Running Head: PROSPECTIVE MEMORY IN VR AND REAL LIFE 1
*** This manuscript draft is under review. Do not cite without authors’ permission! ***
Immersive virtual reality gameplay captures age differences in naturalistic prospective memory
Joseph Saito & Nathan S. Rose*
University of Notre Dame
*Address Correspondence to:
Nathan S. Rose
390 Corbett Family Hall
Notre Dame, IN 46556 [email protected]
PROSPECTIVE MEMORY IN VR AND REAL LIFE 2
Abstract
Age differences in prospective memory (PM)—memory for delayed intentions—have shown paradoxical patterns between laboratory and naturalistic settings. Virtual reality (VR) has been used to try and enhance the ecological validity of PM assessments, but methodological differences and limited validation have undermined interpretation of previous findings. We compared age differences between VR- and naturalistic-based measures of PM performance for younger (18-30 years) and older (56-83 years) adults (N = 111) to explore the role of task context and familiarity. Participants completed PM tasks embedded in the Job Simulator VR videogame and a Breakfast task that involved setting a table and simulating breakfast food preparation. We also included two real-world measures in which participants tried to remember to exchange personal belongings with the experimenter (Belongings task) and return phone calls at specific times outside the lab (Call-back task). We found comparable age deficits in Job Simulator and the Breakfast task. However, the age-PM paradox persisted in the Belongings and Call-back tasks. Hierarchical regression modeling was conducted to determine the roles of working memory, vigilance, and personality traits in each. Regression analyses revealed that significant variance in lab-based PM performance was accounted for by individual differences in working memory and agreeableness in older adults, while vigilance and neuroticism in young adults accounted for variance in naturalistic PM performance. This study suggests that immersive VR gameplay provides ecologically valid PM assessment and advances a theoretical account of the age-PM paradox with a systematic, task-based analysis of age and individual differences in PM.
Keywords: prospective memory, aging, virtual reality, naturalistic, vigilance, working memory, personality
PROSPECTIVE MEMORY IN VR AND REAL LIFE 3
Prospective memory (PM) refers to the collection of cognitive processes that allow individuals to remember to perform intended actions at the appropriate moment in the future.
Daily life is critically dependent on PM (e.g. remembering to buy milk at the market, taking medication at the proper time) (Einstein & McDaniel, 1990). As such, the preservation of PM in later stages of life is critical for maintaining an independent lifestyle (Crovitz & Daniel, 1984;
Terry, 1988; Einstein & McDaniel, 1990; Ellis, 1996; Kliegel & Martin, 2003; Hering et al.,
2018; Woods et al., 2012). A substantial body of research has been conducted to try and understand the origins of PM differences between younger and older adults (Brandimonte,
Einstein, & McDaniel, 2014). The multi-process framework of PM was developed by McDaniel
& Einstein (2000) to describe how strategic monitoring and spontaneous retrieval processes support PM differently as a function of task features, such as the type of retrieval cue, as well as the differences between groups of individuals, such as younger and older adults.
The Multi-process Framework of Prospective Memory
PM tasks with event-based and time-based PM cues have been shown to place different levels of demand on individuals’ cognitive abilities (Will et al., 2009; Rose et al., 2010; Aberle et al., 2010; Rose et al., 2015). Event-based tasks are triggered by an event in one’s environment, and have typically displayed better performance compared to time-based tasks—which consist of intentions cued by the passage of a set period of time. This benefit for event-based tasks is suggested to result from decreased demand on the controlled attentional and working memory processes required for active monitoring and retrieval (Einstein et al., 1995; Park et al., 1997;
Brandimonte, Ferrante, Feresin, & Delbello, 2001; d’Ydewalle, Bouckaert, & Brunfaut, 2001;
Cona, Bisiacchi, & Moscovitch, 2013; Rose et al., 2015). Older adults have been shown to display significant age-related impairments on time-based task performance relative to younger
PROSPECTIVE MEMORY IN VR AND REAL LIFE 4 adults (d’Yedwalle et al., 2000; Einstein et al., 1995; Park et al., 1997; Rendell & Craik, 2000).
While individuals display age decrements in event-based tasks as well, the size of group differences can be mitigated or eliminated by adjusting the salience of the retrieval cue
(McGann, Defeyter, Ellis, & Reid, 2005; Rose et al., 2010). A cue is more salient when it is centrally-embedded in the ongoing task, making it easier to detect via spontaneous retrieval processes (McDaniel & Einstein, 2000). This is in line with Craik, Klix, & Hagendorf’s (1986) hypothesis that older adults show greater deficits on tasks requiring self-initiated monitoring and retrieval. However, age differences in PM performance are not consistent across studies. Older adults are sometimes found to perform comparably or even outperform younger adults—a pattern referred to as the age-prospective memory paradox (Rendell & Thomson, 1999; Rendell
& Craik, 2000).
The Age-Prospective Memory Paradox
While age-related declines in PM are typically observed in conventional lab-based paradigms, older adults have been shown to perform comparably, and sometimes outperform younger counterparts when the task is to be performed in naturalistic settings (Aberle et al.,
2010; Rendell & Thomson, 1999; Rendell & Craik, 2000; Kliegel, McDaniel & Einstein, 2008).
One hypothesis is that this reversal of age-related deficits is due to differences in the ecological validity of naturalistic tasks (e.g. having subjects simulate adhering to a medication regimen in real life over the course of several days) and conventional lab-based measures of PM (e.g. pressing a computer key when a specific word is presented during a computerized lexical decision task). It may be the case that the latter does not capture PM processes as they operate in everyday life. This concern about ecological validity is common with regards to psychological assessment. Parsons’ (2015) review describes many traditional psychological assessments as
PROSPECTIVE MEMORY IN VR AND REAL LIFE 5
“construct-driven” rather than “functionally-driven”, meaning that attempts to isolate psychological constructs may counterproductively limit prediction of real-world behavior. As it pertains to age differences in PM research, few studies have systematically compared differences across multiple different lab-based and naturalistic task settings.
While lab-based measures enable greater experimental control than naturalistic measures
(Einstein and McDaniel, 1990; Einstein, Holland, McDaniel, & Guynn, 1992), their abstract, unfamiliar attributes may disproportionately impact older adults compared to younger adults
(Altgassan, Kliegel, Brandimonte, & Filippello, 2010). Unfortunately, attempts to use naturalistic task designs that are more familiar to older adults make it difficult to account for differences in lifestyle demands and motivation between age groups (Henry et al., 2004; Rendell & Thomson,
1999, Aberle et al., 2010). As a result of these limitations, a comprehensive account of the age-
PM paradox is still lacking. In an attempt to bridge the gap in ecological validity between naturalistic and lab-based measures of PM, research using virtual reality (VR) has become more prominent in recent years.
Virtual Reality and Prospective Memory
Virtual reality, a technology developed and marketed largely as an entertainment product, has been utilized to induce naturalistic perceptual experiences without costs in experimental control. Not surprisingly, the rapid evolution of VR in recent years has created inconsistency in the way individuals define “virtual reality” when describing their methods. These methodological differences are important because they have implications about the extent to which VR is able to simulate real-world interaction.
In the case of the extant PM literature, visual stimuli are predominantly presented via either standard computer monitors (Debarnot et al., 2015; Gonneaud et al., 2012; Sakai et al.,
PROSPECTIVE MEMORY IN VR AND REAL LIFE 6
2018) or head-mounted displays (HMD) (Parsons & Barnett, 2017; Brown et al., 2016; Banville et al., 2010). While each of these methods can make use of three-dimensional environments, they are critically different in the way they embody them. HMDs allow for visual displays that adjust in real-time with physical head movements. The duality of synchronized visual input and motor function has been shown to recreate real-world patterns of neural activation to a greater extent than visual input alone (Taube, Valerio, & Yoder, 2013). One study used virtual reality with mice and found that only 25% of localized place cell activation was achievable with visual input alone, while the remaining 75% activation required additional proprioceptive and vestibular information (Chen, King, Burgess, & O’Keefe, 2012). Another drawback cited in Rizzo,
Buckwalter, & Neumann’s (1997) review of VR’s use in cognitive rehabilitation argued that
“cyber sickness”, a form of motion sickness resulting from perceptual “lag” between visual and motor modalities was a significant barrier in the expansion of VR. Fortunately, many modern
HMD refresh rates and pixel densities are large enough to minimize this concern (Scarfe &
Glennerster, 2015). HMDs may offer the best available means of preserving real-world visuospatial immersion while mitigating concerns of cyber sickness.
Additionally, it is critical that individuals navigate virtual environments in the same manner as they do in the real world. Many VR-based PM studies on healthy adults, including those using HMDs, predominantly utilize keyboards, joysticks, or touchscreens to allow movement in the virtual environment (Debartnot et al., 2015; O’rear & Radvansky, 2018;
Parsons & Barnett, 2017, Okahashi et al., 2013, Sakai et al., 2018). Participants are often seated and static throughout the experiment. This is especially true for VR-based PM studies that combine neuroimaging techniques (Brown et al., 2016; Dong, Wong & Luo, 2018; Kalpouzos et al., 2010). However, much like the aforementioned studies comparing HMDs to two-dimensional
PROSPECTIVE MEMORY IN VR AND REAL LIFE 7 displays, static motor conditions may not allow individuals to fully recruit cognitive and neurological processes permitted by natural locomotion (Clark, 1997, 2008; Shapiro, 2011;
Varela, Thompson & Rosch, 1991). A study by Chance, Gaunet, Beall, & Loomis (1998) compared navigational skills between subjects who wore an HMD and walked naturally within a room-scale virtual environment and those that simulated locomotion with a joystick. The additional vestibular and proprioceptive input from the walking condition lead to improved performance relative to the joystick condition, as well as lower incidence of cyber sickness. This same pattern was also evidenced in other studies that found significant performance differences between active motion and passive transport conditions in visual discrimination tasks (Simons &
Wang, 1998; Wang & Simons, 1999). These improvements in immersion and performance that resulted from increased locomotive freedom suggests that individuals’ ability to apply their structured knowledge and experience from the real-world may be limited in less immersive conditions.
The Present Study
In order to address the impact of ecological validity on PM and the plausible remedy provided by VR, the present study employed the use of a HMD, wireless handheld controllers, and free walking through a motion-tracked, room-scale virtual environment. To our knowledge, this is the first to employ such a design to explore age differences in PM. One recently-published study has paired a HMD and a handheld pointer to explore the efficacy of a VR shopping task for capturing age differences in everyday memory (Ouellet et al., 2018), and they validated their VR paradigm by establishing a significant relationship between PM performance in VR and subjective reports of everyday memory. While these results are encouraging, Ouellet et al.,
(2018) acknowledged that the use of subjective measures of real-world memory limit the extent
PROSPECTIVE MEMORY IN VR AND REAL LIFE 8 of this validation. To build upon these preliminary findings, conducted a systematic investigation of several objective measures of naturalistic PM to compare against our immersive VR task.
