CALIFORNIA STATE UNIVERSITY, NORTHRIDGE
EVENT-BASED PROSPECTIVE MEMORY PERFORMANCE: MONITORING AND SPONTANEOUS RETRIEVAL COMPARING ICONS AND WORDS
A thesis submitted in partial fulfillment of the requirements for the degree of Master of Art in Psychology, Human Factors and Applied Psychology By Guglielmo Cavazza
December 2007
Signature page
The thesis of Guglielmo Cavazza is approved:
___________________________________ Professor Barry Berson
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___________________________________ Dr. Sun-Mee Kang, Ph.D.
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___________________________________ Dr. Jill Quilici, Ph.D., Chair
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California State University, Northridge
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Acknowledgement I am very grateful to all my committee members specifically; Dr. Barry Berson for his unselfish dedication and insights, Dr. Sun-Mee Kang for continually challenging me and Dr. Jill Quilici for mentoring me and being my beacon throughout my academic years.
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TABLE OF CONTENTS Signature page..................................................................................................................... ii Acknowledgement ............................................................................................................. iii List of Figures .................................................................................................................... vi List of Tables .................................................................................................................... vii Abstract ............................................................................................................................ viii Introduction......................................................................................................................... 1 Current research topic ..................................................................................................... 2 Why prospective memory research is important ............................................................ 3 Previous research ............................................................................................................ 5 Monitoring ...................................................................................................................... 6 Spontaneous retrieval...................................................................................................... 8 The Multiprocess Framework ......................................................................................... 9 Importance of the task................................................................................................... 12 Icons versus words........................................................................................................ 13 Hypotheses.................................................................................................................... 15 Method .............................................................................................................................. 18 Participants.................................................................................................................... 18 Design ........................................................................................................................... 19 Materials ....................................................................................................................... 20 Procedure ...................................................................................................................... 21 Results............................................................................................................................... 25 Preliminary analyses ..................................................................................................... 25 Prospective memory accuracy ...................................................................................... 28 Prospective memory monitoring................................................................................... 30 Further Analyses ........................................................................................................... 36 Discussion ......................................................................................................................... 43 Prospective memory accuracy ...................................................................................... 43 Prospective memory monitoring................................................................................... 45 Adjunct examinations ................................................................................................... 48 iv
Practical applications .................................................................................................... 49 Limitations .................................................................................................................... 51 Future investigations..................................................................................................... 51 References......................................................................................................................... 53 Appendix A....................................................................................................................... 58 Appendix B ....................................................................................................................... 59 Appendix C ....................................................................................................................... 60 Appendix D....................................................................................................................... 61 Appendix E ....................................................................................................................... 62 Appendix F........................................................................................................................ 63 Appendix G....................................................................................................................... 64 Appendix H....................................................................................................................... 65 Appendix I ........................................................................................................................ 66 Appendix J ........................................................................................................................ 67
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LIST OF FIGURES
Figure 1
Expected PM accuracy of emphasis and stimulus type.............................. 16
Figure 2
Expected mean RTs of emphasis and stimulus type..................................
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Figure 3
Reported mean GPA of emphasis and stimulus type.................................
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Figure 4
Predicted PMN target accuracy.................................................................. 30
Figure 5
Obtained PM target accuracy.....................................................................
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Figure 6
Predicted mean RTs of monitoring............................................................
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Figure 7
Obtained mean RTs of monitoring............................................................. 32
Figure 8
Mean RTs for PM task on emphasis and stimulus type............................
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Figure 9
Mean RTs for No-PM task on emphasis and stimulus type (14 after).......
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Figure 10
Mean RTs for PM task on emphasis and stimulus type.............................
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Figure 11
Mean RTs for No-PM task on emphasis and stimulus type (overall)........
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Figure 12
Number of errors on moderate emphasis...................................................
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Figure 13
Number of errors on strong emphasis........................................................
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Figure 14
RTs of 14 events prior to PM targets.........................................................
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Figure 15
RTs of 5 events after PM targets................................................................
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Figure 16
RTs of monitoring and rumination on emphasis and stimulus type........... 39
Figure 17
RTs of PM target detection........................................................................
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Figure 18
Self-assessment of monitoring on emphasis and stimulus type................
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LIST OF TABLES Table 1
Demographic summaries............................................................................ 19
Table 2
Correlation results......................................................................................
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ABSTRACT
EVENT-BASED PROSPECTIVE MEMORY PERFORMANCE: MONITORING AND SPONTANEOUS RETRIEVAL COMPARING ICONS AND WORDS By Guglielmo Cavazza A thesis submitted in partial fulfillment of the requirements for the degree of Master of Art in Psychology, Human Factors and Applied Psychology Objective: This study investigates whether monitoring increases when a Prospective Memory (PM) task is added to an ongoing, categorization task and whether PM accuracy and monitoring during the PM-added task are a function of different degrees of emphasis and types of stimuli. Background: PM can be defined as recalling an action to complete a delayed intention that is triggered by either a stimulus (event-based) or time (timebased). PM retrieval can arise either through capacity-demanding attentional processes of monitoring the environment for the target event or by spontaneous attentional processes. The Multiprocess Framework (McDaniel & Einstein, 2000) proposes that whether people employ strategic monitoring or automatic processes depends on several factors within which there are degrees of emphasis of instructions and target distinctiveness. Hypotheses: It is hypothesized that a) strong emphasis on the importance of the PM task at the time of instruction will lead to a higher accuracy in detecting PM targets than moderate emphasis, b) picture PM target cues will yield higher accuracy in PM performance than word cues, c) PM accuracy will be better for either picture or strong emphasis conditions than for the word/moderate emphasis condition, d) strong emphasis viii
at the time of instructions will yield to longer Reaction Times (RT) on the ongoing task than moderate emphasis, e) picture cues will yield to longer RTs on the ongoing task than word cues, f) PM performance on type of emphasis will be a function of type of PM stimuli, in that participants performing in either the icons or strong emphasis condition will achieve higher PM accuracy detection than those performing in the word/moderate emphasis condition, and g) RTs to categorize items during the no-PM task condition will be shorter than those in the PM task condition. Method: Eighty participants were tested on a categorization task that either showed icon-icon or word-word pairs. In addition, participants were asked to remember to press a “/” key any time they encountered either an icon or a word PM target depicting a bird. A 2 x 2 x 2 mixed-design was employed. Accuracy to detect PM target and reaction time to perform the categorization task were assessed as measures of PM performance and type of cognitive strategy used, respectively. Results: PM accuracy detection did not differ across condition. Participants performed equally well with different degrees of emphasis and different types of stimuli. Monitoring was longer for words than icons during the PM task but monitoring was present on both icons and words when a secondary PM task was added. Emphasis did not affect monitoring, nor was it a function of stimulus type. Conclusions: Icons are processed faster and achieve the same PM accuracy performance as words but performing with icons does not decrease monitoring. The assumptions derived from the Multiprocess Framework theory whereby PM accuracy and PM monitoring are functions of degree of emphasis and stimulus type were only partially supported in this study.
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Introduction In recent years an increasing focus has been devoted to research on Prospective Memory (PM), which, simply stated, is humans’ ability to remember to perform an action in the future. PM differs from retrospective memory in that the former refers to memory for actions to be carried out after a delayed time while the latter refers to memory for past events, for instance memory recall for a list of words previously learned. PM-related studies have predominantly included basic research and in some measure applied research, distinguished different PM types, and investigations into whether PM performance is the result of monitoring processes or spontaneous retrieval. Most of the PM-related studies focus on basic research (Bradimonte & Passolunghi, 1994; Kvavilashvili, 1998; Marsh & Hicks, 1998; Marsh, Hicks, Cook, Hansen, & Pallos, 2003; McDaniel & Einstein, 2000; Meier, Zimmermann & Perrig, 2006; Smith, 2004). Applied PM research investigations have dealt with the health care domain (Dieckmann, Reddersen, Wehner, & Rall, 2006) and aviation (Dismukes, Young, & Sumwalt, 1998; Dismukes, Loukopoulos, & Jobe, 2001). Prospective Memory may be defined as remembering to remember; thus a successful performance refers to one’s ability to complete a delayed intention (Penningroth, 2005). Recalling an action or intention is triggered by either a stimulus or cue (event-based) or a time (time-based). An example of an event-based task is remembering to buy some bread after seeing a bakery while driving, and an example of a time-based task is remembering to take a pill every morning at the same hour. Timebased PM tasks bank on self-initiated retrieval processes and successful performance depends on time estimation, while event-based PM tasks rely on both the strength of
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association between cue and action and on the ease of identifying the target event (Cicogna, Nigro, Occhionero & Esposito, 2005; Craik, 1986; Einstein & McDaniel, 1990; Einstein, McDaniel, Richardson, Guynn & Cunfer, 1995). If agreement has been reached on differentiating PM types, there is still much debate regarding whether PM performance involves monitoring processes or spontaneous retrieval or both, and what conditions favor one over the other. Current research topic This paper is an investigation of event-based PM performance, specifically on identifying whether different types of stimuli (icons versus words) and different levels of emphasis on the PM task will lead to better accuracy in remembering to perform an action in the future, and on understanding what kind of task interference, if any, these variables will have on reaction time to perform an ongoing, categorization task. Task interference is thought to assess what attentional resources, either monitoring or spontaneous retrieval, participants would employ in executing the PM task. If participants engage in monitoring processes to remember to perform an action in the future, then the reaction time to perform the ongoing task should increase due to the commitment participants put towards looking for PM target cues. If, on the other hand, spontaneous mechanisms are employed, then reaction time to perform the ongoing task should not slow down. Also, if participants’ accuracy in performing the PM task is a function of type of PM target cues, then it is possible to assess what type of stimuli yield better PM performance and whether these stimuli are differentially linked to the use of monitoring or spontaneous retrieval mechanisms.
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Why prospective memory research is important PM research is relevant for everyday life, clinical application, and cognitive theory. According to Kliegel and Martin (2003), 50 to 80 percent of all everyday memory problems are, at least in part, PM problems. Also, several studies that addressed the issue of PM problems in neuropsychological patients and PM research efforts in this context study what mechanisms cause forgetting as well as possible strategies for rehabilitation (Fortin, Godbout & Braun, 2002). Conducting PM research is essential for advancing theoretical understanding of a) what attentional processes people might engage to remember to carry out a delayed intention, b) how these processes affect ongoing activities, c) what external conditions might favor monitoring or spontaneous retrieval of intentions, d) which cue attributes can be used in what context and with what populations to pinpoint variables that influence cue detection (Marsh et al., 2003). Applied research should focus on how factors such as delayed intentions, divided attention, cue salience, and individual differences affect attentional processes, and on what attentional processes should be encouraged when these factors are present in different task scenarios. For instance, if spontaneous retrieval is the process more likely employed in long-term real-world PM conditions, then it would be beneficial to discourage monitoring processes, which would likely cause a workload increase, instead of increasing cue salience (Einstein et al., 2005). Dismukes et al. (2001) found that 22% of the neglected activities in airplane flight operations involved failing to perform an intended action, including either routine actions or actions deferred to a later time but eventually forgotten. He states that it is apparently
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simple to forget to carry out an action in aviation operations because the intention to carry out a deferred action has to be retrieved from memory at a time when, more than likely, a pilot is occupied with other task demands. Thus, actions that are deferred may also become removed from the normal environmental cues, such as displays, callouts and procedural flows that can serve to activate memory retrieval. Another empirical study (Dieckmann et al., 2006) about PM failures in emergency room simulation scenarios tested medical interns on type of intentions (educational, external to the patient, and internal to the patient) and influence of subjective importance (more important, less important), measuring for executed actions. Educational intentions consisted of instructions to correctly perform specific, lecturerelated medical steps. Internal intentions were directly part of the medical scenario, and external intentions were related to the simulation situation, addressing a specific task which had to be performed outside the scenario. Although there were not significant differences in missed executions on the three types of intentions, percentages of missed actions were substantially high (30%) across conditions. Dieckmann et al. also found a significant interaction between type of intentions and level of importance in that interns were more likely to execute unimportant, external actions than important, external ones while the pattern of results for the other two conditions was reversed. In both educational and internal conditions, participants were more likely to execute important actions than unimportant actions. They concluded that external intentions might have the highest salience in that intentions were neither related to the students’ course nor to the scenario itself and therefore could have led interns to the conclusion that, although unclear, it was “safe” to execute the actions anyway.
