Neurocomputational Dynamics of Sequence Learning

• Published in: Neuron (Cambridge, Mass.) Vol. 98; no. 6; pp. 1282 - 1293.e4
• Main Authors: Konovalov, Arkady, Krajbich, Ian
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 27.06.2018; Elsevier Limited
• Subjects: Bayesian analysis; Bayesian learning; Brain mapping; Brain research; Data analysis; fMRI; Functional magnetic resonance imaging; hippocampus; IPS; lateral PFC; Learning; Neuroimaging; pattern learning; prediction; sequence learning; SRT; Studies; Variables; vmPFC; fMRI; lateral PFC; SRT; hippocampus; Bayesian learning; vmPFC; prediction; sequence learning; IPS; pattern learning
• ISSN: 0896-6273; 1097-4199; 1097-4199
• DOI: 10.1016/j.neuron.2018.05.013

The brain is often able to learn complex structures of the environment using a very limited amount of evidence, which is crucial for model-based planning and sequential prediction. However, little is known about the neurocomputational mechanisms of deterministic sequential prediction, as prior work has primarily focused on stochastic transition structures. Here we find that human subjects’ beliefs about a sequence of states, captured by reaction times, are well explained by a Bayesian pattern-learning model that tracks beliefs about both the current state and the underlying structure of the environment, taking into account prior beliefs about possible patterns in the sequence. Using functional magnetic resonance imaging, we find distinct neural signatures of uncertainty computations on both levels. These results support the hypothesis that structure learning in the brain employs Bayesian inference. •Human subjects learn to detect patterns in sequences of images while undergoing fMRI•Behavior is well explained by a Bayesian pattern-learning model•Distinct networks track uncertainty about the pattern and the predicted image Konovalov and Krajbich present a computational model for how the human brain detects deterministic patterns in its environment. Using fMRI, they identify distinct brain networks that track uncertainty about the specific temporal prediction and the underlying pattern.