Visual spatial attention is typically thought to have a facilitatory effect on processing that monotonically decreases with the distance from the center of attention (Posner, 1980). Some studies suggest that the distribution of attention across space is nonmonotonic, with suppression around the target object (Cutzu & Tsotsos, 2003;Müller et al., 2005). We show in two flanker-task experiments that discrepancies in past work can be unified by a surround inhibition account in which the shape of the attentional distribution is determined by individual differences in selective attention. The distance from the target at which flanker interference was locally suppressed differed greatly among participants and correlated negatively with working memory capacity. The results suggest that attentional control modulates the breadth of the attentional distribution, constrained by limited cognitive capacity, to enhance target identification.
A key challenge in understanding mental (dys)functions is their etiological and functional heterogeneity, and several multi-dimensional assessments have been proposed for their comprehensive characterization. However, such assessments require lengthy testing, which may hinder reliable and efficient characterization of individual differences due to increased fatigue and distraction especially in clinical populations. Computational modeling may address the challenge as it often provides more reliable measures of latent neurocognitive processes underlying observed behaviors and captures individual differences better than traditional assessments. However, even with a state-of-art hierarchical modeling approach, reliable estimation of model parameters still requires a large number of trials. Recent works suggest that Bayesian adaptive design optimization (ADO) is a promising way to address the challenges. With ADO, experimental design is optimized adaptively from trial to trial to extract the maximum amount of information about an individual’s characteristics. In this review, we first describe the ADO methodology and then summarize recent works demonstrating that ADO increases reliability and efficiency of latent neurocognitive measures. We conclude by discussing the challenges and future directions of ADO and propose we develop ADO-based computational fingerprints to reliably and efficiently characterize the heterogeneous profiles of psychiatric disorders.
A major challenge in assessing psychological constructs such as impulsivity is the weak correlation between self-report and behavioral task measures that are supposed to assess the same construct. To address this issue, we developed a real-time driving task called the “highway task”, where participants often exhibit impulsive behaviors, such as reckless driving, thereby mirroring real-life impulsive traits captured by self-report surveys. Here, we first show that a self-report measure of impulsivity is highly correlated with performance in the highway task, but not with traditional behavioral task measures of impulsivity. By integrating deep neural networks with an inverse reinforcement learning (IRL) algorithm, we inferred dynamic changes of subjective rewards during the highway task. The IRL results indicated that impulsive participants attribute high subjective rewards to irrational or risky driving behaviors and situations. Overall, our results suggest that using real-time tasks combined with IRL can help reconcile the discrepancy between self-report and behavioral task measures of psychological constructs including impulsivity, with IRL being a practical modeling framework for multidimensional data from real-time tasks.
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