Joshua C. Poore

The role of executive function and the dorsolateral prefrontal cortex in the expression of neuroticism and conscientiousness.

The current study examined how specific neurological systems contribute to the expression of multiple personality dimensions. We used individuals with traumatic brain injuries to examine the contribution of the dorsolateral prefrontal cortex (DLPFC)—a region important for executive function and attention—to the expression of neuroticism and conscientiousness factors and facets. Results from Voxel-Based Lesion-Symptom Mapping analyses revealed that focal damage to the left DLPFC (Brodmann’s area 9) was associated with high neuroticism and low conscientious factor and facet scores (anxiety and self-discipline, respectively). Compared with lesioned and normal controls, veterans with damage in left DLPFC also reported higher neuroticism and lower conscientiousness facet scores, slower reaction times on the California Computerized Assessment Package assessment, and lower scores on the Delis–Kaplan executive function battery. Findings suggest that while neuroticism and conscientiousness remain psychometrically independent personality dimensions, their component facets may rely on a common neurocognitive infrastructure and executive function resources in general.

Modeling strategic use of human computer interfaces with novel hidden Markov models

Immersive software tools are virtual environments designed to give their users an augmented view of real-world data and ways of manipulating that data. As virtual environments, every action users make while interacting with these tools can be carefully logged, as can the state of the software and the information it presents to the user, giving these actions context. This data provides a high-resolution lens through which dynamic cognitive and behavioral processes can be viewed. In this report, we describe new methods for the analysis and interpretation of such data, utilizing a novel implementation of the Beta Process Hidden Markov Model (BP-HMM) for analysis of software activity logs. We further report the results of a preliminary study designed to establish the validity of our modeling approach. A group of 20 participants were asked to play a simple computer game, instrumented to log every interaction with the interface. Participants had no previous experience with the games functionality or rules, so the activity logs collected during their naïve interactions capture patterns of exploratory behavior and skill acquisition as they attempted to learn the rules of the game. Pre- and post-task questionnaires probed for self-reported styles of problem solving, as well as task engagement, difficulty, and workload. We jointly modeled the activity log sequences collected from all participants using the BP-HMM approach, identifying a global library of activity patterns representative of the collective behavior of all the participants. Analyses show systematic relationships between both pre- and post-task questionnaires, self-reported approaches to analytic problem solving, and metrics extracted from the BP-HMM decomposition. Overall, we find that this novel approach to decomposing unstructured behavioral data within software environments provides a sensible means for understanding how users learn to integrate software functionality for strategic task pursuit.

Operationalizing Engagement with Multimedia as User Coherence with Context

Traditional approaches for assessing user engagement within multimedia environments rely on methods that are removed from the human computer interaction itself, such as surveys, interviews and baselined physiology. We propose a context coherence approach that operationalizes engagement as the amount of independent user variation that covaries in time with multimedia contextual events during unscripted interactions. This can address questions about the features of multimedia users are most engaged and how engaged users are without the need for prescribed interactions or baselining. We assessed the validity of this approach in a psychophysiological study. Forty participants played interactive video games. Intake and post-stimulus questionnaires collected subjective engagement reports that provided convergent and divergent validity criteria to evaluate our approach. Estimates of coherence between physiological variation and in-game contextual events predicted subjective engagement and added information beyond physiological metrics computed from baselines taken outside of the multimedia context. Our coherence metric accounted for task-dependent engagement, independent of predispositions; this was not true of a baselined physiological approach that was used for comparison. Our findings show compelling evidence that a context-sensitive approach to measuring engagement overcomes shortcomings of traditional methods by making best use of contextual information sampled from multimedia in time-series analyses.

Personality, Cognitive Style, Motivation, and Aptitude Predict Systematic Trends in Analytic Forecasting Behavior

The decision sciences are increasingly challenged to advance methods for modeling analysts, accounting for both analytic strengths and weaknesses, to improve inferences taken from increasingly large and complex sources of data. We examine whether psychometric measures—personality, cognitive style, motivated cognition—predict analytic performance and whether psychometric measures are competitive with aptitude measures (i.e., SAT scores) as analyst sample selection criteria. A heterogeneous, national sample of 927 participants completed an extensive battery of psychometric measures and aptitude tests and was asked 129 geopolitical forecasting questions over the course of 1 year. Factor analysis reveals four dimensions among psychometric measures; dimensions characterized by differently motivated “top-down” cognitive styles predicted distinctive patterns in aptitude and forecasting behavior. These dimensions were not better predictors of forecasting accuracy than aptitude measures. However, multiple regression and mediation analysis reveals that these dimensions influenced forecasting accuracy primarily through bias in forecasting confidence. We also found that these facets were competitive with aptitude tests as forecast sampling criteria designed to mitigate biases in forecasting confidence while maximizing accuracy. These findings inform the understanding of individual difference dimensions at the intersection of analytic aptitude and demonstrate that they wield predictive power in applied, analytic domains.

