Modeling subjective relevance in schizophrenia and its relation to aberrant salience

Abstract

In schizophrenia, increased aberrant salience to irrelevant events and reduced learning of relevant information may relate to an underlying deficit in relevance detection. So far, subjective estimates of relevance have not been probed in schizophrenia patients. The mechanisms underlying belief formation about relevance and their translation into decisions are unclear. Using novel computational methods, we investigated relevance detection during implicit learning in 42 schizophrenia patients and 42 healthy individuals. Participants underwent functional magnetic resonance imaging while detecting the outcomes in a learning task. These were preceded by cues differing in color and shape, which were either relevant or irrelevant for outcome prediction. We provided a novel definition of relevance based on Bayesian precision and modeled reaction times as a function of relevance weighted unsigned prediction errors (UPE). For aberrant salience, we assessed responses to subjectively irrelevant cue manifestations. Participants learned the contingencies and slowed down their responses following unexpected events. Model selection revealed that individuals inferred the relevance of cue features and used it for behavioral adaption to the relevant cue feature. Relevance weighted UPEs correlated with dorsal anterior cingulate cortex activation and hippocampus deactivation. In patients, the aberrant salience bias to subjectively task-irrelevant information was increased and correlated with decreased striatal UPE activation and increased negative symptoms. This study shows that relevance estimates based on Bayesian precision can be inferred from observed behavior. This underscores the importance of relevance detection as an underlying mechanism for behavioral adaptation in complex environments and enhances the understanding of aberrant salience in schizophrenia.

Precision-weighting of superior frontal cortex unsigned prediction error signals benefits learning, is mediated by dopamine, and is impaired in psychosis

“…The coding of unsigned prediction errors in the superior and middle frontal gyri and dACC is in line with earlier findings by Hayden (et al, 2011) who found unsigned prediction errors in the dACC of monkeys, and with prior fMRI studies in humans (Fletcher et al 2001, Turner et al 2004, Fouragnan et al, 2017; Fouragnan et al 2018; Metereau & Dreher 2012; Ide et al, 2013). Our findings are consistent with those of Katthagen et al (2018), who used reaction time data (rather than choice data) from a human fMRI reversal learning study to derive a relevance weighted unsigned prediction error signal, which was also represented in the dACC. Our data in the dopaminergic modulation study, replicated in the psychosis study, show (for the first time to our knowledge) that cortical prediction error signals based on choice data are precision-weighted in humans.…”

Dopaminergic basis for signaling belief updates, but not surprise, and the link to paranoia

“…The findings of our study are highly relevant for dopaminergic and neurocomputational theories of schizophrenia (59,72). The aberrant salience hypothesis proposes that symptoms such as paranoia arise when unwarranted meaning and behavioral salience is attributed to ambiguous, irrelevant, or unreliable stimuli (17,18,(20)(21)(22)(23).…”

“…Second, by dissociating the meaningful information content of an observation from its simple unexpectedness, and showing a dopaminergic relationship with the former, our findings point to the possibility of advances that might accrue from reformulating constructs such as "salience" in a more mathematically rigorous fashion. In fact, one hypothesis from our findings is that the central feature of "aberrant salience" in psychotic disorders is a failure to dissociate between meaningful (task-relevant) and meaningless (task-irrelevant) information, resulting in belief updating arising out of merely surprising inputs (59).…”

Developmental Changes in Learning: Computational Mechanisms and Social Influences

“…Computationally, fitting r(s ′ ) as a free parameter (i.e., as a measure of reinforcement sensitivity; Gold et al, 2012 ), allows to compare sensitivity toward different types of reinforcers (e.g., social vs. monetary) between age groups. Recent modeling accounts have also captured subjective relevance in a Pavlovian conditioning approach (Katthagen, 2017 ). These computational approaches could be particularly suitable to re-assess the postulated higher relevance of social feedback in adolescence (Blakemore, 2008 ; Foulkes and Blakemore, 2016 ) using a modeling approach.…”