Differential effects of instructed and objective feedback reliability on feedback-related brain activity.
ERPs
P3
decision making
feedback reliability
feedback-related negativity
Journal
Psychophysiology
ISSN: 1540-5958
Titre abrégé: Psychophysiology
Pays: United States
ID NLM: 0142657
Informations de publication
Date de publication:
09 2019
09 2019
Historique:
received:
10
12
2018
revised:
22
03
2019
accepted:
26
03
2019
pubmed:
28
5
2019
medline:
24
6
2020
entrez:
28
5
2019
Statut:
ppublish
Résumé
Feedback reliability refers to the probability that the same decision leads to the same positive or negative feedback in the future. Previous research has shown that unreliable feedback is associated with attenuated feedback-related brain activity in ERPs, represented by a reduced fronto-central valence effect (feedback-related negativity or reward positivity) and a reduced feedback-related P3. Here, we asked whether these effects reflect top-down mechanisms or whether they can be explained by implicit feedback-outcome contingency learning. In two experiments, participants performed a trial-and-error learning task while subjective or objective feedback reliability was varied across blocks. In Experiment 1, we manipulated instructed feedback reliability while holding objective feedback reliability constant. Low instructed feedback reliability led to an attenuation of the fronto-central valence effect and the P3. In Experiment 2, we manipulated objective feedback reliability while holding instructed feedback reliability constant. Here, no modulation of feedback-related brain activity was observed. These results suggest that effects of feedback reliability are driven by top-down mechanisms based on explicit knowledge. Specifically, effects on the fronto-central valence effect could indicate a devaluation of unreliable feedback or a bias on the generation or utilization of reward prediction errors.
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
e13399Informations de copyright
© 2019 Society for Psychophysiological Research.
Références
Baker, T. E., & Holroyd, C. B. (2011). Dissociated roles of the anterior cingulate cortex in reward and conflict processing as revealed by the feedback error-related negativity and N200. Biological Psychology, 87, 25-34. https://doi.org/10.1016/j.biopsycho.2011.01.010
Bell, A. J., & Sejnowski, T. J. (1989). An information-maximisation approach to blind separation and blind deconvolution. Neural Computation, 6, 1004-1034. https://doi.org/10.1162/neco.1995.7.6.1129
Chase, H. W., Swainson, R., Durham, L., Benham, L., & Cools, R. (2011). Feedback-related negativity codes prediction error but not behavioral adjustment during probabilistic reversal learning. Journal of Cognitive Neuroscience, 23, 936-946. https://doi.org/10.1162/jocn.2010.21456
Collins, A. G. E., & Frank, M. J. (2012). How much of reinforcement learning is working memory, not reinforcement learning? A behavioral, computational, and neurogenetic analysis. European Journal of Neuroscience, 35, 1024-1035. https://doi.org/10.1111/j.1460-9568.2011.07980.x
Daw, N. D., Gershman, S. J., Seymour, B., Dayan, P., & Dolan, R. J. (2011). Model-based influences on humans' choices and striatal prediction errors. Neuron, 69, 1204-1215. https://doi.org/10.1016/j.neuron.2011.02.027
Delorme, A., Sejnowski, T., & Makeig, S. (2007). Enhanced detection of artifacts in EEG data using higher-order statistics and independent component analysis. NeuroImage, 34, 1443-1449. https://doi.org/10.1016/j.neuroimage.2006.11.004
Doll, B. B., Hutchison, K. E., & Frank, M. J. (2011). Dopaminergic genes predict individual differences in susceptibility to confirmation bias. Journal of Neuroscience, 31, 6188-6198. https://doi.org/10.1523/JNEUROSCI.6486-10.2011
Doll, B. B., Jacobs, W. J., Sanfey, A. G., & Frank, M. J. (2009). Instructional control of reinforcement learning: A behavioral and neurocomputational investigation. Brain Research, 1299, 74-94. https://doi.org/10.1016/j.brainres.2009.07.007
Donchin, E., & Coles, M. G. H. (1988). Is the P300 component a manifestation of context updating? Behavioral and Brain Sciences, 11, 357-374. https://doi.org/10.1017/S0140525X00058027
Ernst, B., & Steinhauser, M. (2012). Feedback-related brain activity predicts learning from feedback in multiple-choice testing. Cognitive, Affective, & Behavioral Neuroscience, 12, 323-336. https://doi.org/10.3758/s13415-012-0087-9
Ernst, B., & Steinhauser, M. (2015). Effects of invalid feedback on learning and feedback-related brain activity in decision-making. Brain and Cognition, 99, 78-86. https://doi.org/10.1016/j.bandc.2015.07.006
Ernst, B., & Steinhauser, M. (2017). Top-down control over feedback processing: The probability of valid feedback affects feedback-related brain activity. Brain and Cognition, 115, 33-40. https://doi.org/10.1016/j.bandc.2017.03.008
Ernst, B., & Steinhauser, M. (2018). Effects of feedback reliability on feedback-related brain activity: A feedback valuation account. Cognitive, Affective, & Behavioral Neuroscience, 18, 596-608. https://doi.org/10.3758/s13415-018-0591-7
