ali.ma@wecistanche.com

Kimberly M. Newberry 1 & Daniel P. Feller2 & Heather R. Bailey3

Accepted: 12 November 2020 / Published online: 7 January 2021

# The Psychonomic Society, Inc. 2021

Click to Cistanche DHT can improve memory 

Abstract

Much research has shown that experts possess superior memory in their domain of expertise. This memory benefit has been proposed to be the result of various encoding mechanisms, such as chunking and differentiation. Another potential encoding mechanism that is associated with memory is event segmentation, which is the process by which people parse continuous information into meaningful, discrete units. Previous research has found evidence that segmentation, to some extent, is affected by top-down processing. To date, few studies have investigated the influence of expertise on segmentation, and questions about expertise, segmentation ability, and their impact on memory remain. The goal of the current study was to investigate the influence of expertise on segmentation and memory ability for two different domains: basketball and Overwatch. Participants with high and low knowledge for basketball and with low knowledge for Overwatch viewed and segmented videos at coarse and fine grains, then completed memory tests. Differences in segmentation ability and memory were present between experts and control novices, specifically for the basketball videos; however, experts’ segmentation only predicted memory for activities for which knowledge was lacking. Overall, this research suggests that experts’ superior memory is not due to their segmentation ability and contributes to a growing body of literature showing evidence supporting conceptual effects on segmentation.

Keywords:Domain knowledge. Event segmentation. Memory. Expertise,cistanche extract

Decades ofwork on domain knowledge (semantic knowledge for a particular field) have shown that experts possess superior memory for information in their expert field. This memory benefit has been explained by various encoding mechanisms, including chunking (Chase & Simon, 1973), differentiation, and unitization (Herzmann & Curran, 2011). Recently, anoth- er encoding mechanism has been shown to influence memory for event information: event segmentation (Bailey et al., 2013; Flores, Bailey, Eisenberg, & Zacks, 2017; Newberry &The data presented in this manuscript were previously presented at both the 59th annual meeting of the Psychonomic Society in November of 2018, and the 91st annual meeting of the Midwestern Psychological Association in April of 2019.

* Kimberly M. Newberry

knewberr@su.edu

1. Department of Psychology, Shenandoah University, 600 Millwood Ave., Halpin Harrison Hall 117, Winchester, VA 22601, USA

2. Department of Learning Sciences, Georgia State University, Atlanta, GA, USA

3. Department of Psychological Sciences, Kansas State University, Manhattan, KS, USA

Bailey, 2019; Sargent et al., 2013; Zacks, Speer, Vettel, & Jacoby, 2006).

Event segmentation is an encoding mechanism in which people parse continuous event information into meaningful, discrete units (e.g., Zacks, Speer, Swallow, Braver, & Reynolds, 2007). How people segment an event influences how they perceive, comprehend, and remember events (for review, see Radvansky & Zacks, 2014). This process may be influenced by both perceptual and conceptual factors, which suggests prior knowledge may affect how someone perceives and segments an event, which in turn may influence memory. While some studies suggest that domain knowledge influences segmentation (e.g., experts identify fewer boundaries: Bläsing,2015; experts agree more coarse boundaries: Levine, Hirsh-Pasek, Pace, & Michnick Golinkoff, 2017; Zacks & Tversky, 2003), several questions remain: To what extent do people agree on how activities are segmented within and outside of their knowledge domain? Do high-domain-knowledge individuals organize events at encoding differently from low-domain-knowledge individuals? If so, does this ex- plain the observed memory benefit?

Thus, the current study investigated the influence of domain knowledge on the segmentation and memory of basketball and Overwatch games. These activities were chosen for their popularity as well as for testing the generalizability of knowledge effects on the segmentation across different activities. To begin, theories of event cognition, event segmentation theory and the event horizon model, are discussed, followed by the relationship between segmentation and knowledge. Afterward, the literature on expertise is described and integrated with event segmentation, and general predictions about the current study are presented.

