Latent structure in random sequences drives neural learning toward a rational bias
Abstract
The human mind has a unique capacity to find order in chaos. The way the neocortex integrates information over time enables the mind to capture rich statistical structures embedded in random sequences. We show that a biologically motivated neural network model reacts to not only how often a pattern occurs (mean time) but also when a pattern is first encountered (waiting time). This behavior naturally produces the alternation bias in the gambler’s fallacy and provides a neural grounding for the Bayesian models of human behavior in randomness judgments. Our findings support a rational account for human probabilistic reasoning and a unifying perspective that connects the implicit learning without instruction with the generalization under structured and expressive rules.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Mar 09, 2015
- Source ID
- 10.1073/pnas.1422036112
Entities
People
- Hongbin Wang
- Jack W. Smith
- Rajan Bhattacharyya
- Randall C. O'Reilly
- Xun Liu
- Yanlong Sun
Organizations
- Air Force Office of Scientific Research
- Chinese Academy of Sciences
- Office of Naval Research Global
- United States Department of the Interior
- University of Colorado