Title |
Optimal Behavioral Hierarchy
|
---|---|
Published in |
PLoS Computational Biology, August 2014
|
DOI | 10.1371/journal.pcbi.1003779 |
Pubmed ID | |
Authors |
Alec Solway, Carlos Diuk, Natalia Córdova, Debbie Yee, Andrew G. Barto, Yael Niv, Matthew M. Botvinick |
Abstract |
Human behavior has long been recognized to display hierarchical structure: actions fit together into subtasks, which cohere into extended goal-directed activities. Arranging actions hierarchically has well established benefits, allowing behaviors to be represented efficiently by the brain, and allowing solutions to new tasks to be discovered easily. However, these payoffs depend on the particular way in which actions are organized into a hierarchy, the specific way in which tasks are carved up into subtasks. We provide a mathematical account for what makes some hierarchies better than others, an account that allows an optimal hierarchy to be identified for any set of tasks. We then present results from four behavioral experiments, suggesting that human learners spontaneously discover optimal action hierarchies. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 2 | 29% |
Unknown | 5 | 71% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 5 | 71% |
Science communicators (journalists, bloggers, editors) | 1 | 14% |
Practitioners (doctors, other healthcare professionals) | 1 | 14% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 7 | 2% |
Spain | 2 | <1% |
France | 2 | <1% |
Portugal | 1 | <1% |
Netherlands | 1 | <1% |
Italy | 1 | <1% |
Germany | 1 | <1% |
Belgium | 1 | <1% |
Switzerland | 1 | <1% |
Other | 2 | <1% |
Unknown | 276 | 94% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 88 | 30% |
Researcher | 54 | 18% |
Student > Bachelor | 35 | 12% |
Student > Master | 34 | 12% |
Student > Postgraduate | 15 | 5% |
Other | 32 | 11% |
Unknown | 37 | 13% |
Readers by discipline | Count | As % |
---|---|---|
Neuroscience | 57 | 19% |
Psychology | 56 | 19% |
Computer Science | 49 | 17% |
Agricultural and Biological Sciences | 38 | 13% |
Engineering | 17 | 6% |
Other | 29 | 10% |
Unknown | 49 | 17% |