopped at traffic lights, could increase the chance of forgetting to resume at a recently reduced speed limit. In Experiment 2, we showed that the probability of corrected speeding increased from 1% to 11% when an additional task was introduced, where corrected speeding refers to drivers initially speeding but then remembering to drive at the new re- duced limit and slowing down some time later. These findings suggest that while unprompted recall of the reduced limit did occur shortly after interruption, it occurred more slowly for drivers who engaged in distracting tasks while stopped at traffic lights. It is crucial to note that just because these drivers eventually corrected their speeding, their driving was not necessarily less dangerous. There is a much higher likelihood that pedestrians will be stuck by vehicles immediately after traffic lights (Palamara & Broughton, 2013), where vehicle speed is directly related to acci- dent severity (Rosén & Sander, 2009). Our findings highlight the importance of continuing current education and enforcement cam- paigns aimed at reducing driver distraction (McEvoy, Stevenson, & Woodward, 2006), even when drivers are stopped in traffic. Another nonintrusive intervention, which we show can help counter forgetting-induced speeding, is increasing the time be- tween a speed limit change and an interruption. This intervention helps reduce forgetting by increasing the time available to encode the new speed intention (Smith & Bayen, 2004;Trafton et al., 2003). In Experiment 3, increasing the time spent traveling at a new lower speed before interruption from5sto15sdecreased the likelihood of uncorrected speeding after an interruption from 30% to 20%. This suggests that shifting existing speed limit transitions at known problem locations could help reduce speeding by a third. A major benefit of this kind of intervention is that it would place no additional demands on drivers and would be relatively low cost. Ensuring adequate separation between interruptions (e.g., traffic lights, stop signs) and speed limit transition points should be a consideration in future planning of roads and improvements to existing roadways. As mentioned previously, the current study recruited a sample of younger, more inexperienced drivers since they are disproportion- ately represented in accidents where speeding is involved (Pala- mara, Kaura, & Fraser, 2013). This group may be more at risk of forgetting after interruptions because they have been shown to be more susceptible to distractions (Klauer et al., 2014) and their lack of experience means they may need to dedicate more resources to the driving task itself (Crundall & Underwood, 1998;Fisher et al., 2002;Triggs & Regan, 1998). As such, inexperienced drivers could be more likely to forget and speed than more experienced drivers. However, research also suggests that PM performance declines with age, particularly for older adults over 70 years of age (Einstein, McDaniel, Richardson, Guynn, & Cunfer, 1995;May- This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. 188 BOWDEN, VISSER, AND LOFT lor, 1990). It is therefore likely that both inexperience and age- related declines may affect the extent of postinterruption speeding. Future studies should investigate the relative impacts of each of these factors on forgetting induced speeding. Conclusions Although there is no doubt that some drivers are choosing to speed (Bolderdijk, Knockaert, Steg, & Verhoef, 2011;Machin & Sankey, 2008), the current experiments make the case that PM failure also plays a crucial role in speeding under certain driving conditions. We show here that interrupted drivers are approxi- mately three times more likely to speed after a recent speed limit decrease than those who are not interrupted. The probability of speeding is potentially further increased when drivers fill their interruption with an additional task, such as conversing with a passenger or using an in-vehicle communication or entertainment system. We suggest that a simple way of reducing this forgetting- induced speeding would be by increasing the separation between speed limit transitions and interruptions to give drivers more time to encode a stronger PM intention. References Altmann, E. M., & Trafton, J. G. (2002). Memory for goals: An activation- based model.Cognitive Science, 26,39 – 83.http://dx.doi.org/10.1207/ s15516709cog2601_2 Anderson, J. R., & Lebiere, C. J. (2014).The atomic components of thought. Hove, United Kingdom: Psychology Press. Atchley, P., Atwood, S., & Boulton, A. (2011). The choice to text and drive in younger drivers: Behavior may shape attitude.Accident Analysis and Prevention, 43,134 –142.http://dx.doi.org/10.1016/j.aap.2010.08.003 Bendak, S., & Al-Saleh, K. (2010). The role of roadside advertising signs in distracting drivers.International Journal of Industrial Ergonomics, 40,233–236.http://dx.doi.org/10.1016/j.ergon.2009.12.001 Blincoe, K. M., Jones, A. P., Sauerzapf, V., & Haynes, R. (2006). Speeding drivers’ attitudes and perceptions of speed cameras in rural England. Accident; Analysis and Prevention, 38,371–378.http://dx.doi.org/10 .1016/j.aap.2005.10.008 Boehm-Davis, D. A., & Remington, R. (2009). Reducing the disruptive effects of interruption: A cognitive framework for analysing the costs and benefits of intervention strategies.Accident Analysis and Preven- tion, 41,1124 –1129.http://dx.doi.org/10.1016/j.aap.2009.06.029 Bolderdijk, J. W., Knockaert, J., Steg, E. M., & Verhoef, E. T. (2011). Effects of pay-as-you-drive vehicle insurance on young drivers’ speed choice: Results of a Dutch field experiment.Accident Analysis and Prevention, 43,1181–1186.http://dx.doi.org/10.1016/j.aap.2010.12.032 Bowden, V. K., Loft, S., Tatasciore, M., & Visser, T. A. W. (2017). Lowering thresholds for speed limit enforcement impairs peripheral object detection and increases driver subjective workload.Accident Analysis and Prevention, 98,118 –122.http://dx.doi.org/10.1016/j.aap .2016.09.029 Brandimonte, M. A., Einstein, G. O., & McDaniel, M. A. (2014).Pro- spective memory: Theory and applications. Hove, United Kingdom: Psychology Press. Chan, M., & Singhal, A. (2013). The emotional side of cognitive