van den Berg, Berry

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   University of GroningenIndividual differences in learning rate are reflected in feedback related brain processesvan den Berg, BerryIMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite fromit. Please check the document version below.Publication date:2020Link to publication in University of Groningen/UMCG research databaseCitation for published version (APA):van den Berg, B. (2020). Individual differences in learning rate are reflected in feedback related brainprocesses.CopyrightOther than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of theauthor(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons).The publication may also be distributed here under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license.More information can be found on the University of Groningen website: https://www.rug.nl/library/open-access/self-archiving-pure/taverne-amendment.Take-down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediatelyand investigate your claim.Downloaded from the University of Groningen/UMCG research database (Pure): http://www.rug.nl/research/portal. For technical reasons thenumber of authors shown on this cover page is limited to 10 maximum.Download date: 10-09-2023DataModelled dataDataINDIVIDUAL DIFFERENCES IN LEARNING RATE ARE REFLECTED IN FEEDBACK RELATED BRAIN PROCESSESVIRTUAL Cognitive Neuroscience Society 2020 berry.van.den.berg@rug.nlHere, participants chose on each trial either a face or a house, which was fol-lowed by receiving either a zero (0) gain (+) or a loss (-) of different magni-tudes (0:8)On each set of 20 trials either the face or house was the set-winner and was more likely to yield net gains. SummaryFeedback processing was marked by amplitude modulations induced by both magnitude and valence in the earlyl atency range with distinct topo-graphical effects. Specifically, va-lence showed a classical negative polarity feedback related negativity (FRN). Magnitude showed a frontal postive deflection for larger out-comesParticipants learned over the course of 20 trials to choose the stimulus that yielded higher net-gains. There was substantial variability in  how well participants were able to do so. ERP amplitudes in the late latency range were modulated by feedbackn-1. High learn-ing rates were characterized by an LPC that was stronger modulated by previous feedback information as opposed to low learning rates. The ability to use and integrate feedback information over time is key to our ability to learn and decision making. Although it is fairly well established how the brain processes outcomes on a single trial, it is less well studied how these processes depend on encountered information on previous trials. In sum, this study provides a novel and important set of findings providing more in-sight into how the brain dy-namically integrates feedback information over multiple trials to guide decision making in an uncertain world. ++++++8duration (ms)1000-1500 until resp. (max 1200) 300700-900500FixationFixationFeedbackHighlightchoiceChoiceCue2000-2500Berry van den Berg, Timothy Sondej, Celina Pütz, Marty G Woldorff & Monicque M Lorist University of Groningen; Duke UniversityPROCESSING OF FEEDBACK INTEGRATION OF FEEDBACKACROSS TRIALS[-7; -8] 0[-5; -6] [-3; -4] [-1; -2] [+1; +2] [+3; +4] [+5; +6] [+7; +8] feedback-226101418-200 0 200 400 600 800-226101418time (ms)amplitude (μV)HOW DO WE USE FEEDBACK?0.000.050.10individual learning rateβ trialtrial number0.50.60.70.85 10 15 20p(choosing set−winner)learning rate < medianlearning rate > medianaverage250 to 350Loss minus Gain250 to 350 250 to 350Gain: [+5:+8] minus Gain [+1:+4] Loss: [-8:-5] minus Loss [-4:-1] amplitude (μV)-3 3500 to 600Loss minus GainGain: [+5:+8] minus Gain [+1:+4] Loss: [-8:-5] minus Loss[-4:-1] 500 to 600 500 to 600amplitude (μV)-3 3feedbackn7.510.012.515.017.5−8 −6 −4 −2 0 +2 +4 +6 +8feedbacknfeedbackn-12 14amplitude (µV)−8−6−4−20+2+4+6+8−8−6−4−2 0 +2+4+6+84 20amplitude (µV)−8−6−4−20+2+4+6+8−8−6−4−2 0 +2+4+6+8feedbackn-1feedbacknamplitude (µV)amplitude (µV)feedbackn681012−8 −6 −4 −2 0 +2 +4 +6 +8 −8−6−4−20+2+4+6+8−8−6−4−2 0 +2+4+6+8−8−6−4−20+2+4+6+8−8−6−4−2 0 +2+4+6+8−8−6−4−2 0 2 4 6 8 −8−6−4−2 0 2 4 6 8−8−6−4−20+2+4+6+8feedbacknlearning rate < medianamplitude (μV)3 20Datafeedbacknfeedbackn-1Modelled datafeedbackn-1learning rate > medianamplitude (μV)m3 20−8−6−4−2 0 +2+4+6+8 −8−6−4−2 0 +2+4+6+8−8−6−4−20+2+4+6+8−8−6−4−2 0 +2+4+6+8 −8−6−4−2 0 +2+4+6+8−8−6−4−20+2+4+6+8Late latency rangeEarly latency rangeProcesses in the late latency range (500-600ms) were modulated by both cur-rent feedback contents, and also by the feedback on the previous trial, indicating an integrative role. Strikingly, this integration was even further modulated by the individu-al participants’ learning rate. As such, the processes that are marked by the LPC sub-serve a dynamic updating role that is highly susceptible to prior information. Feedback processing was characterized by amplitudes in the early latency range (250-350ms) being modulated by the magni-tude and valence on the current trial. In this early time range we found minimal influence of the feedback of the previous trial, sug-gesting a feedback registration mechanism, that is not modulated by prior information (i.e. expectation). Modelled dataBoth magnitude and valence modu-lated amplitudes in the later latency range. These modulations had si-miliar scalp topographies (sugges-tive of a modulation of the Late Pos-tive Complex [LPC]), suggesting a similiar neuro-cognitive process by both factors is involved in this later time period.feedbackonsetEarly processing(250-350ms)Late processing(500-600ms)feedbackn-1learning rate < median learning rate > medianERP amplitudes in the early latency range were slightly modulated by feedbackn-1 but not learning rate.Early Late

