Working and short-term memory in children with ADHD: an examination of prefrontal cortical functioning using functional Near-Infrared Spectroscopy (fNIRS).

Working memory impairments are an oft-reported deficit among children with ADHD, and complementary neuroimaging studies implicate reductions in prefrontal cortex (PFC) structure and function as a neurobiological explanation. Most imaging studies, however, rely on costly, movement-intolerant, and/or invasive methods to examine cortical differences. This is the first study to use a newer neuroimaging tool that overcomes these limitations, functional Near Infrared Spectroscopy (fNIRS), to investigate hypothesized prefrontal differences. Children (aged 8-12) with ADHD (N = 22) and typically developing (N = 18) children completed phonological working memory (PHWM) and short-term memory (PHSTM) tasks. Children with ADHD evinced poorer performance on both tasks, with greater differences observed in PHWM (Hedges' g = 0.67) relative to PHSTM (g = 0.39). fNIRS revealed reduced hemodynamic response among children with ADHD in the dorsolateral PFC while completing the PHWM task, but not within the anterior or posterior PFC. No between-group fNIRS differences were observed during the PHSTM task. Findings suggest that children with ADHD exhibit an inadequate hemodynamic response in a region of the brain that underlies PHWM abilities. The study also highlights the use of fNIRS as a cost-effective, noninvasive neuroimaging technique to localize/quantify neural activation patterns associated with executive functions.

Explicit and implicit category learning in categorical visual search.

Categorical search has been heavily investigated over the past decade, mostly using natural categories that leave the underlying category mental representation unknown. The categorization literature offers several theoretical accounts of category mental representations. One prominent account is that separate learning systems account for classification: an explicit learning system that relies on easily verbalized rules and an implicit learning system that relies on an associatively learned (nonverbalizable) information integration strategy. The current study assessed the contributions of these separate category learning systems in the context of categorical search using simple stimuli. Participants learned to classify sinusoidal grating stimuli according to explicit or implicit categorization strategies, followed by a categorical search task using these same stimulus categories. Computational modeling determined which participants used the appropriate classification strategy during training and search, and eye movements collected during categorical search were assessed. We found that the trained categorization strategies overwhelmingly transferred to the verification (classification response) phase of search. Implicit category learning led to faster search response and shorter target dwell times relative to explicit category learning, consistent with the notion that explicit rule classification relies on a more deliberative response strategy. Participants who transferred the correct category learning strategy to the search guidance phase produced stronger search guidance (defined as the proportion of trials on which the target was the first item fixated) with evidence of greater guidance in implicit-strategy learners. This demonstrates that both implicit and explicit categorization systems contribute to categorical search and produce dissociable patterns of data.

Training detection of camouflaged targets in natural scenes: Backgrounds and targets both matter.

As target-background similarity increases, search performance declines, but this pattern can be attenuated with training. In the present study we (1) characterized training and transfer effects in visual search for camouflaged targets in naturalistic scenes, (2) evaluated whether transfer effects are preserved 3 months after training, (3) tested the suitability of the perceptual learning hypothesis (i.e., using learned scene statistics to aid camouflaged target detection) for explaining camouflage search improvements over training, and (4) provide guidance for camouflage detection training in practice. Participants were assigned to one of three training groups: adaptive camouflage (difficulty varied by performance), massed camouflage (difficulty increased over time), or an active control (no camouflage), and trained over 14 sessions. Additional sessions measured transfer (immediately post training) and retention of training benefits (10 days and 3 months post training). Both the adaptive and massed training groups showed improved camouflaged target detection up to 3 months following training, relative to the control. These benefits were observed only with backgrounds and targets that were similar to those experienced during training and are broadly consistent with the perceptual learning hypothesis. In practice, training interventions should utilize stimuli similar to the operational environment in which detection is expected to occur.

Training away face-type bias: perception and decisions about emotional expression in stereotypically Black faces.

Prior research indicates that stereotypical Black faces (e.g., wide nose, full lips) are perceived negatively relative to non-stereotypical faces (face-type bias). The current study investigated whether stereotypical faces may bias the interpretation of a neutral facial expression to seem threatening. Moreover, could biased responses be trained away with feedback? In two experiments, stimuli (face images) were presented in a speeded identification task that included corrective feedback, and participants indicated whether the face stimuli were stereotypical or not and threatening or not. Stimuli were pre-rated by face-type (stereotypical, non-stereotypical) and expression (neutral, threatening). Computational modeling based on General Recognition Theory indicated that training increased perceptual discriminability between all the faces. By the end of training (in both experiments), discriminability for emotional expression was slightly higher for stereotypical faces. Model parameters (for both experiments) also showed that, early in training, decision boundaries were more biased toward the threatening response for stereotypical faces relative to non-stereotypical faces. The results suggest that decision bias may be malleable with training.

Which Phish Is on the Hook? Phishing Vulnerability for Older Versus Younger Adults.