Event segmentation theory

According to event segmentation theory (EST; Kurby & Zacks, 2008; Zacks et al., 2007), events are experienced continuously, but the perception of those events is not. Rather, people use perceptual (e.g., motion, body position; Newtson, Enquist, & Bois, 1977; Zacks, 2004) and conceptual (e.g., knowledge, goals; Levine et al., 2017; Radvansky & Zacks, 2014; Zacks, 2004) information to construct mental representations of ongoing activity, such that the current event representation is held in working memory until a change is perceived, at which point a new representation is constructed to reflect the new event (e.g., Zacks et al., 2007). This updating process is thought to occur when there is a mismatch between expectation and reality (Rescorla & Wagner, 1972) that is driven by prediction failures (Zacks et al., 2007), a lack of coherence (Gernsbacher, 1991), or changes in context (Clewett & Davachi, 2017).

EST posits that people generate predictions for upcoming auctions, and the accuracy of these predictions is monitored. For example, after a basketball player makes a shot, it is likely that a player from the opposite team will inbound the ball and dribble it to the other end of the court. However, when the player with the ball reaches the opposite end of the court, the event becomes less predictable. Will the player pass the ball or take a shot? The points in time when predictions fail, or when people perceive a change and update their event representation, are called event boundaries. Research suggests that within an event, predictability is high, but across event boundaries, predictability is low (e.g., Reynolds, Zacks, & Braver, 2007; Zacks, Kurby, Eisenberg, & Haroutunian, 2011). Interestingly, people reliably parse events at consistent boundaries (e.g., Bower, Black, & Turner, 1979; Hard, Tversky, & Lang, 2006b; Newtson, 1973; Speer, Swallow, & Zacks, 2003; Zacks, Tversky, & Iyer, 2001a), even up to 1 year later (test-retest; Speer et al., 2003).

Research using a unitization paradigm, in which people denote boundaries while watching events unfold, suggests that events are hierarchically structured (e.g., Newtson, 1973; Sargent et al., 2013; Zacks, Tversky, et al., 2001a) such that larger, coarse-grain events are made up of smaller, fine-grain events (Tversky, Zacks, & Martin, 2008; Zacks & Swallow, 2007; Zacks, Tversky, et al., 2001a). For instance, a college basketball game may consist of the first half and the second

half. However, the first half could be further divided into smaller subevents, such as a series of plays executed by each team. Previous work has found individual differences in the

extent to which people perceive alignment between fine and coarse-grain events (e.g., Hard, Lozano, & Tversky, 2006a; Kurby & Zacks, 2011; Sargent et al., 2013; Zacks et al., 2001b), and evidence suggests that hierarchical encoding may be important for memory (Kurby & Zacks, 2011).

Importantly, the event horizon model (Radvansky, 2012), which subsumes event segmentation theory (e.g., Radvansky & Zacks, 2014, 2017), explains that event boundaries reduce retroactive interference by separating information into sepa- rate event models, which leads to better overall memory for the activity. Indeed, evidence suggests that the extent to which people demonstrate normative segmentation (i.e., the degree to which they agree on locations of event boundaries and have better hierarchical alignment) predicts how well they later re-member the activity (Bailey et al., 2013; Flores et al., 2017; Kurby & Zacks, 2011; McGatlin, Newberry, & Bailey, 2018; Newberry & Bailey, 2019; Sargent et al., 2013; Zacks et al., 2006).

What influences segmentation behavior?

Two types of factors presumably influence segmentation: perceptual and conceptual (e.g., Zacks, 2004; Zacks et al., 2007). Much of the research on segmentation has focused on the influence of perceptual cues. For example, perceived event boundaries tend to align with changes in body position (Newtson et al., 1977), spatial location (Magliano, Miller, & Zwaan, 2001), object motion (Zacks et al., 2001b), and perceptual change (Hard et al., 2006b). For example, perceptual change in basketball may involve changes occurring around the ball (e.g., passes, shots; Huff et al., 2017). Further, regions of the brain that process emotion (e.g., extrastriate motion com- plex) show increased activity at event boundaries (Speer et al., 2003; Zacks et al. 2001b), suggesting that motion is a strong predictor of event boundary perception.