Participants completed PM tasks embedded in the VR game, Job Simulator (Owlchemy
Labs, Co., Austin, TX), as well as a naturalistic measure: the Breakfast task (Altgassan, Koban &
Kliegel, 2012). Job Simulator is a commercially-marketed video game in which players participate in futuristic, tongue-in-cheek simulations of what it was like when humans had to work. Players simulate role-playing different jobs such as a convenience store clerk or a short- order chef while interacting with and following instructions given by a “Job-bot”—an anthropomorphic robot with a computer monitor for a face.
The Breakfast Task was modeled after the previously-validated Dresden Breakfast Task
(Altgassan, Koban & Kliegel, 2012) in which participants were asked to prepare a breakfast for a large group and set the table while abiding by time and procedural constraints that are common in everyday meal preparation. Despite the abstract, unfamiliar nature of the Job Simulator VR videogame to older adult participants, because of the high degree of immersion and the engagement of familiar behaviors used to complete naturalistic PM tasks in VR, we hypothesized that age differences in PM performance on Job Simulator would be comparable to those found on the Breakfast task.
We further verified these demand characteristics by having participants complete two tasks assessing working memory (WM) and attentional vigilance, respectively. WM has been repeatedly shown to decline with age and modulate PM performance (Salthouse, 1994; Park,
2000; Rypma & D’Esposito, 2000; Park et al., 2002; Sarter & Bruno, 2004; Bopp & Verhaegan,
2005; Rose, Myerson, Somers, & Hale, 2009; Rose et al., 2010). WM allows for maintenance of information relevant to one’s current task goals in the face of distraction and shifts in one’s focus
PROSPECTIVE MEMORY IN VR AND REAL LIFE 9 of attention, and has been shown to predict PM performance on tasks that require balancing PM intentions and ongoing task demands (Engle, Tuholski, Laughlin, & Conway, 1999; Kane,
Conways, Hambrick, & Engle, 2007; Rose et al., 2010, Unsworth, McMillan, Brewer, &
Spillers, 2012). Vigilance has also been implicated as underlying successful PM task performance, particularly in PM task conditions that require a high degree of sustained attention
(Brandimonte et al., 2001; Graf & Uttle, 2001). Previous research found significant relations between age and individual differences in working memory capacity, but not vigilance, and performance on PM tasks during the Virtual Week game, particularly for tasks with less focal cues such as irregularly-occurring time-based cues (Rose et al., 2010). Such findings provide some support for Lindenberger & Potter’s (1998) suggestion that age-related variance in PM may be independent of vigilance ability. We hypothesized that our measure of working memory, but not psychomotor vigilance, would significantly predict time-based PM performance on the
Job Simulator and Breakfast tasks.
We also included a personality trait inventory (i.e. BFI-II) to evaluate how developmental changes in personality may help to explain the paradoxical patterns of age differences between lab-based and real-world settings. Studies assessing the changes in personality across the lifespan have found evidence for increased agreeableness and conscientiousness, coupled with decreases in neuroticism, extraversion, and openness as a function of adulthood (Allemand, Zimprich, &
Hendriks, 2008; McCrae et al., 1999). Cuttler & Graf’s (2007) study on the relationship between personality traits and PM found that socially-prescribed perfectionism & neuroticism each predicted performance on one of their two lab-based PM measures, while conscientiousness predicted performance on both one of the lab-based tasks and their real-world PM task. Given
PROSPECTIVE MEMORY IN VR AND REAL LIFE 10 this, we hypothesized that age-related increases in agreeableness and conscientiousness would mirror the “paradoxical” pattern of age-related benefits to real-world PM performance.
Yet another aim of the current study was to account for the “task context” dimension of ecological validity with regards to PM assessments. Previous studies have shown that increasing the ecological validity of a lab-based PM measure may not sufficiently account for paradoxical age-related findings (Rendell & Craik, 2000; Hering, Cortez, Kliegel, & Altgassan, 2014).
Rendell & Craik’s (2000) original Virtual Week task consisted of a computerized board game, where subjects progress through the course of five consecutive days of “normal life”, completing
PM behaviors reasonably familiar to everyday experience (e.g. taking medication at 11 a.m., dropping off dry cleaning after lunch). They found that, despite including tasks that may benefit those with greater experience performing them in everyday life, younger adults still outperformed older adults. To account for this, we included two secondary measures of PM that took place outside of the formal testing session. One task involved remembering to retrieve one’s cell phone and return a movement tracker attached to the subject’s clothing at the end of the experiment (Belongings Task). The other involved calling the experimenter’s office by phone multiple times while out in the real world at times between the in-lab sessions (Call-back task;
Rose et al., 2015). Our goal was to determine if the PM paradox would persist in these naturalistic measures performed outside the lab and to construct hierarchical regression models of PM using the potential mediator variables that were assessed (i.e. WM, vigilance, and personality traits). This approach was used to try and determine if the paradoxical outcomes can be explained by differences in the abilities that are recruited to perform tasks between laboratory and naturalistic settings.
PROSPECTIVE MEMORY IN VR AND REAL LIFE 11
Taken together, this study sought to validate the use of immersive VR for measuring PM in younger and older adults, and reveal the role of task familiarity and context in age differences in PM. In doing so, we hoped to build on the existing literature that has examined the role of ecological validity in PM assessment and determine if variation in task setting leads to reliance on different cognitive and personality factors that explain paradoxical differences between naturalistic and lab-based PM performance.
Method
Participants
Fifty-nine younger adults (18-30 years; Mage = 19.4; SDage = 1.8; Medu = 13.0; 32M: 27F) and fifty-two older adults (58-83 years; Mage = 70.4; SDage = 5.1; Medu = 16.3; 23M: 29F) (N =
111) completed the experiment. Younger adults were undergraduate students from the University of Notre Dame who were compensated with course credit. Older adults were community volunteers who were compensated $10 per hour for participating. Both groups were screened for risk factors associated with using virtual reality equipment (e.g. history of seizures, frequent or severe headaches, etc.; see Appendix A for full screening). Older adults’ cognitive status was screened using the Telephone Interview for Cognitive Status (TICS; Brandt, Spencer, & Folstein,
1988) (M = 38.1, SD = 3.3). Individuals who scored below a 32 on the TICS or who were identified as having any risk factors associated with VR usage were excluded from participation.
Data for ten participants were removed due to early withdrawal (five older; did not return for second session), not following directions (one older), or computer malfunction during their session (one younger, three older). Additionally, data for the out-of-lab PM tasks were unavailable for ten participants (four young, six old) due to researcher error in task administration (four young), because the participant did not own a cell phone (five older), or due
PROSPECTIVE MEMORY IN VR AND REAL LIFE 12 to a scheduling error (one older). The University of Notre Dame Institutional Review Board and
Ethics Committee approved all procedures and methods included in this study (protocol: 17-06-
3930).
Job Simulator
Job Simulator is a commercially-available VR video game that entails role-playing through simulations of real-world jobs and completing various tasks that are common to the simulated job. Participants completed two job simulations: one as a short-order cook, and the other as a convenience store clerk. These simulations included the ongoing videogame narrative as well as the PM tasks that the researchers added to the game (see Figure 1). A floating computer screen “Job-bot” narrator provided audible instructions for what participants needed to do in the virtual environment to advance to the next in-game task. These audio instructions were delivered through a speaker in the experimental room at a comfortable listening for each participant. Interacting with and completing the Job-bot’s requests served as the ongoing task that participants completed (see Figure 1).
Players used a wired HMD and wireless, handheld controllers. The controllers consisted of two identical handheld wands, each with a small button on the top side (i.e. “menu button”) and a trigger on the back (i.e. “trigger”). In the virtual environment, controllers visually appear as hands that mimic controller movement to provide visual feedback of behavior, such as when a user grasps an object (see Figure 1). Specifically, when participants moved either hand close to an object that could be interacted with, the item glowed to indicate that it could be moved.
Participants could then pull the trigger on the controller to grasp the object and release the trigger to let go of it. Movement with the HMD and controllers was tracked using laser-based positional
PROSPECTIVE MEMORY IN VR AND REAL LIFE 13 systems implanted in two base stations located at opposite corners of the room. The base stations face inward towards the play space that participants physically navigate while immersed in VR.
Figure 1. Job Simulator Task Sequence. All participants first performed a VR Tutorial to familiarize them with the experience of exploring a virtual environment and interacting with objects using the controllers (A); then participants memorized the PM tasks that the experimenter added to the game (B), watched a short video that introduced participants to the location and arrangement of objects in the virtual environment (C), recited the target cues for the PM tasks (D), before playing each of the Convenience Store Clerk and Short-Order Cook scenarios (E) [the callout panels depict examples of Event-Based and Time-Based PM tasks that were added (1), when the triggering cue was presented (2), and the participant executing the task (3)]. Following completion of both scenarios, a retrospective assessment ensured that participants remembered the to-be-performed task content (F). See text for further details.
All participants completed a tutorial in which they were given a general introduction to
VR and how to use the equipment in the experiment (Figure 1A). They learned to both reach out and depress the trigger to grasp and move objects, and check the time of day by pressing the menu button. The digital clock embedded in the menu displayed the current time of day in hours
PROSPECTIVE MEMORY IN VR AND REAL LIFE 14 and minutes. The tutorial was complete after participants successfully demonstrated how to perform each of these actions without any assistance.
Following the tutorial, the experimenter removed the HMD and seated participants in front of a computer screen with written text containing seven PM tasks (Figure 1B). PM tasks consisted of four event-based measures (e.g. turn the sink on and off every three orders to clear the drain) and three time-based measures (e.g. put the wine into the fridge after 12 minutes to chill it). In the list that participants studied, the tasks were not separated by task type or by the order in which they were to be performed in the game. Participants were told that the tasks needed to be memorized and executed in the simulation that they were about to play. After one minute of studying the task list, the screen was turned off and participants were asked to verbally recite as many of the tasks as they could remember without prompting. Participants recall continued until they recited all of the tasks, or stated that they could not remember anymore.
Once finished, the researcher repeated the tasks that participants remembered aloud, indicated that they were correct, and then recited those that were recalled incorrectly, or were not recalled at all. This process was repeated until participants were able to correctly recite all of the tasks that they studied without assistance from the researcher (Y: M = 1.99, SD = 0.49; O: M = 3.31,
SD = 1.46). Participants then watched a 2.5 minute, narrated introduction video about the impending simulation to familiarize them with the environment and to provide context for the to- be-performed PM tasks (i.e., arrangement of objects in the environment) (see Figure 1C).
In summary, interacting with and performing the in-game tasks instructed by the Job-bot made up the ongoing task that participants completed. During the course of the ongoing task, participants were to perform the previously memorized PM tasks in response to the corresponding event-based or time-based cue. Event-based cues consisted of external events
PROSPECTIVE MEMORY IN VR AND REAL LIFE 15 intended to trigger retrieval of the PM intention and time-based cues required monitoring of the menu clock to perform intentions at the appropriate time.
