Alle Publikationen
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2017
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(2017): Learning a commonsense moral theory. In: Cognition 167, S. 107-123. DOI: 10.1016/j.cognition.2017.03.005
DOI: http://www.ncbi.nlm.nih.gov/pubmed/28351662 Abstract: We introduce a computational framework for understanding the structure and dynamics of moral learning, with a focus on how people learn to trade off the interests and welfare of different individuals in their social groups and the larger society. We posit a minimal set of cognitive capacities that together can solve this learning problem: (1) an abstract and recursive utility calculus to quantitatively represent welfare trade-offs; (2) hierarchical Bayesian inference to understand the actions and judgments of others; and (3) meta-values for learning by value alignment both externally to the values of others and internally to make moral theories consistent with one's own attachments and feelings. Our model explains how children can build from sparse noisy observations of how a small set of individuals make moral decisions to a broad moral competence, able to support an infinite range of judgments and decisions that generalizes even to people they have never met and situations they have not been in or observed. It also provides insight into the causes and dynamics of moral change across time, including cases when moral change can be rapidly progressive, changing values significantly in just a few generations, and cases when it is likely to move more slowly. zitiert von: 7
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(2017): Choice predicts the feedback negativity. In: Psychophysiology 54 (12), S. 1800-1811. DOI: 10.1111/psyp.12961
DOI: http://www.ncbi.nlm.nih.gov/pubmed/28752549 Abstract: Choosing the appropriate response given the circumstance is integral to all aspects of human behavior. One way of elucidating the mechanisms of choice is to relate behavior to neural correlates. Electrophysiological evidence implicates the ERP feedback-negativity (FN) and the P300 as promising neural correlates of reward processing, an integral component of learning. However, prior research has not adequately addressed how the development of a preference to select one option over another (choice preference) relates to the FN and the P300. We assessed whether variation in choice preference predicted the FN and P300 amplitude within subjects. We used a discrete-trials two-alternative choice procedure, where the reinforcer rate for each option was dependently scheduled by a concurrent variable interval. The reinforcer ratio for selecting each option was varied between sessions. Choice was quantified using both the generalized matching law sensitivity and the log odds of staying on the same versus switching to the other alternative (stay preference). The relationship between stay preference, FN, and P300 amplitudes was assessed using the innovative application of hierarchical Bayesian linear regression. The results demonstrate that stay preference was controlled by the reinforcer ratios and credibly predicted the FN amplitude but not P300 amplitude. The findings are consistent with the view that reinforcers may guide behavior by what they signal about future reinforcement, with the FN related to such a process.
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