Frank, M. J., & Claus, E. D. (2006). Anatomy of a decision: Striato-orbitofrontal interactions in reinforcement learning, decision making, and reversal. Psychological Review, 113, 300-326. https://doi.org/10.1037/0033-295X.113.2.300
Gehring, W. J., & Willoughby, A. R. (2002). The medial frontal cortex and the rapid processing of monetary gains and losses. Science, 295, 2279-2282. https://doi.org/10.1126/science.1066893
Greenhouse, S. W., & Geisser, S. (1959). On methods in the analysis of profile data. Psychometrika, 24, 95-112. https://doi.org/10.1007/BF02289823
Holroyd, C. B., & Coles, M. G. H. (2002). The neural basis of human error processing: Reinforcement learning, dopamine, and the error-related negativity. Psychological Review, 109, 679-708. https://doi.org/10.1037/0033-295X.109.4.679
Holroyd, C. B., Pakzad-Vaezi, K. L., & Krigolson, O. E. (2008). The feedback correct-related positivity: Sensitivity of the event-related brain potential to unexpected positive feedback. Psychophysiology, 45, 688-697. https://doi.org/10.1111/j.1469-8986.2008.00668.x
Li, J., Delgado, M. R., & Phelps, E. A. (2011). How instructed knowledge modulates the neural systems of reward learning. Proceedings of the National Academy of Sciences, 108, 55-60. https://doi.org/10.1073/pnas.1014938108
Li, P., Peng, W., Li, H., & Holroyd, C. B. (2018). Electrophysiological measures reveal the role of anterior cingulate cortex in learning from unreliable feedback. Cognitive, Affective, & Behavioral Neuroscience, 18, 949-963. https://doi.org/10.3758/s13415-018-0615-3
Miltner, W. H. R., Braun, C. H., & Coles, M. G. H. (1997). Event-related brain potentials following incorrect feedback in a time-estimation task: Evidence for a “generic” neural system for error detection. Journal of Cognitive Neuroscience, 9, 788-798. https://doi.org/10.1162/jocn.1997.9.6.788
Nieuwenhuis, S. (2011). Learning, the P3, and the locus coeruleus-norepinephrine system. In R. B. Mars, J. Sallet, M. Rushworth, & N. Yeung (Eds.), Neural basis of motivational and cognitive control (pp. 209-222). Oxford, UK: Oxford University Press. https://doi.org/10.7551/mitpress/9780262016438.003.0012
Nieuwenhuis, S., Aston-Jones, G., & Cohen, J. D. (2005). Decision making, the P3, and the locus coeruleus-norepinephrine system. Psychological Bulletin, 131, 510-532. https://doi.org/10.1037/0033-2909.131.4.510
Polich, J. (2007). Updating P300: An integrative theory of P3a and P3b. Clinical Neurophysiology, 118, 2128-2148. https://doi.org/10.1016/j.clinph.2007.04.019
Rescorla, R. A., & Wagner, A. R. (1972). A theory of Pavlovian conditioning: Variations in the effectiveness of reinforcement and nonreinforcement. In A. H. Black, & W. F. Prokasy (Eds.), Classical conditioning II: Current research and theory (pp. 64-99). New York, NY: Appelton-Century-Crofts.
San Martín, R. (2012). Event-related potential studies of outcome processing and feedback-guided learning. Frontiers in Human Neuroscience, 6, 304. https://doi.org/10.3389/fnhum.2012.00304
Schiffer, A.-M., Siletti, K., Waszak, F., & Yeung, N. (2017). Adaptive behaviour and feedback processing integrate experience and instruction in reinforcement learning. NeuroImage, 146, 626-641. https://doi.org/10.1016/j.neuroimage.2016.08.057
Steinhauser, M., & Yeung, N. (2010). Decision processes in human performance monitoring. Journal of Neuroscience, 30, 15643-15653. https://doi.org/10.1523/JNEUROSCI.1899-10.2010
Steinhauser, M., & Yeung, N. (2012). Error awareness as evidence accumulation: Effects of speed-accuracy trade-off on error signaling. Frontiers in Human Neuroscience, 6, 240. https://doi.org/10.3389/fnhum.2012.00240
Sutton, R. S., & Barto, A. G. (1998). Reinforcement learning (Vol. 9). Cambridge, MA: MIT Press.
Viola, C. F., Thorne, J., Edmonds, B., Schneider, T., Eichele, T., & Debener, S. (2009). Semi-automatic identification of independent components representing EEG artifact. Clinical Neurophysiology, 120, 868-877.
Walentowska, W., Moors, A., Paul, K., & Pourtois, G. (2016). Goal relevance influences performance monitoring at the level of the FRN and P3 components. Psychophysiology, 53, 1020-1033. https://doi.org/10.1111/psyp.12651
Walsh, M. M., & Anderson, J. R. (2011). Modulation of the feedback-related negativity by instruction and experience. Proceedings of the National Academy of Sciences, 108, 19048-19053. https://doi.org/10.1073/pnas.1117189108
Walsh, M. M., & Anderson, J. R. (2012). Learning from experience: Event-related potential correlates of reward processing, neural adaptation, and behavioral choice. Neuroscience & Biobehavioral Reviews, 36, 1870-1884. https://doi.org/10.1016/j.neubiorev.2012.05.008
Winer, B. J. (1971). Use of analysis of variance to estimate reliability of measurements. In B. J. Winer (Ed.), Statistical principles in experimental design (2nd ed., pp. 283-295). New York, NY: McGraw-Hill.