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Emotional Valence and the Free-Energy Principle

Overview of attention for article published in PLoS Computational Biology, June 2013
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Title
Emotional Valence and the Free-Energy Principle
Published in
PLoS Computational Biology, June 2013
DOI 10.1371/journal.pcbi.1003094
Pubmed ID
Authors

Mateus Joffily, Giorgio Coricelli

Abstract

The free-energy principle has recently been proposed as a unified Bayesian account of perception, learning and action. Despite the inextricable link between emotion and cognition, emotion has not yet been formulated under this framework. A core concept that permeates many perspectives on emotion is valence, which broadly refers to the positive and negative character of emotion or some of its aspects. In the present paper, we propose a definition of emotional valence in terms of the negative rate of change of free-energy over time. If the second time-derivative of free-energy is taken into account, the dynamics of basic forms of emotion such as happiness, unhappiness, hope, fear, disappointment and relief can be explained. In this formulation, an important function of emotional valence turns out to regulate the learning rate of the causes of sensory inputs. When sensations increasingly violate the agent's expectations, valence is negative and increases the learning rate. Conversely, when sensations increasingly fulfil the agent's expectations, valence is positive and decreases the learning rate. This dynamic interaction between emotional valence and learning rate highlights the crucial role played by emotions in biological agents' adaptation to unexpected changes in their world.

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Geographical breakdown

Country Count As %
Germany 3 1%
United Kingdom 3 1%
France 3 1%
Switzerland 1 <1%
Austria 1 <1%
Portugal 1 <1%
Iceland 1 <1%
Spain 1 <1%
Japan 1 <1%
Other 1 <1%
Unknown 282 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 69 23%
Researcher 59 20%
Student > Master 44 15%
Student > Bachelor 27 9%
Student > Doctoral Student 14 5%
Other 47 16%
Unknown 38 13%
Readers by discipline Count As %
Psychology 80 27%
Neuroscience 46 15%
Agricultural and Biological Sciences 23 8%
Computer Science 19 6%
Engineering 19 6%
Other 58 19%
Unknown 53 18%