distrac- tion: Implications for road safety.Accident Analysis and Prevention, 50, 147–154.http://dx.doi.org/10.1016/j.aap.2012.04.004 Clark-Foos, A., & Marsh, R. L. (2008). Recognition memory for valenced and arousing materials under conditions of divided attention.Memory, 16,530 –537.http://dx.doi.org/10.1080/09658210802007493 Corbett, C. (2001). Explanations for “understating” in self-reported speed- ing behaviour.Transportation Research Part F: Traffic Psychology andBehaviour, 4,133–150.http://dx.doi.org/10.1016/S1369-8478(01) 00019-5 Crundall, D. E., & Underwood, G. (1998). Effects of experience and processing demands on visual information acquisition in drivers.Ergo- nomics, 41,448 – 458.http://dx.doi.org/10.1080/001401398186937 Delaney, A., Ward, H., Cameron, M., & Williams, A. F. (2005). Contro- versies and speed cameras: Lessons learnt internationally.Journal of Public Health Policy, 26,404 – 415.http://dx.doi.org/10.1057/palgrave .jphp.3200044 Dodhia, R. M., & Dismukes, R. K. (2009). Interruptions create prospective memory tasks.Applied Cognitive Psychology, 23,73– 89.http://dx.doi .org/10.1002/acp.1441 Einstein, G. O., & McDaniel, M. A. (1990). Normal aging and prospective memory.Journal of Experimental Psychology: Learning, Memory, and Cognition, 16,717–726.http://dx.doi.org/10.1037/0278-73126.96.36.1997 Einstein, G. O., McDaniel, M. A., Richardson, S. L., Guynn, M. J., & Cunfer, A. R. (1995). Aging and prospective memory: Examining the influences of self-initiated retrieval processes.Journal of Experimental Psychology: Learning, Memory, and Cognition, 21,996 –1007.http:// dx.doi.org/10.1037/0278-73188.8.131.526 Einstein, G. O., Smith, R. E., McDaniel, M. A., & Shaw, P. (1997). Aging and prospective memory: The influence of increased task demands at encoding and retrieval.Psychology and Aging, 12,479 – 488.http://dx .doi.org/10.1037/0882-79184.108.40.2069 Fisher, D. L., Laurie, N. E., Glaser, R., Connerney, K., Pollatsek, A., Duffy, S. A., & Brock, J. (2002). Use of a fixed-base driving simulator to evaluate the effects of experience and PC-based risk awareness training on drivers’ decisions.Human Factors: The Journal of the Human Factors and Ergonomics Society, 44,287–302.http://dx.doi.org/ 10.1518/0018720024497853 Fleiter, J. J., Lennon, A., & Watson, B. (2010). How do other people influence your driving speed? Exploring the ‘who’ and the ‘how’ of social influences on speeding from a qualitative perspective.Transpor- tation Research Part F: Traffic Psychology and Behaviour, 13,49 – 62. http://dx.doi.org/10.1016/j.trf.2009.10.002 Gregory, B., Irwin, J. D., Faulks, I. J., & Chekaluk, E. (2014). Speeding in school zones: Violation or lapse in prospective memory?Journal of Experimental Psychology: Applied, 20,191–198.http://dx.doi.org/10 .1037/xap0000019 Grundgeiger, T., Sanderson, P. M., Orihuela, C. B., Thompson, A., Mac- Dougall, H. G., Nunnink, L., & Venkatesh, B. (2013). Prospective memory in the ICU: The effect of visual cues on task execution in a representative simulation.Ergonomics, 56,579 –589.http://dx.doi.org/ 10.1080/00140139.2013.765604 Huth, V., Sanchez, Y., & Brusque, C. (2015). Drivers’ phone use at red traffic lights: A roadside observation study comparing calls and visual- manual interactions.Accident Analysis and Prevention, 74,42– 48. http://dx.doi.org/10.1016/j.aap.2014.10.008 Kanellaidis, G., Golias, J., & Zarifopoulos, K. (1995). A survey of drivers’ attitudes toward speed limit violations.Journal of Safety Research, 26, 31– 40.http://dx.doi.org/10.1016/0022-4375(94)00025-5 Klauer, S. G., Guo, F., Simons-Morton, B. G., Ouimet, M. C., Lee, S. E., & Dingus, T. A. (2014). Distracted driving and risk of road crashes among novice and experienced drivers.The New England Journal of Medicine, 370,54 –59.http://dx.doi.org/10.1056/NEJMsa1204142 Kliegel, M., McDaniel, M. A., & Einstein, G. (2008).Prospective memory: Cognitive, neuroscience, developmental, and applied perspectives. New York, NY: Taylor & Francis. Lewis, I., Watson, B., Tay, R., & White, K. M. (2007). The role of fear appeals in improving driver safety: A review of the effectiveness of fear-arousing (threat) appeals in road safety advertising.International Journal of Behavioral and Consultation Therapy, 3,203–222.http://dx .doi.org/10.1037/h0100799 This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. 189 PROSPECTIVE MEMORY FAILURE INDUCED SPEEDING Loft, S., Smith, R. E., & Bhaskara, A. (2011). Prospective memory in an air traffic control simulation: External aids that signal when to act. Journal of Experimental Psychology: Applied, 17,60 –70.http://dx.doi .org/10.1037/a0022845 Logan, G. D., & Gordon, R. D. (2001). Executive control of visual attention in dual-task situations.Psychological Review, 108,393– 434. http://dx.doi.org/10.1037/0033-295X.108.2.393 Machin, M. A., & Sankey, K. S. (2008). Relationships between young drivers’ personality characteristics, risk perceptions, and driving behav- iour.Accident; Analysis and Prevention, 40,541–547.http://dx.doi.org/ 10.1016/j.aap.2007.08.010 Marsh, R. L., & Hicks, J. L. (1998). Event-based prospective memory and executive control of working memory.Journal of Experimental Psychol- ogy: Learning, Memory, and Cognition, 24,336 –349.http://dx.doi.org/ 10.1037/0278-73220.127.116.116 Maylor, E. A. (1990). Age and prospective memory.The Quarterly Journal of Experimental Psychology, 42,471– 493.http://dx.doi.org/10.1080/ 14640749008401233 McEvoy, S. P., Stevenson, M. R., & Woodward, M. (2006). The impact of driver distraction on road safety: Results from a representative survey in two Australian states.Injury Prevention, 12,242–247.http://dx.doi.org/ 10.1136/ip.2006.012336 Monk, C. A., Trafton, J. G., & Boehm-Davis, D. A. (2008). The effect of interruption duration and demand on resuming suspended goals.Journal of Experimental Psychology: Applied, 14,299 –313.http://dx.doi.org/10 .1037/a0014402 Morey, R. D. (2008). Confidence intervals from normalized data: A cor- rection to Cousineau (2005).Reason, 4,61– 64. Nelson, E., Atchley, P., & Little, T. D. (2009). The effects of perception of risk and importance of answering and initiating a cellular phone call while driving.Accident; Analysis and Prevention, 41,438 – 444.http:// dx.doi.org/10.1016/j.aap.2009.01.006 Nowinski, J. L., & Dismukes, K. R. (2005). Effects of ongoing task context and target typicality on prospective memory performance: The impor- tance of associative cueing.Memory, 13,649 – 657.http://dx.doi.org/10 .1080/09658210444000313 Oei, H.