Reward Sensitivity and Noise Contribute to Negative Affective Bias: A Learning Signal Detection Theory Approach in Decision-Making. Lütkenherm, I. K., Locke, S. M., & Robinson, O. J. Computational Psychiatry, May, 2024. ![Reward Sensitivity and Noise Contribute to Negative Affective Bias: A Learning Signal Detection Theory Approach in Decision-Making [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex In patients with mood disorders, negative affective biases – systematically prioritising and interpreting information negatively – are common. A translational cognitive task testing this bias has shown that depressed patients have a reduced preference for a high reward under ambiguous decision-making conditions. The precise mechanisms underscoring this bias are, however, not yet understood. We therefore developed a set of measures to probe the underlying source of the behavioural bias by testing its relationship to a participant’s reward sensitivity, value sensitivity and reward learning rate. One-hundred-forty-eight participants completed three online behavioural tasks: the original ambiguous-cue decision-making task probing negative affective bias, a probabilistic reward learning task probing reward sensitivity and reward learning rate, and a gambling task probing value sensitivity. We modelled the learning task through a dynamic signal detection theory model and the gambling task through an expectation-maximisation prospect theory model. Reward sensitivity from the probabilistic reward task (β = 0.131, p = 0.024) and setting noise from the probabilistic reward task (β = –0.187, p = 0.028) both predicted the affective bias score in a logistic regression. Increased negative affective bias, at least on this specific task, may therefore be driven in part by a combination of reduced sensitivity to rewards and more variable responses.
@article{lutkenherm_reward_2024,
title = {Reward {Sensitivity} and {Noise} {Contribute} to {Negative} {Affective} {Bias}: {A} {Learning} {Signal} {Detection} {Theory} {Approach} in {Decision}-{Making}},
volume = {8},
issn = {2379-6227},
shorttitle = {Reward {Sensitivity} and {Noise} {Contribute} to {Negative} {Affective} {Bias}},
url = {https://account.cpsyjournal.org/index.php/up-j-cp/article/view/102},
doi = {10.5334/cpsy.102},
abstract = {In patients with mood disorders, negative affective biases – systematically prioritising and interpreting information negatively – are common. A translational cognitive task testing this bias has shown that depressed patients have a reduced preference for a high reward under ambiguous decision-making conditions. The precise mechanisms underscoring this bias are, however, not yet understood. We therefore developed a set of measures to probe the underlying source of the behavioural bias by testing its relationship to a participant’s reward sensitivity, value sensitivity and reward learning rate. One-hundred-forty-eight participants completed three online behavioural tasks: the original ambiguous-cue decision-making task probing negative affective bias, a probabilistic reward learning task probing reward sensitivity and reward learning rate, and a gambling task probing value sensitivity. We modelled the learning task through a dynamic signal detection theory model and the gambling task through an expectation-maximisation prospect theory model. Reward sensitivity from the probabilistic reward task (β = 0.131, p = 0.024) and setting noise from the probabilistic reward task (β = –0.187, p = 0.028) both predicted the affective bias score in a logistic regression. Increased negative affective bias, at least on this specific task, may therefore be driven in part by a combination of reduced sensitivity to rewards and more variable responses.},
number = {1},
urldate = {2024-11-06},
journal = {Computational Psychiatry},
author = {Lütkenherm, Isabel K. and Locke, Shannon M. and Robinson, Oliver J.},
month = may,
year = {2024},
}
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