In contrast, research investigating effects of conceptual fac- tors on segmentation is mixed: Some studies suggest that conceptual factors have no influence on segmentation (e.g., Hard et al., 2006b; Huff et al., 2017; Zacks, Kumar, Abrams, & Metha, 2009), whereas others suggest that they do (context: Loschky, Larson, Magliano, & Smith, 2015; Newberry & Bailey, 2019; familiarity: McGatlin et al., 2018; Smith, Newberry & Bailey, 2020; Zacks & Tversky, 2003; perspec- tive: Newberry & Bailey, 2019; schema and scripts: Bartlett, 1932; McGatlin et al., 2018; Schank & Abelson, 1977; goals: Baldwin, Baird, Saylor & Clark; 2001; Wilder, 1978a, 1978b; Zacks, 2004). For example, Wilder (1978a, 1978b) showed that participants segmented more often when an actor’s goals were unclear as compared with when the activity was goal-directed and predictable, indicating that goals affect how peo- ple perceive an activity. Similarly, Zacks (2004) found that movement predicted segmentation less when events were goal-directed as opposed to random. Though altogether these results suggest that when goal-related knowledge is present, people rely less on perceptual cues while perceiving an event, the effects have been moderate to small.

A stronger manipulation: Expertise Recent research on knowledge and segmentation has moved toward using a stronger manipulation of prior knowledge: expertise (e.g., Bläsing, 2015; Levine et al., 2017). The use of expertise to evaluate knowledge effects on segmentation fits well with EST and the event horizon model because ample evidence suggests that having prior knowledge about an activity improves prediction when viewing similar activi- ties (e.g., Ambrosini et al., 2013; Kanakogi & Itakura, 2011; Möller, Zimmer, & Aschersleben, 2015; Sommerville, Woodward, & Needham, 2005), and re- search has shown that people with prior knowledge or experience for an activity also have better memory for that activity (e.g., basketball: Allard, Graham, & Parsalu, 1980; dance: Allard & Starkes, 1991; chess: Chase & Simon, 1973; baseball: Chiesi, Spilich, & Voss, 1979; bridge: Engle & Bukstel, 1978; maps: Gilhooly, Wood, Kinnear, & Green, 1988; music: Meinz & Salthouse, 1998). Given that prediction is posited to be the mechanism upon which segmentation operates (e.g., Zacks, Braver, et al., 2001b; Zacks, Kurby, et al., 2011) and event boundary identification is important for memory (e.g., Radvansky & Zacks, 2014), this would suggest that segmentation behavior and memory may differ when one has prior knowledge or experience with an activity com- pared with no knowledge or experience.

Such a presumption has been supported in the expertise literature focusing on other mechanisms involved in percep- tual learning (Goldstone, 1998): differentiation (ability to sep- arate initially fused categories) and unitization (ability to inte- grate individual parts into functional wholes). Evidence suggests that experts better judge when to engage in each process (Herzmann & Curran, 2011). When encoding dynamic activ- ity, experts may be better at identifying conceptual units of information and distinguishing between fine details for events within their domain (e.g., Piras, Lobietti, & Squatrito, 2010). For example, a basketball expert may be able to identify the steps involved in a pick and roll (i.e., better differentiation) whereas a novice might perceive these steps as one action or not at all, or the basketball expert may perceive that same pick and roll as part of a larger play, whereas the novice may perceive it as its own event (i.e., better unitization). If experts identify meaningful event boundaries based on a shared knowledgebase that improves their prediction accuracy, one might expect experts to show more normative segmentation ability, in terms of higher agreement on event boundary loca- tions and/or better alignment of coarse and fine boundaries.

Two studies have investigated effects of expertise on segmentation behavior. In the dance domain, Bläsing (2015) in- vestigated effects of expertise and movement-specific famil- iarity on the segmentation of a dance phrase. Dancers and nondancers watched and segmented videos of a dancer completing a choreographed phrase. Bläsing (2015) found that dancers segmented less often compared with nondancers, suggesting that expertise reduces the number of perceived boundaries for events within one’s area of expertise. In another experiment, Bläsing evaluated the causal role of knowledge on segmentation by having intermediate dancers segment a dance phrase, then learn and practice the motor movements, and segment the phrase again. Like the first experiment, increased familiarity and motor experience with the dance phrase caused dancers to segment less often. Similarly, Levine et al. (2017) found that figure skating experts identi- fied more similar coarse-grain events compared with novices when segmenting an Olympic figure skating routine. These studies have provided initial evidence that expertise influences segmentation behavior; however, some limitations remain. One limitation is that these studies only evaluated segmentation at one grain size. They either provided no specific grain size instruction (Bläsing, 2015) or they only instructed participants to segment at the coarse-grain level (Levine et al., 2017). By including both coarse and fine-grained segmentation in one study, we can evaluate the hierarchical alignment of small events into larger events, and whether domain knowledge increases this alignment. Critically, neither study inves- tigated experts’ segmentation ability in a domain outside their expertise. Furthermore, neither study measured memory, so effects of domain knowledge and segmentation on memory have not yet been evaluated.