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As we have seen, in real-world domains people forget to perform intended actions, monitor extensively, and exhibit high error rates. The occurrence of any of these three outcomes is oftentimes linked to the importance of the PM task and to the type of external PM cues. Emphasis of task importance and stimulus types are two factors that influence what attentional processes individuals might adopt to perform a PM task. Distinguishing monitoring processes from spontaneous retrieval is essential to understanding Prospective Memory. This study investigates whether manipulating the importance of a task and the stimulus type has an effect on participants’ monitoring, PM accuracy detection, and error rates and whether the results could lead to insight into reducing monitoring, increasing PM accuracy and decreasing errors. Previous research To provide a thorough account of what PM entails and what assumptions it draws on, it is essential to supply theoretical explanations from previous research. A typical paradigm for studying PM in a laboratory context involves asking participants to remember to press a key whenever they see a particular target item in the context of an ongoing task. Participants are supposed to remember to carry out a delayed intention with no hints given by the experimenter. Thus when a PM target is encountered, participants need to switch from seeing the item as one to be rated to considering the item as a cue for the intended PM action (Einstein et al., 2005). There are different approaches to understanding how people carry out this kind of retrieval. One is to assume that humans have an executive attentional system that consciously monitors the environment for PM target events (strategic monitoring) and another is to assume that the cognitive
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system relatively automatically responds to the occurrence of the target events in the environment (automatic process). Monitoring theories assume that PM retrieval occurs through the capacitydemanding attentional process of monitoring the environment for the target event. Spontaneous Retrieval theory proposes that people rely on spontaneous attentional processes to retrieve intentions when PM targets are encountered. A different explanation is suggested by the Multiprocess Framework (McDaniel & Einstein, 2000); whereby some event-based tasks are accomplished by reflex while others require attentionaldemanding resources to be deployed for successful PM performance. The option depends on the influence of priming, typicality, salience of the target event, level of task absorption, and individual differences, all of which play a role in holding information to be employed in future time. In the next section, these three theories will be described in more detail. Monitoring The idea underlying the monitoring view is that in developing intention, an executive attentional system monitors the environment for the target event (Shallice & Burgess, 1991). Smith and Bayen (2004) argue that retrieval of an intention cannot be automatic, because capacity-consuming preparatory processes must be employed to successfully perform a PM task, and that these processes may involve monitoring of the environment for the PM-target events. Since the preparatory attentional processes happen prior to encountering a PM-target event, when a PM task is embedded in an ongoing task the reaction times to perform the latter should increase because there are less available resources, and this should occur even when the PM target is not present. According to
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this claim, Smith and Bayen (2004) maintain that preparatory attentional processes are functionally related to PM task performance in that PM detection accuracy is positively related to an increase in monitoring; that is, the better PM accuracy, the longer the reaction times in the ongoing task. There is evidence that the dual-task nature of PM paradigms is analogous to research on divided attention where a primary task relies in some way on limited resources and the addition of an attention-demanding secondary task has a negative impact on the primary task. Smith (2003) tested whether participants’ performance would differ on a lexical decision ongoing task, with either a delayed (after completion of the ongoing task) or an embedded (concurrent to the ongoing task) PM assignment. She found that the delayed PM task group responded faster than did the embedded PM task group, and concluded that the addition of a concurrent PM task produced longer reaction times on the ongoing task. Smith and Bayen (2004) introduced the preparatory attentional and memory process (PAM) theory, stating that PM performance is never automatic and suggesting that capacity-consuming preparatory processes are required for successful, event-based prospective remembering. Thus, embedding a PM task in an ongoing task reduces the available resources for the ongoing task, even though the target event is not present, and aiming to achieve an improved PM performance will always lead to some costs which are reflected in the ongoing task. According to the PAM theory, retrospective memory processes are also required to carry out successful PM performance in that they allow for discrimination between PM targets and non-target events as well as recollection of the
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intended action and they are likely to absorb resources when the target is present (Marsh, Hicks, & Watson, 2002; Smith, 2003). Smith and Bayen (2004) not only argue that retrospective components may be engaged in event-based PM tasks but they also contend that traditional statistical analyses have limitations in determining how variables affect PM performance. They believe that multinomial models for analyzing categorical data are better suited to detect associations among variables, in that “in these models, it is assumed that there are discrete cognitive states that participants attain with certain probabilities during task performance. These probabilities are represented as model parameters that can be estimated from observed raw data via maximum likelihood parameter estimation” (Smith & Bayen, 2004, page 757). The fit of the resulting model to the empirical data can be evaluated via goodnessof-fit tests (see Hu & Batchelder, 1994 or Riefer & Batchelder, 1988, for technical details). Spontaneous retrieval The assumption in spontaneous retrieval theory is that remembering occurs when the presence of the target event initiates successful retrieval processes without engaging in monitoring the environment. People might occasionally think about the PM task but no resources need to be devoted to evaluating the target event at the moment a PM cue is first processed (Einstein et al., 2005). An example of a spontaneous retrieval theory is the reflexive-associative theory, which states that, during planning, people tend to form an association between the target cue and the intended action. When they eventually encounter the target event, the intended action is brought to consciousness by an automatic associative system (Einstein & McDaniel, 1996; McDaniel & Einstein, 2000).
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In this case, retrieval is supposed to happen automatically, using few cognitive resources. However, for the retrieval to occur, a cue target needs to be fully processed at retrieval and the individual needs to form a solid encoding between the cue and the intended action (Einstein et al., 2005). In their experiment, Reese & Cherry (2002) provided evidence of spontaneous retrieval. They had participants perform an ongoing task and a PM task. Subsequently, they probed participants while still engaged in the experiment, asking them whether they were thinking about the PM task or the ongoing task. Sixty-nine percent of participants mentioned thinking about the ongoing task, while less than five percent reported thinking about the PM task, despite performing relatively well on the PM task. Reese & Cherry reasoned that if participants were relying on monitoring processes they should have mentioned thinking about the PM task more often and concluded that this was evidence that participants were engaging in spontaneous retrieval. The Multiprocess Framework This theory accounts for when individuals use monitoring versus automatic processes. McDaniel and Einstein (2000) argue that, since people have multiple, simultaneous PM demands and often delay occurs before one performs an intended action, it would be maladaptive for humans to constantly engage in monitoring activities because this would heavily tax one’s working memory ability. On the other hand, although there is a bias to prefer relying exclusively on spontaneous retrieval, there exist certain conditions during which people employ monitoring processes. The importance of the task, the characteristics of the target event and their relation to the target actions, the nature of the ongoing task and individual differences seem to be factors determining
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whether people use spontaneous mechanisms or monitoring processes to aid in remembering to perform actions in the future (Einstein, et al., 2005). The Multiprocess Framework theory appears to offer a more complete explanation of the mechanisms underlying PM performance, in that it accounts for both monitoring and spontaneous retrieval processes. The theory supports the idea that people count on strategic or attention-demanding processes for successful PM retrieval as well as the notion that there are automatic processes supporting PM retrieval, one mediated by an attentional system and the other mediated by a memory system. Monitoring is thought to involve a central executive system, which would assist individuals in a voluntary rehearsal for the PM performance and thereby increase the likelihood of PM task success. On the other hand, the attentional system based on exogenous events is thought to help individuals to better respond to salient, external stimuli and produce involuntary orienting responses without engaging in intentional monitoring. The associative-based memory process, borrowing from spreading activation theory (Anderson, 1983), suggests that unfamiliar targets produce better PM performance than familiar ones because unfamiliar targets have fewer associations and therefore higher chances that the activation spreading from the target would activate the associated intended action. Thus, the intensity of the activation of a highly salient stimulus is inversely related to the number of links connected to the target node (McDaniel & Einstein, 2000). Also, spreading activation theory accounts for high PM performance when two stimuli are closely related to each other. The closer the nodes, the higher the chance the association would activate the intended action.
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A typical approach to evaluating the cost of performing a PM task is to measure the response time and accuracy of evaluating the PM non-target ongoing task events. If remembering to perform an additional PM task causes a considerable drop in accuracy or a reduction in speed on the ongoing task compared to when the ongoing task is performed alone, then this would be an indication that individuals relied on monitoring. Furthermore, to the extent that monitoring processes are needed for achieving PM performance, longer reaction times should provide an indication of PM accomplishment. On the other hand, if remembering to perform an additional PM task does not increase reaction time or reduce accuracy to carry out an ongoing task compared to when the ongoing task is performed alone, then this would be evidence that individuals relied more on spontaneous retrieval processes. The Multiprocess Framework indicates that both patterns can surface and this depends on a variety of variables; for instance, how important the PM task is perceived to be by the individual, the number of PM target items, the level of focal processing, the target item typicality, the target distinctiveness, whether the PM target has been primed, and how well a PM target event matches with the ongoing activity (Einstein et al., 2005; Marsh et al., 2003; Penningroth, 2005). The current study will examine the effects of manipulating the importance of the PM task (strong emphasis at the time of instruction versus moderate emphasis) and the type of stimuli (icons versus words) on PM detection accuracy and ongoing task reaction time. The idea for this comes from Experiment 1 in Einstein et al. (2005), where different emphases at the time of PM instructions (explained in the next paragraph) and different types of focal stimuli were employed to observe the outcome on PM recognition accuracy and reaction time in performing the ongoing task. Instead of manipulating types of focal
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cues, the research interest here is to investigate if PM icon targets lead to higher PM detection accuracy than PM word targets, as it did for the focal cues compared to the nonfocal cues in Einstein at al. (2005), and whether this relation is a function of the type of emphasis given at the time of PM instructions. The following paragraphs will explain in detail the rationale for using different types of emphasis and different types of stimuli as variables for the current research. Importance of the task According to the Multiprocess Framework, PM tasks perceived as important will tend to encourage more monitoring processes because the main goal becomes ensuring the achievement of improved performance on the PM task. On the contrary, when a PM task is perceived as less important, PM performance may be left to more automatic processes because individuals see strategic monitoring as being effortful (McDaniel & Einstein, 2000). Einstein et al. (2005) assessed latencies on the ongoing task and accuracy to detect a PM target as a function of type of PM emphasis. Participants were given either strong emphasis or moderate emphasis on the importance of the PM task at the time of PM instructions. They found a significantly higher PM accuracy for the strong emphasis condition than the moderate emphasis condition and a longer reaction time to perform the ongoing task in the strong emphasis condition than the moderate emphasis condition. They concluded that, although motivation had a positive effect on PM accuracy, participants experienced more task interference as shown by the slower RT to perform the ongoing task relative to the moderate emphasis condition. Cost in speed to perform the ongoing task implies that participants relied on strategic monitoring rather than spontaneous retrieval.