Anhedonia in combat veterans with penetrating head injury

Anhedonia is a common symptom following traumatic brain injury. The neural basis of anhedonia is poorly understood, but believed to involve disturbed reward processing, rather than the loss of sense of pleasure. This analysis was undertaken to determine if injury to specific regions of prefrontal cortex (PFC) result in anhedonia. A CT-based lesion analysis was undertaken in 192 participants of the Vietnam Head Injury Study, most with penetrating head injury. Participants were divided into left and right ventrolateral prefrontal, bilateral ventromedial prefrontal, and other injury locations. Anhedonia was measured by self-report in each group using the four-item anhedonia subscale score of the Beck Depression Inventory-II. Individuals with right ventrolateral injury reported greater severity of anhedonia compared to those with injury in the left ventrolateral region. These findings support an association between injury in the right ventrolateral PFC and anhedonia.

Fine Distinctions within Cognitive Style Predict Forecasting Accuracy

Leveraging data from over 1,000 users in the System for Prediction, Aggregation, Display and Elicitation (SPADE) research program, we present preliminary data on the factor structure of individual variation in decision making ability and the associations of this variance with errors in cognitive reasoning and accuracy in making socio-political forecasts. Generally, prior research has identified two factors, or styles—intuitive and analytic—that account for significant variance in how individuals reach solutions to complex numerical and logical problems. Though sometimes named differently across research programs, an intuitive style is a tendency to use instincts, experiential knowledge, and intuition to solve problems, where an analytic style is a tendency to apply formal logic, methods of inquiry and theory to confront problems. Within a large research sample, factor analytic techniques define finer distinctions among these styles. In particular, we find distinctions within the analytical style, such that certain measures of analytic style (REI; Norris, Pacini, & Epstein, 1998) capture variance related to tendencies to express a deep interest in complex problem solving and openness to new information. In contrast, other measures (CSI; Allinson et al., 1996) capture variance related to tendencies to solve problems that are driven by a need for closure and conscientiousness. Subsequent correlation analysis suggests that the latter tendency covaries with susceptibility to commit errors in logical reasoning and poor performance on socio-economic forecasts elicited through the iSPADE system. Future work will clarify the relationship between cognitive styles and errors in reasoning and forecasting behavior through multi-level modeling techniques.

Software as a Medium for Understanding Human Behavior

Our ability to understand users constrains our ability to design, create, and develop for them new ways of interfacing with technology. In turn, our ability to measure and derive insights from user behavior in real-world environments constrains our ability to truly understand them and how they will use the technology we develop for them. The psychological sciences (broadly defined) remain steadfastly locked in a tradition of experimental artifice—they make sense of our observations of humans by artificially constraining the environment (the laboratory) and human experience (experiments). However, the rate at which real-world endeavors are finding analogous virtualized platforms (e.g., entertainment, productivity, and sociality) is dramatically increasing; people are using software for more aspects of their lives than ever. This presents new measurement opportunities because software is a tool, and if we can instrument tools while they are used to perform tasks, then we can understand how humans approach those tasks. In this way, software is a new, virtualized medium for understanding real-human behavior in new compelling ways that bridge the gap between foundational research in the psychological science and applied research in the fields of human computer interaction (HCI). In this review paper, we will describe advances in gathering meaningful data from human in software environments and how it may be used to improve how people interface with their technology, understand cognition, as well as our understanding of people.

Editorial: Virtual Environments as Study Platforms for Realistic Human Behavior

As of 2013, US persons spend an average of 37 h per month using smartphone applications, another 27 h using the internet on personal computers, and 133 h watching live television (Nielsen, 2014). Coupled with the estimated 64% of US persons that own a smartphone (Pew, 2015), people are spending a large sum of their waking lives plugged into virtualized, but very real, software-mediated environments. In these cases, and where virtual environments (VEs) are meant to emulate the real-world for gaming and simulation, the psychological sciences now have an unprecedented opportunity to use them as mediums for understanding humans in the real-world (Bowers et al., 2008). VEs can be used to capture high fidelity data about human behavior in a way that far exceeds what can be captured in existing laboratory and daily experience research methods. The current challenge is to harness this data for observing and modeling how people use to manipulate their environment, forage for information, and work with people in organic, unstructured interactions. This research topic introduces new approaches and use-cases for how VEs can enhance the ecological validity of laboratory studies, and the generalizable study of human behavior in naturalistic, real-world environments, even though they may be virtual.