Confirmation Bias in the Course of Instructed Reinforcement Learning in Schizophrenia-Spectrum Disorders

Abstract

A large body of research attributes learning deficits in schizophrenia (SZ) to the systems involved in value representation (prefrontal cortex, PFC) and reinforcement learning (basal ganglia, BG) as well as to the compromised connectivity of these regions. In this study, we employed learning tasks hypothesized to probe the function and interaction of the PFC and BG in patients with SZ-spectrum disorders in comparison to healthy control (HC) subjects. In the Instructed Probabilistic Selection task (IPST), participants received false instruction about one of the stimuli used in the course of probabilistic learning which creates confirmation bias, whereby the instructed stimulus is overvalued in comparison to its real experienced value. The IPST was administered to 102 patients with SZ and 120 HC subjects. We have shown that SZ patients and HC subjects were equally influenced by false instruction in reinforcement learning (RL) probabilistic task (IPST) (p-value = 0.441); however, HC subjects had significantly higher learning rates associated with the process of overcoming cognitive bias in comparison to SZ patients (p-value = 0.018). The behavioral results of our study could be hypothesized to provide further evidence for impairments in the SZ-BG circuitry; however, this should be verified by neurofunctional imaging studies.

Coarse-Grained Neural Network Model of the Basal Ganglia to Simulate Reinforcement Learning Tasks

“…This work presents a new CGNN model of the BG derived from previous FGNN models [ 21 , 24 , 46 ] accounting for typical behavioral results from human studies on reinforcement learning tasks, such as the Probabilistic Selection Task [ 22 ], Instructed Probabilistic Selection Task [ 43 ] and Probabilistic Reversal Learning Task [ 44 ]. Although there is a natural tendency to design more detailed neural network models with an increasing number of neurons (with a notable exception described in Reference [ 46 ]), we decided to take a step in the opposite direction.…”

“…The misleading cue is a part of the instruction manual read by the subject before the game starts. The Probabilistic Reversal Learning Task [ 44 ] is reduced to only a single pair of stimuli that is presented at each trial. The tricky thing is that, after several dozen trials, the contingencies are swapped (the reversal event).…”

“…We refer to this network as the fine-grained neural network (FGNN), and based on its structure, we developed a new coarse-grained neural network (CGNN) replacing layers of neurons with single nodes that represent the collective behavior of the given layer. We then compared the functionality of both the FGNN and CGNN models with respect to several reinforcement learning tasks that are based on the BG circuitry, such as the Probabilistic Selection Task [ 22 , 41 ], Probabilistic Reversal Learning Task [ 42 ] and Instructed Probabilistic Selection Task [ 43 , 44 ], to assess whether a more simple approach in modeling can produce similar results.…”

Validation of a modified Chinese‐language version of the Davos Assessment of Cognitive Biases Scale (MCL‐DACOBS) in a sample of Chinese patients with schizophrenia

“…Cognitive biases affect several cognitive domains, including decision‐making/reasoning, attention, motivation, memory recall, and style of attribution of meaning (Beck et al., 2011). Increasing numbers of studies have been reporting cognitive biases that contribute to cognitive impairments in patients with schizophrenia, and cognitive bias and cognitive impairments appear to produce reciprocal deterioration and worsen disability (Frydecka et al., 2022; Hu et al., 2022; Navalón et al., 2021; Tang et al., 2022). Accordingly, assessing cognitive bias in patients with schizophrenia may enable the cognitive performance of patients with schizophrenia to be improved, thus improving their prognosis.…”