Title |
Optimal policy for value-based decision-making
|
---|---|
Published in |
Nature Communications, August 2016
|
DOI | 10.1038/ncomms12400 |
Pubmed ID | |
Authors |
Satohiro Tajima, Jan Drugowitsch, Alexandre Pouget |
Abstract |
For decades now, normative theories of perceptual decisions, and their implementation as drift diffusion models, have driven and significantly improved our understanding of human and animal behaviour and the underlying neural processes. While similar processes seem to govern value-based decisions, we still lack the theoretical understanding of why this ought to be the case. Here, we show that, similar to perceptual decisions, drift diffusion models implement the optimal strategy for value-based decisions. Such optimal decisions require the models' decision boundaries to collapse over time, and to depend on the a priori knowledge about reward contingencies. Diffusion models only implement the optimal strategy under specific task assumptions, and cease to be optimal once we start relaxing these assumptions, by, for example, using non-linear utility functions. Our findings thus provide the much-needed theory for value-based decisions, explain the apparent similarity to perceptual decisions, and predict conditions under which this similarity should break down. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 6 | 17% |
United States | 6 | 17% |
Germany | 2 | 6% |
Japan | 1 | 3% |
Sweden | 1 | 3% |
Netherlands | 1 | 3% |
Switzerland | 1 | 3% |
Unknown | 17 | 49% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 20 | 57% |
Scientists | 12 | 34% |
Practitioners (doctors, other healthcare professionals) | 2 | 6% |
Science communicators (journalists, bloggers, editors) | 1 | 3% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Germany | 3 | <1% |
United States | 2 | <1% |
France | 1 | <1% |
Spain | 1 | <1% |
Korea, Republic of | 1 | <1% |
Unknown | 305 | 97% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 90 | 29% |
Researcher | 52 | 17% |
Student > Master | 36 | 12% |
Student > Bachelor | 25 | 8% |
Student > Doctoral Student | 23 | 7% |
Other | 44 | 14% |
Unknown | 43 | 14% |
Readers by discipline | Count | As % |
---|---|---|
Neuroscience | 80 | 26% |
Psychology | 70 | 22% |
Agricultural and Biological Sciences | 26 | 8% |
Computer Science | 16 | 5% |
Engineering | 10 | 3% |
Other | 57 | 18% |
Unknown | 54 | 17% |