-L. (1996). Automatic speed management in the Netherlands. Transportation Research Record, 1560,57– 64.http://dx.doi.org/10 .3141/1560-09 Palamara, P., & Broughton, M. (2013). An investigation of pedestrian crashes at traffic intersections in the Perth Central Business.Journal of Public Health, 93,1456 –1463. Palamara, P., Kaura, K., & Fraser, M. (2013).An investigation of serious injury motor vehicle crashes across metropolitan, regional and remote Western Australia. Retrieved fromhttp://c-marc.curtin.edu.au/local/ docs/ISIMVCMRRWA.pdf Pilkington, P., & Kinra, S. (2005). Effectiveness of speed cameras in preventing road traffic collisions and related casualties: Systematic review.British Medical Journal, 330,331–334.Rosén, E., & Sander, U. (2009). Pedestrian fatality risk as a function of car impact speed.Accident Analysis and Prevention, 41,536 –542.http://dx .doi.org/10.1016/j.aap.2009.02.002 Rosenthal, R., & Rosnow, R. L. (1985).Contrast analysis: Focused comparisons in the analysis of variance. Cambridge, MA: Cambridge University Press. Shiffrin, R. M., & Schneider, W. (1977). Controlled and automatic human information processing: II. Perceptual learning, automatic attending and a general theory.Psychological Review, 84,127–190.http://dx.doi.org/ 10.1037/0033-295X.84.2.127 Shinar, D. (1998). Aggressive driving: The contribution of the drivers and the situation1.Transportation Research Part F: Traffic Psychology and Behaviour, 1,137–160.http://dx.doi.org/10.1016/S1369-8478(99) 00002-9 Smith, R. E., & Bayen, U. J. (2004). A multinomial model of event-based prospective memory.Journal of Experimental Psychology: Learning, Memory, and Cognition, 30,756 –777.http://dx.doi.org/10.1037/0278- 7318.104.22.1686 Stone, M., Dismukes, K., & Remington, R. (2001). Prospective memory in dynamic environments: Effects of load, delay, and phonological re- hearsal.Memory, 9,165–176.http://dx.doi.org/10.1080/0965821014 3000100 Strayer, D. L., Drews, F. A., & Johnston, W. A. (2003). Cell phone- induced failures of visual attention during simulated driving.Journal of Experimental Psychology: Applied, 9,23–32.http://dx.doi.org/10.1037/ 1076-898X.9.1.23 Trafton, J. G., Altmann, E. M., Brock, D. P., & Mintz, F. E. (2003). Preparing to resume an interrupted task: Effects of prospective goal encoding and retrospective rehearsal.International Journal of Human- Computer Studies, 58,583– 603.http://dx.doi.org/10.1016/S1071- 5819(03)00023-5 Trafton, J. G., & Monk, C. A. (2007). Task interruptions.Review of Human Factors and Ergonomics, 3,111–126.http://dx.doi.org/10.1518/ 155723408X299852 Triggs, T. J., & Regan, M. A. (1998). Development of a cognitive skills training product for novice drivers. InProceedings of the 1998 Road Safety Research, Policing and Education Conference(pp. 46 –50). Wel- lington, New Zealand: Land Transport Authority. World Health Organization. (2015).Road traffic injuries—Fact sheet 358. Geneva, Switzerland: Author. Yanko, M. R., & Spalek, T. M. (2013). Route familiarity breeds inatten- tion: A driving simulator study.Accident; Analysis and Prevention, 57, 80 – 86.http://dx.doi.org/10.1016/j.aap.2013.04.003 Received July 27, 2016 Revision received November 25, 2016 Accepted December 4, 2016 This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. 190 BOWDEN, VISSER, AND LOFT
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T. G. Horgan et al.: Gender Differences in Memory Journal of Individual Differences2012; Vol. 33( 3):169–17 4 © 2012 Hogrefe Publishing Original Article Gender Differences in Memory for What Others Say About Themselves and Their Family Members Terrence G. Horgan, Jeannette M. Stein, Jeremy Southworth, and Michelle Swarbrick University of Michigan-Flint, Flint, MI, USA Abstract.Do women have a recall advantage for what others say? And does it matter what type of information another person shares with them? Women’s greater interdependence in self-construal was predicted to give them an advantage over men in their memory for information shared about close others. In an experimental study, 124 undergraduate students (64 women and 60 men) from a Midwestern university in the United States watched either a videotaped male or femaletarget discussing his or her lifestyle habits, health goals, and family. Participants then completed a surprise recognition test of their memory for what the target had said. Results show men were as accurate as women at remembering personal information shared by the targets, but women more accurately recalled what was said about the targets’ family members. The implications of thesefindings for various professional relationships are discussed. Keywords:gender, memory, verbal information, self-construal Two people enter a party. One says to the other, “There’s Derrick. Remember our neighbor told us that Derrick is her nephew and a fireman in town?” The other replies, “Oh, Derrick, the fireman. He’s what to our neighbor?” In ste- reotype, the one who remembers that Derrick is the neigh- bor’s nephew is a woman; the one who does not is a man. However, when it comes to remembering what people say about their family members, it is not known whether wom- en actually outperform men. The present study addressed this question by having participants watch a videotaped male or female talking about lifestyle habits, health goals, and family. Women’s greater interdependence in self-con- strual was predicted to give them an advantage over men in memory for what the targets had shared about close oth- ers, namely, their family members (Cross & Madson, 1997). Research has shown that women outperform men on tests of memory for nonverbal information. For example, Hall, Murphy, and Schmid Mast (2006b) found that, rela- tive to men, women have superior memory for their inter- action partner’s gazing, gesturing, and smiling. However, the literature is strangely silent as