# Quantifying the cognitive processes supporting computations of stochasticity and volatility in humans

> **NIH NIH R21** · UNIVERSITY OF SOUTHERN CALIFORNIA · 2023 · $247,500

## Abstract

Maladaptive processes of uncertainty are linked to cognitive dysfunctions in mental illness, such as
anxiety, behavioral addictions, and schizophrenia. However, it is not clear what specific computations
support this general notion of uncertainty, how they go wrong, and how these same computational
constructs transdiagnostically influence many mental illnesses. We plan to address these questions
by drawing on our recent theoretical work (Piray and Daw, 2021), which identifies specific
computational hypotheses about how uncertainty processes may go wrong when organisms are
faced with observations that are corrupted by two types of noise: moment-to-moment stochasticity of
observations and volatility, i.e., how quickly they change. Using a novel task, we will address these
questions both cross-sectionally (Aim 1) and longitudinally (Aim 2) in a large general population.
Statistical principles indicate that volatility and stochasticity have opposite effects on the learning rate,
a parameter that determines the reliance on each new outcome during learning. But earlier research
in computational neuroscience and computational psychiatry failed to consider mutual dependencies
in computing volatility and stochasticity, leaving open the question of how the brain separates these
two types of noise in the real world in which they are both unknown. In recent work, we addressed
this issue and introduced a model for the joint estimation of both factors. A key prediction of the
model is that individuals who are less sensitive to stochasticity are more likely to mistake stochasticity
for volatility, and vice versa. This situation might, in principle, arise in psychiatric conditions. Here, we
propose a behavioral task that systematically manipulates both volatility and stochasticity. The task,
together with the model, allows us to find two key parameters for each subject: sensitivity to
stochasticity and sensitivity to volatility. In Aim 1, we will use the task to characterize the cognitive
process supporting computations of volatility and stochasticity and link the process parameters to
transdiagnostic constructs of psychopathology. In Aim 2, we will determine whether model
parameters predict trajectories of clinically relevant symptoms. This project attempts to model
complicated cognitive processes that are relevant for understanding learning and decision-making
dysfunctions in mental illness by utilizing cutting-edge data collection technologies and by mapping
subject-level parameters reflecting individual variations in this process to transdiagnostic self-report
measures. This work makes it possible to study the neurophysiological underpinnings of volatility,
stochasticity, and uncertainty in the future. This project also has the potential to pave the way for the
development of biomarkers for transdiagnostic constructs related to uncertainty computations, which
are relevant to anxiety, depression, behavioral addictions, and schizophrenia.

## Key facts

- **NIH application ID:** 10732422
- **Project number:** 1R21MH134217-01
- **Recipient organization:** UNIVERSITY OF SOUTHERN CALIFORNIA
- **Principal Investigator:** Payam Piray
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $247,500
- **Award type:** 1
- **Project period:** 2023-06-01 → 2025-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10732422

## Citation

> US National Institutes of Health, RePORTER application 10732422, Quantifying the cognitive processes supporting computations of stochasticity and volatility in humans (1R21MH134217-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10732422. Licensed CC0.

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