OBJECTIVE: To determine if there are age-related differences in phishing vulnerability and if those differences exist under various task conditions (e.g., framing and time pressure). BACKGROUND: Previous research suggests that older adults may be a vulnerable population to phishing attacks. Most research exploring age differences has used limiting designs, including retrospective self-report measures and restricted email sets. METHOD: The present studies explored how older and younger adults classify a diverse sample of 100 legitimate and phishing emails. In Experiment 1, participants rated the emails as either spam or not spam. Experiment 2 explored how framing would alter the results when participants rated emails as safe or not safe. In Experiment 3, participants performed the same task as Experiment 1, but were put under time pressure. RESULTS: No age differences were observed in overall classification accuracy across the three experiments, rather all participants exhibited poor performance (20%-30% errors). Older adults took significantly longer to make classifications and were more liberal in classifying emails as spam or not safe. Time pressure seemed to remove this bias but did not influence overall accuracy. CONCLUSION: Older adults appear to be more cautious when classifying emails. However, being extra careful may come at the cost of classification speed and does not seem to improve accuracy. APPLICATION: Age demographics should be considered in the implementation of a cyber-training methodology. Younger adults may be less vigilant against cyber threats than initially predicted; older adults might be less prone to deception when given unlimited time to respond.

The psychological interaction of spam email features.

This study explored distinct perceptual and decisional contributions to spam email mental construal. Participants classified spam emails according to pairings of three stimulus features - presence or absence of awkward prose, abnormal message structure, and implausible premise. We examined dimensional interactions within general recognition theory (GRT; a multidimensional extension of signal detection theory). Classification accuracy was highest for categories containing either two non-normal dimension levels (e.g. awkward prose and implausible premise) or two normal dimension levels (e.g. normal prose and plausible premise). Modelling indicated both perceptual and decisional contributions to classification responding. In most cases, perceptual discriminability was higher along one dimension when stimuli contained a non-normal level of the paired dimension (e.g. prose discriminability was higher with abnormal structure). Similarly, decision criteria along one dimension were biased in favour of the non-normal response when stimuli contained a non-normal level of the paired dimension. Potential applications for training are discussed. Practitioner summary: We applied general recognition theory (i.e. multivariate signal detection theory) to spam email classification at low or high levels of three stimulus dimensions: premise plausibility, prose quality, and email structure. Relevant to training, this approach helped identify perceptual and decisional biases that could be leveraged to individualise training.

Neurophysiological Response to Olfactory Stimuli in Combat Veterans With Posttraumatic Stress Disorder.

There is a need for a better understanding of underlying pathology in posttraumatic stress disorder (PTSD) to develop more effective treatments. The late positive potential (LPP) amplitude from electroencephalogram has been used to assess individual differences in emotional reactivity. There is evidence that olfaction is particularly important in emotional processing in PTSD. The current study examined LPP amplitudes in response to olfactory stimuli in 24 combat veterans with PTSD and 24 nonmilitary/non-PTSD controls. An olfactometer delivered three negatively valenced odorants, with 12 trials of each delivered in a random order. The groups did not differ in LPP amplitude across odorants. However, within the PTSD group, higher Clinician-Administered PTSD Scale scores related to an increased LPP amplitude after diesel fuel and rotten egg, but not n_butanol, odorants. Results provide specific targets and theory for further research into clinical applications such as selection of idiographic odorants for use in virtual-reality exposure therapy.

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.

A Functional Near-Infrared Spectroscopy Study of Trauma-Related Auditory and Olfactory Cues: Posttraumatic Stress Disorder or Combat Experience?

The prevalence of posttraumatic stress disorder (PTSD) among U.S. veterans deployed to Iraq or Afghanistan necessitates the need for comprehensive assessment and treatment strategies. This study investigated the utility of a combat-related PTSD symptom provocation paradigm to elicit unique neurological responses across three groups: combat veterans with PTSD, combat veterans without PTSD, and nonmilitary participants without PTSD. Using functional near-infrared spectroscopy (fNIRS) the results indicated that combat veterans with PTSD demonstrated significant activation to a trauma-related sound compared with nonmilitary personnel, channel 14: d = 1.03, 95% confidence interval (CI) [0.28, 1.76]; channel 15: d = 1.30, 95% CI [0.53, 2.06]; and combat veterans without PTSD, channel 14: d = 0.87, 95% CI [0.14, 1.59]. Specifically, this increased neural activation was approximately located in the right medial superior prefrontal cortex (Brodmann areas 9/10), an area associated with experiencing negative or threatening stimuli and emotional detachment. There were no differences across the groups for nontrauma-related sounds. Results were less clear with respect to a combat-related odor. These results suggest a specific neurophysiological response to trauma-related cues and, if replicated, may offer a biomarker for combat-related PTSD. Such a response could provide incremental validity over diagnostic assessments alone and assist in planning and monitoring of treatment outcome.

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.

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.

Best-classifier feedback in diagnostic classification training.