Once participants entered the simulation environment, before beginning to play, they were asked to press the menu button and verbally recite the current time. The researcher informed them that this represented the starting time, and asked participants to verbally report the target times at which they were to perform the time-based tasks (Figure 1D). For example, if the participant began the chef simulation at 12:00PM, the participant was to state that they had to place a cup of wine into the fridge at 12:12PM. The researcher provided correction if the target time stated by the participant was incorrect. After correctly orienting the time-based tasks to their relevant target times, participants began the simulation (Figure 1E). During simulation performance, screen-capture software was used to record in-game footage of participant performance. These videos were used to score performance offline.
After completing the simulation, participants were again asked to verbally recite the PM tasks that they had memorized (Figure 1F). This procedure tested participants’ retrospective memory for the PM tasks. Tasks that were not successfully recalled post-simulation were excluded from analysis, since failure to complete the task could have resulted from an inability to initially form the PM intention and/or retrieve the task content (i.e. retrospective memory failure) rather than remembering to perform the delayed intention at the appropriate moment (i.e. PM failure). The number of tasks that were excluded from analysis due to retrospective memory failure was 4% for younger adults and 15% for older adults.1
Breakfast Task
1 Analyses were repeated using an ANCOVA to control for differences in retrospective memory performance. This confirmed that none of the relationships between our variables of interest were significantly altered by differences in retrospective memory performance. 2 We re-ran our subsequent PM analysis while including session of completion and found no significant between- subject effects for session, F(1,107) = 0.70, p = 0.41 nor group by session interactions F(1,107) = 1.17, p = 0.28. 3 The time-accuracy threshold for one event-based task was set to within 60 seconds surrounding event-cue onset
PROSPECTIVE MEMORY IN VR AND REAL LIFE 16
The Breakfast Task was administered to provide an objective measure of naturalistic PM in the lab under direct observation. The task design and analysis was based on similar paradigms developed by Craik & Bialystok (2006); Altgassan et al., (2012), Hering et al., (2014) and
Feinkohl, Cress & Kimmerle (2016). Participants were asked to prepare a breakfast and set as many table settings as they could at each of the four stations at the table (See Figure 2). The breakfast required preparing five different foods and beverages of varying complexity, each requiring a different series of steps and time constraints to complete them within. The overarching goals were to set the table as many times as possible, to have each of the foods and beverages finish at the same time, and to complete the entire meal preparation in under 13 minutes. At the end of the meal preparation, participants were to arrange the food onto the plates in a particular manner, by placing the egg on top of the toast with the bacon on the side, and serve it onto the table with a drink at each station.
PROSPECTIVE MEMORY IN VR AND REAL LIFE 17
Figure 2. Breakfast Task configuration of the kitchen area (A) and the dining area (B), which were positioned on opposite ends of a 3.5 m testing room. The lab space was divided into two areas: the kitchen area, for cooking the food (Figure
2A), and the dining area, for setting the table (Figure 2B). After first introducing the task and its rules, the researcher demonstrated how to prepare each food and beverage item using objects in the kitchen area (e.g. turning the stove power on and off). Participants were then given one minute to memorize the rules and procedures for the experiment, using a summary sheet that they were allowed to reference during task introduction. Afterwards, the summary sheet was removed and the participants were asked to recall the rules to be adhered to and the tasks to be completed. This process was repeated until participants successfully reported all the rules independently. They were then provided with a blank sheet of paper and ten minutes to write a detailed plan about how they intended to execute the task to meet all the requirements and goals.
PROSPECTIVE MEMORY IN VR AND REAL LIFE 18
Participants verbally reported their plans to the researcher following the planning phase. Task performance began after a filled delay in which the Operation Span task was completed. Verbal reports of plans and task performance were both video-recorded for scoring at a later date.
Task performance was based on the following measures: (1) absolute deviation from the target time for each food, including required sub-steps (e.g. flipping the bacon to its other side)
(i.e. local task goals; Craik & Bialystok, 2006) and absolute deviation from the target time to complete the entire meal preparation (i.e. global task goal; Craik & Bialystok, 2006) (2) the total number of complete table settings (plate, fork, spoon, and knife), (3) the number of appliances left powered on after the participant indicated the task was complete, and (4) whether or not food was served on the table correctly (i.e. correct/incorrect). Plans were scored based on a composite of three point-based categories: prioritization (number of tasks mentioned in the plan at any point), rule description (number of rules mentioned in the plan at any point), and specification of action (number of food sub-steps specified and number of specifically elaborated orders of tasks mentioned in the plan at any point) (Hering et al., 2014). See Results section for further description of scoring criteria.
Belongings Task
A modified version of a personal belongings task (Wilson, Cockburn, & Baddeley, 1985;
Cuttler & Graf, 2008) was administered in order to measure naturalistic, in-lab PM that involved exchanging personal items between the researcher and participant to be retrieved later. When a participant arrived and completed the consent process, the researcher asked to retain the participants’ cell phone either before or after they entered the main testing room. The phone was stowed out of sight in a separate location unseen by the participant. Additionally, the researcher gave the participant a small, clip-on activity tracker device to attach to the waistband on the back
PROSPECTIVE MEMORY IN VR AND REAL LIFE 19 of their pants; participants were told that the device would track their “fidgeting” during the experiment. The purpose of the exchange was to measure precisely when and where the participant would remember to ask for his/her cell phone back and return the activity tracker at the end of the experiment, prior to leaving the lab. The initial exchange of the items took place in separate locations. That is, the cell phone task was encoded inside the main testing room and the activity tracker task was encoded outside the main testing room, or vice versa. The encoding context was counterbalanced across participants. During the initial exchange, the researcher asked the participant to verbally confirm that they intended to retrieve/return the item at the end of the experiment (i.e. “Remember to give/get this back at the end of the session today, okay?”).
Following the completion of the first testing session, participants were guided out towards the exit of the lab. Many participants initially forgot to exchange one or both items, left the lab, but then stopped in the hallway, returned to the lab and asked about their phone or returned the tracker. The precise location where the participant stopped to return or retrieve either item was noted and the distance from the target location was recorded. Participants who forgot to exchange either item were stopped before they completely left the floor of the building and were asked “Are you forgetting something?” Then they were asked to follow the researcher back to the main testing space. If participants returned to the main testing space and had not successfully recalled both items, they were asked “Is there anything that you are supposed to get back from me or return to me?” If this was also unsuccessful, the experimenter identified which of the items that had not been returned to its owner. There were two dependent measures taken,
1) recall accuracy: 0, forgot; 1, remembered one item; 2, remembered both, and 2) the distance travelled from the target retrieval location, in meters, before they recalled both items.
Participants who were unable to recall one or both items independently before prompting were
PROSPECTIVE MEMORY IN VR AND REAL LIFE 20 assessed a maximum distance that was equivalent to where participants were stopped before they left the floor of the building.
Call-back Task
The Call-back task was administered to measure naturalistic PM performance in the real world, outside of the lab (Rose et al., 2015). For this task, participants identified a two-hour window of time between their first and second in-lab testing session when they would be available to call the lab and leave a voicemail with minimal distraction or interruption. At the beginning of the window, a researcher called the participant and re-explained the directions of the task. The participant was instructed to call the researcher back at two specific times in the first hour (i.e. exactly 15 minutes and 40 minutes after hanging up) and leave a voicemail with their initials. For example, if, upon hanging up the phone, the time of day was 11:13AM, the target times to return a call to the lab would be 11:28AM and 11:53AM. The target time to return each phone call was not mentioned to the participant. They were to calculate it on their own and remember to call as close to the target time as possible and leave a voicemail that stated the time they intended to call, which confirmed that any discrepancies between the target time to call and actual time of calling were not due to calculation errors in the encoding stage. The answering machine recorded the time of the phone call. At the start of the second hour, the researcher called the participant a second time and communicated two specific times to call back within the second hour (i.e. exactly 20 minutes and 35 minutes after hanging up). The participants complied with the same procedure as the first hour. In each phone call that the researcher made to the participant, explicit instructions were given to avoid using reminders, timers, or any other form of memory “offloading” during the delay. Participants were told that the purpose of the task was for them to remember on their own. Participants verbally agreed to comply with this rule before
PROSPECTIVE MEMORY IN VR AND REAL LIFE 21 the first hour, and confirmed compliance during the second call from the researcher. The researchers recorded the two target times on a call-log after hanging up. The actual time of the return call was compared against the target time to obtain an absolute deviation measure from the target time for each of the four calls. If a call was not returned at any point, the actual time assessed was imputed to three standard deviations longer than the median deviation time for that respective group. Mean deviation from target times across the four calls represented the participants’ Call-back score.
Psychomotor Vigilance Task (PVT)
The PVT was administered to measure a participant’s vigilance ability. It is a reaction time measure that consists of monitoring a stimulus (in this case, a black dot presented in the middle of the computer screen). Participants were instructed to press the space bar as quickly as possible when the dot changed from black to red. Pressing the space bar changed the dot back to black, and the participant waited until the next time the dot changed to red. Importantly, the time the dot changed color was unpredictable—the inter-trial interval was random, ranging from
3000-7000 milliseconds (ms) in steps of 500 ms. The variable interval requires that participants remain vigilant in monitoring the dot’s color change. Trials with response times less than 200 ms or greater than 1000 ms were removed, resulting in loss of 0.82% of the data. A final PVT score was obtained by averaging response times across trials in the trimmed data set.
Big Five Inventory (BFI-II)
The BFI-II was administered to measure the Big Five personality dimensions (Soto &
John, 2017; Watson & Clark, 1992). It is a 60-item self-reported questionnaire and is a well- validated assessment of personality. Each item contained a statement that described a general characteristic (e.g. “I am someone who is outgoing, sociable”), and participants identified the
PROSPECTIVE MEMORY IN VR AND REAL LIFE 22 extent to which they agreed it was representative of themselves on a 5-point scale, from 1
(strongly disagree) to 5 (strongly agree). Each participant’s inventory was scored for domain scales of personality: open-mindedness, conscientiousness, extraversion, agreeableness, neuroticism (see Soto & John, 2017 for scoring criteria).
Operation Span Task (OSPAN)
The Operation Span Task (OSPAN) was administered to measure working memory capacity (Turner & Engle, 1989). It is a well-validated measure of working memory capacity that consists of a dual-task procedure in which participants are asked to answer arithmetic questions while trying to remember an interspersed sequence of letters. Letters were presented one at a time, separated by a math problem between each letter. The number of letters ranged from three to seven per trial. After each trial, participants were asked to select the letters they had seen, in order of presentation, from a set of twelve possible letters. This recall test was performed by clicking the mouse over each appropriate letter. Three trials of each set length were performed in a randomized order across the entire task. The dependent measure was the total number of letters that were recalled in correct order across all trials (i.e. partial score; Conway et al., 2005).