Individual differences in learning rate are reflected in feedback related brain processes

University of Groningen

   University of GroningenIndividual differences in learning rate are reflected in feedback related brain processesvan den Berg, BerryIMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite fromit. Please check the document version below.Publication date:2020Link to publication in University of Groningen/UMCG research databaseCitation for published version (APA):van den Berg, B. (2020). Individual differences in learning rate are reflected in feedback related brainprocesses.CopyrightOther than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of theauthor(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons).Take-down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediatelyand investigate your claim.Downloaded from the University of Groningen/UMCG research database (Pure): http://www.rug.nl/research/portal. For technical reasons thenumber of authors shown on this cover page is limited to 10 maximum.Download date: 26-12-2020DataModelled dataDataINDIVIDUAL DIFFERENCES IN LEARNING RATE ARE REFLECTED IN FEEDBACK RELATED BRAIN PROCESSESVIRTUAL Cognitive Neuroscience Society 2020 berry.van.den.berg@rug.nlHere, participants chose on each trial either a face or a house, which was fol-lowed by receiving either a zero (0) gain (+) or a loss (-) of different magni-tudes (0:8)On each set of 20 trials either the face or house was the set-winner and was more likely to yield net gains. SummaryFeedback processing was marked by amplitude modulations induced by both magnitude and valence in the earlyl atency range with distinct topo-graphical effects. Specifically, va-lence showed a classical negative polarity feedback related negativity (FRN). Magnitude showed a frontal postive deflection for larger out-comesParticipants learned over the course of 20 trials to choose the stimulus that yielded higher net-gains. There was substantial variability in  how well participants were able to do so. ERP amplitudes in the late latency range were modulated by feedbackn-1. High learn-ing rates were characterized by an LPC that was stronger modulated by previous feedback information as opposed to low learning rates. The ability to use and integrate feedback information over time is key to our ability to learn and decision making. Although it is fairly well established how the brain processes outcomes on a single trial, it is less well studied how these processes depend on encountered information on previous trials. In sum, this study provides a novel and important set of findings providing more in-sight into how the brain dy-namically integrates feedback information over multiple trials to guide decision making in an uncertain world. ++++++8duration (ms)1000-1500 until resp. (max 1200) 300700-900500FixationFixationFeedbackHighlightchoiceChoiceCue2000-2500Berry van den Berg, Timothy Sondej, Celina Pütz, Marty G Woldorff & Monicque M Lorist University of Groningen; Duke UniversityPROCESSING OF FEEDBACK INTEGRATION OF FEEDBACKACROSS TRIALS[-7; -8] 0[-5; -6] [-3; -4] [-1; -2] [+1; +2] [+3; +4] [+5; +6] [+7; +8] feedback-226101418-200 0 200 400 600 800-226101418time (ms)amplitude (μV)HOW DO WE USE FEEDBACK?0.000.050.10individual learning rateβ trialtrial number0.50.60.70.85 10 15 20p(choosing set−winner)learning rate < medianlearning rate > medianaverage250 to 350Loss minus Gain250 to 350 250 to 350Gain: [+5:+8] minus Gain [+1:+4] Loss: [-8:-5] minus Loss [-4:-1] amplitude (μV)-3 3500 to 600Loss minus GainGain: [+5:+8] minus Gain [+1:+4] Loss: [-8:-5] minus Loss[-4:-1] 500 to 600 500 to 600amplitude (μV)-3 3feedbackn7.510.012.515.017.5−8 −6 −4 −2 0 +2 +4 +6 +8feedbacknfeedbackn-12 14amplitude (µV)−8−6−4−20+2+4+6+8−8−6−4−2 0 +2+4+6+84 20amplitude (µV)−8−6−4−20+2+4+6+8−8−6−4−2 0 +2+4+6+8feedbackn-1feedbacknamplitude (µV)amplitude (µV)feedbackn681012−8 −6 −4 −2 0 +2 +4 +6 +8 −8−6−4−20+2+4+6+8−8−6−4−2 0 +2+4+6+8−8−6−4−20+2+4+6+8−8−6−4−2 0 +2+4+6+8−8−6−4−2 0 2 4 6 8 −8−6−4−2 0 2 4 6 8−8−6−4−20+2+4+6+8feedbacknlearning rate < medianamplitude (μV)3 20Datafeedbacknfeedbackn-1Modelled datafeedbackn-1learning rate > medianamplitude (μV)m3 20−8−6−4−2 0 +2+4+6+8 −8−6−4−2 0 +2+4+6+8−8−6−4−20+2+4+6+8−8−6−4−2 0 +2+4+6+8 −8−6−4−2 0 +2+4+6+8−8−6−4−20+2+4+6+8Late latency rangeEarly latency rangeProcesses in the late latency range (500-600ms) were modulated by both cur-rent feedback contents, and also by the feedback on the previous trial, indicating an integrative role. Strikingly, this integration was even further modulated by the individu-al participants’ learning rate. As such, the processes that are marked by the LPC sub-serve a dynamic updating role that is highly susceptible to prior information. Feedback processing was characterized by amplitudes in the early latency range (250-350ms) being modulated by the magni-tude and valence on the current trial. In this early time range we found minimal influence of the feedback of the previous trial, sug-gesting a feedback registration mechanism, that is not modulated by prior information (i.e. expectation). Modelled dataBoth magnitude and valence modu-lated amplitudes in the later latency range. These modulations had si-miliar scalp topographies (sugges-tive of a modulation of the Late Pos-tive Complex [LPC]), suggesting a similiar neuro-cognitive process by both factors is involved in this later time period.feedbackonsetEarly processing(250-350ms)Late processing(500-600ms)feedbackn-1learning rate < median learning rate > medianERP amplitudes in the early latency range were slightly modulated by feedbackn-1 but not learning rate.Early Late

