Don't Think, Just Feel the Music: Individuals with Strong Pavlovian-to-Instrumental Transfer Effects Rely Less on Model-based Reinforcement Learning
Open accessAbstract
Behavioral choice can be characterized along two axes. One axis distinguishes reflexive, model-free systems that slowly accumulate values through experience and a model-based system that uses knowledge to reason prospectively. The second axis distinguishes Pavlovian valuation of stimuli from instrumental valuation of actions or stimulus–action pairs. This results in four values and many possible interactions between them, with important consequences for accounts of individual variation. We here explored whether individual variation along one axis was related to individual variation along the other. Specifically, we asked whether individuals' balance between model-based and model-free learning was related to their tendency to show Pavlovian interferences with instrumental decisions. In two independent samples with a total of 243 participants, Pavlovian–instrumental transfer effects were negatively correlated with the strength of model-based reasoning in a two-step task. This suggests a potential common underlying substrate predisposing individuals to both have strong Pavlovian interference and be less model-based and provides a framework within which to interpret the observation of both effects in addiction.
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Pavlovian learning facilitates execution of operant behavior through Pavlovian-instrumental transfer, particularly when the response is habitual.
“Pavlovian learning is known to facilitates execution of operant behavior through Pavlovian-instrumental transfer, particularly when the response is habitual49,50.”
From Dissociable roles of prefrontal plasticity in decision-making strategy and execution of habitual behavior (2026) · cites this paper as [50]
The Introduction (page unavailable in our copy) says Pavlovian stimuli can enhance instrumental responding through Pavlovian-instrumental transfer, supporting the first part of the claim. The Results and Additional information (pages unavailable) associate stronger transfer with less model-based decision-making, but report no association with model-free behavior. The paper therefore does not establish that transfer particularly facilitates habitual responses. (LLM, conf=0.88)
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Enhanced Pavlovian-to-instrumental transfer in internet gaming disorder
Qin, C., Feng, S., Chen, Y., Liao, X., Cheng, X., Tian, M., Zhou, X., Deng, J., Peng, Y., Gong, K., et al. (2023) · Journal of Behavioral Addictions · doi:10.1556/2006.2023.00023 · Cited by 12 · see citing works
Xu, L., Zhang, J., Geng, X., Song, K., Zeng, P.-P., Potenza, M. N., Schad, D. J., & Zhang, J. (2024) · Computers in Human Behavior · doi:10.1016/j.chb.2024.108188 · Cited by 12 · see citing works
Self-citationInteractive influences of food, contexts and neurocognitive systems on addictive eating
Ho, D., & Verdejo‐García, A. (2021) · Progress in Neuro-Psychopharmacology and Biological Psychiatry · doi:10.1016/j.pnpbp.2021.110295 · Cited by 11 · see citing works
da Costa, R. Q. M., Furukawa, E., Hoefle, S., Moll, J., Tripp, G., & Mattos, P. (2020) · Frontiers in Psychology · doi:10.3389/fpsyg.2020.00195 · Cited by 11 · see citing works
Afshar, N. M., Cinotti, F., Martin, D. A., Khamassi, M., Calu, D. J., Taylor, J. R., & Groman, S. M. (2022) · Journal of Neuroscience · doi:10.1523/jneurosci.1113-22.2022 · Cited by 7 · see citing works
Kuitunen‐Paul, S., Pfab, S., Garbusow, M., Heinz, A., Kuitunen, P. T., Manthey, J., Nebe, S., Smolka, M. N., & Wïttchen, H. (2017) · Journal of Substance Abuse Treatment · doi:10.1016/j.jsat.2017.12.011 · Cited by 7 · see citing works
Self-citationIndividual concerns modulate reward-related learning and behaviors involving sexual outcomes.
