Early versus late noise differentially enhances or degrades context-dependent choice

被引:0
作者
Shen, Bo [1 ]
Nguyen, Duc [2 ]
Wilson, Jailyn [3 ]
Glimcher, Paul W. [1 ,2 ]
Louie, Kenway [1 ,2 ]
机构
[1] NYU, Grossman Sch Med, New York, NY 10016 USA
[2] NYU, Ctr Neural Sci, New York, NY 10003 USA
[3] Cornell Univ, Dept Psychol, Ithaca, NY 14853 USA
关键词
DIVISIVE NORMALIZATION; DECISION-MAKING; INTEGRATING MEMORIES; NEURAL MECHANISM; UTILITY; COMPETITION; VIOLATIONS; MODELS; SIGNAL;
D O I
10.1038/s41467-025-59140-3
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Noise is a fundamental problem for information processing in neural systems. In decision-making, noise is thought to cause stochastic errors in choice. However, little is known about how noise arising from different sources may contribute differently to value coding and choice behaviors. Here, we examine how noise arising early versus late in the decision process differentially impacts context-dependent choice behavior. We find in model simulations that under early noise, contextual information enhances choice accuracy, while under late noise, context degrades choice accuracy. Furthermore, we verify these opposing predictions in experimental human choice behavior. Manipulating early and late noise - by inducing uncertainty in option values and controlling time pressure - produces dissociable positive and negative context effects. These findings reconcile controversial experimental findings in the literature, suggesting a unified mechanism for context-dependent choice. More broadly, these findings highlight how different sources of noise can interact with neural computations to differentially modulate behavior.
引用
收藏
页数:15
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