Optimizing vs. Matching: Response Strategy in a Probabilistic Learning Task is associated with Negative Symptoms of Schizophrenia

Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, PO Box 21247, Baltimore, MD 21228, USA.
Schizophrenia Research (Impact Factor: 3.92). 04/2011; 127(1-3):215-22. DOI: 10.1016/j.schres.2010.12.003
Source: PubMed


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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Available from: James Waltz, Mar 05, 2014
    • "Motivation and reinforcement learning was highlighted by a CNTRICS working group (Markou et al. 2013) as a reflection of extensive evidence of impairments in patients with schizophrenia (e.g. Gard et al. 2007, 2014; Gold et al. 2008, 2013; Weiler et al. 2009; Barch and Dowd 2010; Kasanova et al. 2011; Dowd and Barch 2012; Yılmaz et al. 2012; Barch et al. 2014; Griffiths et al. 2014). Some of the tasks proposed by the working group are already established in the touchscreen, such as autoshaping, which has been successfully used in both rat and mouse models (Bussey et al. 1997b; Parkinson et al. 2000a, b; Dalley et al. 2005; Ito et al. 2005; DePoy et al. 2013; Horner et al. 2013). "
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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). "
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