Computational Modeling of Cognition and Behavior

2018 ◽  
Author(s):  
Simon Farrell ◽  
Stephan Lewandowsky
Keyword(s):  
Computational Modeling ◽  
And Behavior

scholarly journals Host-microbiome coevolution promotes cooperation in a rock-paper-scissor dynamic

2019 ◽  
Author(s):  
Ohad Lewin-Epstein ◽  
Lilach Hadany
Keyword(s):  
Computational Modeling ◽  
Potential Role ◽  
Evolution Of Cooperation ◽  
Host Behavior ◽  
Wide Range ◽  
Forms Of Life ◽  
New Perspective ◽  
Almost All ◽  
And Behavior

AbstractCooperation is a fundamental behavior observed in all forms of life. The evolution of cooperation has been widely studied, but almost all theories focused on the cooperating individual and its genes. We suggest a different approach, taking into account the microbes carried by the interacting individuals. Accumulating evidence reveal that microbes can affect their host wellbeing and behavior, yet hosts can evolve mechanisms to resist the manipulations of their microbes. We thus propose that coevolution of microbes with their hosts may favor microbes that induce their host to cooperate. Using computational modeling, we show that microbe-induced cooperation can evolve and be maintained in a wide range of conditions, including when facing hosts’ resistance to the microbial effect. We find that host-microbe coevolution leads the population to a rock-paper-scissors dynamic, that enables maintenance of cooperation in a polymorphic state. This theory may help explain occurrences of cooperation in a wide variety of organisms, including in cases that are difficult to explain by current theories. In addition, this study provides a new perspective on the coevolution of hosts and their microbiome, emphasizing the potential role of microbes in shaping their host behavior.

scholarly journals Spatiotemporal ontogeny of brain wiring

2019 ◽  
Vol 5 (6) ◽  
pp. eaav9694 ◽  
Author(s):  
A. Goulas ◽  
R. F. Betzel ◽  
C. C. Hilgetag
Keyword(s):  
Computational Modeling ◽  
Network Topology ◽  
Brain Regions ◽  
Adult Brain ◽  
Axonal Guidance ◽  
Global Network ◽  
Dependent Plasticity ◽  
Guidance Cues ◽  
And Behavior ◽  
Activity Dependent

The wiring of vertebrate and invertebrate brains provides the anatomical skeleton for cognition and behavior. Connections among brain regions are characterized by heterogeneous strength that is parsimoniously described by the wiring cost and homophily principles. Moreover, brains exhibit a characteristic global network topology, including modules and hubs. However, the mechanisms resulting in the observed interregional wiring principles and network topology of brains are unknown. Here, with the aid of computational modeling, we demonstrate that a mechanism based on heterochronous and spatially ordered neurodevelopmental gradients, without the involvement of activity-dependent plasticity or axonal guidance cues, can reconstruct a large part of the wiring principles (on average, 83%) and global network topology (on average, 80%) of diverse adult brain connectomes, including fly and human connectomes. In sum, space and time are key components of a parsimonious, plausible neurodevelopmental mechanism of brain wiring with a potential universal scope, encompassing vertebrate and invertebrate brains.

Modeling the Experience of Emotion

2010 ◽  
Vol 1 (1) ◽  
pp. 1-17 ◽  
Author(s):  
Joost Broekens
Keyword(s):  
Computational Modeling ◽  
Affective Computing ◽  
Computational Models ◽  
Appraisal Theory ◽  
Content Change ◽  
Primary Focus ◽  
Computational Models Of Emotion ◽  
Research Initiatives ◽  
And Behavior ◽  
Do So

