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Getting interaction theory (IT) together: Integrating developmental, phenomenological, enactive, and dynamical approaches to social interaction

We argue that progress in our scientific understanding of the ‘social mind’ is hampered by a number of unfounded assumptions. We single out the widely shared assumption that social behavior depends solely on the capacities of an individual agent. In contrast, both developmental and phenomenological studies suggest that the personal-level capacity for detached ‘social cognition’ (conceived as a process of theorizing about and/or simulating another mind) is a secondary achievement that is dependent on more immediate processes of embodied social interaction. We draw on the enactive approach to cognitive science to further clarify this strong notion of ‘social interaction’ in theoretical terms. In addition, we indicate how this interaction theory (IT) could eventually be formalized with the help of a dynamical systems perspective on the interaction process, especially by making use of evolutionary robotics modeling. We conclude that bringing together the methods and insights of developmental, phenomenological, enactive and dynamical approaches to social interaction can provide a promising framework for future research. Keywords: theory of mind; cognitive science; phenomenology; embodied cognition; dynamical systems theory; enactive approach; social cognition; interaction theory; evolutionary robotics

 

Getting interaction theory (IT) together

Integrating developmental, phenomenological, enactive, and dynamical approaches to social interaction

Tom Froese, Shaun Gallagher

Interaction Studies 13:3. 2012. iii, 164 pp. (pp. 436–468)

http://benjamins.com/#catalog/journals/is.13.3.06fro/details

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Emergent Criticality through Adaptive Information Processing in Boolean Networks

Emergent Criticality through Adaptive Information Processing in Boolean Networks | Papers | Scoop.it

We study information processing in populations of Boolean networks with evolving connectivity and systematically explore the interplay between the learning capability, robustness, the network topology, and the task complexity. We solve a long-standing open question and find computationally that, for large system sizes N, adaptive information processing drives the networks to a critical connectivity Kc=2. For finite size networks, the connectivity approaches the critical value with a power law of the system size N. We show that network learning and generalization are optimized near criticality, given that the task complexity and the amount of information provided surpass threshold values. Both random and evolved networks exhibit maximal topological diversity near Kc. We hypothesize that this diversity supports efficient exploration and robustness of solutions. Also reflected in our observation is that the variance of the fitness values is maximal in critical network populations. Finally, we discuss implications of our results for determining the optimal topology of adaptive dynamical networks that solve computational tasks.

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