   University of GroningenIndividual differences in learning rate are reflected in feedback related brain processesvan den Berg, BerryIMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite fromit. Please check the document version below.Publication date:2020Link to publication in University of Groningen/UMCG research databaseCitation for published version (APA):van den Berg, B. (2020). Individual differences in learning rate are reflected in feedback related brainprocesses.CopyrightOther than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of theauthor(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons).The publication may also be distributed here under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license.More information can be found on the University of Groningen website: https://www.rug.nl/library/open-access/self-archiving-pure/taverne-amendment.Take-down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediatelyand investigate your claim.Downloaded from the University of Groningen/UMCG research database (Pure): http://www.rug.nl/research/portal. For technical reasons thenumber of authors shown on this cover page is limited to 10 maximum.Download date: 05-12-2023DataModelled dataDataINDIVIDUAL DIFFERENCES IN LEARNING RATE ARE REFLECTED IN FEEDBACK RELATED BRAIN PROCESSESVIRTUAL Cognitive Neuroscience Society 2020 berry.van.den.berg@rug.nlHere, participants chose on each trial either a face or a house, which was fol-lowed by receiving either a zero (0) gain (+) or a loss (-) of different magni-tudes (0:8)On each set of 20 trials either the face or house was the set-winner and was more likely to yield net gains. SummaryFeedback processing was marked by amplitude modulations induced by both magnitude and valence in the earlyl atency range with distinct topo-graphical effects. Specifically, va-lence showed a classical negative polarity feedback related negativity (FRN). Magnitude showed a frontal postive deflection for larger out-comesParticipants learned over the course of 20 trials to choose the stimulus that yielded higher net-gains. There was substantial variability in  how well participants were able to do so. ERP amplitudes in the late latency range were modulated by feedbackn-1. High learn-ing rates were characterized by an LPC that was stronger modulated by previous feedback information as opposed to low learning rates. The ability to use and integrate feedback information over time is key to our ability to learn and decision making. Although it is fairly well established how the brain processes outcomes on a single trial, it is less well studied how these processes depend on encountered information on previous trials. In sum, this study provides a novel and important set of findings providing more in-sight into how the brain dy-namically integrates feedback information over multiple trials to guide decision making in an uncertain world. ++++++8duration (ms)1000-1500 until resp. (max 1200) 300700-900500FixationFixationFeedbackHighlightchoiceChoiceCue2000-2500Berry van den