Affective computing has proven to be a viable field of research comprised of a large number of multidisciplinary researchers, resulting in work that is widely published. The majority of this work consists of emotion recognition technology, computational modeling of causal factors of emotion and emotion expression in virtual characters and robots. A smaller part is concerned with modeling the effects of emotion on cognition and behavior, formal modeling of cognitive appraisal theory and models of emergent emotions. Part of the motivation for affective computing as a field is to better understand emotion through computational modeling. In psychology, a critical and neglected aspect of having emotions is the experience of emotion: what does the content of an emotional episode look like, how does this content change over time, and when do we call the episode emotional. Few modeling efforts in affective computing have these topics as a primary focus. The launch of a journal on synthetic emotions should motivate research initiatives in this direction, and this research should have a measurable impact on emotion research in psychology. In this article, I show that a good way to do so is to investigate the psychological core of what an emotion is: an experience. I present ideas on how computational modeling of emotion can help to better understand the experience of motion, and provide evidence that several computational models of emotion already address the issue.

scholarly journals 29.1 COMPUTATIONAL MODELING OF PERCEPTION AND BEHAVIOR REVEALS HIDDEN INSIGHTS INTO MECHANISMS OF PSYCHOTIC SYMPTOMS

2019 ◽  
Vol 45 (Supplement_2) ◽  
pp. S136-S136
Author(s):  
Albert Powers ◽  
Christoph Mathys ◽  
Philip Corlett
Keyword(s):  
Computational Modeling ◽  
Psychotic Symptoms ◽  
And Behavior

scholarly journals Modeling the Experience of Emotion

2012 ◽  
pp. 1-18 ◽  
Author(s):  
Joost Broekens
Keyword(s):  
Computational Modeling ◽  
Affective Computing ◽  
Computational Models ◽  
Appraisal Theory ◽  
Content Change ◽  
Primary Focus ◽  
Computational Models Of Emotion ◽  
Research Initiatives ◽  
And Behavior ◽  
Over Time

Affective computing has proven to be a viable field of research comprised of a large number of multidisciplinary researchers, resulting in work that is widely published. The majority of this work consists of emotion recognition technology, computational modeling of causal factors of emotion and emotion expression in virtual characters and robots. A smaller part is concerned with modeling the effects of emotion on cognition and behavior, formal modeling of cognitive appraisal theory and models of emergent emotions. Part of the motivation for affective computing as a field is to better understand emotion through computational modeling. In psychology, a critical and neglected aspect of having emotions is the experience of emotion: what does the content of an emotional episode look like, how does this content change over time and when do we call the episode emotional. Few modeling efforts in affective computing have these topics as a primary focus. The launch of a journal on synthetic emotions should motivate research initiatives in this direction, and this research should have a measurable impact on emotion research in psychology. In this paper, I show that a good way to do so is to investigate the psychological core of what an emotion is: an experience. I present ideas on how computational modeling of emotion can help to better understand the experience of emotion, and provide evidence that several computational models of emotion already address the issue.

scholarly journals Achilles Subtendon Structure and Behavior as Evidenced From Tendon Imaging and Computational Modeling

2020 ◽  
Vol 2 ◽  
Author(s):  
Geoffrey G. Handsfield ◽  
Joachim Greiner ◽  
Josef Madl ◽  
Eva A. Rog-Zielinska ◽  
Enzo Hollville ◽  
...  
Keyword(s):  
Computational Modeling ◽  
And Behavior

scholarly journals The use of computational modeling to link sensory processing with behavior in Drosophila

2017 ◽  
Author(s):  
Jan Clemens ◽  
Mala Murthy
Keyword(s):  
Computational Modeling ◽  
Neural Activity ◽  
Genetic Model ◽  
Model Organisms ◽  
Neuronal Responses ◽  
Natural Behavior ◽  
Sensory Stimuli ◽  
Making Sense ◽  
Modelling Framework ◽  
And Behavior

A major goal of systems neuroscience is to understand how the brain represents information, and how those representations are used to drive behavior. Even though genetic model organisms like Drosophila grant unprecedented experimental access to the nervous system for manipulating and recording neural activity, the complexity of natural stimuli and natural behaviors still poses a challenge for solving the connections between neural activity and behavior. Here, we advocate for the use of computational modeling to complement (and enhance) the Drosophila toolkit. We first lay out a modelling framework for making sense of the relation between natural sensory stimuli, neuronal responses, and natural behavior. We then highlight how this framework can be used to reveal how neural circuits drive behavior, using selected case studies.

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