Elsevier

NeuroImage

Volume 213, June 2020, 116719
NeuroImage

Neurocomputational correlates of learned irrelevance in humans

https://doi.org/10.1016/j.neuroimage.2020.116719Get rights and content
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open access

Highlights

  • Model-based fMRI reveals the neural correlates of learned irrelevance (LIrr) in humans.

  • LIrr expression correlates positively with entorhinal cortex activity.

  • LIrr expression correlates negatively with nucleus accumbens (NAcc) activity.

  • Prediction error signals regulate LIrr and correlates with amygdala/NAcc activity.

  • The results indicate neuronal overlap between human and animal LIrr.

Abstract

Inappropriate behaviors may result from acquiring maladaptive associations between irrelevant information in the environment and important events, such as reward or punishment. Pre-exposure effects are believed to prevent the expression of irrelevant associations. For example, learned irrelevance delays the expression of associations between conditioned (CS) and unconditioned (US) stimuli following their uncorrelated presentation. The neuronal substrates of pre-exposure effects in humans are largely unknown because these effects rapidly attenuate when using traditional pre-exposure paradigms. The latter are therefore incompatible with neuroimaging approaches that require many trial repetitions. Moreover, large methodological differences between animal and human research on pre-exposure effects challenge the presumption of shared neurocognitive substrates, and question the prevalent use of pre-exposure effects in animals to model symptoms of human mental disorders. To overcome these limitations, we combined a novel learned irrelevance task with model-based fMRI. We report the results of a model that describes learned irrelevance as a dynamic process, which evolves across trials and integrates the weighting between two state-action values pertaining to ‘CS-no US’ associations (acquired during pre-exposure) and ‘CS-US’ associations (acquired during subsequent conditioning). This relative weighting correlated i) positively with the learned irrelevance effect observed in the behavioral task, ii) positively with activity in the entorhinal cortex, and iii) negatively with activity in the nucleus accumbens (NAcc). Furthermore, the model updates the relative weighting of the two state-action values via two separate prediction error (PE) signals that allow the dynamic accumulation of evidence for the CS to predict the ‘US’ or a ‘no US’ outcome. One PE signal, designed to increase the relative weight of ‘CS-US’ associations following ‘US’ outcomes, correlated with activity in the NAcc, while another PE signal, designed to increase the relative weight of ‘CS-no US’ associations following ‘no US’ outcomes, correlated with activity in the basolateral amygdala. By extending previous animal observations to humans, the present study provides a novel approach to foster translational research on pre-exposure effects.

Keywords

Associative learning
Decision making
Entorhinal cortex
Maladaptive behavior
Nucleus accumbens

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