Given that normative segmentation is associated with better memory for events (Bailey et al., 2013; Flores et al., 2017; Zacks et al., 2006), it is possible that the superior memory of experts may be due to more normative segmentation of the activity within their knowledge domain. If segmentation is a process that is enhanced by accumulation of prior knowledge and experience, one might expect the memory benefit to only be present for the more knowledgeable activity. However, prior work has shown that people use prior knowledge to fill in the gaps at retrieval (e.g., Hasher & Griffin, 1978). Thus, knowledge could override effects of segmentation on memory and some evidence suggests that segmentation and knowledge affect memory independently (Sargent et al., 2013). If this is true, one might expect segmentation to predict memory only for the novice activity, as novices would not have knowledge 1 Segmentation frequency and agreement are different. Someone may segment less often, but still identify several boundaries identified by the group, and thus have high agreement.

to rely on at retrieval, other than the event representations they built while encoding the activity for the first time.

Thus, the current study expanded upon Bläsing (2015)and Levine et al. (2017) by investigating segmentation behavior and its relationship to memory performance in people with high and low knowledge (for simplicity, we heretofore refer to them as “experts” and “control novices,” respectively), across two different domains: basketball (sport) and Overwatch (video game). Basketball is a limited-contact, team sport that involves players working together to achieve a com- mon goal (i.e., shooting the ball through the hoop to earn points). Overwatch, though also team-based, is a multiplayer first-person shooter video game developed by Blizzard Entertainment, Inc.©. Basketball and Overwatch were chosen as the activities in this study for two reasons. First, the inclusion of two activities makes the current study unique in that experts were tested on activities both within and outside their field of expertise. Second, basketball and Overwatch are dif- ferent from dance and figure skating (e.g., Ericsson & Smith, 1991), which allows the research questions to be extended from single-actor to team-based activities.

Hypotheses

If expertise influences segmentation behavior, then experts should segment less often at the coarse grain (segmentation frequency; Bläsing 2015) and agree more on boundary locations (segmentation agreement; Levine et al., 2017) for activities within their expert field. Alternatively, experts may segment more often, particularly at the fine grain, if they engage in perceptual processes such as differentiation to better distinguish between finer subevents (Piras et al., 2010). We also

hypothesized that experts would show the greater alignment of coarse and fine boundaries for activities within their expert area (hierarchical alignment). However, if perceptual cues have a stronger influence on segmentation than do conceptual

factors (Hard et al., 2006b; Huff et al., 2017; Zacks, Speer, &Reynolds, 2009), then experts and control novices may demonstrate similar segmentation behavior because perceptual cues (motion) are readily available to both groups. Further,

we hypothesized that experts would show better memory performance for activities within their field of expertise, based on the significant body of expertise research (for review, see Ericsson & Smith, 1991; Furley & Wood, 2016).

Prior work suggests that normative segmentation is associated with better memory for events (e.g., Bailey et al., 2013). Thus, we hypothesized that segmentation ability would predict memory performance, regardless of activity or domain knowledge, such that those with better segmentation agreement and/or hierarchical alignment would have a better memory. However, we also predicted that the relationship between segmentation and memory would be stronger in the expert

activity if domain knowledge improves memory by enhancing segmentation. Alternatively, some work suggests that general knowledge may influence memory independently of segmentation (Sargent et al., 2013), such that people may rely on knowledge (e.g., schemas, scripts, expectations), when it is available, to help them remember the activity, as opposed to how they encode (segment) that particular instance of that activity. In this case, knowledge may override the relationship between segmentation and memory, such that experts who segment well and those who segment poorly remember similar amounts of information.

