Reduced susceptibility to confirmation bias in schizophrenia

Abstract

Patients with schizophrenia (SZ) show cognitive impairments on a wide range of tasks, with clear deficiencies in tasks reliant on prefrontal cortex function and less consistently observed impairments in tasks recruiting the striatum. This study leverages tasks hypothesized to differentially recruit these neural structures to assess relative deficiencies of each. Forty-eight patients and 38 controls completed two reinforcement learning tasks hypothesized to interrogate pre-frontal and striatal functions and their interaction. In each task, participants learned reward discriminations by trial and error and were tested on novel stimulus combinations to assess learned values. In the task putatively assessing fronto-striatal interaction, participants were (inaccurately) instructed that one of the stimuli was valuable. Consistent with prior reports and a model of confirmation bias, this manipulation resulted in overvaluation of the instructed stimulus after its true value had been experienced. Patients showed less susceptibility to this confirmation bias effect than did controls. In the choice bias task hypothesized to more purely assess striatal function, biases in endogenously and exogenously chosen actions were assessed. No group differences were observed. In the subset of participants who showed learning in both tasks, larger group differences were observed in the confirmation bias task than in the choice bias task. In the confirmation bias task, patients also showed impairment in the task conditions with no prior instruction. This deficit was most readily observed on the most deterministic discriminations. Taken together, these results suggest impairments in fronto-striatal interaction in SZ, rather than in striatal function per se.

The drift diffusion model as the choice rule in reinforcement learning

“…In addition to supporting the exploration of basic RL processes, the RLDD model should also be useful in shedding light on cognitive deficiencies of learning and on decision making in clinical groups (Maia & Frank, 2011; Montague, Dolan, Friston, & Dayan, 2012; Ziegler, Pedersen, Mowinckel, & Biele, 2016), as in the effect of stimulant medication on cognitive processes in ADHD shown here (Fig. 5), but also in other groups with deficient learning and decision making (Mowinckel, Pedersen, Eilertsen, & Biele, 2015), such as in drug addiction (Everitt & Robbins, 2013; Schoenbaum, Roesch, & Stalnaker, 2006), schizophrenia (Doll et al, 2014), and Parkinson’s disease (Frank, Samanta, et al, 2007; Moustafa, Sherman, & Frank, 2008; Yechiam, Busemeyer, Stout, & Bechara, 2005). …”

“…On the basis of a detailed neural-network model of the basal ganglia, these effects are thought to be due to the selective modulation of striatal D1 and D2 receptors through dopamine (Frank et al, 2004). The task has been used to investigate the effects of dopamine on learning and decision making in ADHD (Frank, Santamaria, et al, 2007), autism spectrum disorder (Solomon, Frank, & Ragland, 2015), Parkinson’s disease (Frank et al, 2004), and schizophrenia (Doll et al, 2014), among others.…”

“…Also, whereas the difference between the proportions of correct responses between learning conditions ON medication was larger at the beginning of learning than at the end, the model predicted larger differences at the end. Such patterns could potentially be captured by models with time-varying learning rates (Frank et al, 2015; Krugel et al, 2009; Nassar, Wilson, Heasly, & Gold, 2010), by models that could explicitly account for capacity-limited and delay-sensitive working memory in learning (Collins & Frank, 2012), or by more elaborate model-based approaches to instrumental learning (Collins & Frank, 2012; Biele, Erev, & Ert, 2009; Doll et al, 2014; Doll, Simon, & Daw, 2012). Hence, whereas the model implements important fundamental aspects of instrumental learning and decision making, the implementation of additional processes might be needed to fully account for the data from other experiments.…”

Probabilistic Reversal Learning in Schizophrenia: Stability of Deficits and Potential Causal Mechanisms

“…These findings are consistent with previous studies that show reinforcement learning and working memory abilities are closely linked. 15,[38][39][40] There were somewhat weaker relationships between the cognitive and PRL variables in the reversal than the discrimination phases, which may reflect more restricted range in the neurocognitive variables. Notably, we found that working memory and processing speed were the only subdomains that separated the patient subgroups.…”

Predicting the future in schizophrenia: The discrepancy between anticipatory and consummatory pleasure

“…In schizophrenia the role of context or recency biases and the impact of fewer pleasant experiences in everyday life on anticipatory pleasure are unclear. A recent study suggests individuals with schizophrenia are less influenced by well-established confirmation biases in a gambling task (Doll et al, 2014) but did not report the forecasted pleasure that accompanied this behaviour. Studies examining anticipatory pleasure in controls have used autobiographical scenarios such as birthday parties or a Monday morning at work (Robinson and Clore, 2002;Wilson and Gilbert, 2005).…”