Diagnostic classification training requires viewing many examples along with category membership feedback. "Objective" feedback based on category membership suggests that perfect accuracy is attainable when it may not be (e.g., with confusable categories). Previous work shows that feedback based on an "optimal" responder (that sometimes makes classification errors) leads to higher long-run reward, especially in unequal category payoff conditions. In the current study, participants learned to classify normal or cancerous mammography images, earning more points for correct "cancer" than "normal" responses. Feedback was either objective or based on performance of an empirically determined "best" classifier. This approach is necessary because theoretically optimal responses cannot be determined with complex real-world stimuli with unknown perceptual distributions. Replicating earlier work that used simple artificial stimuli, we found that best-classifier performance led to decision-criterion values (ß) closer to the reward-maximizing criterion, along with higher point totals and a slight reduction (as predicted) in overall accuracy.

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 ß 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.

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. VR's 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.

Optimal classifier feedback improves cost-benefit but not base-rate decision criterion learning in perceptual categorization.

Unequal payoffs engender separate reward- and accuracy-maximizing decision criteria; unequal base rates do not. When payoffs are unequal, observers place greater emphasis on accuracy than is optimal. This study compares objective classifier (the objectively correct response) with optimal classifier feedback (the optimal classifier's response) when payoffs or base rates are unequal. It provides a critical test of Maddox and Bohil's (1998) competition between reward and accuracy maximization (COBRA) hypothesis, comparing it with a competition between reward and probability matching (COBRM) and a competition between reward and equal response frequencies (COBRE) hypothesis. The COBRA prediction that optimal classifier feedback leads to better decision criterion leaning relative to objective classifier feedback when payoffs are unequal, but not when base rates are unequal, was supported. Model-based analyses suggested that the weight placed on accuracy was reduced for optimal classifier feedback relative to objective classifier feedback. In addition, delayed feedback affected learning of the reward-maximizing decision criterion.

Probability matching, accuracy maximization, and a test of the optimal classifier's independence assumption in perceptual categorization.

Observers completed perceptual categorization tasks that included 25 base-rate/payoff conditions constructed from the factorial combination of five base-rate ratios (1:3, 1:2, 1:1, 2:1, and 3:1) with five payoff ratios (1:3, 1:2, 1:1, 2:1, and 3:1). This large database allowed an initial comparison of the competition between reward and accuracy maximization (COBRA) hypothesis with a competition between reward maximization and probability matching (COBRM) hypothesis, and an extensive and critical comparison of the flat-maxima hypothesis with the independence assumption of the optimal classifier. Model-based instantiations of the COBRA and COBRM hypotheses provided good accounts of the data, but there was a consistent advantage for the COBRM instantiation early in learning and for the COBRA instantiation later in learning. This pattern held in the present study and in a reanalysis of Bohil and Maddox (2003). Strong support was obtained for the flat-maxima hypothesis over the independence assumption, especially as the observers gained experience with the task. Model parameters indicated that observers' reward-maximizing decision criterion rapidly approaches the optimal value and that more weight is placed on accuracy maximization in separate base-rate/payoff conditions than in simultaneous base-rate/payoff conditions. The superiority of the flat-maxima hypothesis suggests that violations of the independence assumption are to be expected, and are well captured by the flat-maxima hypothesis, with no need for any additional assumptions.

Evidence for a procedural-learning-based system in perceptual category learning.

The consistency of the mapping from category to response location was investigated to test the hypothesis that abstract category labels are learned by the hypothesis testing system to solve rule-based tasks, whereas response position is learned by the procedural-learning system to solve information-integration tasks. Accuracy rates were examined to isolate global performance deficits, and model-based analyses were performed to identify the types of response strategies used by observers. A-B training (consistent mapping) led to more accurate responding relative to yes-no training (variable mapping) in the information-integration category learning task. Model-based analyses indicated that the yes-no accuracy decline was due to an increase in the use of rule-based strategies to solve the information-integration task. Yes-no training had no effect on the accuracy of responding or distribution of best-fitting models relative to A-B training in the rule-based category learning tasks. These results both provide support for a multiple-systems approach to category learning in which one system is procedural-learning-based and argue against the validity of single-system approaches.

Linear transformations of the payoff matrix and decision criterion learning in perceptual categorization.

The effects of payoff-matrix multiplication, payoff-matrix addition, the presence of long-run gains versus long-run losses, category discriminability, and base rate on decision criterion learning were examined in 2 perceptual categorization experiments. Observers were found to be sensitive to the effects of payoff-matrix multiplication (and category discriminability) on the steepness of the objective reward function in line with predictions from the flat-maxima hypothesis and contrary to the predictions from the payoff-variance hypothesis. Decision criterion learning was best in base-rate conditions, was worst when losses were associated with incorrect responding, and was intermediate when no losses were associated with incorrect responding. This performance profile was well captured by the competition between reward and accuracy (COBRA) hypothesis. A hybrid model framework that instantiates both the flat-maxima and COBRA hypotheses was necessary to account for the data from both experiments.