The current study

The purpose ofthis experiment was to investigate the relationship between domain knowledge, segmentation ability, and memory for events within and outside of one’s knowledge area. Previous work has observed the effects of expertise on the segmentation of dance phrases (Bläsing, 2015) and a figure skating routine (Levine et al., 2017); however, these studies only evaluated experts’ segmentation behavior for events within their field of expertise. Additionally, the hierarchical alignment of different segmentation grains and their effects on memory have yet to be evaluated in this context. In the current experiment, basketball and Overwatch experts and control novices viewed and segmented videos of basketball and Overwatch. Due to recruitment issues, only a very small sample of Overwatch experts participated in the study (see Method section). The current experiment ultimately focused on a within-subjects comparison of basketball experts’ segmentation and memory for basketball (area of expertise) and for Overwatch (area outside of expertise) videos as well as a between-subjects comparison of segmentation and memory for basketball activities between basketball experts and control novices.

Method

Participants A total of 165 participants (see Table 1) were recruited from Kansas State University (KSU). Participants were recruited from psychology courses and from other

organizations across campus. To increase recruitment of Overwatch experts, the study was advertised through the KSU eSports Club, which promotes professional competition and spectatorship for Overwatch videogame players and fans. Recruitment yielded 35 basketball experts (Overwatch nov- ices), 12 Overwatch experts (three of which were basketball novices, nine of which had “intermediate” or expert basketball scores), 61 control novices (novices in both activities), two uncategorized, and 55 “intermediate” individuals who scored above the novice, but below the expert thresholds in both areas (see Knowledge Surveys, below).

Predictions for the current experiment were based on an “expert” versus “control novice” comparison. Only individuals who met the criteria for expert or control novice were included in the main analyses. Participants who scored in the “intermediate” range for either activity were only included in the exploratory analyses where knowledge was treated as a continuous variable (see Supplemental Materials). Unfortunately, recruitment of Overwatch experts proved difficult, even after targeting Overwatch players from eSports for several months. Therefore, due to the low sample size, the main analyses of the current experiment also exclude this group (though they are included in the exploratory analyses in the Supplemental Materials). Additionally, eight partici- pants’ data (two basketball experts, two control novices, two intermediates, and two uncategorized) were lost due to technical issues. Participants were compensated with course credit or entered into a gift card raffle, depending on from where they were recruited.

Because participants were not randomly assigned to groups, all participants completed a series of cognitive measures (processing speed, vocabulary, semantic knowledge, and working memory; see Supplemental Materials for a full description) to assess individual differences that may have otherwise explained possible segmentation and memory effects. Bayes factors were used to test for evidence of the null hypothesis (i.e., no difference between groups; see Table 2). Bayes factors of less than 1 suggested substantial evidence for the null (e.g., Wetzels & Wagenmakers, 2012), suggesting no differences between groups on these cognitive abilities.

Materials

Knowledge survey Knowledge surveys were used to identify experts and novices in basketball and Overwatch. The basket- ball portion of the survey was a modified version of Feller, Schwan, Wiemer, and Magliano (2018; adapted from French & Thomas, 1987), such that it was reduced to 23 questions to match the Overwatch survey, which was developed for use in the current study. Both the basketball and Overwatch surveys included 23 questions each about general information regard- ing each activity, as well as seven self-report familiarity and expertise questions. All questions had five answer options, with the fifth option (e) always stating “I don’t know.” Experts were identified with scores ranging from 17 to 23, while novices were identified with scores ranging from 0 to 7 (based on percentage cutoffs from previous work using knowledge surveys; Rawson & van Overschelde, 2008). Both surveys are included in the Appendix.

