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Published on: April 15, 2014
On Bayesian integration in sensorimotor learning: Another look at Kording and Wolpert (2004)
Sean Duffy1, Johanna Hertel2, Deniz Igan3
1Rutgers University-Camden, Department of Psychology, USA.
Summary
Reanalyzing Kording and Wolpert's (2004) data reveals that apparent optimal Bayesian learning in stochastic environments may be a statistical artifact. Trial-level analysis suggests recency bias, not learning of distributions.
Area of Science:
- Cognitive Science
- Computational Neuroscience
- Decision Science
Background:
- Kording and Wolpert (2004) proposed subjects are optimal Bayesian learners in stochastic environments.
- Their influential paper suggests learning from post-trial feedback and sensitivity to mid-trial feedback size.
Purpose of the Study:
- To re-evaluate the conclusions of Kording and Wolpert (2004) using trial-level data.
- To investigate whether observed behaviors reflect optimal Bayesian learning or other biases.
Main Methods:
- Analysis of trial-level data from Kording and Wolpert (2004).
- Re-examination of feedback accuracy and visual size characterizations.
- Statistical analysis to identify learning or recency bias.
Main Results:
- Subjects did not show increased sensitivity to mid-trial feedback based on visual size.
- Trial-level analysis indicated a recency bias, not evidence of learning the stochastic distribution.
- Apparent optimal Bayesian learning in the original study appears to be a statistical artifact of averaged data.
Conclusions:
- The findings challenge the interpretation of Kording and Wolpert (2004) regarding optimal Bayesian learning.
- Results suggest that averaged data can obscure underlying biases like recency.
- Implications for the broader literature on Bayesian judgments in stochastic environments.
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