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Based on the assumption that assignments perceived as more important will likely lead to better PM accuracy and more task interference, it is hypothesized that strong emphasis on the PM task at the time of instructions will cause participants to detect more PM target cues than when moderate emphasis is given. Also, it is hypothesized that strong emphasis of the PM task at the time of instructions will cause more task interference than moderate emphasis as measured by longer reaction times to perform the ongoing task. Icons versus words Distinctiveness is a second factor that may engage an involuntary orienting process that improves PM performance. Target events can be unusual relative to prior knowledge, distinctive relative to the existing context, or salient in some way. McDaniel and Einstein (2000) suggest that salient stimuli, such as uppercase words, involuntarily capture ones attention prompting an analysis of the significance of the item beyond its function in the ongoing activity. This distinctiveness of the target might produce attentional switching from the ongoing activity and provide a support for rapidly identifying its importance, thus prompting an association of the target event with the intended target action (Penningroth, 2005). Involuntary orienting processes prompted by a distinctive target should provide a constant and reliable mechanism for PM performance. When perceptually distinctive targets are used, absolute levels of PM performance are nearly perfect (Einstein, McDaniel, Manzi, Cochran, & Baker, 2000). These processes should also produce better performance than other PM processes such as resource-demanding monitoring or probabilistic associative memory, which are prone to fluctuation and may be somewhat
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inconstant. In line with this, when target cues are presented, for instance in all lower case, PM levels should be significantly smaller than when target cues are presented with the first letter in upper case (Einstein et al., 2000). Ultimately, involuntary orienting processes should be less subject to disruption than more strategic, resource-demanding targets. Einstein et al. (2000) also found that distinctive targets maintained high PM performance in the face of a secondary task, while when the targets were not distinctive, PM performance declined with a secondary task added. Could target distinctiveness be increased by selecting appropriate stimuli? Would using icons in a PM task be less absorbing with respect to an ongoing task than using words? Dual Coding theory (Paivio, 1971) claims that picture stimuli hold an advantage over word stimuli because pictures are dually encoded, both verbally and visually, while words are encoded only verbally. This picture-superiority effect facilitates people to recall and recognize pictures more easily than words. The ease with which pictures, or icons for that matter, are decoded might be caused by the stimulus distinctiveness. If icons are more distinctive as stimuli than words then they should lead to less monitoring to search for PM target and to greater PM accuracy detection than words. This study will examine whether using icon PM targets versus word PM targets leads to better PM target detection accuracy and interferes less with reaction time in performing an ongoing activity. It is hypothesized that, when participants are asked to remember to perform a delayed intention, the accuracy of identifying a PM target cue and taking the appropriate action will be superior when the target stimulus is an icon rather than when a word. Also, if participants are monitoring and essentially inquiring whether each item is a target, the form of the item should not matter much. But, if participants are
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relying more on icons than on words, where it is the type of cue that helps trigger the retrieval, seeing the item in the form of an icon should facilitate the use of automatic processes to retrieve PM cues. If this is true, then the reaction time to perform the ongoing task while engaging with icons will be faster than when engaging with words. Hypotheses Following the assumptions stressed by the Multiprocess Framework theory, when people engage in prospective remembering they tend to either adopt strategic monitoring or automatic processes and that this choice depends on factors such as importance of the task, the distinctiveness of cues, the focal processing, the type of planning, and individual differences. In this study only the first two factors are considered: the importance of the task by manipulating the type of emphasis on the PM task at the time of instruction, and the distinctiveness of cues by manipulating targets presented as icons versus words. Since this study includes an ongoing, primary task (categorization task) and a secondary task (PM task), two types of measurements are examined; namely accuracy to perform the PM task and response time to carry out the ongoing categorization task. The current research hypotheses maintain that accuracy in detecting PM targets (secondary task) will be more likely when; a) PM cues are distinctive, and b) when strong emphasis on the PM task is given at the time of instructions. Furthermore, cognitive costs caused by the PM task interfering with the primary, ongoing task will be more likely when: a) the importance of the PM task is strongly emphasized, and b) PM cues are not very distinctive. There should be no or minimal cognitive costs when: a) moderate emphasis is given to the PM task in instructions, and b) cues are distinctive.
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Three hypotheses are to be tested in relation to accuracy of PM performance. Strong emphasis about the importance of the PM task at the time of instructions is expected to lead to a significantly higher PM performance (number of PM targets found) than moderate emphasis instructions and icon PM target cues are expected to yield higher PM performance than word PM target cues. Also, an interaction is expected between type of emphasis at the time of instruction and type of PM target cues; strongly emphasizing the PM task at the time of instructions should lead to a high PM task performance with both icon targets and word targets, while when moderate emphasis on PM task is given at the time of instructions PM task performance should lead to a poor performance with word targets but not with icon targets (see Figure 1). 3.4 3.3 3.2 3.1 # of PM 3 Targets 2.9
Icons Words
2.8 2.7 2.6 2.5 Moderate
Strong Emphasis
Figure 1. Expected accuracy to detect a PM target as a function of degree of emphasis and stimulus type
Three hypotheses are proposed to be tested in relation to response times (RT) for performing the ongoing task. RT to perform the ongoing task should be slower with word PM targets than icon PM targets. RT is expected to be slower when high emphasis on the PM task is given at the time of instructions than when moderate emphasis is given. An interaction is expected to be significant in that the difference in RTs to perform the ongoing task between icon and words on the strong emphasis will be significantly greater 16
that the difference in RTs for the same stimuli on the moderate emphasis. There might not be a significant difference on participants’ RT between words/moderate emphasis condition, icons/strong emphasis condition, and icon/moderate emphasis but these three conditions will be significantly different than words/strong emphasis (see Figure 2). Also, RT to perform the ongoing task should be slower when the PM task is present than when it is not. The three-way interaction is not expected to be significant. 3.5 3 2.5 Mean RTs in 2 Milliseconds 1.5
Icons Words
1 0.5 0 Moderate
Strong Emphasis
Figure 2. Expected mean RTs as a function of degree of emphasis and stimulus type
To examine these hypotheses, 80 participants were tested on a categorization ongoing task and on a PM task, measuring for PM detection accuracy and RT to perform the categorization task. Half of the participants examined icon stimuli and the other half examined word stimuli. Both groups were asked to judge if the two stimuli belonged to the same category or not. Participants were told to perform a secondary PM task, in that they were asked to remember to press a forward-slash key anytime they encountered either an icon or a word describing “wing(s)”. Also, at the time when PM instructions were specified participants were either given strong emphasis on the importance of the PM task or moderate emphasis on the importance of the PM task.
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Method Participants One hundred participants from the Psychology Subject Pool at California State University, Northridge, were tested in the study. Twelve participants were discarded because they did not understand the instructions; they either pressed the wrong key(s) to categorize item pairs or reported a PM target much more frequently (more than 10 times) than they were actually presented (4 times). Eight participants were discarded because they incorrectly categorized more than 25% of trials. All the 80 remaining participants but one received class credit for their participation (the person who participated for no credit volunteered in the study). There were 28 males and 52 females, with a mean age of 21.32 years, ranging from 18 to 44 years. Mean reported GPA was 3.04 with a range of from 2.00 to 3.93. Forty-eight participants were native English speakers and 32 were ESL (English as second language) speakers. Of these, 22 learned English before the age of 12 and 7 learned English after the age of 12. Three participants failed to indicate when they had started to learn English. Table 1 displays frequencies and percentages of students’ academic status and type of major. Twenty participants were randomly assigned to each of the four between-subject conditions and were tested individually or in groups of up to four individuals. All participants within the same group were tested on the same condition.