Procedure
The experiment was completed in two separate sessions, each approximately two hours in length, and spaced approximately one week apart (M = 7.2 days, range = 1-28 days). In the first session, participants provided informed consent and completed the initial exchanges necessary for the Belongings task. Following the exchange, participants completed a demographics questionnaire. Then they completed the VR tutorial and received instructions on how to play Job
Simulator. Prior to starting the task, they memorized the PM tasks and watched the introduction video for the short-order cook simulation. Participants then completed the short-order cook
PROSPECTIVE MEMORY IN VR AND REAL LIFE 23 simulation. Each of these steps were repeated to prepare participants for the convenience store clerk simulation, except for the initial VR tutorial. After completing both VR games, participants completed the BFI-II and PVT task. Participants then coordinated scheduling for the Call-back task, and were told by the researcher that the first testing session was over and completed the
Belongings task.
Participants performed the Call-back task at home any time between the first and second sessions, including the day of either session. On the second in-lab testing session, participants first received instructions for the Breakfast task and completed the planning stage. Each participant then performed the OSPAN filled delay task before beginning the Breakfast task.
Retention intervals were comparable for each age group during the filled delay (Y: M = 0:13:30,
SD = 0:02:07; O: M = 0:13:38, SD = 0:02:19; t(109) = -0.31, p = 0.76). Due to time constraints on the first session, 48 participants (40 old, 8 young) completed the convenience store clerk simulation at the start of the second session; 22 (16 old, 6 young) completed BFI-II and PVT at the start of the second session.2
Results
Scoring Criteria
PM task performance in Job Simulator and the Breakfast task was measured as the absolute difference between the ideal and actual times of performance. These deviation measures were converted to accuracy scores for each trial based on whether or not performance happened within a particular time window surrounding the ideal time of performance. Time-based PM tasks performed within 60 seconds of the ideal performance marker were deemed correct. This range was appropriate because the clock that participants could monitor displayed hours and
2 We re-ran our subsequent PM analysis while including session of completion and found no significant between- subject effects for session, F(1,107) = 0.70, p = 0.41 nor group by session interactions F(1,107) = 1.17, p = 0.28.
PROSPECTIVE MEMORY IN VR AND REAL LIFE 24 minutes, but not seconds. Event-based PM accuracy thresholds were set to ten seconds surrounding the ideal time at which the event cue occurred.3 Performance was collapsed across the two Job Simulator simulations to double the number of observations per task type with the goal of maximizing the reliability of assessment. Responses were also coded to capture the proportion of responses that were “a little late”, “very late”, or those that were completely forgotten (i.e. not performed at all). Younger and older adults’ mean proportions of responses for each task type are presented in Table 1.4
Age Differences in Prospective Memory
Before we conducted correlational analyses, we first examined the psychometric properties of each task and the extent to which it reliably measured its intended construct. To do so, we computed Spearman-Brown split-half reliability coefficients for each age group, which can be found in Table 2. Table 2 also presents the bivariate correlations among all the other measures above and below the diagonal for the older and younger adults, respectively. Scores from the Call-back task, PVT, and Belongings task were square root transformed to account for non-normality in their distributions. After correction, measures of skewness and kurtosis ranged from -0.99 to 1.64 and -1.16 to 3.63, respectively, which reflect approximately normal distributions (skewness < 2, kurtosis < 4, Kline, 1998).
3 The time-accuracy threshold for one event-based task was set to within 60 seconds surrounding event-cue onset (i.e. turning the sink on and off every 3 orders) because the time between completing an order and beginning the next one was approximately 60 seconds, and accurate performance could reasonably fall outside of the ten second window established for the other event-based tasks and up to 60 seconds at which point the next order occurred. 4 We re-ran our mixed-design ANOVA to include responses that were binned as “a little late”. Using this more lenient scoring criterion did not change any of the age group relationships. There was a main effect of regularity due to performance being better on irregular tasks than regular tasks. This was qualified by a cue type by regularity interaction which found this pattern to be more pronounced in event-based measures than time-based measures. This suggests that the benefit of task regularity was not observed when a more lenient criterion was used.
PROSPECTIVE MEMORY IN VR AND REAL LIFE 25
Figure 3. Mean proportion of correctly performed VR-based prospective memory tasks in younger and older adults. Error bars: SEM. * p < 0.05; ** p < 0.01
Job Simulator
First we examined age differences and potential interactions with PM cue type and task regularity. A mixed-design ANOVA was conducted on PM accuracy with cue type (event-based, time-based) and task regularity (regular, irregular) as within-subjects factors, and age group as a between-subjects factor. The analysis revealed a main effect of group because younger adults
2 outperformed older adults across the four task types, F(1,109) = 108.90, p < 0.001, ηp = 0.500
(See Figure 3). The analysis also revealed a cue type by age interaction, F(1,109) = 13.35, p <
2 0.01, ηp = 0.11. Post hoc (Bonferroni corrected) analysis of this interaction showed that there were larger age differences on time-based tasks (Y: M = 0.75, SD = 0.21; O: M = 0.36, SD =
0.21) than event-based tasks (Y: M = 0.64, SD = 0.18; O: M = 0.41, SD = 0.18). There was
PROSPECTIVE MEMORY IN VR AND REAL LIFE 26 neither a main effect, F(1,109) = 1.34, p = 0.25, nor interaction, F(1,109) = 2.16, p = 0.15, with task regularity; thus, further analysis collapsed over this factor.
Given the cue type by age interaction, we wanted to investigate the extent to which time monitoring behavior (i.e. number of clock checks) impacted participants’ ability to perform time- based PM tasks correctly. We separated the data between younger and older adults and conducted bivariate correlations on individuals’ mean number of clock checks and mean time- based PM task accuracy. This initial analysis showed that both younger adults’, r = 0.50, p <
0.01, and older adults’, r = 0.66, p < 0.01, time-based PM performance was positively correlated with the number of clock checks. There was a significant difference in the average number of clock checks between younger (M = 46.0, SD = 20.2) and older adults (M = 24.5, SD = 17.9), t(109) = 5.89, p < 0.01. Therefore, we then conducted an ANCOVA on time-based PM accuracy using the number of clock checks as a covariate to determine whether differences in performance would persist if clock-checking behavior was accounted for in both age groups. After controlling for time monitoring behavior, the age differences in time-based PM accuracy remained, F(1,108)
2 = 49.05, p < 0.01, ηp = 0.31, with younger adults (M = 0.69, SD = 0.18) still significantly outperforming older adults (M = 0.43, SD = 0.18). Taken together, younger adults outperformed older adults in all PM tasks, particularly time-based measures—a pattern which was not solely explained by the observed differences in time-monitoring behavior. The correlational analysis below help to explain the source of this differential age deficit.
Breakfast Task
Next, a mixed-design ANOVA was conducted with ten levels for the different cooking tasks as a within-subjects factor and age group as a between-subjects factor to investigate possible age-related differences in local task goals. The test for between-subjects effects revealed
PROSPECTIVE MEMORY IN VR AND REAL LIFE 27 significant age differences in the proportion of PM tasks accurately performed, F(1,109) = 90.55,
2 2 p < 0.01, ηp = 0.45. A main effect of task was also observed, F(9,981) = 4.58, p < 0.01, ηp =
2 0.04. The task by age group interaction was not significant, F(9,981) = 0.53, p = 0.85, ηp = 0.01.
A pair of independent samples t-tests were also conducted on (1) the number of appliances left powered on and (2) food serving accuracy (i.e. whether food was arranged properly on the plate) to determine if age differences existed in these secondary PM measures. There was not a statistically significant difference in the number of appliances left on between younger adults (M
= 1.08, SD = 1.42) and older adults (M = 0.94, SD = 1.19), t(109) = 0.57, p = 0.57. Younger adults (M = 0.90, SD = 0.30) were significantly better at remembering to serve the food properly at the end of the task than older adults (M = 0.58, SD = 0.50), t(109) = 4.15, p < 0.01.
While a majority of the local task goals showed significant age-related deficits, we also wanted to see if this same pattern held for the global task goals of finishing all of the foods and drinks at the same time and in under 13 minutes. The outcome measure for assessing the prior question is the stop range. Stop range is defined as the time difference between stopping the first item and the last item, which participants were attempting to get as close to zero seconds as possible. An independent samples t-test revealed that younger adults (M = 56 sec., SD = 90 sec.) stopped the foods closer together than older adults (M = 94 sec., SD = 122 sec.), but this difference failed to reach significance, t(109) = -1.87, p = 0.06. However, older adults showed larger discrepancies (M = 98 sec., SD = 102 sec.) with the 13 minute total cooking time cutoff than younger adults (M = 28 sec., SD = 48 sec.). Overall, older adults showed decrements in performance on each cooking task (i.e. local tasks) relative to younger adults, while their performance on the two global task goals revealed a prioritization to finish food and beverages at the same time at the cost of finishing in under 13 minutes.
PROSPECTIVE MEMORY IN VR AND REAL LIFE 28
Given this contrast in performance between local and global task objectives, we investigated the role of clock-checking behavior and planning ability in Breakfast Task performance. To review, planning ability was a point-based, composite score across three categories: prioritization, rule description, and specification of action (see Methods). A pair of independent samples t-tests were conducted on the number of clock checks and plan scores to determine if any age differences existed. Results indicated that younger adults (M = 16.46, SD =
5.67) checked the clock significantly more often than older adults (M = 11.15, SD = 4.94), t(109)
= 5.22, p < 0.001. However, as with clock checking in Job Simulator, significant age-differences remained for an ANCOVA on Breakfast task accuracy with the number of clock checks as a
2 covariate, F(1,108) = 55.10, p < 0.01, ηp = 0.34, with younger adults (M = 0.79, SD = 0.20) still performing significantly more accurately than older adults (M = 0.49, SD = 0.20). In contrast, planning ability was shown to yield no significant differences between younger (M = 28.89, SD
= 7.87) and older adults (M = 26.90, SD = 6.84), t(109) = 1.41, p = 0.16. Thus, despite the lack of age differences in planning, and presumably a considerable difference between groups in experience preparing breakfast over their lifetime, younger adults still performed the breakfast task more efficiently and accurately than older adults. This age-deficit in simulating an everyday
PM task dovetails with the age-deficit observed in PM performance during the VR game.
