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Normative evidence accumulation in unpredictable environments
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AI
DOI：10.7554/eLife.08825.png)
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摘要
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In our dynamic world, decisions about noisy stimuli can require temporal accumulation of evidence to identify steady signals, differentiation to detect unpredictable changes in those signals, or both. Normative models can account for learning in these environments but have not yet been applied to faster decision processes. We present a novel, normative formulation of adaptive learning models that forms decisions by acting as a leaky accumulator with non-absorbing bounds. These dynamics, derived for both discrete and continuous cases, depend on the expected rate of change of the statistics of the evidence and balance signal identification and change detection. We found that, for two different tasks, human subjects learned these expectations, albeit imperfectly, then used them to make decisions in accordance with the normative model. The results represent a unified, empirically supported account of decision-making in unpredictable environments that provides new insights into the expectation-driven dynamics of the underlying neural signals.
Keyword:
PERCEPTUAL DECISION
VISUAL-MOTION
MODELS
DYNAMICS
PARIETAL
URGENCY
NEUROMODULATION
REPRESENTATION
PROBABILITY
UNCERTAINTY
期刊
IF:
0
论文数:
1.8W
被引数:
16
机构
引用论文
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PROCEEDINGS OF THE IEEE
PROCEEDINGS OF THE IEEEIF25.9
PREAI
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DECISION FIELD-THEORY - A DYNAMIC COGNITIVE APPROACH TO DECISION-MAKING IN AN UNCERTAIN ENVIRONMENTPSYCHOLOGICAL REVIEW
PSYCHOLOGICAL REVIEWIF5.8
PREAI
分享收藏PartsNetProceedings of the 2018 2nd International Conference on Computer Science and Artificial Intelligence
Proceedings of the 2018 2nd International Conference on Computer Science and Artificial IntelligenceIF0
PREAI
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