Psychotic Experiences and Overhasty Inferences Are Related to Maladaptive Learning

Abstract

Theoretical accounts suggest that an alteration in the brain’s learning mechanisms might lead to overhasty inferences, resulting in psychotic symptoms. Here, we sought to elucidate the suggested link between maladaptive learning and psychosis. Ninety-eight healthy individuals with varying degrees of delusional ideation and hallucinatory experiences performed a probabilistic reasoning task that allowed us to quantify overhasty inferences. Replicating previous results, we found a relationship between psychotic experiences and overhasty inferences during probabilistic reasoning. Computational modelling revealed that the behavioral data was best explained by a novel computational learning model that formalizes the adaptiveness of learning by a non-linear distortion of prediction error processing, where an increased non-linearity implies a growing resilience against learning from surprising and thus unreliable information (large prediction errors). Most importantly, a decreased adaptiveness of learning predicted delusional ideation and hallucinatory experiences. Our current findings provide a formal description of the computational mechanisms underlying overhasty inferences, thereby empirically substantiating theories that link psychosis to maladaptive learning.

Attractor-like Dynamics in Belief Updating in Schizophrenia

“…Left, Protected exceedance probabilities for the six models in each group in each dataset. The protected exceedance probability is the probability a particular model is more likely than any other tested model, above and beyond chance, given the group data (Rigoux et al, 2014). Model 6 wins in all groups in both datasets (top row, controls; middle row, Scz; bottom row, clinical controls).…”

“…These changes in network dynamics may also be reflected in the inferences the network computes (i.e., easier switching between attractor basins may correspond to easier switching between beliefs), although this is yet to be demonstrated experimentally. NMDAR hypofunction could contribute to an increased tendency to switch between beliefs and increased stochasticity in responding in several ways (Rolls et al, 2008): (1) by reducing inhibitory interneuron activity, via weakened NMDAR synapses from pyramidal cells to interneurons, such that other attractor states are less suppressed when one is active (a spiking network model has shown that this leads to more rapid initial belief updating in perceptual tasks) (Lam et al, 2017); (2) by reducing pyramidal cell activity, via weakened recurrent NMDAR synapses on pyramidal cells, such that attractor states are harder to sustain; and (3) by reducing the NMDAR time constant, making states more vulnerable to random fluctuations in neural activity. See also similar schematics elsewhere (Durstewitz and Seamans, 2008;Rolls et al, 2008).…”

“…After fitting the six models to each subject's data, we performed Bayesian model selection on all groups separately in both Dataset 1 (at baseline and follow-up) and Dataset 2. This procedure weights models according to their accuracy but penalizes them for complexity (i.e., unnecessary extra parameters) to prevent overfitting (Stephan et al, 2009;). The winning model in all eight groups was Model 6 (see Fig.…”

“…In Scz, attractor states in prefrontal cortex are thought to be less stable, so it is easier for the network to switch between them, but harder to become more confident about (i.e., increase the stability of) any particular one (Rolls et al, 2008). This loss of stable neuronal states, recently demonstrated in two animal models of Scz (Hamm et al, 2017), is thought to be due to hypofunction of NMDARs or cortical dopamine 1 receptors in Scz (Fig.…”

Overly strong priors for socially meaningful visual signals in psychosis proneness

“…The intensity of every anomalous perception is quantified on subscales for intrusiveness, frequency and distress. As in our previous work (Stuke et al, 2017(Stuke et al, , 2018, we used total PDI and CAPS scores obtained by adding up their three subscales, and Spearman correlations to relate psychosis proneness to task behavior. Basic demographic information and average psychosis proneness scores of the participants are summarized in Table 1.…”