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The why, what, where, when and how of goal-directed choice: neuronal and computational principles

Overview of attention for article published in Philosophical Transactions of the Royal Society B: Biological Sciences, November 2014
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (88th percentile)
  • Good Attention Score compared to outputs of the same age and source (72nd percentile)

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6 Facebook pages

Citations

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112 Dimensions

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350 Mendeley
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2 CiteULike
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Title
The why, what, where, when and how of goal-directed choice: neuronal and computational principles
Published in
Philosophical Transactions of the Royal Society B: Biological Sciences, November 2014
DOI 10.1098/rstb.2013.0483
Pubmed ID
Authors

Paul F. M. J. Verschure, Cyriel M. A. Pennartz, Giovanni Pezzulo

Abstract

The central problems that goal-directed animals must solve are: 'What do I need and Why, Where and When can this be obtained, and How do I get it?' or the H4W problem. Here, we elucidate the principles underlying the neuronal solutions to H4W using a combination of neurobiological and neurorobotic approaches. First, we analyse H4W from a system-level perspective by mapping its objectives onto the Distributed Adaptive Control embodied cognitive architecture which sees the generation of adaptive action in the real world as the primary task of the brain rather than optimally solving abstract problems. We next map this functional decomposition to the architecture of the rodent brain to test its consistency. Following this approach, we propose that the mammalian brain solves the H4W problem on the basis of multiple kinds of outcome predictions, integrating central representations of needs and drives (e.g. hypothalamus), valence (e.g. amygdala), world, self and task state spaces (e.g. neocortex, hippocampus and prefrontal cortex, respectively) combined with multi-modal selection (e.g. basal ganglia). In our analysis, goal-directed behaviour results from a well-structured architecture in which goals are bootstrapped on the basis of predefined needs, valence and multiple learning, memory and planning mechanisms rather than being generated by a singular computation.

X Demographics

X Demographics

The data shown below were collected from the profiles of 20 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 350 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 4 1%
Canada 3 <1%
United States 3 <1%
Spain 2 <1%
Chile 1 <1%
Ireland 1 <1%
United Kingdom 1 <1%
Netherlands 1 <1%
Portugal 1 <1%
Other 1 <1%
Unknown 332 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 81 23%
Researcher 75 21%
Student > Master 43 12%
Student > Doctoral Student 25 7%
Student > Bachelor 23 7%
Other 64 18%
Unknown 39 11%
Readers by discipline Count As %
Neuroscience 72 21%
Psychology 69 20%
Agricultural and Biological Sciences 51 15%
Computer Science 39 11%
Engineering 17 5%
Other 45 13%
Unknown 57 16%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 13. 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 01 September 2022.
All research outputs
#2,891,677
of 25,711,518 outputs
Outputs from Philosophical Transactions of the Royal Society B: Biological Sciences
#2,343
of 7,143 outputs
Outputs of similar age
#33,085
of 277,199 outputs
Outputs of similar age from Philosophical Transactions of the Royal Society B: Biological Sciences
#27
of 98 outputs
Altmetric has tracked 25,711,518 research outputs across all sources so far. Compared to these this one has done well and is in the 88th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,143 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 24.8. This one has gotten more attention than average, scoring higher than 67% of its peers.
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 277,199 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 88% of its contemporaries.
We're also able to compare this research output to 98 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 72% of its contemporaries.