to whether there are gen- der differences in memory for verbal information shared within an interpersonal context. A female advantage over males in memory for what oth- er people say might be expected given that women have been shown to outperform men on most verbal learningtasks, including remembering lists of words (Herlitz & Rehnman, 2008; Yonker, Eriksson, Nilsson, & Herlitz, 2003). Yet studies that have tested participants’ memory for what others have said to them did not find any gender differences (Hall, Murphy, & Schmid Mast, 2006a). Per- haps men and women are equally accustomed to remem- bering what others tell them in day-to-day life, negating any advantage women might have over men on verbal- learning tasks. Consequently, the nature of the to-be-recalled verbal in- formation might be crucial to revealing gender differences in this memory domain as it has been in others. Horgan, McGrath, and Long (2009) found that, compared to men, women show superior memory for the people, but not the objects, in their surroundings. This female advantage in memory for people is consistent with women’s overall greater interpersonal orientation. Tests of semantic memo- ry also suggest that gender differences are dependent on the type of material to be learned. For instance, Herrmann, Crawford, and Holdsworth (1992, Study 1) found that women did better than men at remembering a list of grocery items but worse than men at remembering a set of travel directions. Such findings may be related to gender-relevant experiences. Within an interpersonal context, personal information about others (hereafter referred to astargets) and informa- tion about targets’ family members might act as two dis- DOI: 10.1027/1614-0001/a000087 © 2012 Hogrefe PublishingJournal of Individual Differences2012; Vol. 33(3):169–174 This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. tinctive types of information, with gender differences in memory for each type also being linked to gender-relevant experiences. For the purpose of this study, personal infor- mation refers to details about the person but not his or her relationships with others. When memory for personal in- formation about targets is tested, men and women may per- form similarly because both are used to processing such information about targets, such as where they are from and what school they attend. But women might have more ex- perience than men processing targets’ family information (e.g., sibling’s age), given that women’s “self” tends to be more interdependent (Cross & Madson, 1997). Cross and Madson (1997) argued that gender differences exist in self-construal (i.e., how men and women tend to define their “self”). Women’s sense of self tends to be more interdependent or relationship-oriented, whereas men’s tends to be more independent or separate from others. Ev- idence of a gender difference in self-construal includes women thinking more about the people in their lives and, in dyadic interactions, reporting more thoughts and feelings about their partners as well as adopting the perspective of their partners more often than men (Ickes, Robertson, Tooke, & Teng, 1986). When reflecting on the “self,” a person is likely to think about his or her own personal qualities (e.g., height, inter- ests, etc.) as well as who he or she is in relation to others (e.g., daughter, girlfriend, etc.). Both sources of descriptive information (personal, relational) can also be taken into consideration when trying to understand others (e.g., she is a vegetarian and the mother of two). To the extent that women’s sense of self is more interdependent than men’s, information about the people they have close relations with should be more self-relevant to women than men (Cross & Madson, 1997). By extension, we suggest that information about those who are close to a target, such as his or her family members, might also be more relevant to how wom- en come to understand who the target is (i.e., the target’s “self”) than is the case with men. Consequently, when a target shares details about his or her family members, wom- en might more elaboratively process – and thus have a bet- ter memory for – this type of information than men (Fiske & Taylor, 1991; Singh & Mishra, 2006). There is suggestive evidence that women’s memory for information pertaining to relationships is superior to men’s. Women and men recalled newly acquired personal facts about famous people equally well (Yonker et al., 2003). Yet women reported more accurate memories (compared to men) for whatother peopledid in their relationships (Ga- briel & Gardner, 1999, Study 4) as well as for what hap- pened in their own relationships (Ross & Holmberg, 1992). However, at present, there is no evidence to suggest that women have an advantage over men in remembering what a target has said about close others. In the present study, participants listened to a videotaped male or female target sharing information about lifestyle, health goals, and family. Participants’ memory for what the target had said was tested. Questions focused on personalinformation shared by the target as well as what was said about his or her family members. It was hypothesized that, although men’s and women’s memory for target informa- tion should be comparable, women should have better memory for target family information than men, given that such information might be more relevant to women’s great- er interdependence in self-construal (Cross & Madson, 1997). Finding that women have a memory advantage (viz., a greater ability to recognize what had previously been said about others) over men in this social domain would be im- portant for three reasons: 1. It could have practical implications for some profession- al relationships in which the quality of help provided by one person (e.g., psychologist) might depend upon the details that can be remembered about the other person (e.g., client). 2. Previous research has not examined how gender differ- ences in self-construal might theoretically impact men’s and women’s memory for different types of information that other people verbally share (viz., personal, family). 