Previous research indicates that behavioral performance in simple probability learning tasks can be organized into response strategy classifications that are thought to predict important personal characteristics and individual differences. Typically, relatively small proportion of subjects can be identified as optimizers for effectively exploiting the environment and choosing the more rewarding stimulus nearly all of the time. In contrast, the vast majority of subjects behaves sub-optimally and adopts the matching or super-matching strategy, apportioning their responses in a way that matches or slightly exceeds the probabilities of reinforcement. In the present study, we administered a two-choice probability learning paradigm to 51 individuals with schizophrenia (SZ) and 29 healthy controls (NC) to examine whether there are differences in the proportion of subjects falling into these response strategy classifications, and to determine whether task performance is differentially associated with symptom severity and neuropsychological functioning. Although the sample of SZ patients did not differ from NC in overall rate of learning or end performance, significant clinical differences emerged when patients were divided into optimizing, super-matching and matching subgroups based upon task performance. Patients classified as optimizers, who adopted the most advantageous learning strategy, exhibited higher levels of positive and negative symptoms than their matching and super-matching counterparts. Importantly, when both positive and negative symptoms were considered together, only negative symptom severity was a significant predictor of whether a subject would behave optimally, with each one standard deviation increase in negative symptoms increasing the odds of a patient being an optimizer by as much as 80%. These data provide a rare example of a greater clinical impairment being associated with better behavioral performance.
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"However, individual variability in the direction of validity effects leave open the possibility that some participants may have used a strategy of weighted resource distribution that favored the high probability location . Individual differences in probabilistic strategy use have been reported in a perceptual and decision tasks and subsequent experiments investigating such differences in attention are needed (e.g., Kasanova et al., 2011; Wozny et al., 2010; Frank et al., 2009; Miller et al., 2005; Shanks et al., 2002; Friedman & Massaro, 1998). "
[Show abstract][Hide abstract] ABSTRACT: When predicting where a target or reward will be, subjects tend to choose each location commensurate with the true underlying probability (i.e., to probability match). The strategy of probability matching includes sampling high and low probability locations on some proportion of trials. In contrast, models of probabilistic spatial attention hypothesize that on any given trial attention will either be weighted toward the high probability location or be distributed equally across all locations. Thus, the strategies of probabilistic sampling by choice decisions and spatial attention appear to differ with regard to low-probability events. This distinction is somewhat surprising because similar brain mechanisms (e.g., pFC-mediated cognitive control) are thought to be important in both functions. Thus, the goal of the current study was to examine the relationship between choice decisions and attentional selection within single trials to test for any strategic differences, then to determine whether that relationship is malleable to manipulations of catecholamine-modulated cognitive control with the drug modafinil. Our results demonstrate that spatial attention and choice decisions followed different strategies of probabilistic information selection on placebo, but that modafinil brought the pattern of spatial attention into alignment with that of predictive choices. Modafinil also enhanced learning of the probability distribution, evidenced by earlier learning of the probability distribution. Together, these results suggest that enhancing cognitive control mechanisms (e.g., through prefrontal cortical function) leads spatial attention to follow choice decisions in selecting information according to rule-based expectations.
[Show abstract][Hide abstract] ABSTRACT: Negative symptoms are core features of schizophrenia (SZ); however, the cognitive and neural basis for individual negative symptom domains remains unclear. Converging evidence suggests a role for striatal and prefrontal dopamine in reward learning and the exploration of actions that might produce outcomes that are better than the status quo. The current study examines whether deficits in reinforcement learning and uncertainty-driven exploration predict specific negative symptom domains.
We administered a temporal decision-making task, which required trial-by-trial adjustment of reaction time to maximize reward receipt, to 51 patients with SZ and 39 age-matched healthy control subjects. Task conditions were designed such that expected value (probability × magnitude) increased, decreased, or remained constant with increasing response times. Computational analyses were applied to estimate the degree to which trial-by-trial responses are influenced by reinforcement history.
Individuals with SZ showed impaired Go learning but intact NoGo learning relative to control subjects. These effects were most pronounced in patients with higher levels of negative symptoms. Uncertainty-based exploration was substantially reduced in individuals with SZ and selectively correlated with clinical ratings of anhedonia.
Schizophrenia patients, particularly those with high negative symptoms, failed to speed reaction times to increase positive outcomes and showed reduced tendency to explore when alternative actions could lead to better outcomes than the status quo. Results are interpreted in the context of current computational, genetic, and pharmacological data supporting the roles of striatal and prefrontal dopamine in these processes.
[Show abstract][Hide abstract] ABSTRACT: Abstract This article presents a very simple definition of executive functioning (EF). Although EF is traditionally understood as a cognitive function dependent upon top-down cortical control, we challenge this model. We propose that the functional architecture of the brain evolved to meet the needs of interactive behavior and that cognition develops to control the motor system, which is of paramount importance in adaptation, essentially a manifestation of EF. We propose that traditional models of cognition are incomplete characterizations of EF and that procedural learning and "automatic" behaviors are the most basic, bottom-up functions that support all EF. We propose that motor development in children demonstrates how all knowledge is grounded in sensorimotor interaction and how interactive behavior generates both procedural and declarative knowledge, which later interact to generate EF. This model emphasizes the critical importance of motor behavior in children and stresses the importance of the pediatric motor examination in understanding the development of EF. This model also has implications for why traditional tests of EF have little predictive validity in both children and adults.