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Table 1: Demographic summaries for the 80 participants who took part in the study Status Standing: Freshman Sophomore Junior Senior Graduate
N: 25 15 8 26 6
Percentage: 31.30 % 18.80 % 10.00 % 32.00 % 7.50 %
N: 37 7 5 3 28
Percentage: 46.30% 8.80% 6.30% 3.80% 23.20%
Major Type: Psychology Undeclared Biology Business Other Majors
Design The design was a 2x2x2 mixed factorial that included the between-subject variables of type of stimuli (words, icons) and type of PM emphasis given at the time of instruction (moderate emphasis, strong emphasis) and the within-subject variable of presence of a PM target (no PM task, PM task). Accuracy in detecting PM targets, accuracy in detecting pair matches and pair mismatches in the ongoing categorization task, and mean response time to perform the ongoing categorization task were measured. Only the RTs from the correct trial responses were used to assess whether task interference was present during the ongoing categorization. Also, when analyzing the effect of type of stimuli (icons versus words) on the RT to perform the ongoing categorization task, the RTs for the five trials following the occurrence of the PM target were excluded. Einstein et al. (2005) found that people tend to slow down after
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encountering a PM target, thus the five trials that follow the PM target appearance could mask monitoring when it is present. Materials SuperLab 4.0 (Cedrus Corporation, 2007) was used to present pair stimuli, which were in digital format and manipulated to achieve a 2x2-inch size homogeneity and a 110 pixel per inch resolution on a white background for icons (see Appendix A) and a size 30 font, Times style of black characters on a white background for words (see Appendix B). All stimuli pairs were separated by a black rectangular divider positioned vertically between the pairs. Stimuli content symbolized Animals, Artifacts, and Vegetation, which roughly occurred in equal number. The Animal category included mammals, reptiles, birds, amphibians, fish and insects. The Artifacts category included a variety of humanmade objects and the Vegetation category included plants, trees, fruits, and vegetables. The icon-icon pair half and the word-word pair half perfectly mirrored each other; that is, for every icon-icon match there was a word-word match equivalent. Fifty percent of the pair items in the ongoing categorization task contained category matches and 50 percent contained category mismatches. For every block there were 50 percent of pair matches and 50 percent of pair mismatches, accounting for equal number of Animal-Animal, Artifact-Artifact, Vegetation-Vegetation matches and Animal-Artifact, Animal-Vegetation, Artifact-Vegetation mismatches. All the word pairs in the word-word condition almost matched in number of syllables; whenever this was not possible (10 word pairs out of 160) the word pairs matched in number of letters. The Icons were in RGB (hues of red, green and black) colors and represented animals, plants and artifacts in their naturalistic forms. Four of the animal icons represented only the
20
animal’s head and all the rest pictured the animals’ lateral full body. Some of the icons of the three categories had a colored spot between the white background and the actual figure. Icons were downloaded from classroomclipart.com and clipart.com and enhanced using Adobe® Photoshop® software to achieve visual uniformity. The PM target items occurred four times in the PM half of the experiment and zero times in the control half. There were two PM target icons, one representing an airplane and the other representing an eagle, both portrayed sideways, and two PM target words indicating the same items (see appendix C). Each item appeared twice in its relative condition. The first PM target pair was the eagle paired with a swordfish, the second was the plane paired with a lemon, the third was the airplane coupled with a zebra, and the last was the eagle coupled with a piano. There was one match (animalanimal) and three mismatches (object-vegetation, object-animal, and animal-object). The same arrangement was used for the corresponding PM word targets in the word condition. Sheets of paper containing instructions for each condition were used to maintain uniformity in instruction deliverance (see Appendices D, E, F for icons and Appendices G, H, I for words). At the end of the experimental session, participants received a questionnaire. The questions included demographic, academic-related, and performance-related queries (see Appendix I). Procedure The experiment was divided into two parts, each consisting of eight blocks of either icon-icon or word-word pair trials with 20 pairs in each block presented on a
21
computer screen. The two pair sets (one with 160 icon pairs and the other with 160 word pairs) were used for one half of the experiment (presence or absence of PM task); that is, everyone performed one half with no PM task embedded either with an icon or a word categorization task. For the other half, everyone performed a similar categorization task (those tested with icons continued to use icons and those who tested with words continued to used words) conjointly with a PM task. The icon-icon, word-word pairs were fixed. Trials, blocks, and conditions (PM task, no-PM task) were fully counterbalanced as a correction for practice and fatigue effects. For the PM half, the order of the PM targets was fixed and appeared on the first, third, sixth and seventh block each positioned on the fifteenth trial. Having each PM target fixed on the fifteenth trial allowed for the measurements of the monitoring in the 14 events prior to the PM target cue and for the measurement of a possible rumination effect hypothesized in the RTs of 5 events after the PM target cue. For the categorization task, participants were asked to press the “g” key if the pair matched and the “h” if the pair did not. The “g” and the “h” keys were labeled “yes” and “no” respectively, using white stickers. For the PM task participants were asked to press the “/” key on the computer keyboard whenever the PM target item occurs, ignoring performing the categorization task. The “/” key was labeled with a blue sticker. The reason for labeling the three keys was to avoid participants being distracted by looking for the proper keys to press at the cost of the experiment. Participants were first given instructions (see Appendix D for Icons and Appendix G for Words) about the ongoing categorization task to make their decision as quickly and accurately as possible and they were given a block of 24 practice trials. They were
22
presented with stimulus pairs on a computer screen, and their ongoing task was to decide whether the stimulus to the right of the screen was a member of the category represented by the stimulus to the left of it. They were told to press the “yes” labeled key on the keyboard if the two stimuli matched and the “no” labeled key if the two stimuli did not match. The pair stayed on the screen until participants responded, and the response triggered presentation of the next pair. Participants received feedback as to whether they were doing the task correctly. After the practice trials, participants were invited to ask any questions they may have had. Since presence/absence of PM task was counterbalanced, type and amount of instructions would change depending on what condition started first. Participants received either strong- or moderate-emphasis PM instructions (with the difference between the two conditions being in the degree of emphasis placed on the PM tasks relative to the ongoing task) and they were told to look for target items that belong to categories that have wing(s). In the strong-emphasis condition (see sample of instructions in Appendix E for Icons and in Appendix H for words), participants were told to perform the ongoing categorization task as quickly as possible but they were advised that what the researcher was really interested in was the participants’ ability to remember to perform an action in the future, i.e., to press the target key any time a PM target was encountered. Participants in the moderate emphasis condition (see sample of instructions in Appendix F for icons and Appendix I for words) received the same directions except that the emphasis on PM instructions was waned. For instance, participants were told that, in addition to performing the categorization task, they were to perform the PM task but advised to
23
direct their focus primarily to the ongoing categorization task speed and accuracy and to try not to monitor for the PM target cues. First, all participants were requested to read the sheets of instructions, then asked to listen to the experimenter’s oral directions, and finally invited to ask questions. In the PM condition, participants were told to look for target items that belong to categories that have wing(s) and instructed to press the blue-labeled key any time, during the ongoing task, when they encountered them (in the form of icons for one group and in the form of words for the other). Participants were told that, when detecting a target cue, they had to press the blue-labeled key as a substitute for answering for the categorization task; that is, when a PM target was encountered, instead of pressing either the yes- or nolabeled keys they would press the blue-labeled key. If participants were able to detect a PM target cue but, for any reason, failed to press the blue key on the current trial, they were allowed to press the blue key in subsequent trials if they remembered to do so. There were a total of 320 trials, 160 for the non-PM condition and 160 for the PM condition. Participants had a break between conditions. Those who performed the no-PM task first and the PM task next listened to the PM instructions between the conditions and filled out a questionnaire (see Appendix J) at the end. Those who performed the PM task first and the no-PM task next filled out the questionnaire between conditions and had a one-minute pause before starting the next condition. The whole experiment lasted between 12 and 25 minutes. Participants were then debriefed, thanked and dismissed.
24
Results Preliminary analyses Preliminary analyses were conducted to examine mean differences, correlations and normality of distributions between demographic data and self-reported dependent variables collected from the questionnaire and the dependent variables collected from the experiment. Demographic continuous variables included Age and GPA. Demographic categorical variables included Gender, Major, Academic Status, and English as primary language. Moreover, Self-reported dependent variables were collected and used to assess participants’ awareness of their performance. These included: Perception of Performance (on a 1-to-5 scale, where 1 was poor and 5 excellent), Retrospective Memory of actual target hits participants remembered, and Monitoring Self-Assessment (on a 1-to-5 scale where 1 was never and 5 always). See Appendix J. Dependent variables collected from the experiment included the mean RTs of the 14 events prior to the PM targets (rt_before), which were employed to detect monitoring during PM categorization task; mean RTs of the five events after a PM target has been presented and found during the PM categorization task (rt_after); mean RTs of the no-PM categorization task (control_rt); mean RTs of the 14 events—prior to where PM target locations were positioned in the PM condition—for the no-PM categorization task (control_rt_before); number of targets correctly detected (target_detection); item categorization error rate during the PM task (PM_errors); and item categorization error rate during the no-PM task (No-PM_errors). Analyses of normality revealed that only Age had a significant and severe skewness, (sobtained = 12 > scritical value = 3.2), which was caused by the presence of two
25
extreme outliers. Since the variable Age did not affect the overall analyses, nor was it a variable tied to a particular research question, no transformation was utilized. All the other variables had no significant skewness and were normally distributed or approaching a normal distribution. Correlation analyses between the experiment dependent variables and demographic measures are shown in Table 2. The first row indicates that grade point average (GPA) is significantly positively correlated with the mean RT of the 14 events prior to the target items, which means that the higher the reported GPA value is the higher the RTs are as well. Rows 2 and 3 indicate that GPA is negatively correlated with the number of wrong responses (item categorization error rate) during both PM and noPM categorization tasks, which means that the higher the GPA reported value is, the fewer errors a participant committed (note, that only the latter correlation is statistically significant). Lastly, Age (row 4) significantly correlates with the mean RT of no-PM categorization task. Table 2: Correlation results
1 2 3 4
Variables GPA & rt _before (RTs on monitoring) GPA & error_ PM (errors during PM task) GPA & error_no-PM (errors during no-PM task) Age & rt_control (mean RTs of no-PM task)
r .29 -.18 -.23
p < .05 n.s. < .05
.23
< .05
Analyses of relationships between demographic continuous variables and experiment independent variables revealed that only participants’ GPA significantly differed as a function of type of stimuli and degree of emphasis ( F(1, 74) = 4.76, p = 0.033, η2 = .06, Power = .57). Participants performing with words and given strong emphasis at the time the instructions (M = 3.259, SD = .467) had a significantly higher GPA than 26
those performing with icons on the same degree of emphasis (M = 2.935, SD = .391). However, those participants performing with words and given moderate emphasis had (M = 2.916, SD = .404) a significantly lower GPA than those performing with icons on the same degree of emphasis (M = 3.02, SD = .467). Figure 3 illustrates the relationship of GPA and the 4 experimental conditions. 3.3 3.26 3.2 3.1 Mean GPA
3 2.9
Icons
3.02 2.94
2.92
Words
2.8 2.7 Moderate
Strong
Emphasis
Figure 3: Reported mean GPA as a function of degree of emphasis and stimulus type Analyses of relationships between categorical demographic variables and experiment DVs revealed that only academic Status significantly differed on mean RTs on the no-PM categorization task; control_rt, ( F(4, 75) = 3.27, p = 0.016, η2 = .149, Power = .81). When simple comparisons were analyzed, only freshmen’s RTs (N = 25, M = 1169.38, SD = 250.59) were significantly faster (p = .043) than seniors’ times (N = 26, M= 1460.50, SD = 430.67). All the other simple comparisons did not reach statistical significance. However, when the variable student “status” was analyzed using control_rt_before (the RTs of the no-PM task for the 14 events prior to where the PM target locations were in the PM task condition), the overall F ratio still resulted significant ( F(4, 75) = 3.02, p = 0.023, η2 = .139, Power = .78), but Tukey simple comparisons analysis revealed that only freshmen (N = 25, M = 1160.65, SD = 262.24) were significantly faster (p = .018) in performing the no-PM categorization task than 27
sophomores (N = 15, M = 1606.84, SD = 586.24). All the other simple comparisons did not reach statistical significance. Given the significant difference in RTs among status groups a cross tabulation was conducted to verify if student status differed in frequency in the 4 experimental conditions. A Pearson chi square analysis revealed that student status did not differ across condition (Χ2(12) = 12.76, p = .387). The preliminary analyses suggest that the GPA variable be tried as covariate only for the between-subject analyses involving RTs of PM task to determine whether GPA scores affect variance. Age was not used as covariate on analyses of monitoring and PM categorization accuracy mainly because there was only a significant correlation with categorization RTs of no-PM task, there were two extreme outliers which made the distribution severely skewed, and the age range was not large (excluding the 2 outliers, from 18 to 27). Student status was not used as a covariate on the analyses of monitoring because, despite the significant difference in RTs among student status, group counts did not significantly vary across the experimental conditions. The next paragraphs describe the results of the statistical analyses for each of the research hypotheses. PM accuracy results are presented first, followed by results for PM monitoring. Results of repeated measures analyses are presented last. Prospective memory accuracy Did participants receiving strong emphasis at the time of instructions find more PM targets than those receiving moderate emphasis? It was expected that participants given strong emphasis to look for PM cues at the time of instructions would detect more PM target cues than those given moderate emphasis. Contrary to the prediction, a factorial ANOVA did not indicate a significant main effect of participants’ accuracy on
28
degree of emphasis (F(1, 76) = 3.19, p = .08, η2 = .04, Power = .42). Participants given strong emphasis at the time of instruction did not find significantly more PM target items (M = 3.25, SD = .84) than participants given moderate emphasis (M = 2.8, SD = 1.34). Although the p value approached significance, this pattern of results did not support the prediction. Did participants in the icon condition find more PM targets that those in the word condition? It was expected that participants categorizing icons would detect more PM target cues than those categorizing words. Contrary to the prediction, a factorial ANOVA did not indicate a significant main effect of type of stimuli on participants’ PM target detection accuracy (F(1, 76) = 0, p = 1.0). Participants categorizing icon events did not find significantly more PM target items (Marginal mean = 3.03, Sd = 1.025) than participants categorizing word events (Marginal mean = 3.03, Sd = 1.25). This pattern of results does not support the prediction. Was there an interaction affecting participants’ accuracy in searching for icon or word PM targets as a function of whether they had received strong or moderate emphasis at the time of instructions? According to the assumptions drawn from the Multiprocess Framework theory, it was predicted that PM accuracy detection should be higher when participants either search for PM target icons or receive strong PM-search emphasis or a combination of both than when they search for PM target words and receiving moderate PM-search emphasis. Contrary to the prediction, a factorial ANOVA did not indicate a significant interaction between degree of emphasis and type of stimuli on PM accuracy (F(1, 76) = .156, p = .694). As shown in Figures 4 and 5, the pattern obtained from the experiment analysis differed from the expected one and did not support the prediction.
