Corey Bohil

Virtual reality in neuroscience research and therapy

Virtual reality (VR) environments are increasingly being used by neuroscientists to simulate natural events and social interactions. VR creates interactive, multimodal sensory stimuli that offer unique advantages over other approaches to neuroscientific research and applications. VRs compatibility with imaging technologies such as functional MRI allows researchers to present multimodal stimuli with a high degree of ecological validity and control while recording changes in brain activity. Therapists, too, stand to gain from progress in VR technology, which provides a high degree of control over the therapeutic experience. Here we review the latest advances in VR technology and its applications in neuroscience research.

ON THE HUNT: VISUAL SEARCH FOR CAMOUFLAGED TARGETS IN REALISTIC ENVIRONMENTS

Visual search tasks commonly involve manipulating the number of targets and distractors to change difficulty levels and observe differences in reaction time and accuracy. What happens when the search background itself serves as a distractor? By creating a target that has been camouflaged to match the environment, we observed RT and accuracy both with and without a target preview. We hypothesized that 1) as target size decreased (increasing difficulty), RT would increase and accuracy would decrease, and 2) that the target preview would not aid in search. Our results support for our first hypothesis, and partially support our second. Reaction times were unaffected by the availability of the target preview, but search accuracy displayed a small cost. Overall, our findings suggest that search for camouflaged targets in the real world, in some cases, is predicated on a categorical representation of the target.

Implicit learning mediates base rate acquisition in perceptual categorization

We explored the possibility, suggested by Koehler (Behavioral and Brain Sciences, 19, 1-53, 1996; also Spellman Behavioral and Brain Sciences, 19, 38, 1996), that implicit learning mediates the influence of base-rates on category knowledge acquired through direct experience. In two experiments, participants learned simple perceptual categories with unequal base-rates (i.e., presentation frequency). In Experiment 1, participants received either response training or observational training. In Experiment 2, participants received response training with either immediate or delayed feedback. In previous studies, observational training and delayed feedback training have been shown to disrupt implicit learning. We found that base-rate influence was weaker in these conditions when category discriminability was low (i.e., when category membership was difficult to determine). This conclusion was based on signal detection β values as well as decision-bound modeling results. Because these disruptions to implicit learning attenuate the base-rate effect, we conclude that implicit learning does indeed underlie the influence of base-rates learned through direct experience. This suggests that the implicit learning system postulated by the COVIS theory of categorization (Ashby, Alfonso-Reese, Turken, & Waldron Psychological Review, 105, 442-481, 1998) may be involved in developing sensitivity to category base-rates.

Predicting and interpreting identification errors in military vehicle training using multidimensional scaling

We compared methods for predicting and understanding the source of confusion errors during military vehicle identification training. Participants completed training to identify main battle tanks. They also completed card-sorting and similarity-rating tasks to express their mental representation of resemblance across the set of training items. We expected participants to selectively attend to a subset of vehicle features during these tasks, and we hypothesised that we could predict identification confusion errors based on the outcomes of the card-sort and similarity-rating tasks. Based on card-sorting results, we were able to predict about 45% of observed identification confusions. Based on multidimensional scaling of the similarity-rating data, we could predict more than 80% of identification confusions. These methods also enabled us to infer the dimensions receiving significant attention from each participant. This understanding of mental representation may be crucial in creating personalised training that directs attention to features that are critical for accurate identification. Practitioner Summary: Participants completed military vehicle identification training and testing, along with card-sorting and similarity-rating tasks. The data enabled us to predict up to 84% of identification confusion errors and to understand the mental representation underlying these errors. These methods have potential to improve training and reduce identification errors leading to fratricide.

On the Hunt: Searching for Poorly Defined Camouflaged Targets.

As camouflaged targets share visual characteristics with the environment within which they are embedded, searchers rarely have access to a perfect visual template of such targets. Instead, they must rely on less specific representations to guide search. Although search for camouflaged and non-specified targets have both received attention in the literature, to date they have not been explored in a combined context. Here we introduce a new paradigm for characterizing behavior during search for camouflaged targets in natural scenes, while also exploring how the fidelity of the target template affects search processes. Search scenes were created from forest images, with targets a distortion (varied size) of that image at a random location. In Experiment 1 a preview of the target was provided; in Experiment 2 there was no preview. No differences were found between experiments on nearly all measures. Generally, reaction times and accuracy improved with familiarity on the task (more so for small targets). Analysis of eye movements indicated that performance benefits were related to improvements in both Search and Target Verification time. Combined, our data suggest that search for camouflaged targets can be improved over a short time-scale, even when targets are poorly defined.

Applications of Optical Neuroimaging in Usability Research

In this article we review recent and potential applications of optical neuroimaging to human factors and usability research. We focus specifically on functional near-infrared spectroscopy (fNIRS) because of its cost-effectiveness and ease of implementation. Researchers have used fNIRS to assess a range of psychological phenomena relevant to human factors, such as cognitive workload, attention, motor activity, and more. It offers the opportunity to measure hemodynamic correlates of mental activity during task completion in human factors and usability studies. We also consider some limitations and future research directions.

