Back to Search

Emotion as an emergent phenomenon of the neurocomputational energy regulation mechanism of a cognitive agent in a decision-making task

Title	Emotion as an emergent phenomenon of the neurocomputational energy regulation mechanism of a cognitive agent in a decision-making task
Author	Kırtay, M., Vannucci, L., Albanese, U., Laschi, C., Öztop, Erhan, Falotico, E.
Publication Date:	2021-02
Publication Place	- Sage
Subject	Decision making, Emergent behavior, Emotion, Energy regulation, Visual recalling
Type	Periodical
Language	English
Digital	Yes
Manuscript	No
Library:	Özyeğin University
Library Asset ID	1059-7123
Record ID	6b0f2447-b8c5-4a9d-a37f-a7297b8c23b0
Library Location	Computer Science
Date	2021-02
Notes	Horizon 2020 Framework Programme
Sample Text	Biological agents need to complete perception-action cycles to perform various cognitive and biological tasks such as maximizing their wellbeing and their chances of genetic continuation. However, the processes performed in these cycles come at a cost. Such costs force the agent to evaluate a tradeoff between the optimality of the decision making and the time and computational effort required to make it. Several cognitive mechanisms that play critical roles in managing this tradeoff have been identified. These mechanisms include adaptation, learning, memory, attention, and planning. One of the often overlooked outcomes of these cognitive mechanisms, in spite of the critical effect that they may have on the perception-action cycle of organisms, is “emotion.” In this study, we hold that emotion can be considered as an emergent phenomenon of a plausible neurocomputational energy regulation mechanism, which generates an internal reward signal to minimize the neural energy consumption of a sequence of actions (decisions), where each action triggers a visual memory recall process. To realize an optimal action selection over a sequence of actions in a visual recalling task, we adopted a model-free reinforcement learning framework, in which the reward signal—that is, the cost—was based on the iteration steps of the convergence state of an associative memory network. The proposed mechanism has been implemented in simulation and on a robotic platform: the iCub humanoid robot. The results show that the computational energy regulation mechanism enables the agent to modulate its behavior to minimize the required neurocomputational energy in performing the visual recalling task.
DOI	10.1177/1059712319880649
Cilt	29

View in source Özyeğin University Özyeğin University - Historical works, archives, and periodicals search engine

Özyeğin University

Emotion as an emergent phenomenon of the neurocomputational energy regulation mechanism of a cognitive agent in a decision-making task

Author Kırtay, M., Vannucci, L., Albanese, U., Laschi, C., Öztop, Erhan, Falotico, E.

Publication Date 2021-02

Publication Place - Sage

Subject Decision making, Emergent behavior, Emotion, Energy regulation, Visual recalling

Type Periodical

Language English

Digital Yes

Manuscript No

Library Özyeğin University

Library Asset ID 1059-7123

Record ID 6b0f2447-b8c5-4a9d-a37f-a7297b8c23b0

Library Location Computer Science

Date 2021-02

Notes Horizon 2020 Framework Programme

Sample Text Biological agents need to complete perception-action cycles to perform various cognitive and biological tasks such as maximizing their wellbeing and their chances of genetic continuation. However, the processes performed in these cycles come at a cost. Such costs force the agent to evaluate a tradeoff between the optimality of the decision making and the time and computational effort required to make it. Several cognitive mechanisms that play critical roles in managing this tradeoff have been identified. These mechanisms include adaptation, learning, memory, attention, and planning. One of the often overlooked outcomes of these cognitive mechanisms, in spite of the critical effect that they may have on the perception-action cycle of organisms, is “emotion.” In this study, we hold that emotion can be considered as an emergent phenomenon of a plausible neurocomputational energy regulation mechanism, which generates an internal reward signal to minimize the neural energy consumption of a sequence of actions (decisions), where each action triggers a visual memory recall process. To realize an optimal action selection over a sequence of actions in a visual recalling task, we adopted a model-free reinforcement learning framework, in which the reward signal—that is, the cost—was based on the iteration steps of the convergence state of an associative memory network. The proposed mechanism has been implemented in simulation and on a robotic platform: the iCub humanoid robot. The results show that the computational energy regulation mechanism enables the agent to modulate its behavior to minimize the required neurocomputational energy in performing the visual recalling task.

DOI 10.1177/1059712319880649

Cilt 29