Stussi, Y., Sennwald, V., Pool, E. R., Delplanque, S., Brosch, T., Bianchi‐Demicheli, F., & Sander, D. (2021) · Motivation Science · doi:10.1037/mot0000249 · Cited by 6 · see citing works
Kasal, M. I., Beşiroğlu, L., Zorlu, N., Dikmeer, N., Bektaş, A. B., Durmaz, E., Polat, S., Gelal, F. M., Rapp, M. A., Heinz, A., et al. (2021) · Scientific Reports · doi:10.1038/s41598-021-91179-2 · Cited by 4 · see citing works
Self-citationAlpha EEG power reflects the suppression of Pavlovian bias during social reinforcement learningPreprint
Thompson, J. C., & Westwater, M. (2017) · bioRxiv · doi:10.1101/153668 · Cited by 5 · see citing works
(Neuro)therapeutic Approaches in the Field of Alcohol Use Disorders
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da Costa, R. Q. M., Furukawa, E., Hoefle, S., Moll, J., Tripp, G., & Mattos, P. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/601195 · Cited by 2 · see citing works
Komputationale Modelle in der psychiatrischen Forschung
Heinz, A., Schlagenhauf, F., Mascarell-Maricic, L., & Daedelow, L. S. (2017) · Zeitschrift für Psychiatrie Psychologie und Psychotherapie · doi:10.1024/1661-4747/a000298 · Cited by 1 · see citing works
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Afshar, N. M., Cinotti, F., Martin, D., Khamassi, M., Calu, D. J., Taylor, J. R., & Groman, S. M. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.06.12.495805 · Cited by 1 · see citing works
Asaoka, N., Pagano, D., & Hayashi, Y. (2026) · Nature Communications · doi:10.1038/s41467-026-75706-1
“Pavlovian learning is known to facilitates execution of operantbehaviorthroughPavlovian-instrumentaltransfer,particularly when the response is habitual 49,50 .”
The Neurobiology of Gambling Disorder: Neuroscientific Studies and Computational Perspectives
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Stronger Prejudices Are Associated With Decreased Model-Based Control
Sebold, M., Chen, H., Önal, A., Kuitunen‐Paul, S., Mojtahedzadeh, N., Garbusow, M., Nebe, S., Wïttchen, H., Huys, Q. J. M., Schlagenhauf, F., et al. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2021.767022
Self-citationBorn, M., Krasniqi, S., Riemerschmid, C., Chen, H., Thill, L., Smolka, M. N., Schlagenhauf, F., Heinz, A., Friedel, E., Ebrahimi, C., et al. (2026) · medRxiv · doi:10.64898/2026.09.01.26361915
Self-citation
- Authors
- Sebold, M., Schad, D. J., Nebe, S., Garbusow, M., Jünger, E., Kroemer, N. B., Kathmann, N., Zimmermann, U. S., Smolka, M. N., Rapp, M. A., Heinz, A., & Huys, Q. J. M.
- Affiliations
- Humboldt-Universität zu Berlin (DE)
- University of Potsdam (DE)
- Technische Universität Dresden (DE)
- Yale University (US)
- Charité – Universitätsmedizin Berlin (DE)
- University of Zurich (CH)
- ORCID
- Venue
- Journal of Cognitive Neuroscience
- Publisher
- MIT Press - Journals
- Published
- 2016-07-01
- Volume
- 28
- Issue
- 7
- Pages
- 985-995
- ISSN
- 0898-929X (ISSN-L)
- 1530-8898
- OpenAlex
- W2289068127
- Cited by
- 41
- References
- 72
- Citations by year
- 2026: 2 · 2024: 1 · 2023: 2 · 2022: 3 · 2021: 6 · 2020: 5 · 2019: 8 · 2018: 4 · 2017: 7 · 2016: 3
- FWCI
- 4.68
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of Cognitive Neuroscience (publishedVersion)
- PubMed (publishedVersion)
- Zurich Open Repository and Archive (University of Zurich) (acceptedVersion, other-oa)
- publish.UP (University of Potsdam) (submittedVersion)
- Zurich Open Repository and Archive (University of Zurich) (submittedVersion, other-oa)
- Zurich Open Repository and Archive (University of Zurich)
- Funders
- Deutsche Forschungsgemeinschaft (RA 1047/2-2, ZI 1119/3‐2, FOR1617, HE2597/15‐2, WI709/10-2, WI 709/10-1, HE2597/14-2, HE 2597/13-1, SM 80/7–2, HE2597/13-2, ZI1119/3-1, RA 1047/2-1, /14-2, HE2597/15‐1, /14-1, HE 2597/14-1, SM 80/7-1)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neurotransmitter Receptor Influence on Behavior
- Keywords
- Pavlovian-to-instrumental transfer, model-based reinforcement learning, model-free learning, individual differences, addiction, behavioral choice
- MeSH terms
- Adult, Choice Behavior, Computer Simulation, Conditioning, Classical, Conditioning, Operant, Female, Humans, Individuality, Longitudinal Studies, Male, Models, Psychological, Probability Learning, Reaction Time, Reinforcement, Psychology, Transfer, Psychology, Linear Models, Feedback, Psychological
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