The effect of novelty on reinforcement learning
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The effect of novelty on reinforcement learning. / Houillon, A; Lorenz, R C; Boehmer, W; Rapp, M A; Heinz, A; Gallinat, J; Obermayer, K.
in: PROG BRAIN RES, Jahrgang 202, 01.01.2013, S. 415-39.Publikationen: SCORING: Beitrag in Fachzeitschrift/Zeitung › SCORING: Zeitschriftenaufsatz › Forschung › Begutachtung
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TY - JOUR
T1 - The effect of novelty on reinforcement learning
AU - Houillon, A
AU - Lorenz, R C
AU - Boehmer, W
AU - Rapp, M A
AU - Heinz, A
AU - Gallinat, J
AU - Obermayer, K
N1 - Copyright © 2013 Elsevier B.V. All rights reserved.
PY - 2013/1/1
Y1 - 2013/1/1
N2 - Recent research suggests that novelty has an influence on reward-related learning. Here, we showed that novel stimuli presented from a pre-familiarized category can accelerate or decelerate learning of the most rewarding category, depending on the condition. The extent of this influence depended on the individual trait of novelty seeking. Different reinforcement learning models were developed to quantify subjects' choices. We introduced a bias parameter to model explorative behavior toward novel stimuli and characterize individual variation in novelty response. The theoretical framework allowed us to test different assumptions, concerning the motivational value of novelty. The best fitting-model combined all novelty components and had a significant positive correlation with both the experimentally measured novelty bias and the independent novelty-seeking trait. Altogether, we have not only shown that novelty by itself enhances behavioral responses underlying reward processing, but also that novelty has a direct influence on reward-dependent learning processes, consistently with computational predictions.
AB - Recent research suggests that novelty has an influence on reward-related learning. Here, we showed that novel stimuli presented from a pre-familiarized category can accelerate or decelerate learning of the most rewarding category, depending on the condition. The extent of this influence depended on the individual trait of novelty seeking. Different reinforcement learning models were developed to quantify subjects' choices. We introduced a bias parameter to model explorative behavior toward novel stimuli and characterize individual variation in novelty response. The theoretical framework allowed us to test different assumptions, concerning the motivational value of novelty. The best fitting-model combined all novelty components and had a significant positive correlation with both the experimentally measured novelty bias and the independent novelty-seeking trait. Altogether, we have not only shown that novelty by itself enhances behavioral responses underlying reward processing, but also that novelty has a direct influence on reward-dependent learning processes, consistently with computational predictions.
KW - Adult
KW - Bias (Epidemiology)
KW - Computer Simulation
KW - Decision Making
KW - Exploratory Behavior
KW - Female
KW - Humans
KW - Individuality
KW - Male
KW - Markov Chains
KW - Models, Neurological
KW - Models, Psychological
KW - Probability Learning
KW - Reinforcement (Psychology)
KW - Young Adult
U2 - 10.1016/B978-0-444-62604-2.00021-6
DO - 10.1016/B978-0-444-62604-2.00021-6
M3 - SCORING: Journal article
C2 - 23317843
VL - 202
SP - 415
EP - 439
JO - PROG BRAIN RES
JF - PROG BRAIN RES
SN - 0079-6123
ER -