29
When GPA was included in the equation as a covariate, the pattern of results did not vary.
Predicted Results
Obtained Results
3.9
3.9
3.7
3.7 3.5
3.5 Icons
# of PM 3.3 Targets 3.1
Words
3.3 3.2
# of PM 3.3 Targets 3.1
2.9
2.9
2.7
2.7
Icons Words
2.85 2.75
2.5
2.5 Moderate
Moderate
Strong
Strong
Emphasis
Emphasis
Figure 4: Predicted PM target accuracy results Figure 5: Obtained PM target accuracy results as a function of degree of emphasis and type as a function of degree of emphasis and type of stimuli of stimuli
Prospective memory monitoring Were participants’ RTs to perform the PM categorization task faster when receiving strong emphasis than when receiving moderate emphasis? It was predicted that individuals receiving moderate emphasis at the time when the instructions to perform a categorization task are given should show faster RTs than those receiving strong emphasis. A factorial ANOVA comparing RTs of 14 events prior to target cues revealed that there was a marginal, though not significant, main effect on degree of emphasis (F(2, 75) =
3.28, p = .074, η2 = .041, Power = .43) in that participants given strong emphasis at
the time of instructions (M = 1726.2, SD = 425.28) were marginally faster in categorizing items than participants given moderate emphasis (M = 1601.26, SD = 394.88). This pattern of results did not support the research hypothesis. A different factorial analysis 30
was conducted on type of stimuli and degree of emphasis including grade point average (GPA) as a covariate. When GPA was added in the equation as a covariate, the main effect of type of stimuli was still significant (F(1, 73) = 64.72, p < .001, η2 = .47, Power = 1) but the main effect of degree of emphasis was no longer approaching significance (F(1, 73) =
2.4, p = .126), while the effect of GPA was statistically significant (F(1, 73) = 4.28, p =
.042, η2 = .055, Power = .53). This pattern of results did not support the research hypothesis. Were participants’ RTs slower when categorizing icons than when categorizing words during a PM task? It was expected that the reaction times in categorizing icons would be shorter than categorizing words. A factorial ANOVA analysis comparing mean RTs of 14 events prior to target cues (rt_before) revealed a significant main effect on type of stimuli (F(1, 76) = 71.13, p < .001, η2 = .483, Power = 1), in that participants categorizing icons (M = 1372.98, SD = 244.99) were significantly faster than participants categorizing words (M = 1954.48, SD = 368.28). The pattern of results did support the research hypothesis. Was there an interaction on participants’ RTs in categorizing different types of stimuli as a function of degrees of emphasis? An interaction was expected to be significant in that the difference in RTs to perform the ongoing task between icon and words in the moderate emphasis conditions will be significantly greater than the difference in RTs for the same stimuli in the strong emphasis conditions. A factorial ANOVA did not find a significant interaction between type of stimuli and degree of emphasis given at the time of instruction (F(1, 76) = .97, p = .33). As shown in Figures 6 and 7, the pattern obtained from the analysis differed from the predicted pattern. What the 31
results show is that there is no interaction between independent variables but only a significant main effect of type of stimuli and a marginal effect of degree of emphasis. When GPA was added in the equation as a covariate the results did not vary. The pattern of results did not support the research hypothesis.
2500
2500
2000
2000
2050.93 1758.02
Mean RTs in milliseconds
1500
Icons Words
1000 500
Mean RTs in milliseconds
1500
1401.47
1344.49
Icons Words
1000 500
0
0 Moderate
Strong
Moderate
Emphasis
Strong Emphasis
Figure 6: Predicted RTs of monitoring for degree of emphasis as a function of stimulus type
Figure 7: Obtained RTs of monitoring for degree of emphasis as a function of stimulus type
Were participants’ RTs in categorizing items during the no-PM condition significantly faster than those RTs in categorizing items during the PM condition? It was predicted that categorizing items during the no-PM task would be faster than when a PM task was present. A 2 X 2 X 2 Mixed ANOVA on RTs of the 14 events prior to the target cues for both the PM categorization task (rt_before) and the no-PM categorization task (control_rt_before) for type of stimuli and degree of emphasis revealed a significant main effect of presence or absence of PM task (F(1, 76) = 53.16, p < .001, η2 = .412, Power = 1). Categorizing items during the no-PM task (M = 1390.67, SD = 452.33) was significantly faster than when the PM task was included (M = 1663.73, SD = 426.84). This pattern of
32
result does support the research hypothesis stating that monitoring is present when a PM task is added to an ongoing categorization task. The effect of degree of emphasis did not indicate that RTs to categorize items differed based on emphasis (F(1, 76) = 2.07, p = .154). Strong emphasis at the time of instructions (M = 1726.20, SD = 452.89) did not lead to faster categorization than when receiving moderate emphasis (M = 1601.26, SD = 394.88). This pattern of results, though in line with the results obtained from the previous factorial ANOVA of emphasis and type on RTs of 14 events before the PM target cues for the PM task, does not support the research hypothesis that strong emphasis should generate slower RTs than moderate emphasis. The Mixed ANOVA also revealed a significant main effect on type of stimuli (F(1, 76)=
59.02, p = .001, η2 = .437, Power = 1). Participants categorizing icons (M = 1263.50,
Std. Error = 48.54) were significantly faster that those categorizing words (M = 1790.90, Std. Error = 48.54). This pattern of results is consistent with the results obtained for a factorial ANOVA of type and emphasis on RTs of 14 events before the PM target cues previously discussed, in that categorizing icons is faster than words. The Mixed ANOVA did not reveal significant two-way interactions of item categorization as a function of degree of emphasis and task type (F(1, 76) = .49, p = .488), or as a function of type of stimuli and task type (F(1, 76) = 2.09, p = .153). The betweensubject interaction between stimulus type and degree of emphasis was not significant (F(1, 76) =
.218, p = .614), nor was there present a significant three-way interaction (F(1, 76)=
.22, p = .642).
33
Figures 8 and 9 show the pattern of results for the three-way interaction.
PM Task
No-PM Task
2500 2000 Mean RTs in 1500 milliseconds 1000
2500 1858.02 1344.49
2050.94 1401.47
2000 Icons Words
Mean RTs in 1500 milliseconds 1000
500
1539.47 1169.09
1715.17 Icons 1138.95
Words
500
0
0 Moderate
Strong
Moderate
Emphasis
Figure 8: Mean RTs for PM task
Strong Emphasis
Figure 9: Mean RTs for No-PM task
A 2 X 2 X 2 Mixed ANOVA was conducted using RTs of 14 events before the PM target occurrence (rt_before) and the RTs of the overall events during the No-PM task (control_rt). There was a significant effect of presence or absence of PM task (F(1, 76) = 92.99, p < .001, η2 = .550, Power = 1). Participants’ RTs were faster when categorizing items alone (M = 1379.45, SD = 384.39) than when a PM task was included (M = 1663.73, SD = 426.84). The main effect of degree of emphasis was not statistically significant (F(1, 76) = 1.22, p = .272), but the main effect of stimulus type was significant (F(1, 76) = 72.075, p < .001, η2 = .487, Power = 1). Participants categorizing icons (M = 1285.15, SD = 242.07) were significantly faster than those categorizing words (M = 1785.03, SD = 362.84). This pattern of results is consistent with the results obtained for a factorial ANOVA of item type and emphasis on the RTs of 14 events before the PM target cues and in the mixed ANOVA of type and emphasis on rt_before and control_rt_before previously discussed.
34
The interaction between task type and degree of emphasis approached significance (F(1, 76) = 3.65, p = .060, η2 = .046, Power = .47). Participants’ RTs to categorize items during the no-PM task were similar for both those who received moderate emphasis (M = 1373.32, SD = 399.81) and those who received strong emphasis (M = 1385.59, SD = 373.33). But during the PM half of the experiment, the RTs for strong emphasis (M = 1726.20, SD = 452.90) were marginally higher than those for moderate emphasis (M = 1601.26, SD = 394.88). Moreover, the interaction between task type and type of stimuli was marginal (F(1, 76) = 3.43, p = .068, η2 = .043, Power = .45). Although the main effect of PM task was strong, the difference in participants’ mean RTs to categorize items between icon/PM task (M = 1372.98, SD = 244.99) and icons/no-PM task (M = 1154.02, SD = 61.14) was smaller than the difference between word/PM task (M = 1954.48, SD = 48.75) and word/no-PM task (M = 1627.48, SD = 61.14). The between-subject interaction of degree of emphasis and stimulus type was not significant (F(1, 76) = 1.143, p = .288), nor was the three-way interaction (F(1, 76) = .003, p = .956). Figures 10 and 11 illustrate the three-way interaction between degree of emphasis and stimulus type on RTs of presence (rt_before) and absence (control_rt) of PM task. PM Task
No-PM Task 2500
2500 2000 Mean RTs in 1500 milliseconds 1000
1858.02 1344.49
2050.94 1401.47
2000 Mean RTs in 1500 milliseconds 1000
Icons Words
1577.09
1654.08
1169.54
1117.1
Icons
500
500
0
0 Moderate
Moderate
Strong
Figure 10: Mean RTs for PM task
Strong Emphasis
Emphasis
Figure 11: Mean RTs for PM task
35
Words
Further Analyses Although there was no hypothesis to test categorization accuracy, a mixed ANOVA was employed to compare the number of incorrect responses during item categorization in both the PM and the no-PM task. The analysis revealed no significant effect between PM and no-PM task on number of errors (F1, 76 = 1.56, p = .215), in that participants performing the PM task were not more likely to have incorrect responses (M = 14.58, SD = 7.85) than when they performed the no-PM task (M = 13.83, SD = 7.67). For the between-subject component of the mixed ANOVA, the effect of degree of emphasis was not significant (F1, 76 = .196, p = .66), nor were the effect of stimulus type (F1, 76 = 1.452, p = .23) and the interaction between stimulus type and emphasis (F1, 76 = .209, p = .659). There was a marginal, though not significant, interaction between task type and type of stimuli (F1, 76 = 3.22, p = .077, ή2 = .041, Power = .24), in that participants categorizing words made marginally more incorrect responses during the PM task (M = 16.10, SD = 7.22) than during no-PM task (M = 14.28, SD = 7.02), while there was no difference for those categorizing icons during PM task (M = 13.05, SD = 8.24) and No-PM task (M = 13.38, SD = 8.34). The interaction between task type and degree of emphasis was not significant (F1, 76 = .628, p = .431), nor was the three-way interaction of task type, degree of emphasis, and stimulus type (F1, 76 = .000, p = 1). Figures 12 and 13 illustrate the results of the mixed ANOVA on number of errors.