3. It would add to a growing body of research showing that women have enhanced interpersonal sensitivity relative to men, which includes demonstrating better memory for others’ nonverbal cues and appearance (Hall & Ber- nieri, 2001; Hall et al., 2006b; Horgan, Schmid Mast, Hall, & Carter, 2004). Method Participants A total of 124 undergraduates (64 women, 60 men) from a Midwestern university in the United States participated in the study. Participants were primarily first-year students from a wide variety of majors within the university, which has a student population that is approximately 75% White, 12.5% Black, 8% multi-racial, 2.5% Hispanic, and 2% Asian. Data from three participants were discarded because they knew the target shown in the video. Analyses were performed on the remaining 121 participants. Participants were tested in small groups of 1–7, and all were treated in accordance with APA ethical guidelines. Materials Target Stimulus Videos There were two videos: one of a White male and one of a White female, both in their early 20s. By using a target from each sex, we could increase the generalizability of our findings and rule out the possibility that any recall advan- tage for personal or family information or both was linked to gender-congruent pairings (i.e., women recalling rela- tively more information about the female target than the 170 T. G. Horgan et al.: Gender Differences in Memory Journal of Individual Differences2012; Vol. 33(3):169–174 © 2012 Hogrefe Publishing This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. male target, whereas the reverse being true for men). Each target was shown sitting on an exercise mat in workout clothes. Targets were instructed to talk about their health goals and basic lifestyle habits, and to share general back- ground information about themselves. The topics of health and basic lifestyle habits were chosen because they are gen- der neutral in nature and (usually) do not require listeners to possess any specialized knowledge of the subject, as might be the case with more gender-typical hobbies (e.g., woodworking). Memory Tests Participants’ memory for what the male and female target had said was assessed with a 26-item and 29-item multi- ple-choice test, respectively. Items on each test dealt with personal and family information that the targets had shared. Care was taken in the selection and wording of test items to ensure that participants would not need to possess any specialized knowledge of health or exercise in order to an- swer them. Personal information included the name, age, favorite food, and exercise routine of each target. Family information included what the targets had stated about their family members, such as their illnesses, exercise habits, business ventures, and cooking. For instance, “What school is she currently attending?” was considered a question about the personal information that the female target had shared, whereas “Why does her mom walk as a means to stay fit?” was considered a question about family informa- tion. On the male [female] target test, there were 19  questions relating to personal information and 7  about his [her] family. Participants were required to select only one answer alternative for each test question. There was always only one objectively correct answer for each ques- tion. Procedure Participants were given the informed consent to read and sign. It stated that they would be asked to fill out a ques- tionnaire about a video clip and aspects of their personality for the purpose of exploring first impressions. Keeping the purpose of the study ambiguous helped to ensure that wom- en would not be more motivated than men to do well on the task at hand in order to fulfill gender-role expectations, as might have been the case had participants learned that the study concerned their social skills (Eagly, 1987; Ickes, Gesn, & Graham, 2000). Participants were randomly assigned to view the male or female target video. The experimenter explained that partic- ipants would watch a video. The experimenter exited the room after the video began playing to reduce the possibility of distractions. When the video ended, participants were im- mediately given the memory test. Upon completion of the tests, participants were debriefed, thanked, and dismissed. Results Test Scoring Participants answers were coded as correct (1 point) or in- correct (0 points). Two total scores were then calculated for each participant: the sum of all the correct answers about the male or female target’s personal information; and the sum of all the correct answers pertaining to the male or female target’s family information. In order to account for the different number of items on the male and female tests and the different number of questions pertaining to person- al and family information, each total score was divided by the total number of relevant questions on the test, resulting in proportion correct scores. Analysis Plan Participants’ arcsin-transformed proportion correct memo- ry scores for target personal information and family infor- mation were the dependent variables in the study. Gender differences in memory for target personal and family information were tested using a 2 (Participant gender: men vs. women) × 2 (Target gender: male vs. female) × 2 (Type of information recalled: personal vs. family) mixed-model analysis of variance (ANOVA), with participant gender and target gender as the between- participants factors and type of information recalled as the within-participants variable. Table 1 presents the means for personal and family information by target gen- der and participant gender. Main Effect of Participant Gender No predictions were made concerning a gender difference in overall memory for what the targets had said. However, results revealed a main effect of gender,F(1, 117) = 6.25, p= .01,η p2= .05, with women showing better overall mem- ory (M= .85) for what the targets had said than men (M= .80). Ta b l e 1 .Men’s and women’s mean proportion correct memory scores for the male and female target’s personal and family information Target information Personal Family Participant sex Male Female Male Female Male .83 (0.10) .84 (0.02) .75 (0.03) .79 (0.03) Female .85 (0.02) .87 (0.02) .84 (0.03) .85 (0.03) Notes.Values represent participants’ backtransformed proportion cor- rect memory scores. Scale ranges from 0 to 1.0. Standard deviations are in parentheses. T. G. Horgan et al.: Gender Differences in Memory 171 © 2012 Hogrefe PublishingJournal of Individual Differences2012; Vol. 33(3):169–174 This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. Participant Gender × Type of Information Interaction Results showed the expected interaction,F(1, 117) = 4.94, p= .03,η p2 = .04. The means are shown in Table 2. Of importance, the contrast between male and female recall of family information was significant,t(117) = 2.83,p< .01. In short, men were as accurate as women at remem- bering target personal information. However, the targets also talked about their own family members, and women remembered this information significantly better than did men. Other Possible Effects The targets were allowed to talk freely in an effort to cre- ate a more realistic video while keeping the topics of dis- cussion as gender neutral as possible. Because this led to differences in the male and female target videos (e.g., in length), there was a concern that this might confound the testing of gender differences in memory for target person- al and family information. However, there was no main effect of targetF(1, 117) = .39,p= .54, and no Participant gender × Target gender interaction,F(1, 117) = .01,p= .92. Thus, there was no evidence that participants had bet- ter or worse memory either for what the male or female target had said or that the gender composition of the par- ticipant-target pairing affected men’s and women’s mem- ory differently. The targets also shared more personal information than family information. Yet, within-participants contrasts showed that participants did not recall personal information about the targets better than information about the targets’ families,F(1, 117) = .03,p= .86. Moreover, the female target shared more personal and family information than did the male target. Nonetheless, there was no evidence that type of information recalled (personal vs. family) interact- ed with target genderF(1, 117) = 1.56,p= .22, nor was there a three-way interaction involving participant gender, target gender, and type of information recalledF(1, 117) = .91,p= .34. Discussion We examined whether women have better memory for in- formation about close others than do men. Previous re- search showed that women tend to remember more of the nonverbal information they observe than do their male counterparts (Hall et al., 2006b). Our results expand on this female advantage by showing that women also tend to out- perform men when remembering what targets have said about close others, in this case, members of the target’s family. This is, to our knowledge, the very first evidence to show that women’s memory for specific verbal information shared within an interpersonal context is superior to that of men’s. Our finding corresponds with Cross and Madson’s (1997) position that women are more interdependent than men and thus more attuned to information about close oth- ers. Our results also extend the theoretical implications of a gender difference in self-construal to a domain of social behavior that, although a part of everyday life (i.e., people trying to remember what others say to them), has received little research attention. Moreover, our findings add to the literature showing that gender matters with respect to ver- bal behavior in interpersonal contexts. For example, prior research has revealed gender differences in speaking time in mixed groups, use of filled pauses, and frequency of speech errors (see Hall, 1984). Gender differences in memory for information about other people, such as their nonverbal behavior and appear- ance, have been uncovered but not explained. The specific mechanisms that give women an advantage over men are unknown (Hall et al., 2006b; Schmid Mast & Hall, 2006). Similarly, although we discussedwhy, theoretically speak- ing, our findings make sense in terms of known gender differences in self-construal, we did not investigatehow women were able to outperform men. Research has repeat- edly demonstrated that greater self-relevance, deeper and more elaborative encoding, and better organization of in- formation facilitate recall. People’s self-construals serve as an important encoding structure for self-relevant information (Kuiper & Rogers, 1979; Rogers, Kuiper & Kirker, 1977; for a review, see Fiske & Taylor, 1991). For example, when physical fitness is particularly relevant to individuals, they are likely to pay more attention to and remember more about the physical fitness of those they encounter. Consequently, if informa- tion about close relationships is more relevant to how wom- en view the “self” – their own, as Cross and Madson have argued (1997), and that of others, as we suggest – then women might have paid greater attention to target family information than men. Memory is enhanced when information is processed based on meaning rather than structural or phonemic fea- tures (Bobrow & Bower, 1969; Craik & Lockhart, 1972; Hyde & Jenkins, 1973; Lockhart, Craik, & Jacoby, 1976). Because women tend to be more interdependent than men, Table 2.Men’s and women’s mean proportion correct memory scores for targets’ personal information and family information Target information Participant sex Personal Family Men .83 (0.10) .77 (0.16) Women .86 (0.10) .85 (0.15) Notes.Values represent participants’ backtransformed proportion cor- rect memory scores. Standard deviations are given in parentheses. Participant Gender × Type of Information Recalled,F(1, 117) = 4.94, p= .03,η p2= .04. 