Berg, Timothy Sondej, Celina Pütz, Marty G Woldorff & Monicque M Lorist University of Groningen; Duke UniversityPROCESSING OF FEEDBACK INTEGRATION OF FEEDBACKACROSS TRIALS[-7; -8] 0[-5; -6] [-3; -4] [-1; -2] [+1; +2] [+3; +4] [+5; +6] [+7; +8] feedback-226101418-200 0 200 400 600 800-226101418time (ms)amplitude (μV)HOW DO WE USE FEEDBACK?0.000.050.10individual learning rateβ trialtrial number0.50.60.70.85 10 15 20p(choosing set−winner)learning rate < medianlearning rate > medianaverage250 to 350Loss minus Gain250 to 350 250 to 350Gain: [+5:+8] minus Gain [+1:+4] Loss: [-8:-5] minus Loss [-4:-1] amplitude (μV)-3 3500 to 600Loss minus GainGain: [+5:+8] minus Gain [+1:+4] Loss: [-8:-5] minus Loss[-4:-1] 500 to 600 500 to 600amplitude (μV)-3 3feedbackn7.510.012.515.017.5−8 −6 −4 −2 0 +2 +4 +6 +8feedbacknfeedbackn-12 14amplitude (µV)−8−6−4−20+2+4+6+8−8−6−4−2 0 +2+4+6+84 20amplitude (µV)−8−6−4−20+2+4+6+8−8−6−4−2 0 +2+4+6+8feedbackn-1feedbacknamplitude (µV)amplitude (µV)feedbackn681012−8 −6 −4 −2 0 +2 +4 +6 +8 −8−6−4−20+2+4+6+8−8−6−4−2 0 +2+4+6+8−8−6−4−20+2+4+6+8−8−6−4−2 0 +2+4+6+8−8−6−4−2 0 2 4 6 8 −8−6−4−2 0 2 4 6 8−8−6−4−20+2+4+6+8feedbacknlearning rate < medianamplitude (μV)3 20Datafeedbacknfeedbackn-1Modelled datafeedbackn-1learning rate > medianamplitude (μV)m3 20−8−6−4−2 0 +2+4+6+8 −8−6−4−2 0 +2+4+6+8−8−6−4−20+2+4+6+8−8−6−4−2 0 +2+4+6+8 −8−6−4−2 0 +2+4+6+8−8−6−4−20+2+4+6+8Late latency rangeEarly latency rangeProcesses in the late latency range (500-600ms) were modulated by both cur-rent feedback contents, and also by the feedback on the previous trial, indicating an integrative role. Strikingly, this integration was even further modulated by the individu-al participants’ learning rate. As such, the processes that are marked by the LPC sub-serve a dynamic updating role that is highly susceptible to prior information. Feedback processing was characterized by amplitudes in the early latency range (250-350ms) being modulated by the magni-tude and valence on the current trial. In this early time range we found minimal influence of the feedback of the previous trial, sug-gesting a feedback registration mechanism, that is not modulated by prior information (i.e. expectation). Modelled dataBoth magnitude and valence modu-lated amplitudes in the later latency range. These modulations had si-miliar scalp topographies (sugges-tive of a modulation of the Late Pos-tive Complex [LPC]), suggesting a similiar neuro-cognitive process by both factors is involved in this later time period.feedbackonsetEarly processing(250-350ms)Late processing(500-600ms)feedbackn-1learning rate < median learning rate > medianERP amplitudes in the early latency range were slightly modulated by feedbackn-1 but not learning rate.Early Late

https://pure.rug.nl/ws/files/128196930/posterCNS2020.pdf

Individual differences in learning rate are reflected in feedback related brain processes

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ARTS repository - University of Groningen

University of Groningen

ARTS repository - University of Groningen