PROSPECTIVE MEMORY IN VR AND REAL LIFE 29
Figure 4. Predicting naturalistic PM from VR performance. Note. R2 values estimate the proportion of variance in Breakfast task performance attributable to variability in Job Simulator performance overall (A) and split into event-based (B) and time- based performance (C), with values on the bottom-right and bolded in each panel collapsing across age group, and those on the bottom-left in each panel within each age group. ** p < 0.01
Predicting PM Performance
To assess the ecological validity of our PM tasks embedded in the VR game, we examined the degree to which VR-based PM accuracy predicted performance on the Breakfast task. A simple linear regression model was calculated to predict overall PM accuracy on the
Breakfast Task from overall PM performance on the Job Simulator game. There was a significant positive relationship between Job Simulator and Breakfast Task, F(1,109) = 61.00, p
< 0.01, in which 36% of variability in Breakfast Task performance was explained by that in Job
Simulator. Given the aforementioned interaction between cue type and age group in Job
PROSPECTIVE MEMORY IN VR AND REAL LIFE 30
Simulator performance, we then split Job Simulator into event-based and time-based performance and re-ran the regression analyses for each cue type separately. These revealed significant predictive relationships between Breakfast Task and PM performance on tasks with event-based cues, F(1,109) = 19.12, p < 0.01, R2 = 0.15, and time-based cues, F(1,109) = 72.60, p < 0.01, R2 = 0.40 (see Figure 4). Partial correlation analyses were conducted to determine whether these relationships persisted irrespective of variance attributable to age. The correlation between event-based PM performance and Breakfast task was no longer significant when controlling for age group, r = 0.05, p = 0.64. However, time-based PM performance was still a significant predictor irrespective of the variance due to age, r = 0.31, p < 0.01. Linear regression analyses within each group indicated that older adult time-based VR performance significantly predicted their Breakfast task performance, F(1,50) = 9.77, p < 0.01, R2 = 0.16 (see Figure 4), which persisted even after controlling for age within the older adult group, r = 0.37, p < 0.01.
Thus, much of the association between PM performance in VR and on the Breakfast task was attributable to the sizeable age-related differences in performance, with individual differences in time-based PM performance in the older adult group predicting Breakfast task performance irrespective of age.
PROSPECTIVE MEMORY IN VR AND REAL LIFE 31
Figure 5. Cognitive covariates of VR-based PM performance. Note. R2 values estimate the proportion of variance in event-based (left column) and time-based (right column) VR performance attributable to variability in working memory capacity (operation span, A-B) and vigilance ability (C-D), with values on the bottom-right and bolded in each panel collapsing across age group, and those on the bottom-left in each panel within each age group. * p < 0.05, ** p < 0.01.
Next, we ran a series of hierarchical multiple regression analyses to predict event-based and time-based VR performance using potential moderators of age differences in PM performance including working memory, vigilance and personality traits. Scatterplots depicting individual regression associations between younger and older adults’ PM performance and the cognitive moderating variables can be found in Figure 5. In the hierarchical models, each moderating variable was entered in a step-wise fashion to determine how each moderator contributed to the overall regression line. Age group was entered in the final step to determine if
PROSPECTIVE MEMORY IN VR AND REAL LIFE 32 the prior contributions from working memory, vigilance, and personality traits were able to completely account for all variance due to age. In both event-based and time-based performance, working memory, vigilance, and personality trait measures accounted for a significant amount of the variability (EB: F(7,103) = 6.03, p < 0.01, R2 = 0.29; TB: F(7,103) = 8.85, p < 0.01, R2 =
0.38). However, a total of 8% and 17% of the remaining variability in event- and time-based PM, respectively, was attributable to age. For event-based VR performance, only working memory significantly contributed to the model (β = 0.25, t(110) = 2.38, p = 0.02), while the remaining moderators, including agreeableness (β = -0.08, t(110) = -0.84, p = 0.40) and conscientiousness did not (β = 0.11, t(110) = 1.22, p = 0.23). For time-based VR performance, agreeableness was the only moderator that significantly contributed, albeit negatively, to the model (β = -0.22, t(110) = -2.72, p < 0.01). Conscientiousness did not contribute significantly to the model of time- based VR performance (β = 0.04, t(110) = 0.52, p = 0.60). A significant association between working memory and time-based VR performance (β = 0.47, t(110) = 5.64, p < 0.01) was found while controlling for vigilance and personality, but was no longer significant after also controlling for age (β = 0.15, t(110) = 1.71, p = 0.09). The association between working memory and time-based VR performance within the older adult group (see Figure 5) was also no longer significant after controlling for age differences within the group (r = 0.17, p = 0.22).
PROSPECTIVE MEMORY IN VR AND REAL LIFE 33
Figure 6. Cognitive covariates of breakfast task performance. Note. R2 values collapsed across age group (bottom-right, bolded in each panel) and between age group (bottom-left in each panel) estimate the amount of variability in Breakfast task performance attributable to variability in operation span performance (A) and psychomotor vigilance performance (B); ** p < 0.01
A hierarchical regression analysis was also conducted to predict average Breakfast task accuracy from working memory, vigilance, and the five personality trait measures. To reiterate, moderators were entered in a step-wise fashion to determine the individual contributions of each to the overall model (see Figure 6), with age group entered in the final step. The moderators were able to account for a significant amount of the variance in Breakfast Task performance, F(7,103)
= 5.47, p < 0.01, R2 = 0.27, but left a remaining 26% of the variance in performance to be accounted for by age. Within this model, only agreeableness was found to be significantly associated with average Breakfast task accuracy after controlling for age and the remaining moderators (β = -.28, t(110) = -3.38, p < 0.01). Conscientiousness did not significantly contribute to the model (β = 0.11, t(110) = 1.40, p = 0.16). Similar to the time-based VR performance model, a significant association between working memory and average Breakfast task accuracy (β = 0.35, t(110) = 3.83, p < 0.01) was found while controlling for vigilance and
PROSPECTIVE MEMORY IN VR AND REAL LIFE 34 personality traits, but was no longer significant after also controlling for age (β = -0.05, t(110) =
-0.56, p = 0.58).
Secondary PM Measures
To assess age differences on the Belongings and Call-back tasks, we began by running independent samples t-tests to examine potential age differences on each. An independent samples t-test on the Belongings task showed younger adults (M = 0.79, SD = 0.27) did not remember to retrieve their cell phone and return the activity tracker more than older adults (M =
0.71, SD = 0.37), t(99) = 1.28, p = 0.20. A second independent samples t-test comparing distance travelled between younger (M = 2.81, SD = 2.76) and older adults (M = 3.38, SD = 3.34) also found no significant difference between groups, t(99) = -0.94, p = 0.35. We then split recall performance for cell phone and activity tracker, and ran an ANOVA with item type as a within- subjects variable, and age group as a between-subjects variable. Results of this test showed that both younger and older adults were significantly better at remembering to retrieve their cell phone than remembering to return the activity tracker, F(1,99) = 24.51, p < 0.01. The item by age group interaction was not significant, F(1,99) = 0.20, p = 0.66. Thus, in contrast to the large age-related deficits observed in the PM performance on both the Job Simulator and Breakfast tasks, younger adults did not outperform older adults on the Belongings task.
Also contrasting the age deficits observed in PM performance on the Job Simulator and
Breakfast tasks was the pattern of age differences on the Call-back task. Age-related differences were reversed on the Call-back task, with older adults showing significantly smaller absolute deviation times (M = 58 sec., SD = 76 sec.) in their phone calls than younger adults (M = 192 sec., SD = 232 sec.), t(108) = 3.96, p < 0.01.
PROSPECTIVE MEMORY IN VR AND REAL LIFE 35
We again ran hierarchical multiple regression analyses to determine how variability in working memory, vigilance, and personality was associated with performance. With regards to the Belongings task, which did not show an age differences in performance, the regression analysis revealed that there was no reliable association between the potential moderator variables and Belongings task performance, F(7,93) = 0.49, p = 0.84, R2 = 0.04. Neither conscientiousness
(β = -0.02, t(100) = -0.18, p = 0.86) nor agreeableness (β = -0.08, t(100) = -0.65, p = 0.52) contributed to the hierarchical model of Belongings task performance. With regards to the Call-
Back task, the cognitive and personality measures were able to account for a significant amount of the variance, F(7,102) = 3.92, p < 0.01, R2 = 0.21, but left a remaining 12.7% of the variance in performance to be accounted for by age. Amongst the moderating variables that contributed to the Call-back task regression model, vigilance (β = 0.31, t(109) = 3.51, p < 0.01) and neuroticism (β = -0.23, t(109) = -2.36, p = 0.02) held significant associations, with poorer Call- back performance being associated with poorer vigilance and lower levels of neuroticism.
PM-Paradox Index
Following these hierarchical regression analyses for each PM task, we conducted a follow-up analysis to try and reveal the source of the differential pattern of age-differences in
PM performance across lab and real-world contexts. We hypothesized that there would be different predictors of performance between younger and older adult groups as well as across different contexts—i.e., the age-PM paradox. We created an index of the paradox from the tasks we assessed by including those which contained the most similar task characteristics across the different contexts. The PM measures from the Call-back task most closely resemble the measures of time-based PM performance in Job Simulator and the time-based measures collected in the
Breakfast task.
PROSPECTIVE MEMORY IN VR AND REAL LIFE 36
Due to differences in scales and/or units, we first z-scored performance on each task before computing a difference score between each individual’s Job Simulator time-based performance and the Call-back task (i.e., JS-CB), as well as a difference score between each individual’s Breakfast task performance and the Call-back task (i.e., BF-CB). The same hierarchical regression analyses conducted previously were repeated with this PM-paradox index as the outcome variable to determine the extent to which each predictive variable mediated the difference between younger and older adults’ PM performance across the different contexts. By performing these analyses on both the JS-CB and BF-CB indices, we were able to assess the replicability of the pattern observed.
First, a hierarchical regression analysis was conducted to predict JS-CB difference scores from working memory, vigilance, and the five personality trait measures. Moderators were entered in a step-wise fashion to determine the individual contributions of each to the overall model, with age group entered in the final step.
The moderators were able to account for a significant amount of the variance in the JS-
CB index, F(7,102) = 6.11, p < 0.01, R2 = 0.30, but left a remaining 25.7% of the variance in performance to be accounted for by age. Vigilance (β = 0.17, t(109) = 2.34, p = 0.02), agreeableness (β = -0.19, t(109) = -2.33, p = 0.02), and neuroticism (β = -0.18, t(109) = -2.25, p = 0.03) were found to be significantly associated with the performance differences while controlling for age and the remaining moderators. A significant association with working memory (β = 0.45, t(109) = 5.00, p < 0.01) was found while controlling for vigilance and personality traits, but was no longer significant after also controlling for age (β = 0.05, t(109) =
0.59, p = 0.56).
PROSPECTIVE MEMORY IN VR AND REAL LIFE 37
We repeated this hierarchical regression analyses on the BF-CB index using the same moderating variables, with each entered step-wise into the model. The moderators were again able to account for a significant amount of the variance in the BF-CB index, F(7,102) = 4.77, p <
0.01, R2 = 0.25, but left a remaining 31.8% of the variance in performance to be accounted for by age. The mediating role of vigilance (β = 0.22, t(109) = 3.07, p < 0.01), agreeableness (β = -
0.22, t(109) = -2.81, p < 0.01), and neuroticism (β = -0.18, t(109) = -2.24, p = 0.03) were replicated in this second model, validating the regression model of the JS-CB index. Again, the significant mediating effect of working memory capacity (β = 0.36, t(109) = 3.87, p < 0.01) was no longer significant when age was included (β = -0.08, t(109) = -0.93, p = 0.36). Of note, conscientiousness did not significantly mediate patterns in either the JS-CB index (β = 0.02, t(109) = 0.27, p = 0.79) or the BF-CB index (β = 0.07, t(109) = 0.88, p = 0.38).