36
Strong Emphasis
Moderate Emphasis 18 17 16 15 Number of 14 errors 13 12 11 10 9
16.35 14.05 12.55
PM Task
12.4
Icons Words
18 17 16 15 Number of 14 errors 13 12 11 10 9
15.85 14.5 14.35 13.55
Words
PM Task
No-PM Task
Icons
No-PM Task Task type
Task type
Figure 12: Number of categorization errors in PM and no-PM conditions as a function of stimulus type with moderate emphasis
Figure 13: Number of categorization errors in PM and no-PM conditions as a function of stimulus type with strong emphasis
The RTs to categorize the five events after the PM target cue’s occurrence were thought to cause a rumination effect and were therefore trimmed from the prior analyses. A mixed ANOVA examining the relationship between type of stimuli and degree of emphasis using rt_before (reaction times of 14 events prior to the PM targets) and rt_ after (reaction times of 5 events after the PM targets) as a within-subject variable revealed a significant main effect of monitoring (F(1, 73) = 77.132, p < .001, η2 = .514, Power = 1), in that participants’ RTs were significantly longer when they categorized the 5 events after the PM targets (M = 2161.43, SD = 676.25) than when they categorized the 14 events prior to the PM targets (M = 1657.62, SD = 429.52). The interaction between monitoring and degree of emphasis was not significant (F(1, 73) = .067, p = .796), and neither was the interaction between monitoring and type of stimuli (F(1, 73) = .348, p = .557). However, the three-way interaction was statistically significant (F(1, 73) = 4.001, p = .049, η2 = .052, Power = .51), in that the rumination effect (rt_after) increased when comparing words during moderate emphasis (M = 2230.84, SD = 718.62) and words during strong emphasis (M = 2615.00, SD = 694.04) but decreased when comparing icons during moderate emphasis (M = 2005.00, SD = 440.22) and icons 37
during strong emphasis (M = 1805.28, SD = 582.04). Figures 14 and 15 show the relationship among the RTs of monitoring and rumination as a function of stimulus type and degree of emphasis. Monitoring
Rumination
2600 R Ts i n M i l l i se c o n d s
2614
2600
2100 1864.65 1600 1344.49
2100
2050.94 1401.47
Icons Words
1100 600
RTs i n M i l l i se c o nd s
2230.84 2005
1600
1805.28
Icons Words
1100 600 100
100 Moderat e
Moderat e
St rong
St rong
Em p ha si s
Em p h a si s
Figure 14: RTs of the 14 events prior to the PM target cues of icons and words as a function of degree of emphasis
Figure 15: RTs of the 5 events after to the PM target cues of icons and words as a function of degree of emphasis
The mixed ANOVA on monitoring and rumination did not reveal a significant between subject effect on degree of emphasis (F(1, 73) = 1.257, p = .266) but revealed a significant between subject effect on stimulus type (F(1, 73) = 33.407, p < .001, η2 = .314, Power = 1) and a marginal interaction between emphasis and type (F(1, 73) = 3.494, p = .066, η2 = .046, Power = .46). Once again categorizing icons (M = 1639.06, Std. Error = 68.86) was significantly faster than words (M = 2190.36, Std. Error = 66.01). Also, RTs in categorizing icons tended to decrease for moderate (M = 1669.75, SD = 668.22) compared to strong emphasis (M = 1603.38, SD = 429.52), whereas they tended to increase in categorizing words with strong emphasis (M = 2332.96, SD = 526.96) compared to moderate emphasis (M = 2047.75, SD = 403.81). Figure 16 illustrates the relationship of stimulus type as a function of degree of emphasis for monitoring and rumination combined.
38
2800 2600 2400
2332.97
2200 Mean RTs in 2000 milliseconds 1800 1600
2047.75
Icons Words
1669.75
1603.37
1400 1200 1000 Moderate
Strong Emphasis
Figure 16: RTs of monitoring and rumination combined as a function of degree of emphasis stimulus type
A factorial ANOVA was employed to analyze the mean reaction times of PM target cues—from the moment the stimulus was presented to the moment a participant pressed the assigned key—for each type/emphasis matching. There was no prediction for this analysis. Only those PM target hits that occurred within the first three events after the PM target cue were included. The results of the analysis show that there was not a significant main effect of degree of emphasis (F(1, 73) = .11, p = .737), but there was a significant main effect of type of stimuli (F(1, 73) = 9.11, p = .003, η2 = .111, Power = .85) and a marginally significant interaction (F(1, 73) = 3.88, p = .053, η2 = .050, Power = .49). Participants’ RTs in looking for PM target cues and given strong emphasis instructions (M = 2262.75, SD = 834.74) were not different than those given moderate emphasis (M = 2199.72, SD = 714.17). Participants’ RTs in looking for icon PM target cues (M = 1984.59, SD = 648.01) were significantly lower than those of word PM target cues (M = 2500.43, SD = 818.56). Emphasis and type interacted divergently, in that, when participants given moderate emphasis were looking for PM target cues there was no difference in mean RTs
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between icons (M = 2119.82, SD = 762.86) and words (M n = 2293.73, SD = 662.68). On the other hand, a different pattern emerged in the strong emphasis condition. When participants given strong emphasis were looking for word-PM-target cues (M = 2676.13, SD = 910.51) the RTs increased; contrarily, when they were looking for icon-PM-target cues (M = 1849.43, SD = 491.41) the RTs decreased. Figure 17 shows the relationship of degree of emphasis and stimulus type on RTs to hit a PM target cue. 2800 2676.13
2600 2400 2200
2293.73 2119.82
Mean RTs in 2000 milliseconds 1800
Icons 1849.43
Words
1600 1400 1200 1000 Moderate
Strong Emphasis
Figure 17: PM target detection RTs as a function of stimulus type and degree of emphasis
A Pearson correlation was employed to test whether an increase in monitoring would lead to an increase in PM target accuracy as well. The analysis did not reveal a significant correlation (r(78) = .031, p = .394). Additional monitoring did not improve PM target detection accuracy. Pearson correlations were used to assess the relationship between self-report measures on the questionnaire (see Appendix J) and experiment DVs. The self-report measures included perception of performance, retrospective memory, and self-assessment of monitoring. The experiment DV number of PM targets found significantly positively correlated with perception of performance (r(78) = .226, p < .05) and retrospective
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memory (r(78) = .607, p < .05). The greater the number of PM targets found, the higher participants rated their overall performance and the more PM targets they recalled. Perception of performance significantly negatively correlated with the number of categorization errors in both PM (r(78) = -.369, p < .05) and no-PM tasks (r(78) = -.245, p < .05). The more errors participants made in categorizing items, the worse they rated their performance. All the other correlations were not statistically significant. An ANOVA was used to assess the relationship of degree of emphasis and stimulus type on perception of performance. Though marginal, the effect of emphasis was not significant (F(1, 76) = 2.88, p = .094). Participants marginally rated their performance higher when instructed with strong emphasis (M = 3.6, SD = .778) than with moderate emphasis (M = 3.3, SD = .791). There was not a significant main affect of stimulus type (F(1, 76) = .720, p = .399), nor was there a significant interaction between degree of emphasis and stimulus type (F(1, 76) = .08, p = .778). The analysis of the relationship between degree of emphasis and stimulus type on retrospective memory did not reveal a significant main effect of emphasis (F(1, 73) = 2.598, p = .111), nor did it reveal a main effect of stimulus type (F(1, 73) = .222, p = .639). The interaction between emphasis and type was not significant either (F(1, 73) = .044, p = .834). The analysis of the relationship between emphasis and stimulus type on self-assessment of monitoring revealed a significant main effect of degree of emphasis (F(1, 76) = 16.156, p < .001, η2 = .157, Power = .978). Participants were likely to rate their magnitude of monitoring significantly higher during strong emphasis (M = 4.05, SD = .714) than moderate emphasis (M = 3.35, SD = .834). The main effect of stimulus type was not significant (F(1, 76) = .742, p = .392), nor was the interaction between degree of emphasis
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and stimulus type (F(1, 76) = 742, p = .392). Figure 18 illustrates the relation between degree of emphasis and stimulus type on self-assessment of monitoring. 5 4.5 4 Selfassessment of 3.5 monitoring 3
4.05 Icons
3.5 3.2
Words
2.5 2 Moderate
Strong Emphasis
Figure 18: Self-assessment of monitoring as a function of degree of emphasis and stimulus type
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Discussion In the following paragraphs the results and implications of prospective memory accuracy will be discussed first, then the results of prospective memory monitoring, and the results of the additional analyses will be discussed last. Prospective memory accuracy In this experiment, none of the three research hypotheses related to PM accuracy were confirmed. According to the Multiprocess Framework theory (Einstein et al., 2005; McDaniel & Einstein, 2000), prospective remembering is more likely when the importance of the PM task is strongly emphasized; thus an increase in cognitive monitoring would improve the search for PM target cues during the ongoing categorization task. Also, according to the assumptions drawn from the Multiprocess Framework theory, PM remembering is more likely when the PM target cues are distinctive, thus icons should have a higher distinctiveness potential because icons are dually encoded, both verbally and visually, and this would give them an advantage in being detected and encoded over words. Finally, an interaction was predicted in that PM accuracy performance should have been superior for conditions including either icons or strong emphasis but poor for word/moderate emphasis. Participants did not exhibit greater PM memory when categorizing during strong emphasis than during moderate emphasis, nor was their PM performance better with icons than words. There was also no interaction between degree of emphasis and stimulus type on PM performance. These results might be caused by several factors. First, a small sample size may have led to lower power than expected. In Einstein et al. (2005), the sample size for each condition was greater than the current experiment’s (24 participants
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in each condition compared to 20 in this experiment, and 192 trials compared to this study’s 160). It is possible that with a greater sample size the degree of emphasis effect would be a statistically significant result. Secondly, there might have been a ceiling effect in that all participants performed well despite the experimental manipulation. The assumption that icons are more distinctive than words, thus leading to a better PM performance, was not confirmed. Participants found as many PM target cues in the icon condition as in the word condition. Icons might have a faster encoding, as discussed later, but when it comes to processing information, thus better remembering to perform an intended action, participants are as accurate with words as they are with icons. Lastly, the lack of significant results in PM accuracy might have been caused by individual differences. Dissimilarities in reported GPA were significant for RTs of PM task type. It may be possible that some participants might have monitored more than others or some might have been more motivated than others to perform well. This study’s findings do not support the Multiprocess Framework Theory whereby PM accuracy performance is a function of degree of emphasis and stimulus distinctiveness. In their experiment, Einstein, et al. (2005) found a significant interaction between degree of emphasis and type of focal cue on PM accuracy detection, in that participants found significantly more PM targets when performing with either strong emphasis or focal cues but not when performing with moderate emphasis/non-focal cues. It might be that PM accuracy performance differs with type of focal cues but not with distinctive cues or, since Einstein used only words, it might be that differences in PM performance can be found by manipulating PM targets within stimulus type, i.e., either by using only icons or words.
