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Mendeley readers
Attention Score in Context
| Title |
A Model of Reward- and Effort-Based Optimal Decision Making and Motor Control
|
|---|---|
| Published in |
PLoS Computational Biology, October 2012
|
| DOI | 10.1371/journal.pcbi.1002716 |
| Pubmed ID | |
| Authors |
Lionel Rigoux, Emmanuel Guigon |
| Abstract |
Costs (e.g. energetic expenditure) and benefits (e.g. food) are central determinants of behavior. In ecology and economics, they are combined to form a utility function which is maximized to guide choices. This principle is widely used in neuroscience as a normative model of decision and action, but current versions of this model fail to consider how decisions are actually converted into actions (i.e. the formation of trajectories). Here, we describe an approach where decision making and motor control are optimal, iterative processes derived from the maximization of the discounted, weighted difference between expected rewards and foreseeable motor efforts. The model accounts for decision making in cost/benefit situations, and detailed characteristics of control and goal tracking in realistic motor tasks. As a normative construction, the model is relevant to address the neural bases and pathological aspects of decision making and motor control. |
Mendeley readers
The data shown below were compiled from readership statistics for 258 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 5 | 2% |
| United Kingdom | 5 | 2% |
| Switzerland | 3 | 1% |
| France | 2 | <1% |
| Italy | 1 | <1% |
| Slovenia | 1 | <1% |
| Belgium | 1 | <1% |
| United States | 1 | <1% |
| Unknown | 239 | 93% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 63 | 24% |
| Researcher | 44 | 17% |
| Student > Master | 37 | 14% |
| Professor | 15 | 6% |
| Student > Bachelor | 14 | 5% |
| Other | 39 | 15% |
| Unknown | 46 | 18% |
| Readers by discipline | Count | As % |
|---|---|---|
| Psychology | 49 | 19% |
| Neuroscience | 41 | 16% |
| Engineering | 34 | 13% |
| Agricultural and Biological Sciences | 29 | 11% |
| Medicine and Dentistry | 15 | 6% |
| Other | 32 | 12% |
| Unknown | 58 | 22% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 11 December 2021.
All research outputs
#8,544,090
of 25,394,764 outputs
Outputs from PLoS Computational Biology
#5,639
of 8,964 outputs
Outputs of similar age
#65,398
of 191,611 outputs
Outputs of similar age from PLoS Computational Biology
#51
of 115 outputs
Altmetric has tracked 25,394,764 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 8,964 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one is in the 33rd percentile – i.e., 33% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 191,611 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 44th percentile – i.e., 44% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 115 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 52% of its contemporaries.