172 T. G. Horgan et al.: Gender Differences in Memory Journal of Individual Differences2012; Vol. 33(3):169–174 © 2012 Hogrefe Publishing This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. they may be more likely than men to consider family rela- tionships to be an important context for understanding oth- er people. To the extent that memory is better when infor- mation is processed based on meaning, women might have outperformed men in the current study because family re- lationships have more relevance or meaning to them than is the case with men, leading them to process family infor- mation more deeply than men. This interpretation is in line with Cross and Madson’s (1997) contention that those with an interdependent self-construal engage in deeper process- ing of relational information than those with a more inde- pendent self-construal. Similarly, on a reading task, Singh and Mishra (2006) found that men’s and women’s memo- ries benefitted equally from deeper processing of self-rel- evant information, but only women showed a benefit when the to-be-recalled information was about someone else. Because memory is vast, a greater degree of organiza- tion can facilitate information recall (Mandler & Pearl- stone, 1966; Tulving, 1962). It is easier, for example, to locate a specific item of food, say cereal, if food items are organized in a grocery store. According to Symons and Johnson (1997), people maintain highly elaborative self- representations that serve as powerful mnemonic devices for organizing self-relevant information. If women’s self- representations are more likely to include their relation- ships to close others than is the case with men, as Cross and Madson (1997) maintain, then women might have been more likely to spontaneously organize, and thus better able to retain, recently heard information about the target’s fam- ily members. Women may have been more likely than men to think of the male target, for example, as not only an individual who has personal information to share but also as someone who is likely to be a son and brother. Conse- quently, women might have been more inclined than men to incorporate incoming family information about the tar- get’s dad and siblings into their understanding of who the male target is, making it easier for them to retain and re- trieve these sorts of details. Men and women performed equally well when they were asked to remember personal information about the targets. This might, at first blush, seem inconsistent with previous research showing that women have an advantage over men in memory for social information about targets, such as targets’ appearance and nonverbal cues (e.g., Hall et al., 2006b; Horgan et al., 2004). (Women did have an overall advantage in memory for what the targets had said, although this was due to their superior memory for target family information.) However, like women, men are social beings who must interact with and understand other people in order to conduct their lives successfully. Attending to others’ personal information, such as their name, age, school, interests, and so on, can help in this regard, and thus might be something that both men and women are equally accustomed to, and thus practiced at, doing in everyday life. Of course, attending to others’ family information is beneficial, socially speaking, to men and women alike. Yet family information might be more relevant to how womenview other people, leading to differences in how that infor- mation is typically processed by each sex during verbal interactions. The benefits of having superior memory for others’ family information might extend to the many professional roles that women occupy in society. Research shows that interpersonal relationships influence mental health (Gunlicks-Stoessel, Mufson, Jekal, & Turner, 2010), physical health (Wortman, Dunkel-Schetter, 1979), occupational performance (Inn- strand, Langballe, & Falkum, 2010), and education (Sin Kwok Wong, 1998). Remembering the relationships that people have may be crucial to understanding and possibly helping them at times. Consider a new college student who discloses to her professor that she comes from a family in which her both parents are physicians, and that, although she is not entirely sure of what she would like to do after gradu- ation, she thinks that she might like to pursue medicine, too. Later, she reports that she is struggling in her biology classes and feels conflicted about her growing interest and aptitude in computer science. Discussing her relationship with her parents might offer clues to her current educational concerns. For this to happen, the professor may need to recall that this advisee is not only a student, whose academic interests can change over time, but also the daughter of physicians. It would seem important for professionals and supervisors, such as psychologists, physicians, and educators, to make an effort to remember the family information that their clients, patients, employees, and students have verbally shared with them. Moreover, a superior memory for such details on the part of women could positively impact the quality of help provided to those who seek help or need guidance. Thus, those charged with training therapists, educators, physicians, and the like should be mindful of this female advantage and the possibility that men may benefit from different training in this area. Although future research should address these pos- sibilities, a word of caution is in order. In the current study, we assessed participants’ short-term retention of information. Our findings might not generalize to settings in which people have had the opportunity to store such details about others into long-term memory (long-term therapy between a psy- chologist and his or her client). Nonetheless, it is likely that there are many situations in which there is infrequent contact between two people (e.g., patient and his or her primary care physician) and thus a female advantage in memory for family information might prove beneficial to the quality of that working relationship. It is important to replicate this research in real-world set- tings, especially in situations in which two people meet each other for the first time. Participants and targets from other age groups, races, and educational and cultural backgrounds should be included in future studies as well. Moreover, gen- der was used as a proxy for differences in self-construal. Fu- ture research could assess whether greater interdependence in self-construal is associated with enhanced memory for de- tails about other people’s family members. Finally, cultural differences in self-construal along the independent-interde- pendent continuum have