Discussion
The present study sought to evaluate the dynamic and interactive nature of the processes that underlie individual and age-related differences in PM performance across multiple task contexts. This approach was motivated by the discrepant patterns of performance known as the
“age-PM paradox”, which shows elimination or reversal of stereotyped age deficits in PM in naturalistic tasks compared to conventional lab-based measures. Specifically, we employed a task-based analysis that manipulated both PM variables (e.g. PM cues) and contexts to determine how age- and individual-differences in performance change as a function of these manipulations.
We then tried to determine how age and individual differences in working memory capacity, vigilance ability, and personality factors might underlie PM performance differently across
PROSPECTIVE MEMORY IN VR AND REAL LIFE 38 contexts and age groups in an attempt to provide an explanation for any paradoxical findings that emerge.
Validating Measures of PM during Immersive Virtual Reality Gameplay
Previous attempts to address the role of ecological validity in the age-PM paradox have employed the use of VR-based tasks to recreate PM scenarios as they exist outside of the lab. We used an immersive VR method that preserves real-world perceptual and motor interactions and tried to ecologically-validate this method by showing convergent findings with an objective measure of naturalistic PM. Our approach builds on previous work that has established convergence between immersive VR and a subjective measure of everyday memory (Ouellet et al., 2018). We hypothesized and observed that PM performance during the Job Simulator game significantly predicts performance on our validated, naturalistic PM measure—Breakfast task.
Our regression analyses predicting Breakfast task performance showed that performance on Job Simulator, particularly time-based performance, was able to predict performance above and beyond the variance due to age. Therefore, we were able to support this hypothesis, as well as successfully capture age differences predicted by the multi-process model of PM (McDaniel
& Einstein, 2000). While younger adults outperformed older adults across all PM task types in
Job Simulator, performance differences were substantially larger for tasks that had time-based cues than event-based cues, suggesting that tasks which require conscious monitoring of external cues—rather than spontaneous retrieval—show greater age deficits. We were also able to show that individual differences in working memory played a greater predictive role in time-based task accuracy than in event-based tasks. This supports the multi-process model’s proposal that the degree of controlled, strategic processing modulates the demand on working memory (Figure 4)
PROSPECTIVE MEMORY IN VR AND REAL LIFE 39
(Einstein et al., 1995; Park et al., 1997; Brandimonte et al., 2001; d’Ydewalle et al., 2001; Cona et al., 2013; Rose et al., 2015).
However, the greater amount of time monitoring (i.e. checking the clock) in younger adults compared to older adults in both Job Simulator and the Breakfast task was unable to fully account for significant age differences in performance. This suggests that those older adults who updated their working memory by checking the clock as often as younger adults were still unable to execute PM intentions as effectively as younger adults. It may be the case that differences in time-based PM performance rely less on strategic monitoring of available cues via clock- checking behavior in a reactive fashion, and more so on retaining and making use of cue-related information in a proactive fashion. This proposal applies a prominent theory of cognitive control to PM and aging and is consistent with age differences in proactive versus reactive control mechanisms in other domains (Braver, 2012; Braver, Gray, & Burgess, 2007; Bugg, McDaniel,
& Einstein, 2013).
On the Benefits of Experience to Naturalistic PM Performance
Differences in age-related patterns of performance across the Breakfast task also provided further insight into how real-world experience informs naturalistic task performance.
Older adults showed comparable performance to younger adults in their preparatory planning, their ability to finish all of the food items together, and remembering to turn off cooking appliances. This was contrasted by deficits in meeting the time constraints for each individual food item, serving the food in the proper arrangement, and finishing all items in under 13 minutes. These specific preservations in task performance are contrary to findings in Craik &
Bailystok (2006), who found significant age-related deficits in both global and local task performance on their computerized breakfast task which also involved setting the table and
PROSPECTIVE MEMORY IN VR AND REAL LIFE 40 preparing foods. It may be the case that using realistic items and appliances and requiring physical task switching allows individuals to better engage actions systems and translate their real-world experience to controlled research settings. Furthermore, older adults performed comparably to younger adults on global task goals of preparing all food items so that they were all ready at the same time, which is a common goal of cooking typical meals. In contrast, older adults showed deficits on local task goals specific to the instructions for this Breakfast task with relatively strict, somewhat arbitrary time deadlines, such as flipping the eggs at exactly 3 minutes. It may be that the local task time constraints and serving arrangement rules created a greater sense of arbitrariness in their requirements, while the tasks showing comparable performance across age groups were more universal and allowed older adults to better apply their real-world cooking experience (Kliegel et al., 2008; Altgassan et al., 2010). Future research should elaborate on these findings by investigating why some real-world experience (e.g. finishing cooking foods together and turning appliances on and off) might be easier to apply and translate into successful performance in lab-based simulations of everyday behavior.
Predicting PM Performance across Task Contexts
In addition to establishing convergent findings between Job Simulator and Breakfast task, we also explored the role of working memory, attentional vigilance, and personality traits in predicting performance on PM tasks across varying contexts. In order to accomplish this, we created a PM index of difference scores between time-based PM performance between Job
Simulator and the Call-back task, and repeated the process between the Breakfast task and the
Call-back task in order to evaluate the replicability of our regression analyses. Regression analyses and within-group associations indicated that differences in agreeableness and working memory in older adults were found to modulate lab-based time-based PM performance, while
PROSPECTIVE MEMORY IN VR AND REAL LIFE 41 differences in vigilance and neuroticism in younger adults mediated naturalistic time-based PM performance.
Given that the immersive VR gameplay required switching between performing the Job- bot-instructed ongoing tasks and the experimenter-instructed PM tasks, participants likely had substantial difficulty maintaining all goal-relevant representations in focal attention throughout the game. Thus, the source of age-related deficits and associations with working memory capacity was likely due to the demands placed by switching between actively maintaining one’s current task goal in focal attention and retrieving subsequent task goals from memory. This account is consistent with the dual-component model of working memory (Unsworth & Engle,
2007) and findings that a substantial portion of variance in age-differences in working memory across the adult lifespan are attributable to the component associated with retrieval from memory
(Hale et al., 2011). By controlling for age group between younger and older adults, as well as within the older adult group, we were able to verify that differences in lab-based PM performance attributable to individual differences in working memory were due to developmental changes in working memory with age. Importantly, these findings were observed for PM task performance despite the fact that all participants could accurately recall these tasks on the subsequent retrospective memory test. Thus, the contribution of switching between active maintenance and retrieval from memory cannot be attributable to younger or older adults’ failures to encode or retrieve the tasks from retrospective memory.
In contrast to the findings regarding age differences in Job Simulator and the Breakfast task, findings in the Belongings task and Call-back task replicated patterns consistent with the age-PM paradox (Rendell & Thompson, 1999), with older adults performing comparably and even outperforming younger adults, respectively. As stated earlier, individual differences in
PROSPECTIVE MEMORY IN VR AND REAL LIFE 42 vigilance and neuroticism in younger adults was shown to drive group differences in naturalistic
PM performance. Unlike Job Simulator and the Breakfast task, on the Call-back task, participants may have been able to arrange their daily life schedule during the test period to enable them to maintain PM tasks in focal attention while monitoring for relevant time cues. This suggests that the “ongoing” situational factors of everyday life did not tax controlled attention and working memory retrieval processes to the extent required for switching between the ongoing and PM tasks. It may be that older adults possess lifestyles that permitted greater freedom to keep intentions in focal awareness than younger adults (Aberle et al., 2010; Henry et al., 2004; Rendell & Thomson, 1999). However, tasks that can be performed with processes associated with vigilance alone are not typically deemed to be true measures of PM.
Brandimonte et al. (2001) assert that tasks modulated exclusively by attentional vigilance do not meet the demand criteria of a PM measure, which suggests that, at least in the way that older adults performed the Call-back task, the task measured vigilance more than PM processes.
However, because we could not directly assess participants’ lifestyle demands or the specific cues that participants used to complete the task during the at-home testing period, our ability to interpret their role in these findings is limited. Future research is required to observe potential differences in the ways in which younger and older adults structure their environments to cue themselves to perform PM tasks during at-home testing periods.
With regards to the Belongings task, younger adults did not outperform older adults, yet both groups were better at remembering to retrieve their cell phone than remembering to return the tracker to the researcher. This suggests that both young and older adults were more motivated or incentivized to remember the personally relevant item than the item relevant to the researcher
(Walter & Meier, 2014). Aberle et al. (2010) have shown that using incentives to increase
PROSPECTIVE MEMORY IN VR AND REAL LIFE 43 motivation in naturalistic tasks eliminates the paradox and younger adults perform comparably to older adults. Although it was hypothesized that age- and individual differences in personality traits, and that younger adults’ greater presumed attachment to their cell phone than most older adults, may have affected their ability to retrieve it after the experiment, we found no evidence for such associations.
Associations between personality traits and our PM paradox indices contrast previous research suggesting that developmental increases in conscientiousness are associated with improved performance on lab-based PM measures (Einstein & McDaniel, 1996; Cuttler & Graf,
2007). We found an inverse relationship between agreeableness and time-based performance in
Job Simulator and Breakfast task performance, such that better PM performance was associated with individuals who reported less agreeable responses on the personality inventory, especially in older adults. While a small, but statistically significant relationship between lab-based measures of PM and agreeableness has been previously reported (Salthouse, Berish, & Siedlecki,
2004), the association was in the opposite direction. However, Salthouse et al. (2004) did not find increases in agreeableness as a function of age group in their sample, which contrasts previous work that has shown such a developmental pattern (Allemand, Zimprich, & Hendriks,
2008; McCrae et al., 1999). The direction of the association between Call-back task performance and higher neuroticism may also be somewhat counterintuitive. However, similar to the association between agreeableness and lab-based PM performance in older adults, the association between neuroticism and naturalistic PM performance also seems to be driven by the developmental pattern of higher neuroticism during earlier stages of life. So while it seemed plausible for age-related improvements in conscientiousness and agreeableness to be associated
PROSPECTIVE MEMORY IN VR AND REAL LIFE 44 with the “paradoxical” age-benefits to naturalistic PM as a real, developmental phenomenon, the paradoxical pattern persisted and a comprehensive account remains lacking.