Psychophysical Methods and Signal Detection: Recent Advances in Theory

Signal detection theory (SDT) represents one of the most prominent scientifi c developments in psychology of the past 60 years (Dember, 1998 ; Estes, 2002 ). Its application to perception began with the use of statistical decision theory for radar detection problems (e.g., Peterson, Birdsall, and Fox, 1954 ), and efforts to determine the sensitivity of information transmission via a sensitivity measure that was free of response bias (for an early discussion of the historical antecedents of SDT see Swets, 1973 ). A key insight by the pioneering researchers was that errors of commission in perception tasks are not necessarily the result of guessing, as assumed by threshold theories (Tanner and Swets, 1954 ). The techniques provided by SDT have found wide application, including domains such as radiology, assessment of memory in clinical populations, and many kinds of monitoring tasks. In general, any categorical decision or diagnostic task can be evaluated using SDT, permitting separate assessment of the capacity of the decision maker to discriminate among categories (defi ned as perceptual sensitivity, d’ ) and his or her cognitive bias for selecting one category over another (response criterion or response bias, β ). As a statistical model, SDT rests on a set of assumptions. These include the premises that (1) events to be detected (signals) are always embedded in a background of irrelevant sensory information (noise); (2) the distributions of noise and signal-plus-noise are of normal form and equal variance; (3) observers are both sensors and decision makers, and they adopt a criterion of sensory magnitude for deciding whether a given event is a signal or a nonsignal; and (4) measures of perceptual sensitivity (e.g., d’ ) can be treated as if they were independent of measures of response bias (e.g., β ). Even in some of the seminal work on SDT, Tanner and Swets ( 1954 ) recognized that empirical evidence indicated that the equal variance assumption is not always true, but that it is also not necessary for application of the SDT model (although it is necessary for the use of d’ as a measure of sensitivity; see Swets, 1988 ). Further, the assumption of independence of sensitivity and response bias requires that the observing entity has no previously formed

When a face type is perceived as threatening: Using general recognition theory to understand biased categorization of Afrocentric faces

Prior research indicates that stereotypical Black faces (e.g., wide nose, full lips: Afrocentric) are often associated with crime and violence. The current study investigated whether stereotypical faces may bias the interpretation of facial expression to seem threatening. Stimuli were prerated by face type (stereotypical, nonstereotypical) and expression (neutral, threatening). Later in a forced-choice task, different participants categorized face stimuli as stereotypical or not and threatening or not. Regardless of prerated expression, stereotypical faces were judged as more threatening than were nonstereotypical faces. These findings were supported using computational models based on general recognition theory (GRT), indicating that decision boundaries were more biased toward the threatening response for stereotypical faces than for nonstereotypical faces. GRT analysis also indicated that perception of face stereotypicality and emotional expression are dependent, both across categories and within individual categories. Higher perceived stereotypicality predicts higher perception of threat, and, conversely, higher ratings of threat predict higher perception of stereotypicality. Implications for racial face-type bias influencing perception and decision-making in a variety of social and professional contexts are discussed.

Who are Phishers luring?: A Demographic Analysis of Those Susceptible to Fake Emails

Previous research has identified several populations that are susceptible to inauthentic emails (e.g., spam). However, these studies utilize retrospective, self-report measures to assess email users’ interactions with limited sets of inauthentic emails. In order to fill this gap in the literature, the present study assessed participants’ likelihood to rate a wide variety of emails as spam, authentic, and dangerous. The results highlighted several key findings, 1) there were no gender differences for the email ratings, there were only differences in experience with email, 2) those who do not regularly email and read other electronic documents were more likely to rate emails as spam, possibly indicating an increase in false positives, and 3) the relationship between age and rating an email as spam indicates that younger users may be more susceptible to spam. Overall, the present study identified demographic characteristics that should be considered when training users to detect inauthentic emails.

Base-rate sensitivity through implicit learning

Two experiments assessed the contributions of implicit and explicit learning to base-rate sensitivity. Using a factorial design that included both implicit and explicit learning disruptions, we tested the hypothesis that implicit learning underlies base-rate sensitivity from experience (and that explicit learning contributes comparatively little). Participants learned to classify two categories of simple stimuli (bar graph heights) presented in a 3:1 base-rate ratio. Participants learned either from “observational” training to disrupt implicit learning or “response” training which supports implicit learning. Category label feedback on each trial was followed either immediately or after a 2.5 second delay by onset of a working memory task intended to disrupt explicit reasoning about category membership feedback. Decision criterion values were significantly larger following response training, suggesting that implicit learning underlies base-rate sensitivity. Disrupting explicit processing had no effect on base-rate learning as long as implicit learning was supported. These results suggest base-rate sensitivity develops from experience primarily through implicit learning, consistent with separate learning systems accounts of categorization.