Formation of internal forward model with sensory and reward prediction errors : A behavioral confirmation

Author(s):  
Hitoshi Sato ◽  
Akikazu Sasaki ◽  
Daichi Nozaki ◽  
Hirokazu Tanaka
2020 ◽  
Author(s):  
Kate Ergo ◽  
Luna De Vilder ◽  
Esther De Loof ◽  
Tom Verguts

Recent years have witnessed a steady increase in the number of studies investigating the role of reward prediction errors (RPEs) in declarative learning. Specifically, in several experimental paradigms RPEs drive declarative learning; with larger and more positive RPEs enhancing declarative learning. However, it is unknown whether this RPE must derive from the participant’s own response, or whether instead any RPE is sufficient to obtain the learning effect. To test this, we generated RPEs in the same experimental paradigm where we combined an agency and a non-agency condition. We observed no interaction between RPE and agency, suggesting that any RPE (irrespective of its source) can drive declarative learning. This result holds implications for declarative learning theory.


2021 ◽  
Author(s):  
Joseph Heffner ◽  
Jae-Young Son ◽  
Oriel FeldmanHall

People make decisions based on deviations from expected outcomes, known as prediction errors. Past work has focused on reward prediction errors, largely ignoring violations of expected emotional experiences—emotion prediction errors. We leverage a new method to measure real-time fluctuations in emotion as people decide to punish or forgive others. Across four studies (N=1,016), we reveal that emotion and reward prediction errors have distinguishable contributions to choice, such that emotion prediction errors exert the strongest impact during decision-making. We additionally find that a choice to punish or forgive can be decoded in less than a second from an evolving emotional response, suggesting emotions swiftly influence choice. Finally, individuals reporting significant levels of depression exhibit selective impairments in using emotion—but not reward—prediction errors. Evidence for emotion prediction errors potently guiding social behaviors challenge standard decision-making models that have focused solely on reward.


2017 ◽  
Vol 129 ◽  
pp. 265-272 ◽  
Author(s):  
Chad C. Williams ◽  
Cameron D. Hassall ◽  
Robert Trska ◽  
Clay B. Holroyd ◽  
Olave E. Krigolson

2020 ◽  
Vol 22 (8) ◽  
pp. 849-859
Author(s):  
Julian Macoveanu ◽  
Hanne L. Kjærstad ◽  
Henry W. Chase ◽  
Sophia Frangou ◽  
Gitte M. Knudsen ◽  
...  

2019 ◽  
Vol 3 (7) ◽  
pp. 719-732 ◽  
Author(s):  
Anthony I. Jang ◽  
Matthew R. Nassar ◽  
Daniel G. Dillon ◽  
Michael J. Frank

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Maya G. Mosner ◽  
R. Edward McLaurin ◽  
Jessica L. Kinard ◽  
Shabnam Hakimi ◽  
Jacob Parelman ◽  
...  

Few studies have explored neural mechanisms of reward learning in ASD despite evidence of behavioral impairments of predictive abilities in ASD. To investigate the neural correlates of reward prediction errors in ASD, 16 adults with ASD and 14 typically developing controls performed a prediction error task during fMRI scanning. Results revealed greater activation in the ASD group in the left paracingulate gyrus during signed prediction errors and the left insula and right frontal pole during thresholded unsigned prediction errors. Findings support atypical neural processing of reward prediction errors in ASD in frontostriatal regions critical for prediction coding and reward learning. Results provide a neural basis for impairments in reward learning that may contribute to traits common in ASD (e.g., intolerance of unpredictability).


2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Ulrich Kirk ◽  
Giuseppe Pagnoni ◽  
Sébastien Hétu ◽  
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