Conclusion
Taken together, these findings evidence the importance of directly manipulating PM task contexts and validating performance across them. While the PM tasks presented here were designed to capture similar real-world PM processes, correlational and regression analyses of performance across contexts suggest that the real-world tasks may be measuring different processes, such as vigilance ability, than the naturalistic PM tasks performed under controlled laboratory conditions. The challenge of establishing experimental control over task strategies and lifestyle influences continues to be a significant barrier for assessing differences in PM across lab-based and real-world measures. However, this study demonstrates the value and importance of incorporating multiple assessments of PM and represents a fruitful step towards finally resolving the age-PM paradox. The elucidated differences in cognitive and personality demand characteristics across task settings and age groups indicate that in-lab PM tasks used to evaluate age-related differences should be validated to show comparable associations with PM performance in the real world. Future work should also continue to systematically manipulate task variables and contexts to investigate individual and age differences, as well as include cognitive and personality measures to determine how manipulations in task design change the way participants rely on their cognitive abilities and personality factors. A longitudinal approach would allow for tracking changes in cognitive abilities and personality traits across the time, and allow one to determine how PM performance changes as a function of developmental shifts in the processes that support it.
PROSPECTIVE MEMORY IN VR AND REAL LIFE 45
In conclusion, this study offers one of the first objective validations of immersive VR gameplay for measuring PM functioning in both younger and older adults. Conditions in VR were found to be effective in replicating patterns predicted by the multi-process theory of PM while also weakening concerns about cross-sectional differences in task-familiarity and sense of task arbitrariness and/or abstractness. We were also able to measure performance across multiple task settings and explore the covariates within each. In doing so, we found that naturalistic out- of-lab measures may recruit cognitive abilities differently from ecologically-valid measures in a more controlled environment. While it remains unclear whether this is due to situational factors or strategy differences, these results provide direction for future investigations seeking to resolve paradoxical age-PM findings.
Running Head: PROSPECTIVE MEMORY IN VR AND REAL LIFE 46
References
Aberle, I., Rendell, P. G., Rose, N. S., McDaniel, M. A., & Kliegel, M. (2010). The age
prospective memory paradox: Young adults may not give their best outside of the lab.
Developmental Psychology, 46(6), 1444.
Allemand, M., Zimprich, D., & Hendriks, A. A. (2008). Age differences in five personality
domains across the life span. Developmental Psychology, 44(3), 758.
Altgassen, M., Kliegel, M., Brandimonte, M., & Filippello, P. (2010). Are older adults more
social than younger adults? Social importance increases older adults' prospective memory
performance. Aging, Neuropsychology, and Cognition, 17(3), 312-328.
Altgassen, M., Koban, N., & Kliegel, M. (2012). Do adults with autism spectrum disorders
compensate in naturalistic prospective memory tasks?. Journal of Autism and
Developmental Disorders, 42(10), 2141-2151.
Banville, F., Nolin, P., Lalonde, S., Henry, M., Dery, M. P., & Villemure, R. (2010).
Multitasking and prospective memory: can virtual reality be useful for
diagnosis? Behavioural neurology, 23(4), 209-211.
Bopp, K. L., & Verhaeghen, P. (2005). Aging and verbal memory span: A meta-analysis. The
Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 60(5),
P223-P233.
Brandimonte, M. A., Einstein, G. O., & McDaniel, M. A. (2014). Prospective memory: Theory
and applications. Psychology Press.
Brandimonte, M. A., Ferrante, D., Feresin, C., & Delbello, R. (2001). Dissociating prospective
memory from vigilance processes? Psicológica, 22(1).
PROSPECTIVE MEMORY IN VR AND REAL LIFE 47
Brandt, J., Spencer, M., & Folstein, M. (1988). The telephone interview for cognitive status.
Neuropsychiatry Neuropsychol Behav Neurol, 1(2), 111-117.
Braver, T. S. (2012). The variable nature of cognitive control: a dual mechanisms framework.
Trends in cognitive sciences, 16(2), 106-113.
Braver, T. S., Gray, J. R., & Burgess, G. C. (2007). Explaining the many varieties of working
memory variation: Dual mechanisms of cognitive control. Variation in working memory,
76-106.
Brown, T. I., Carr, V. A., LaRocque, K. F., Favila, S. E., Gordon, A. M., Bowles, B., … &
Wagner, A. D. (2016). Prospective representation of navigational goals in the human
hippocampus. Science, 352(6291), 1323-1326.
Bugg, J. M., McDaniel, M. A., & Einstein, G. O. (2013). Event-based prospective remembering:
An integration of prospective memory and cognitive control theories. The Oxford
handbook of cognitive psychology, 267-282.
Chance, S. S., Gaunet, F., Beall, A. C., & Loomis, J. M. (1998). Locomotion mode affects the
updating of objects encountered during travel: The contribution of vestibular and
proprioceptive inputs to path integration. Presence, 7(2), 168-178.
Chen, G., King, J. A., Burgess, N., & O'Keefe, J. (2013). How vision and movement combine in
the hippocampal place code. Proceedings of the National Academy of Sciences, 110(1),
378-383.
Clark, A. (1997). Being there: Putting brain, body, and world together again. Cambridge, MA:
MIT press.
Clark, A. (2008). Supersizing the mind: Embodiment, action, and cognitive extension. OUP
USA.
PROSPECTIVE MEMORY IN VR AND REAL LIFE 48
Cona, G., Bisiacchi, P. S., & Moscovitch, M. (2013). The effects of focal and nonfocal cues on
the neural correlates of prospective memory: insights from ERPs. Cerebral cortex,
24(10), 2630-2646.
Conway, A. R., Kane, M. J., Bunting, M. F., Hambrick, D. Z., Wilhelm, O., & Engle, R. W.
(2005). Working memory span tasks: A methodological review and user’s guide.
Psychonomic bulletin & review, 12(5), 769-786.
Craik, F. I., & Bialystok, E. (2006). Cognition through the lifespan: mechanisms of change.
Trends in cognitive sciences, 10(3), 131-138.
Craik, F. I., Klix, F., & Hagendorf, H. (1986). A functional account of age differences in
memory (pp. 409-422).
Crovitz, H. F., & Daniel, W. F. (1984). Measurements of everyday memory: Toward the
prevention of forgetting. Bulletin of the Psychonomic Society, 22(5), 413-414.
Cuttler, C., & Graf, P. (2007). Personality predicts prospective memory task performance: An
adult lifespan study. Scandinavian Journal of Psychology, 48(3), 215-231.
Cuttler, C., & Graf, P. (2008). Sub-clinical checking compulsions are related to impaired
prospective memory independently of depression, anxiety and distractibility. Journal of
anxiety disorders, 22(4), 642-654.
Debarnot, U., Crépon, B., Orriols, E., Abram, M., Charron, S., Lion, S.,… & Baron, J. C. (2015).
Intermittent theta burst stimulation over left BA10 enhances virtual reality-based
prospective memory in healthy aged subjects. Neurobiology of aging, 36(8), 2360-2369.
Dong, D., Wong, L. K., & Luo, Z. (2018). Assess BA10 activity in slide-based and immersive
virtual reality prospective memory task using functional near-infrared spectroscopy
(fNIRS). Applied Neuropsychology: Adult, 1-7.
PROSPECTIVE MEMORY IN VR AND REAL LIFE 49 d'Ydewalle, G., Bouckaert, D., & Brunfaut, E. (2001). Age-related differences and complexity of
ongoing activities in time-and event-based prospective memory. The American journal of
psychology, 114(3), 411.
Engle, R. W., Tuholski, S. W., Laughlin, J. E., & Conway, A. R. (1999). Working memory,
short-term memory, and general fluid intelligence: a latent-variable approach. Journal of
experimental psychology: General, 128(3), 309.
Einstein, G. O., Holland, L. J., McDaniel, M. A., & Guynn, M. J. (1992). Age-related deficits in
prospective memory: The influence of task complexity. Psychology and aging, 7(3), 471.
Einstein, G. O., & McDaniel, M. A. (1990). Normal aging and prospective memory. Journal of
Experimental Psychology: Learning, Memory, and Cognition, 16(4), 717.
Einstein, G. O., & McDaniel, M. A. (1996). Retrieval processes in prospective memory:
Theoretical approaches and some new empirical findings. Prospective memory: Theory
and applications, 115-141.
Einstein, G. O., McDaniel, M. A., Richardson, S. L., Guynn, M. J., & Cunfer, A. R. (1995).
Aging and prospective memory: Examining the influences of self-initiated retrieval
processes. Journal of Experimental Psychology: Learning, Memory, and Cognition,
21(4), 996.
Ellis, J. (1996). Prospective memory or the realization of delayed intentions: A conceptual
framework for research. Prospective memory: Theory and applications, 1-22.
Feinkohl, I., Cress, U., & Kimmerle, J. (2016). Reheating breakfast: Age and multitasking on a
computer-based and a non-computer-based task. Computers in Human Behavior, 55, 432-
438.
PROSPECTIVE MEMORY IN VR AND REAL LIFE 50
Gonneaud, J., Piolino, P., Lecouvey, G., Madeleine, S., Orriols, E., Fleury, P., ... & Desgranges,
B. (2012). Assessing prospective memory in young healthy adults using virtual reality.
In Proceedings of the International Conference on Disability, Virtual Reality, and
Associated Technologies. Reading: The University of Reading.
Graf, P., & Uttl, B. (2001). Prospective memory: A new focus for research. Consciousness and
cognition, 10(4), 437-450.
Hale, S., Rose, N. S., Myerson, J., Strube, M. J., Sommers, M., Tye-Murray, N., & Spehar, B.
(2011). The structure of working memory abilities across the adult life span. Psychology
and Aging, 26(1), 92.
Henry, J. D., MacLeod, M. S., Phillips, L. H., & Crawford, J. R. (2004). A meta-analytic review
of prospective memory and aging. Psychology and aging, 19(1), 27.
Hering, A., Cortez, S. A., Kliegel, M., & Altgassen, M. (2014). Revisiting the age-prospective
memory-paradox: the role of planning and task experience. European journal of ageing,
11(1), 99-106.
Hering, A., Kliegel, M., Rendell, P. G., Craik, F. I., & Rose, N. S. (2018). Prospective Memory
Is a Key Predictor of Functional Independence in Older Adults. Journal of the
International Neuropsychological Society, 1-6.
Kalpouzos, G., Eriksson, J., Sjölie, D., Molin, J., & Nyberg, L. (2010). Neurocognitive systems
related to real-world prospective memory. PloS one, 5(10), e13304.
Kane, M. J., Conway, A. R., Hambrick, D. Z., & Engle, R. W. (2007). Variation in working
memory capacity as variation in executive attention and control. Variation in working
memory, 1, 21-48.
PROSPECTIVE MEMORY IN VR AND REAL LIFE 51
Kliegel, M., & Martin, M. (2003). Prospective memory research: Why is it relevant?.
International Journal of Psychology, 38(4), 193-194.
Kliegel, M., McDaniel, M. A., & Einstein, G. O. (Eds.). (2008). Prospective memory: Cognitive,
neuroscience, developmental, and applied perspectives. Taylor & Francis.
Kline, R. B. (1998). Principles and practice of structural equation modeling. New York: Guilford
Press.
Lindenberger, U., & Pötter, U. (1998). The complex nature of unique and shared effects in
hierarchical linear regression: Implications for developmental psychology. Psychological
Methods, 3(2), 218.