been reported (Markus & Kita- T. G. Horgan et al.: Gender Differences in Memory 173 © 2012 Hogrefe PublishingJournal of Individual Differences2012; Vol. 33(3):169–174 This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. yama, 1991). Researchers could test whether students from some Asian cultures, where there is greater interdependence in self-construal, have better memory for target family infor- mation than do students from North America, where there is greater independence in self-construal, as a test of the gener- alizability of our findings. References Bobrow, S. A., & Bower, G. H. (1969). Comprehension and recall of sentences.Journal of Experimental Psychology, 80,455–461. Craik, F. I., & Lockhart, R. S. (1972). Levels of processing: A framework for memory research.Journal of Verbal Learning and Verbal Behavior, 11,671–684. Cross, S. E., & Madson, L. (1997). Models of the self: Self-con- struals and gender.Psychological Bulletin, 122, 5–37. Eagly, A. H. (1987).Sex differences in social behavior: A social- role interpretation. Hillsdale, NJ: Erlbaum. Fiske, S. T., & Taylor, S. E. (1991).Social cognition(2nd ed.). New York: Mcgraw-Hill Book Company. Gabriel, S., & Gardner, W. L. (1999). Are there “his” and “hers” types of interdependence? The implications of gender differ- ences in collective versus relational interdependence for affect, behavior, and cognition.Journal of Personality and Social Psychology, 77, 642–655. Gunlicks-Stoessel, M., Mufson, L., Jekal, A., & Turner, J. B. (2010). The impact of perceived interpersonal functioning on treatment for adolescent depression: IPT-A versus treatment as usual in school-based health clinics.Journal of Consulting and Clinical Psychology, 78,260–267. Hall, J. A. (1984).Nonverbal sex differences: Communication ac- curacy and expressive style. Baltimore, MD: Johns Hopkins University Press. Hall, J. A., & Bernieri, F. J. (Eds.). (2001).Interpersonal sensitiv- ity: Theory and measurement.Mahwah, NJ: Erlbaum. Hall, J. A., Murphy, N. A., & Schmid Mast, M. (2006a).Gender differences in memory. Unpublished raw data, Northeastern University. Hall, J. A., Murphy, N. A., & Schmid Mast, M. (2006b). Recall of nonverbal cues: Exploring a new definition of interpersonal sensitivity.Journal of Nonverbal Behavior, 30, 141–155. Herlitz, A., & Rehnman, J. (2008). Sex differences in episodic memory.Current Directions in Psychological Science, 17, 52–56. Herrmann, D. J., Crawford, M., & Holdsworth, M. (1992). Gen- der-linked differences in everyday memory performance.Brit- ish Journal of Psychology, 83,221–231. Horgan, T. G., McGrath, M. P., & Long, J. A. (2009). The rele- vance of people versus objects in explaining women’s advan- tage over men in appearance accuracy.Sex Roles, 60, 890–899. Horgan, T. G., Schmid Mast, M., Hall, J. A., & Carter, J. D. (2004). Gender differences in memory for the appearance of others.Per- sonality and Social Psychology Bulletin, 30, 185–196. Hyde, T. S., & Jenkins, J. J. (1973). Recall for words as a function of semantic, graphic, and syntactic orienting tasks.Journal of Verbal Learning and Verbal Behavior, 12, 471–480. Ickes, W., Gesn, P. R., & Graham, T. (2000). Gender differencesin empathic accuracy: Differential ability or differential moti- vation?Personal Relationships, 7, 95–109. Ickes, W., Robertson, E., Tooke, W., & Teng, G. (1986). Natural- istic social cognition: Methodology, assessment, and valida- tion.Journal of Personality and Social Psychology, 51, 66–82. Innstrand, S. T., Langballe, E. M., & Falkum, E. (2010). Exploring occupational differences in work-family interaction: Who is at risk?International Journal of Stress Management, 17, 38–55. Kuiper, N. A., & Rogers, T. B. (1979). Encoding of personal in- formation: Self-other differences.Journal of Personality and Social Psychology, 37, 499–514. Lockhart, R. S., Craik, F. I., & Jacoby, L. (1976). Depth of pro- cessing, recognition and recall. In J. Brown (Ed.),Recall and recognition(pp. 1–36). Oxford, UK: Wiley. Mandler, G., & Pearlstone, Z. (1966). Free and constrained con- cept learning and subsequent recall.Journal of Verbal Learn- ing and Verbal Behavior, 5, 126–131. Markus, H. R., & Kitayama, S. (1991). Culture and the self: Im- plications for cognition, emotion, and motivation.Psycholog- ical Review, 98, 224–253. Rogers, T. B., Kuiper, N. A., & Kirker, W. S. (1977). Self-refer- ence and the encoding of personal information.Journal of Per- sonality and Social Psychology, 35,677–688. Ross, M., & Holmberg, D. (1992). Are wives’ memories for events in relationships more vivid than their husbands’ memories?Jour- nal of Social and Personal Relationships, 9, 585–604. Schmid Mast, M., & Hall, J. A. (2006). Women’s advantage at remembering others’ appearance: A systematic look at the why and when of a gender difference.Personality and Social Psy- chology Bulletin, 32, 353–364. Singh, B., & Mishra, S. (2006). Effect of gender and type of en- coding on retention of traits.Journal of the Indian Academy of Applied Psychology, 32, 26–29. Sin-Kwok Wong, R. (1998). Multidimensional influences of fam- ily environment in education: The case of socialist Czechoslo- vakia.Sociology of Education, 71, 1–22. Symons, C. S., & Johnson, B. T. (1997). The self-reference effect in memory: A meta analysis.Psychological Bulletin, 121, 371–394. Tulving, E. (1962). Subjective organization in free recall of “un- related” words.Psychological Review, 69, 344–354. Wortman, C. B., & Dunkel-Schetter, C. (1979). Interpersonal re- lationships and cancer: A theoretical analysis.Journal of So- cial Issues, 35,20–155. Yonker, J. E., Eriksson, E., Nilsson, L.-G., & Herlitz, A. (2003). Sex differences in episodic memory: Minimal influence of estradiol.Brain and Cognition, 52, 231–238. Accepted for publication: February 15, 2012 Terrence G. Horgan Department of Psychology University of Michigan-Flint 411 William R. Murchie Science Building Flint, MI 48502 USA Tel. +1 810 762-3424 Fax +1 810 762-3426 E-mail [email protected] 174 T. G. Horgan et al.: Gender Differences in Memory Journal of Individual Differences2012; Vol. 33(3):169–174 © 2012 Hogrefe Publishing This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.