Name
Abstract | ||
|---|---|---|
Model-based and model-free reinforcement learning (RL) have been suggested as algorithmic realizations of goal-directed and habitual action strategies. Model-based RL is more flexible than model-free but requires sophisticated calculations using a learnt model of the world. This has led model-based RL to be identified with slow, deliberative processing, and model-free RL with fast, automatic processing. In support of this distinction, it has recently been shown that model-based reasoning is impaired by placing subjects under cognitive load-a hallmark of non-automaticity. Here, using the same task, we show that cognitive load does not impair model-based reasoning if subjects receive prior training on the task. This finding is replicated across two studies and a variety of analysis methods. Thus, task familiarity permits use of model-based reasoning in parallel with other cognitive demands. The ability to deploy model-based reasoning in an automatic, parallelizable fashion has widespread theoretical implications, particularly for the learning and execution of complex behaviors. It also suggests a range of important failure modes in psychiatric disorders. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1371/journal.pcbi.1004463 | PLOS COMPUTATIONAL BIOLOGY |
Field | DocType | Volume |
Computer science,Model-based reasoning,Artificial intelligence,Bioinformatics,Automatic processing,Cognitive load,Cognition,Verbal reasoning,Reinforcement,Cognitive impairment,Reinforcement learning | Journal | 11 |
Issue | ISSN | Citations |
9 | 1553-734X | 4 |
PageRank | References | Authors |
0.51 | 4 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| marcos economides | 1 | 10 | 1.41 |
| Zeb Kurth-Nelson | 2 | 4 | 0.51 |
| Annika Lübbert | 3 | 4 | 0.51 |
| Marc Guitart-Masip | 4 | 30 | 4.20 |
| Raymond J Dolan | 5 | 419 | 49.74 |