Videos Five videos were used in this experiment (one practice; four experimental). The practice video depicted a man using Legos to build a ship (155 s). Two of the experimental videos were college basketball games; specifically, Memphis vs. UCLA (153 s; three cuts) and Montana vs. Weber State (130 s; nine cuts; Feller et al., 2018). The other two experimental videos were Overwatch tournament matches; specifically, Houston vs. Boston (144 s; 11 cuts) and London vs. Florida (135 s; seven cuts). All of the experimental videos were shorter clips of continuous game play (maintaining action continuity) taken from longer videos to minimize the influ- ence of cuts on perception, though research suggests that most cuts go unrecognized and do not influence segmentation (Magliano & Zacks, 2011; T. J. Smith & Henderson, 2008). Additionally, evidence from the event cognition literature sug- gests that viewpoint changes also do not influence the events that are perceived (Swallow, Kemp, & Simsek, 2018). The Overwatch videos were chosen because they were profession- ally recorded games played by Overwatch experts. Participants viewed all ofthe experimental videos twice (once per segmentation grain).

Table 2 Performance on cognitive battery by expertise group

Unitization task The unitization task (Newtson, 1973) was used as an overt measure of participants’ perception of event boundaries in the videos. While watching the videos, participants were asked to press the space bar each time “one meaningful unit of activity ends and another begins.” Participants were instructed to identify larger (coarse) or smaller (fine) units of meaningful activity by pressing the space bar (e.g., Sargent et al., 2013). Participants were shaped on this task using a practice video (see Zacks et al., 2009). The shaping procedure required participants to identify at least 3 larger (coarser) units or 6 smaller (finer) units in order to move on to the experimental trials. If this threshold was not met, partici- pants received feedback stating that other people typically identify more units; however, they were not given explicit examples of how the activities in the video could be seg- mented. After receiving this message, participants repeat- ed the shaping procedure until they passed the threshold.

Event memory measures

Recognition memory was assessed using a two-alternative forced-choice test. There were 20 trials per video, each containing one target and one distractor image, simultaneously presented side-by-side. Target im- ages always came from the videos that participants watched, and distractor images always came from portions of the same video that participants did not see. The presentation order of the image pairs was the same for each participant. Participants received 1 point for each correctly identified image (up to 20 total points). Participants’ scores were reported as proportion correct.

Order memory 2 Order memory was assessed using a two-alternative forced-choice test, based on the measure used by Dubrow and Davachi (2014). For each video, participants were presented with eight image pairs on the computer. All the images came from the video's participants watched. A prompt appeared on screen stating “more recent?”, and participants were instructed to choose the image depicting the more recent action.

Design and procedure

Expertise was a between-subjects variable. Participants (NBasketballExperts = 33, NControlNovices = 59) were grouped based on their scores from the knowledge survey about basketball and Overwatch (novice ≤ 7; expert ≥ 17; see Table 3; see Supplemental Materials for analyses that

include expertise as a continuous variable, including par- ticipants with intermediate knowledge). To be clear, ev- eryone in the basketball expert group were also novices in Overwatch, separate from those in the control group, who were identified as control novices in both activities. Activity (basketball & Overwatch) was treated as within-subjects, such that all participants viewed and segmented videos of both activities. Participants segmented each vid- eo twice: once per grain (coarse vs. fine). Video and distractor task were counterbalanced across participants. Segmentation grain was counterbalanced, such that partic- ipants segmented all the videos at one grain, then after completing the last block of tasks for the last video, they segmented all the videos again (in the same order of presentation) at the other grain.

All participants entered the lab in small groups of three or four and were seated at a computer. They first signed an informed consent form and then completed the knowl- edge survey. After, they were given a demographics form and instructed not to fill it out until the experimental pro- gram on the computer told them to do so. Each participant was then presented with the practice video, which shaped each participant’s segmentation behavior to whichever segmentation grain order each participant was assigned (i.e., at least three button presses for coarse-grain; at least six for fine grain). After completing the shaping proce- dure, the experimental trials began. The experimental tri- als consisted of four blocks. In each block, the experimen- tal video was presented, and participants were instructed to “press the space bar any time they felt a meaningful unit of activity ended and a new one began.” After each video, participants completed a distractor task (i.e., one of the individual differences measures listed above), and then moved on to the recognition and order memory tasks. The memory task order was not counterbalanced because the viewing of target images in the order memory task could have aided participants on the recognition task. After the order memory task for the last video of the last block, participants were shown the practice video again and trained on the segmentation task for the alternative grain. Participants then resegmented each video at this new grain in the same order in which the videos were originally presented. At the end of the experiment, participants completed the working memory task. Finally, they were debriefed, thanked, and compensated for their time.