Computational Link Between Motivational Factors and Cognitive Deficits in Depression
- 1Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Chancellor’s Building, 49 Little France Crescent, Edinburgh BioQuarter, Edinburgh, EH16 4SB UK
- 2Institute for Machine Learning, School of Informatics, University of Edinburgh, 10 Crichton Street, Edinburgh, EH8 9AB UK
- 3School of Health and Wellbeing, University of Glasgow, Clarice Pears Building, 90 Byres Road, Glasgow, G12 8TB UK
Abstract
Cognitive deficits are a common symptom of depression and contribute significantly to the disabling effects of the disorder. Experimentally, they are observed as increased reaction times, increased error rates, and deficient performance adaptation after making errors or receiving adverse feedback, in multiple cognitive paradigms. In the current study we aimed to address the cause of these cognitive deficits from a theoretical perspective. We constructed computational models of optimal resource allocation in two cognitive tasks – Delayed Match to Sample (DMS), and Eriksen Flanker (EF). The models explicitly link performance feedback values and beliefs about task controllability with measures of cognitive performance including accuracy, reaction times, and post-error improvement in accuracy (PIA). We then introduced depression-related motivational changes – altered control belief and feedback values (representing learned helplessness, anhedonic valuation and negative bias) – to see if these factors can account for deficits in cognitive performance. In the DMS task, altered control belief and lower valuation of correct performance accounted for decreased accuracy and decreased PIA. In the EF task, these two factors could explain increased response times, decreased accuracy and decreased error-related negativity (ERN) signal magnitude. Increased valuation of adverse feedback, on the other hand, was linked to increased EF accuracy and increased ERN signal magnitude. Furthermore, in the EF task, different combinations of depression-related motivational factors led to different patterns of cognitive performance, which could offer a basis for stratification. Our study offers a novel perspective on how cognitive deficits in depression can be viewed as consequences of altered motivational factors. The developed models provide an explicit computational and algorithmic bridge between the commonly observed cognitive deficits (increased reaction times, decreased performance accuracy, worse post-error adaptation) and factors such as learned helplessness and anhedonic valuation.
Supplementary Information: The online version contains supplementary material available at https://doi.org/10.1007/s12559-026-10635-1.
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BibTeX
@article{stolicyn2026computational,
title = {{Computational Link Between Motivational Factors and Cognitive Deficits in Depression}},
author = {Aleks Stolicyn and Liana Romaniuk and Stephen M. Lawrie and Peggy Seriès},
journal = {Cognitive Computation},
year = {2026},
month = nov,
doi = {10.1007/s12559-026-10635-1},
url = {https://doi.org/10.1007/s12559-026-10635-1},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Computational Link Between Motivational Factors and Cognitive Deficits in Depression
AU - Aleks Stolicyn
AU - Liana Romaniuk
AU - Stephen M. Lawrie
AU - Peggy Seriès
PY - 2026
DA - 2026/11/03
JO - Cognitive Computation
DO - 10.1007/s12559-026-10635-1
UR - https://doi.org/10.1007/s12559-026-10635-1
AB - Cognitive deficits are a common symptom of depression and contribute significantly to the disabling effects of the disorder. Experimentally, they are observed as increased reaction times, increased error rates, and deficient performance adaptation after making errors or receiving adverse feedback, in multiple cognitive paradigms. In the current study we aimed to address the cause of these cognitive deficits from a theoretical perspective. We constructed computational models of optimal resource allocation in two cognitive tasks – Delayed Match to Sample (DMS), and Eriksen Flanker (EF). The models explicitly link performance feedback values and beliefs about task controllability with measures of cognitive performance including accuracy, reaction times, and post-error improvement in accuracy (PIA). We then introduced depression-related motivational changes – altered control belief and feedback values (representing learned helplessness, anhedonic valuation and negative bias) – to see if these factors can account for deficits in cognitive performance. In the DMS task, altered control belief and lower valuation of correct performance accounted for decreased accuracy and decreased PIA. In the EF task, these two factors could explain increased response times, decreased accuracy and decreased error-related negativity (ERN) signal magnitude. Increased valuation of adverse feedback, on the other hand, was linked to increased EF accuracy and increased ERN signal magnitude. Furthermore, in the EF task, different combinations of depression-related motivational factors led to different patterns of cognitive performance, which could offer a basis for stratification. Our study offers a novel perspective on how cognitive deficits in depression can be viewed as consequences of altered motivational factors. The developed models provide an explicit computational and algorithmic bridge between the commonly observed cognitive deficits (increased reaction times, decreased performance accuracy, worse post-error adaptation) and factors such as learned helplessness and anhedonic valuation. Supplementary Information: The online version contains supplementary material available at https://doi.org/10.1007/s12559-026-10635-1.
SN - 1866-9956
SN - 1866-9964
ER -