# Elucidating the relationship between decision-making under second-order uncertainty and dimensions of negative affect using computational modeling

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA BERKELEY · 2021 · $509,912

## Abstract

Project Abstract
Computational modeling can help us formalize how choice behaviors can be optimally adapted to different
situations and investigate the ways in which individuals deviate from optimal behavior. Both anxious and
depressed individuals report difficulties with decision-making; these difficulties have consequences for social
interactions and occupational function. Understanding whether anxiety and depression are associated with
common or unique deficits in decision-making has been hampered by studies focusing on either anxiety or
depression alone and overlooking issues of comorbidity. This is important to address to better identify which
aspects of decision-making should be targets for intervention in different patient groups. The separate
investigation of anxiety- and depression-related deficits in decision-making has also led to a lack of
equivalence of tasks and limited use of both reward-related and aversive outcomes within the same study.
In the proposed research, we will conduct bifactor analysis of item-level responses to anxiety and depression
questionnaires and use participant scores on the dimensions obtained to interrogate whether deficits in decision-
making under second-order uncertainty are common to both anxiety and depression or unique to one or the
other. We focus upon second-order uncertainty as this characterizes many of the situations we encounter in
every-day life but there has been limited investigation of whether anxiety or depression are linked to deficits
in adjusting decision-making to second-order uncertainty. Second-order uncertainty arises both when the
probability of our actions resulting in certain outcomes changes across time (volatility) and when information
needed to estimate how likely a given action is to lead to a given outcome is not fully available (ambiguity).
In the proposed studies, we will use volatility and ambiguity manipulations to examine whether deficits in
decision-making under second-order uncertainty are common to both anxiety and depression or unique to one
or other and whether such deficits are domain general or domain specific (vary by outcome type: aversive,
reward gain or reward loss). On-line studies will be used to conduct replication work and to examine if impaired
decision-making under second-order uncertainty is primarily linked to internalizing symptomatology or common
to a broader range of psychopathology. These online studies will also enable us to test exploratory hypotheses
pertaining to other dimensions of psychopathology. Understanding the extent to which alterations in decision-
making under second order uncertainty are unique to anxiety or depression, common to both anxiety and
depression (i.e. a transdiagnostic marker of Internalizing psychopathology), or associated with
psychopathology more broadly is important to clarify so that we can better tailor cognitive and psycho-
educational interventions to different patient groups. It may also help clarify whether existi...

## Key facts

- **NIH application ID:** 10121524
- **Project number:** 1R01MH122558-01A1
- **Recipient organization:** UNIVERSITY OF CALIFORNIA BERKELEY
- **Principal Investigator:** Sonia Jane Bishop
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $509,912
- **Award type:** 1
- **Project period:** 2021-03-02 → 2026-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10121524

## Citation

> US National Institutes of Health, RePORTER application 10121524, Elucidating the relationship between decision-making under second-order uncertainty and dimensions of negative affect using computational modeling (1R01MH122558-01A1). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10121524. Licensed CC0.

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