McCrae, R. R., Costa, P. T., de Lima, M. P., Simões, A., Ostendorf, F., Angleitner, A., ... &
Chae, J. H. (1999). Age differences in personality across the adult life span: parallels in
five cultures. Developmental psychology, 35(2), 466.
McDaniel, M. A., & Einstein, G. O. (2000). Strategic and automatic processes in prospective
memory retrieval: A multiprocess framework. Applied Cognitive Psychology: The
Official Journal of the Society for Applied Research in Memory and Cognition, 14(7),
S127-S144.
McGann, D., Defeyter, M. A. G., Ellis, J., & Reid, C. (2005). Prospective memory in children:
The effects of age and target salience.
Okahashi, S., Seki, K., Nagano, A., Luo, Z., Kojima, M., & Futaki, T. (2013). A virtual shopping
test for realistic assessment of cognitive function. Journal of neuroengineering and
rehabilitation, 10(1), 59.
O’Rear, A. E., & Radvansky, G. A. (2018). Location-based prospective memory. Quarterly
Journal of Experimental Psychology, 1747021818758608.
PROSPECTIVE MEMORY IN VR AND REAL LIFE 52
Ouellet, É., Boller, B., Corriveau-Lecavalier, N., Cloutier, S., & Belleville, S. (2018). The
Virtual Shop: A new immersive virtual reality environment and scenario for the
assessment of everyday memory. Journal of neuroscience methods, 303, 126-135.
Owlchemy Labs (2018). Job simulator: the 2050 Archives. Retrieved December 21, 2018, from
https://jobsimulatorgame.com/
Park, D. C. (2000). The basic mechanisms accounting for age-related decline in cognitive
function. Cognitive aging: A primer, 11(1), 3-19.
Park, D. C., Hertzog, C., Kidder, D. P., Morrell, R. W., & Mayhorn, C. B. (1997). Effect of age
on event-based and time-based prospective memory. Psychology and aging, 12(2), 314.
Park, D. C., Lautenschlager, G., Hedden, T., Davidson, N. S., Smith, A. D., & Smith, P. K.
(2002). Models of visuospatial and verbal memory across the adult life span. Psychology
and aging, 17(2), 299.
Parsons, T. D. (2015). Virtual reality for enhanced ecological validity and experimental control
in the clinical, affective and social neurosciences. Frontiers in human neuroscience, 9,
660.
Parsons, T. D., & Barnett, M. (2017). Validity of a newly developed measure of memory:
Feasibility study of the virtual environment grocery store. Journal of Alzheimer's
disease, 59(4), 1227-1235.
Rendell, P. G., & Craik, F. I. (2000). Virtual week and actual week: Age related differences in
prospective memory. Applied Cognitive Psychology: The Official Journal of the Society
for Applied Research in Memory and Cognition, 14(7), S43-S62.
PROSPECTIVE MEMORY IN VR AND REAL LIFE 53
Rendell, P. G., & Thomson, D. M. (1999). Aging and prospective memory: Differences between
naturalistic and laboratory tasks. The Journals of Gerontology Series B: Psychological
Sciences and Social Sciences, 54(4), P256-P269.
Rizzo, A. A., Buckwalter, J. G., & Neumann, U. (1997). Virtual reality and cognitive
rehabilitation: A brief review of the future. The Journal of Head Trauma Rehabilitation,
12(6), 1-15
Rose, N. S., Myerson, J., Sommers, M. S., & Hale, S. (2009). Are there age differences in the
executive component of working memory? Evidence from domain-general interference
effects. Aging, Neuropsychology, and Cognition, 16(6), 633-653.
Rose, N. S., Rendell, P. G., Hering, A., Kliegel, M., Bidelman, G. M., & Craik, F. I. (2015).
Cognitive and neural plasticity in older adults’ prospective memory following training
with the Virtual Week computer game. Frontiers in human neuroscience, 9, 592.
Rose, N. S., Rendell, P. G., McDaniel, M. A., Aberle, I., & Kliegel, M. (2010). Age and
individual differences in prospective memory during a" Virtual Week": The roles of
working memory, vigilance, task regularity, and cue focality. Psychology and aging,
25(3), 595.
Rypma, B., & D'Esposito, M. (2000). Isolating the neural mechanisms of age-related changes in
human working memory. Nature neuroscience, 3(5), 509.
Sakai, H., Nagano, A., Seki, K., Okahashi, S., Kojima, M., & Luo, Z. (2018). Development of a
cognitive function test using virtual reality technology: examination in healthy
participants. Aging, Neuropsychology, and Cognition, 25(4), 561-575.
Salthouse, T. A. (1994). The aging of working memory. Neuropsychology, 8(4), 535.
PROSPECTIVE MEMORY IN VR AND REAL LIFE 54
Salthouse, T. A., Berish, D. E., & Siedlecki, K. L. (2004). Construct validity and age sensitivity
of prospective memory. Memory & Cognition, 32(7), 1133-1148.
Sarter, M., & Bruno, J. P. (2004). Developmental origins of the age-related decline in cortical
cholinergic function and associated cognitive abilities. Neurobiology of aging, 25(9),
1127-1139.
Scarfe, P., & Glennerster, A. (2015). Using high-fidelity virtual reality to study perception in
freely moving observers. Journal of vision, 15(9), 3-3.
Shapiro, L. (2011). Embodied Cognition. Abingdon, Oxen: Routledge
Simons, D. J., & Wang, R. F. (1998). Perceiving real-world viewpoint changes. Psychological
Science, 9(4), 315-320.
Soto, C. J., & John, O. P. (2017). The next Big Five Inventory (BFI-2): Developing and
assessing a hierarchical model with 15 facets to enhance bandwidth, fidelity, and
predictive power. Journal of Personality and Social Psychology, 113(1), 117.
Taube, J. S., Valerio, S., & Yoder, R. M. (2013). Is navigation in virtual reality with FMRI really
navigation?. Journal of Cognitive Neuroscience, 25(7), 1008-1019.
Terry, W. S. (1988). Everyday forgetting: Data from a diary study. Psychological reports, 62(1),
299-303.
Turner, M. L., & Engle, R. W. (1989). Is working memory capacity task dependent? Journal of
memory and language, 28(2), 127-154.
Unsworth, N., & Engle, R. W. (2007). The nature of individual differences in working memory
capacity: active maintenance in primary memory and controlled search from secondary
memory. Psychological review, 114(1), 104.
PROSPECTIVE MEMORY IN VR AND REAL LIFE 55
Unsworth, N., McMillan, B. D., Brewer, G. A., & Spillers, G. J. (2012). Everyday attention
failures: An individual differences investigation. Journal of Experimental Psychology:
Learning, Memory, and Cognition, 38(6), 1765.
Varela, F. J., Thompson, E., & Rosch, E. (1991). The embodied mind: Cognitive science and
human experience. Cambridge, MA, US: The MIT Press.
Walter, S., & Meier, B. (2014). How important is importance for prospective memory? A
review. Frontiers in psychology, 5, 657.
Wang, R. F., & Simons, D. J. (1999). Active and passive scene recognition across
views. Cognition, 70(2), 191-210.
Watson, D., & Clark, L. A. (1992). On traits and temperament: General and specific factors of
emotional experience and their relation to the five factor model. Journal of
personality, 60(2), 441-476.
Will, C. M., Rendell, P. G., Ozgis, S., Pierson, J. M., Ong, B., & Henry, J. D. (2009).
Cognitively impaired older adults exhibit comparable difficulties on naturalistic and
laboratory prospective memory tasks. Applied Cognitive Psychology: The Official
Journal of the Society for Applied Research in Memory and Cognition, 23(6), 804-812.
Wilson, B., Cockburn, J., & Baddeley, A. (1985). The Rivermead Behavioral Memory Test.
Thames Valley Test Co. Reading, England.
Woods, B., Aguirre, E., Spector, A. E., & Orrell, M. (2012). Cognitive stimulation to improve
cognitive functioning in people with dementia. Cochrane Database of Systematic
Reviews, (2).
PROSPECTIVE MEMORY IN VR AND REAL LIFE 56
Appendix A.
The HTC Vive creates a virtual reality environment that can be interacted with using hand-held controllers. There is a potential for this technology to interact with nearby metal and/or electrical devices, thus we prevent participation for those who have implanted electrical medical devices. Virtual reality technology may induce dizziness and, in rare cases, cause seizures. Therefore, participants with any history of epilepsy or seizure will be excluded. Participants with heart conditions will also be excluded due to the potential for the technology to raise heart rate and blood pressure. Yes No Have you had an adverse reaction to virtual reality technology? Have you ever had a seizure (epilepsy)? Has anyone in your family been diagnosed with epilepsy? Do you suffer from heart ailments such as hypertension, arrhythmia, or heart diseases? Have you ever had a heart attack or stroke? Have you ever had a head injury (including Neurosurgery)? Do you suffer from frequent or severe headaches? Are you prone to motion sickness? Do you have any metal in your head such as shrapnel, surgical clips, or fragments from welding or metal work? (outside of your mouth) Do you have any implanted devices such as cardiac pacemakers, medical pumps, or intra-cardiac lines? Have you ever had any brain-related conditions? Have you ever had any illness that caused brain injury? (i.e. meningitis, aneurysm, brain tumor) Have you had unstable severe disease such as cardiologic, pulmonary, renal, endocrinal (hyperthyroidism or hypothyroidism), gastrointestinal, or others? Are you currently taking any medication? If yes, please list. ______Have you ever been diagnosed with a psychiatric illness? If you are a woman of childbearing ages; do you suspect that you might be pregnant? Do you need any further explanation of virtual reality and its associated risks? If any item was marked “yes” please provide a comment here: ______I attest that the above information is correct to the best of my knowledge. I have read and understand the contents of this form and have had the opportunity to ask questions regarding the information on this form and regarding the procedure that I am about to undergo.
Signature of Person Completing Form: ______Date (MM/DD/YYYY): ______
Form completed by: ______Participant Name:______
PROSPECTIVE MEMORY IN VR AND REAL LIFE 57
Table 1.
Proportion of total responses for Younger and Older age groups on PM tasks incorporated into
Job Simulator or the Breakfast Task.
Note. Event-based (regular/irregular) thresholds: On time < 10 sec.; Little Late < 30 sec.;
Very Late > 30 sec. Time-based (reg/irreg); Breakfast task thresholds: On time < 60 sec.; Little
Late < 90 sec.; Very Late > 90 sec. (One event-regular task in cook simulation was scored using
<60/<90/>90, respectively)
PROSPECTIVE MEMORY IN VR AND REAL LIFE 58
Table 2.
Split-half and bivariate task correlations for younger and older adults
Note. Below Diagonal: Young (N = 59, Belongings N = 55); Above Diagonal: Old (N=52,
Belongings N = 46, Call-back N = 51); Diagonal: Split-Half Reliabilities (Young/Old);
* p < 0.05; ** p < 0.01