Title
Connecting rule-abstraction and model-based choice across disparate learning tasks.
Abstract
Recent research has identified key differences in the way individuals make decisions in predictive learning tasks, including the use of featureand rule-based strategies in causal learning and model-based versus model-free choices in reinforcement learning. These results suggest that people rely to varying degrees on separable psychological processes. However, the relationship between these types of learning strategies has not been explored in any depth. This study investigated the relationship between featurevs rule-based strategies in a causal learning task and indices of model-free and model-based choice in a two-step reinforcement learning procedure. We found that rule-based transfer was associated with the use of model-based, but not model-free responding in a two-step task.
Year
Venue
Field
2015
CogSci
Social psychology,Predictive learning,Abstraction,Computer science,Cognitive psychology,Reinforcement learning
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
1
4
Name
Order
Citations
PageRank
Hilary Don102.37
Micah B. Goldwater2169.30
A. Ross Otto304.73
Evan Livesey413.78