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None of the alternative theories appear to support the results of this experiment. The Preparatory Attentional and Memory Process Theory proposed by Smith and Bayen (2004) states that PM performance is never automatic and capacity-consuming preparatory processes are necessary for successful PM remembering. If this was true, then strong emphasis given at the time of instruction should have lead to a better PM performance than moderate emphasis. The Spontaneous Retrieval Theory claims that, for successful prospective remembering to occur, a cue target needs to be fully processed at retrieval and that individuals need to form a solid encoding between the cue and the intended action (Einstein et al., 2005). It could be that participants in this study did make a thorough association between cue and intended action but this was not a function of stimulus type, nor was it a function of degree of emphasis. Prospective memory monitoring Two of the 4 research hypotheses related to task interference (monitoring) were substantiated. Adding a prospective memory task to an ongoing, categorization task does increase monitoring, which confirms this study’s prediction and the findings of Preparatory Attentional Monitoring (PAM) and Multiprocess Framework theories. However, the amount of monitoring participants needed to remember to perform an intended action during an ongoing task was not equal across the experimental conditions and was influenced by stimulus type and degree of emphasis. The results supported the research hypothesis predicting that participants would monitor less when confronted with icon pairs than word pairs. During the PM task, participants were faster in categorizing icons than words, which is evidence that they monitored less with icons than with words. Though these findings are not consistent with
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the assumption that icons are more distinctive, and therefore lead to a superior PM accuracy performance compared to words, icons certainly led to more spontaneous retrieval that aided participants in categorizing faster. This validates Paivio’s (1971) dual coding theory whereby icons are dually encoded and therefore processed more quickly than words and the assumptions drawn from the Multiprocess Framework theory whereby icons are more distinctive and lead to less monitoring than words. The research hypothesis regarding the impact of degree of emphasis was not supported. According to the Multiprocess Framework Theory, monitoring should be expected to increase when strong emphasis is given and decrease if moderate emphasis is given. The prediction was that participants focusing on detecting as many PM target cues as possible while categorizing items (strong emphasis) should reveal longer RTs than those focusing on categorizing items as fast as possible while looking for PM target cues (moderate emphasis). Although marginally significant, participants categorizing items in the strong emphasis conditions were not significantly slower than those categorizing items in the moderate emphasis conditions. An interesting pattern of results emerged when participant’s GPA was controlled for in the analysis of monitoring. In this analysis, icons were categorized faster than words but the marginal effect of emphasis was no longer present, while GPA significantly affected the overall variance. This analysis might have been influenced by the fact that GPA was not evenly distributed across conditions. Participants categorizing words and receiving strong emphasis had a significantly higher GPA than those performing on icons with the same degree of emphasis, while the opposite was true for moderate emphasis. Participants declaring a high GPA might have been more motivated
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to perform skillfully than those with lower GPAs, thus dulling the predicted effect of degree of emphasis. The results of the manipulation of degree of emphasis do not support the prediction drawn from the Multiprocess Framework theory whereby strong emphasis would cause more task interference than moderate emphasis, and do not replicate the findings of Einstein’s (2005) experiment. Though the results of the analysis might have been affected by low power, they seem to suggest that controlling for individual differences in self-report measures may be an effective approach to assess PM performance in manipulating degrees of emphasis. The hypothesized interaction between degree of emphasis and stimulus type on monitoring was not supported. According to the assumptions drawn for the Multiprocess Framework Theory, the difference in RTs between icons and words with strong emphasis would be greater than with moderate emphasis. A greater increase in monitoring should occur for words during strong emphasis than for icons during strong degree of emphasis. The reason for this rationale lies in the assumption that participants receiving strong emphasis are more motivated to perform effectively than those receiving moderate emphasis, thus engaging in more cognitive effort, which generates longer RTs (McDaniel & Einstein, 2000). But this drive to perform effectively is mediated by the type of stimuli with which a participant performs. Therefore categorizing icons should mitigate cognitive engagement more than words. The lack of significant interaction did not support the research hypothesis. The assumption drawn from the Multiprocess Framework Theory does not hold true with this experiment. The main effect of type of stimuli and the marginal effect of degree of
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emphasis did not interact and maintained a distinct parallel direction. Although the power was very low, the total variance was affected by the significant result of the covariate GPA, and despite the fact that the obtained trend resembled the predicted one, PM monitoring performance categorizing icons or words does not seem to be a function of degree of emphasis. The assumptions of the Multiprocess Framework theory offer a more thorough explanation to understand the results of this study than PAM theory. In terms of PM performance, icons have a distinctive quality and are processed faster than words on both simple and complex tasks, but this distinctiveness does not lead to better accuracy in remembering to perform an intended action, i.e., icons are faster than and achieve equal PM performance as words. The results of this study overall do not support the assumptions of the PAM theory whereby PM performance increases as does monitoring. Monitoring may decrease as a function of stimulus type and PM target detection accuracy is not positively correlated with amount of monitoring. Adjunct examinations Although no hypothesis was formulated regarding item categorization accuracy (number of correct responses), an analysis revealed that participants were marginally more likely to incorrectly categorize word pairs during the PM task than during No-PM task. This pattern was not evident for icon pairs. A probable explanation for this finding is that monitoring might have led to a cost for categorization accuracy; that is, participants did find just as many PM targets in the word condition as they did in the icon condition but this might have been at the expense of categorization accuracy and longer RTs.
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The RTs of the five events after the PM target cues were significantly longer than the RTs of the 14 events prior to targets, and confirm the rumination effect captured by Einstein’s et al. (2005) experiment, meaning that participants’ RTs slow after they detect a PM target cue. Furthermore, the rumination effect was overall larger for words than for icons and especially noticeable during strong emphasis with words over icons. This means that strong emphasis instructions compel participants to think more when engaging with words whereas with icons such thought processes may not be affected as much due the distinctive nature of icons. Though there was no prediction for examining the RTs to detect the PM target cues, the analysis revealed that icon PM targets were detected faster than word PM targets, which further confirms Paivio’s Dual Coding Theory (1971). Moreover, degree of emphasis interacted with stimulus type. When given strong emphasis, word PM target detection increased while icon PM target detection decreased. This finding indicates that when a task is perceived as important, icon PM target cues are detected faster and more easily than words, further suggesting that icons may be more distinctive than words. Practical applications The major finding in this experiment is that, in general, categorizing icons is faster than categorizing words but when a secondary PM task is added to an ongoing task monitoring increases for both icons and words as well. Icons, though, achieve equal PM accuracy performance as words in less time. Moreover, marginal results suggest the possibility that strongly emphasizing the importance of the task increases monitoring and that performing with words generates more wrong responses in the ongoing task than icons.
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Icons should be employed in product interfaces because users tend to have faster search times without having to pay a cost in accuracy. In website design, icons should be employed whenever a user needs to quickly find a selection. For instance, in building home pages, icons should be used to categorize menu options so that users won’t have to search too long and become frustrated. The same suggestion applies to computer application interfaces. The most common or most used functions in a computer program should be portrayed with icons so that the search time will be greatly reduced. An example of an improved application interface is Word© 2007. The new version enables its users to see the major selections for each tab, almost all the selections are represented by icons, and pop-up menus are drastically minimized The Multiprocess Framework’s account of prospective memory may be applied to investigating failure in airplane flight operations. Why do pilots forget to perform an intended action? The search should look into how important that operation is perceived by a pilot, what cue distinctiveness the interface exhibits, and what level of focal processing is required to encode a symbol. For instance, technology such as the Synthetic and Enhanced Vision System (SVS/EVS) for flights during weather conditions makes use of interfaces that faithfully reproduce the external environment, detect shapes and objects, and display flight information. PM findings may be applied to better understand how pilots operate with SVS/EVS and to determine if the interface can be improved. If high importance tasks have cognitive costs in terms of monitoring then an enhanced interface should include symbols that are distinctive, related to the task, or highly focal. Prospective Memory knowledge may be also applied to design interface devices that aid individuals affected by Alzheimer's disease to carry out simple, daily functions.
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In their study, Jones, Livner and Bäckman (2006) found that episodic memory impairment in preclinical Alzheimer's disease generalizes across prospective and retrospective memory tasks and that this impairment can be seen in preclinical stages of the disease. Using icons to develop interfaces on devices such as a blood sugar analyzer could help a patient to perform the task efficiently and to remember to do it at a proper time. Limitations This study’s findings apply to a university student sample and should not be extended to the general population. Students were all from California State University, Northridge, with an age range from 18 to 27 years (excluding one person of 37 and one of 44). Also, the conclusions of this study are drawn from an experiment manipulation that contained a categorization task and should not be extended to other scenarios. PM target cues included one match and three mismatches and therefore PM accuracy and RT performance might be different if employing a fully counterbalanced PM target set. Finally, individual differences were a random variable, uncontrolled for. For example, the effects of disparity in students’ GPA scores are not fully clear and individual differences in academic standing might also contribute to the variations in response times. More investigation is needed to understand how students perform controlling for GPA and academic standing. Future investigations There are several avenues to further investigate Prospective Memory. One way to expand the probe is to manipulate the levels of search within either icons or words. For instance, icon PM targets could have different focal processing, one in relation to the
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“whole” image and the other to a detail. It would be worthwhile to look into whether monitoring increases when participants are searching for low focal PM target cues, and whether high focal ones generate greater PM performance. A second suggestion is to change the ongoing task with a simulated scenario. For instance; a driving simulation, including a secondary PM task and manipulating degree of emphasis and/or type of stimulus. The research question would be to determine the relationship between degree of emphasis and type of stimuli on PM performance and PM monitoring using an animated scenario rather than a categorization task. A third proposition could be determining whether participants with different GPAs vary in monitoring pattern and in PM performance. The research question would be to investigate whether students with high GPA perform better than those with low GPA and if this has a cost in terms of monitoring effort. These are three possible research areas that could broaden this study’s findings. Conducting investigations about Prospective Memory is not only an intriguing venture, it is also useful because the results can be applied to real-world situations and could lead to insight into reducing monitoring and error rate and increasing PM accuracy in a variety of applied settings.
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References Anderson, J. R. (1983). A spreading activation theory of memory. Journal of Verbal Learning and Verbal Behavior, 22, 261-295. Bradimonte, M. A., Passolunghi, M. C. (1994). The effect of cue-familiarity, cuedistinctiveness and retention interval on prospective remembering. Quarterly Journal of Experimental Psychology, 47, 565-588. Cicogna P., Nigro G., Occhionero M. & Esposito M. J., (2005). Time-based prospective remembering: interference and facilitation in a dual task. European Journal of Cognitive Psychology, 17 (2), 221-240. Craik, F. I. M., (1986). A functional account of age differences in memory. In F. Klix & H. Hagendorf (Eds.), Human memory and cognitive capabilities: Mechanisms and performances (pp. 409-422). Amsterdam: Elsevier-North-Holland. Dieckmann P., Reddersen S., Wehner T., & Rall M. (2006). Prospective memory failures as an unexplored threat to patient safety: Results from a pilot study using patient simulators to investigate the missed execution of intentions. Ergonomics, 49, 526543. Dismukes, K. R., Young, G., & Sumwalt, R. E., (1998). Cockpit interruption and Distractions. ASRS Directline, 10, 4-9. [on-line], available: http:/asrs.arc.nasa.gov/directline_issue/d110_distract.htm Dismukes, K. R., Loukopoulos, L. D., & Jobe, K. K. (2001). The challenges of managing concurrent and deferred tasks. Proceedings of the 11th International Symposium on Aviation Psychology. Columbus, OH: Ohio State University.
