# Investigating the effects of aversive interoceptive states on computations underlying avoidance behavior and their neural basis > **NIH NIH P20** · LAUREATE INSTITUTE FOR BRAIN RESEARCH · 2020 · $300,932 ## Abstract Anxiety disorders are the most ubiquitous form of mental illness, affecting roughly 34% of the population. One major factor that maintains anxiety symptoms is the continued avoidance of feared situations that are in fact tolerable and would often improve quality of life. However, it is unknown how the aversive interoceptive states associated with high anxiety influence maladaptive avoidance behavior – or what the neural underpinnings of such influences are. To answer these questions, we propose to employ a previously validated inspiratory breathing load paradigm capable of reliably inducing aversive visceral states while individuals complete two decision-making tasks. We will use computational modeling to identify dissociable parameters underlying learning and decision-making on an individual basis. Some important individual differences that can be estimated within these models are values of parameters reflecting: (1) how much decisions are driven by seeking information vs. reward, and (2) how far into the future an individual considers when making decisions (planning horizon). Abnormal values for these parameters could drive maladaptive avoidance behavior in anxious populations, because learning to approach uncomfortable situations requires both information-seeking (to test expectations) and a sufficient planning horizon to anticipate that short-term discomfort can lead to long-term benefit. Here we hypothesize that aversive visceral states reduce information-seeking and shorten planning horizon. We further hypothesize that these effects are magnified in anxious populations. In this project will recruit 50 low- and 50 high-anxiety participants (Overall Anxiety Severity and Impairment Scale [OASIS] scores > 8) to test the above-stated hypotheses. Participants will complete two decision-making tasks widely used with computational modelling – the aversive pruning (AP) task 6,7 and the horizon task 8. They will complete each task twice, once with and once without the unpleasant breathing load. The AP task assesses aversive decision true ‘pruning’ – the tendency to not evaluate distal outcomes of a possible course of action if a proximal negative outcome is expected (i.e. effectively reducing planning horizon for policies that may have positive distal outcomes). The horizon task assesses goal-directed information-seeking – the tendency to strategically seek out observations to reduce uncertainty before becoming confident in the best course of action. The AP task will be completed during fMRI. We will compare information-seeking and pruning parameter values with vs. without breathing loads and correlate these parameters with state and trait anxiety levels. We will also test the hypothesis that aversive states during breathing load will amplify the known neural correlates of pruning, by increasing neural responses in subgenual cingulate, insula, and amygdala and reducing dorsal frontoparietal activity associated with future p... ## Key facts - **NIH application ID:** 10399800 - **Project number:** 5P20GM121312-04 - **Recipient organization:** LAUREATE INSTITUTE FOR BRAIN RESEARCH - **Principal Investigator:** Ryan S Smith - **Activity code:** P20 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2020 - **Award amount:** $300,932 - **Award type:** 5 - **Project period:** 2021-05-01 → 2022-06-30 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10399800 ## Citation > US National Institutes of Health, RePORTER application 10399800, Investigating the effects of aversive interoceptive states on computations underlying avoidance behavior and their neural basis (5P20GM121312-04). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10399800. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*