{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,2]],"date-time":"2026-09-02T05:51:48Z","timestamp":1788328308852,"version":"build-2803163510"},"reference-count":33,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T00:00:00Z","timestamp":1761868800000},"content-version":"vor","delay-in-days":303,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,1,18]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Approach-avoidance conflict (AAC) is a core aspect of decision-making, involving competing appetitive and aversive outcomes. Given substantial individual differences in sensitivity to reward and punishment, AAC experiences likely vary across individuals. This study examined the neurocognitive mechanisms underlying AAC, focusing on these individual differences. Participants completed a task with four levels of reward and punishment probabilities (.25, .50, .75, 1), choosing to accept or reject rewards paired with potential punishments. Importantly, we did not find a uniform condition that consistently elicited maximum or minimum AAC across participants. We identified individualized high- and low-conflict conditions using three metrics: reaction time, reward rejection rate, and a composite Behavioural Conflict Index. Mid-frontal theta (MFT) power, an established marker of conflict monitoring, was significantly higher in high-conflict compared to low-conflict trials. Computational modelling revealed variability in participants\u2019 weighting of reward and punishment probabilities, reflecting differences in participants\u2019 sensitivity to reward and punishment outcomes. Furthermore, self-reported Behavioural Inhibition System scores predicted MFT responses, linking personality traits to AAC processing. These findings demonstrate that MFT power reflects AAC-driven cognitive control, shaped by individual sensitivities and traits. Our results emphasize the importance of personalized conflict definitions for understanding adaptive control in motivationally ambiguous contexts.<\/jats:p>","DOI":"10.1093\/scan\/nsaf114","type":"journal-article","created":{"date-parts":[[2025,10,28]],"date-time":"2025-10-28T12:54:49Z","timestamp":1761656089000},"source":"Crossref","is-referenced-by-count":1,"title":["Beyond uniformity: individual sensitivities to reward and punishment shape midfrontal-theta responses to approach avoidance conflict"],"prefix":"10.1093","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5591-8551","authenticated-orcid":false,"given":"Shubham","family":"Pandey","sequence":"first","affiliation":[{"name":"Differential Psychology and Personality Research Unit, Institute of Psychology, Osnabr\u00fcck University , Osnabr\u00fcck 49076,","place":["Germany"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Roman","family":"Osinsky","sequence":"additional","affiliation":[{"name":"Differential Psychology and Personality Research Unit, Institute of Psychology, Osnabr\u00fcck University , Osnabr\u00fcck 49076,","place":["Germany"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2025,10,31]]},"reference":[{"key":"2026010712301676800_nsaf114-B1","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1037\/a0020684","article-title":"A Model-Based fMRI analysis with hierarchical Bayesian parameter estimation","volume":"4","author":"Ahn","year":"2011","journal-title":"J Neurosci Psychol Econ"},{"key":"2026010712301676800_nsaf114-B2","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.jneumeth.2019.01.006","article-title":"Joint modeling of reaction times and choice improves parameter identifiability in reinforcement learning 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