# CRCNS: Investigating the Neurocomputational Mechanisms of Belief Updating

> **NIH NIH R01** · BROWN UNIVERSITY · 2024 · $301,032

## Abstract

PROJECT SUMMARY (See instructions):
Virtually every aspect of human behavior is governed by beliefs about ourselves and the world around us. 
These beliefs are continuously updated in response to the deluge of information we encounter in everyday 
life. The mechanisms that underlie belief updating may hold the key to understanding neuropsychiatric 
conditions involving abnormal belief updating, such as delusions in psychosis or cognitive distortions in 
depression. Despite active research, progress in our understanding of the neural mechanisms of normal 
and abnormal belief updating has been limited, owing in part to i) a focus on individual tasks rather than 
cross-domain constructs, ii) oversimplified models that cannot scale to real world behavior, and iii) a
failure to link computational descriptions to the neural circuitry that performs them. Here, we propose to 
bridge these gaps by implementing our recently developed computational framework of belief updating 
across tasks from different neuroscience domains. We recently established a belief updating framework 
based on contextual inference, which embraces the complexity of real-world belief updating, and can, in 
principle, be applied broadly to tasks of different neuroscience domains. Contextual inference assumes 
that belief updating includes two components: 1) learning what to expect in a given context, and 2)
figuring out which context you are actually in. We have developed a biological instantiation of this model, 
based on cortico-striato-thalamic loops that can perform this contextual inference, capture belief updating 
behavior in canonical tasks, and shed light on pathological belief updating behaviors observed in 
schizophrenia. In our model, individual and clinical differences in belief updating could emerge through 
differences in the stability of context representations, which are stored in cortical attractor networks that 
are sensitive to both stable structural features, such as the ratio of excitation to inhibition within the 
network, as well as dynamic interactions with thalamic inputs. Applying lessons from our model to 
schizophrenia, where prefrontal inhibition is impaired, cortical attractors would be expected to be less 
stable, promoting spurious attractor switches and state transitions. We will extend our contextual
inference model to measure the degree to which it can generalize behavioral tendencies of belief updating 
across neuroscience domains. We will examine how these tendencies relate to real-world behavior and 
mental health traits. We will then test the model’s core predictions regarding the biological origins of

## Key facts

- **NIH application ID:** 11083199
- **Project number:** 1R01MH139351-01
- **Recipient organization:** BROWN UNIVERSITY
- **Principal Investigator:** Matthew Nassar
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $301,032
- **Award type:** 1
- **Project period:** 2024-09-01 → 2029-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11083199

## Citation

> US National Institutes of Health, RePORTER application 11083199, CRCNS: Investigating the Neurocomputational Mechanisms of Belief Updating (1R01MH139351-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/11083199. Licensed CC0.

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