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Einstein, G. O. & McDaniel, M. A. (1990). Normal aging and prospective memory, Journal of Experimental Psychology: Learning, Memory, and Cognition, 16, 717726. Einstein, G. O., & McDaniel, M. A. (1996). Retrieval processes in prospective memory: Theoretical approaches and some new empirical findings. In M. Bradimonte, G. O. Einstein, & M. A. McDaniel (Eds.), Prospective Memory: Theory and Applications (pp. 115-142). Mahwah, NJ: Erlbaum. Einstein, G. O., McDaniel, M. A., Manzi, M., Cochran, B., & Baker, M. (2000). Prospective memory and aging: Forgetting intentions over short delay. Psychology and Aging, 15, 671-683. Einstein, G. O., McDaniel. M. A., Richardson, S. L., Guynn, M. J., & Cunfer, A. R. (1995). Aging and prospective memory. Journal of Experimental Psychology, Learning, Memory, and Cognition, 21, 996-1007. Einstein, G. O., McDaniel, M. A., Thomas, R., Mayfield, S., Shank, H., Morrisette, N., & Breneiser, J. (2005). Multiple processes in prospective memory retrieval: Factors determining monitoring versus spontaneous retrieval. Journal of Experimental Psychology: General, 134, 327-342. Fortin, S., Godbout, L., & Braun, C. M. J. (2002). Strategic sequence planning and prospective memory impairments in frontally lessoned head trauma patients performing activities of daily living. Brain and Cognition, 48, 215-228. Hicks, J. L., Marsh, R. L., & Cook, G. I. (2005). Task interference in time-based, eventbased, and dual intention prospective memory conditions. Journal of Memory and Language, 53, 430-444.
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Hu, X. & Batchelder, W. H. (1994). The statistical analysis of general processing tree models with the EM algorithm. Psychometrika, 59, 21-47. Jones, S., Livner, Ǻ., Bäckman, L. (2006). Patterns of prospective and retrospective memory impairment in preclinical Alzheimer’s disease. Neuropsychology, 20 (2), 144-152. Kliegel, M., Martin, M., McDaniel, M. A., & Einstein, G. O. (2001). Varying the importance of a prospective memory task: Differential effect across time- and event-based memory. Memory, 9(1) 1-11. Kliegel, M., & Martin, M. (2003). Prospective memory research: why is it relevant? International Journal of Psychology, 38 (4), 193-194. Kvavilashvili, L. (1998). Remembering intentions: Testing a new method of investigation. Applied Cognitive Psychology, 12(6), 522-554. Marsh, R. L., & Hicks, J. L. (1998). Event-based prospective memory and executive control of working memory. Journal of Experimental Psychology: Learning, Memory, and Cognition, 24, 336-349. Marsh, R. L., Hicks, J. L., Cook, G. I., Hansen, J. S., & Pallos, A. S. (2003). Interference of ongoing activities covaries with the characteristics of an event-based intention. Journal of Experimental Psychology: Learning, Memory, and Cognition, 29(5), 861-870. Marsh, R. L., Hicks, J. L., & Hancock, T. W. (2000). On the interaction of ongoing cognitive activity and the nature of event-based intention. Applied Cognitive Psychology, 14(7), 29-41.
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Marsh, R. L., & Hicks, J. L., & Watson, V. (2002). The dynamics of intention retrieval and coordination of action in event-based prospective memory. Journal of Experimental Psychology: Learning, Memory, and Cognition, 28, 652-659. McDaniel, M. A., & Einstein, G. O. (2000). Strategic and automatic processes in prospective memory retrieval: A multiprocess framework. Applied Cognitive Psychology, 14, S127-S144. Meier, B., Zimmermann, T., & Perrig, W.J. (2006). Retrieval experience in prospective memory: Strategic monitoring and spontaneous retrieval. Memory, 14, 872-889. Paivio, A. (1971). Imagery and verbal processes. New York: Holt, Rinehart and Winston. Penningroth, L. S., (2005). Effects on attentional demands, cue typicality, and priming on an event-based prospective memory task. Applied Cognitive Psychology, 19, 885897. Reese, C. M., & Cherry, K. E. (2002). The effects of age, ability, and memory monitoring on prospective memory task performance. Aging, Neuropsychology, and Cognition, 9, 98–113. Riefer, D. M., & Batchelder, W. H. (1988). Multinomial modeling and the measurement of cognitive processes. Psychological Review, 95, 318-339. Shallice, T. & Burgess, P. (1991). Deficits in strategy application following frontal lobe damage in man. Brain, 114, 727-741. Smith, R. E. (2003). The cost of remembering to remember in event-based prospective memory: Investigating the capacity demands of delayed intention performance. Journal of Experimental Psychology-Learning Memory and Cognition, 29 (3), 347-361.
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Smith, R. E., & Bayen, U. J. (2004). A multinomial model of event-based prospective memory. Journal of Experimental Psychology-Learning Memory and Cognition, 30 (4), 756-777. SuperLab 4.0 [Computer Software]. (2007). San Pedro, CA: Cedrus Corporation.
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Appendix A Example of Icon-Icon Pairs in the Icon Condition. On the left column are samples of the icon-pair mismatches and on the right column are samples of the icon-pair matches.
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Appendix B Example of Word-Word Pairs in the Word Condition. On the left column are samples of the word-pair mismatches and on the right column are sample of the wordpair matches.
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Appendix C Example Prospective Memory Targets for the Icon-Icon and Word-Word Conditions. On the left column are samples of the pair mismatches and on the right column are samples of the pair matches.
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Appendix D Instructions for the Ongoing Categorization Task for the Icon Condition
“You are requested to perform a categorization task on a computer screen. You will see a series of icon-icon pairs appearing one pair at a time and your task is to decide whether the two items in a pair belong to the same category or not. If the items do match, then you will press the key labeled “Yes” on the keyboard. If the items do not match, then you will press the key labeled “No” on the keyboard. Make sure to respond as quickly and accurately as possible. There are three categories: a) Animals, which includes depictions or caricatures of mammals, reptiles, amphibians, birds, fish and insects; b) Artifacts, which includes a variety of human-made objects; and c) Vegetations, which includes plants, fruits, vegetables, and trees. The items to be categorized are prominent and in the foreground, so they are easy to identify. Before starting the actual experiment you need to do some practice trials so that you will become familiar with the task. Do you have any questions about the instructions? Would you please repeat what you are asked to do?”
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Appendix E Example of the Instructions for the Strong Emphasis on the PM Task for the Icon Condition
“In addition to performing the ongoing task, we also are interested in a secondary task. Any time you see an icon that portrays a category item with “wings” you need remember to press the “blue”-labeled key on the keyboard. In this event, you don’t have to answer the categorization task by pressing either “yes” or “no”, but just press the “blue”-labeled key. If, for any reason, you fail to press the “blue” key but soon after you remember having seen a category item with wing(s), you are allowed to press the “blue” key even during the subsequent trials.
It is extremely important that you concentrate on looking for the icons of category items with wings and to press the “blue” key afterwards. It is critical that you consider your main goal in this section to find absolutely every occurrence of category items with wings.
Do you have any questions about this task?”
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Appendix F Example of the Instructions for the Moderate Emphasis on the PM task in the Icon Condition
“In addition to performing the ongoing task we have a secondary interest in your ability to remember to perform an action in the future. Any time you see an icon portraying a category item with wings you should remember to press the “blue”-labeled key on the keyboard. When you spot an icon portraying a category item with wings you don’t have to answer the categorization task but just press the “blue”-labeled key. If, for any reason, you fail to press the “blue” key but soon after you remember having seen a category item with wings, you are allowed to press the “blue” key even during the subsequent trials.
It is important, though, that you concentrate on the categorization task. It is critical that you consider the categorization task as your main goal and to make the category decisions as quickly and accurately as possible. You should not try to monitor for the icons of category items with wings but mainly focus on the categorization task.
Do you have any question about this task?”
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Appendix G Instructions for the Ongoing Categorization Task for the Word Condition
“You are requested to perform a categorization task on a computer screen. You will see a series of word-word pairs appearing one pair at a time and your task is to decide whether the two items in a pair belong to the same category or not. If the items do match, then you will press the key labeled “Yes” on the keyboard. If the items do not match, then you will press the key labeled “No” on the keyboard. Make sure to respond as quickly and accurately as possible. There are three categories: a) Animals, which includes depictions or caricatures of mammals, reptiles, amphibians, birds, fish and insects; b) Artifacts, which includes a variety of human-made objects; and c) Vegetations, which includes plants, fruits, vegetables, and trees. Before starting the actual experiment you need to do some practice trials so that you will become familiar with the task. Do you have any questions about the instructions? Would you please repeat what you are asked to do?”
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Appendix H Example of the Instructions for the Strong Emphasis on the PM Task for the Word Condition
“In addition to performing the ongoing task, we also are interested in a secondary task. Any time you see a word that represents a category item that has “wings” you need remember to press the “blue”-labeled key on the keyboard. In this event, you don’t have to answer the categorization task by pressing either “yes” or “no”, but just press the “blue”-labeled key. If, for any reason, you fail to press the “blue” key but soon after you remember having seen a word that represents a category item that has wings, you are allowed to press the “blue” key even during the subsequent trials.
It is extremely important that you concentrate on looking for words that represent items that have wings and to press the “blue” key afterwards. It is critical that you consider your main goal in this section to find absolutely every occurrence of words that represent items that have wings.
Do you have any questions about this task?”
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Appendix I Example of the Instructions for the Moderate Emphasis on the PM task in the Word Condition
“In addition to performing the ongoing task we have a secondary interest in your ability to remember to perform an action in the future. Any time you see word that represents a category item that has “wings” you should remember to press the “blue”-labeled key on the keyboard. When you spot a word that represents a category item that has “wings” you don’t have to answer the categorization task but just press the “blue”-labeled key. If, for any reason, you fail to press the “blue” key but soon after you remember having seen a word that represents a category item that has “wings”, you are allowed to press the “blue” key even during the subsequent trials.
It is important, though, that you concentrate on the categorization task. It is critical that you consider the categorization task as your main goal and to make the category decisions as quickly and accurately as possible. You should not try to monitor for words that represent category items that have “wings” but mainly focus on the categorization task.
Do you have any question about this task?”
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Appendix J Questionnaire
1) Age___________________________ 2) Gender________________________ 3) Major_________________________ 4) G.P.A._________________________ 5) Year in school: (circle) Freshman, Sophomore, Junior, Senior, Graduate Yes
6) Is English your first language? (circle one)
No
7) If not, at what age did you start learning English?_______________________ 8) Are you colorblind? (circle one)
Yes
No
9) How do you think your performance in detecting the PM targets (wings) was? (circle one)
5 - excellent
4 - good
3 - average
2 - poor
1- terrible
10) How many “wings” did you encounter?______________________
11) When you were engaging in the categorization task and the secondary (PM) task, how often did you think about looking for wing(s)? (circle one) 5 - always
4 - often
3 - sometimes
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2 - rarely
1 - never
