As part of his doctoral research, Leonardo Zapata-Fonseca coordinated this analysis of embodied social interaction. Great team effort!
Sensitivity to Social Contingency in Adults with High-Functioning Autism during Computer-Mediated Embodied Interaction
Leonardo Zapata-Fonseca, Tom Froese, Leonhard Schilbach, Kai Vogeley, and Bert Timmermans
Autism Spectrum Disorder (ASD) can be understood as a social interaction disorder. This makes the emerging “second-person approach” to social cognition a more promising framework for studying ASD than classical approaches focusing on mindreading capacities in detached, observer-based arrangements. According to the second-person approach, embodied, perceptual, and embedded or interactive capabilities are also required for understanding others, and these are hypothesized to be compromised in ASD. We therefore recorded the dynamics of real-time sensorimotor interaction in pairs of control participants and participants with High-Functioning Autism (HFA), using the minimalistic human-computer interface paradigm known as “perceptual crossing” (PC). We investigated whether HFA is associated with impaired detection of social contingency, i.e., a reduced sensitivity to the other’s responsiveness to one’s own behavior. Surprisingly, our analysis reveals that, at least under the conditions of this highly simplified, computer-mediated, embodied form of social interaction, people with HFA perform equally well as controls. This finding supports the increasing use of virtual reality interfaces for helping people with ASD to better compensate for their social disabilities. Further dynamical analyses are necessary for a better understanding of the mechanisms that are leading to the somewhat surprising results here obtained.
Finally a large part of Mario’s thesis on unsupervised learning in artificial neural networks has been published and is available open access:
Self-modeling in Hopfield Neural Networks with Continuous Activation Function
Mario Zarco and Tom Froese
Hopfield networks can exhibit many different attractors of which most are local optima. It has been demonstrated that combining states randomization and Hebbian learning enlarges the basin of attraction of globally optimal attractors. The procedure is called self-modeling and it has been applied in symmetric Hopfield networks with discrete states and without self-recurrent connections. We are interested in knowing which topological constraints can be relaxed. So, the self-modeling process is tested in asymmetric Hopfield networks with continuous states and self-recurrent connections. The best results are obtained in networks with modular structure.
This semester Dr. Froese will teach the following course, which introduces the foundations of many of this group’s lines of research:
Agentes autónomos, sistemas sociales, y la nueva ciencia cognitiva
When: Mondays and Wednesdays, 13:00 – 14:30 (First class: 29/01/2018)
Where: Anexo del IIMAS, Circuito Escolar, Ciudad Universitaria, DF
This course will introduce ongoing debates in cognitive science about our changing understanding of the mind. Instead of being thought of as a digital computer inside the brain, mind is now widely considered to be an embodied, embedded and extended activity in the world. These ideas will be illustrated based on case studies of research in agent-based models and human-computer interfaces, with special emphasis on demonstrating how social interactions and technologies shape our mind. Students are not expected to program models nor to design interfaces, but to understand the implications of the new cognitive science and to apply them to their own research interests. The course will be taught mainly in English to better prepare students for the special terms used by leading researchers in cognitive science.
Click here for the course website.
Today Mario Zarco graduated with honors from UNAM’s Master’s degree in Computer Science and Engineering for his work on self-optimization in neural networks.
The title and extended abstract of his thesis are as follows:
Estudio de Auto-Optimización en Redes Neuronales de Hopfield
Mario Alberto Zarco López
Las redes neuronales de Hopfield de tiempo discreto, cuya dinámica presentan múltiples atractores de punto fijo, han sido ampliamente usadas en dos casos: (1) memoria asociativa, basada en aprender un conjunto de patrones de entrenamiento los cuales son representados por atractores, y (2) optimización, basado en representar un problema de satisfaccion de restricciones con la topología de la red de tal forma que los atractores sean soluciones de ese problema. En el ultimo caso, la función de energía de la red debe tener la misma forma que la función a ser optimizada, de modo que los m´ınimos de la primera también sean mínimos de la segunda. Aunque se ha demostrado que los atractores de baja energía tienen un amplio domino de atracción, la red usualmente queda atrapada en mínimos locales. Recientemente se demostró que las redes de Hopfield de tiempo-discreto pueden converger en atractores globalmente óptimos ampliando las mejores cuencas de atracción. La red combina el aprendizaje de sus propios atractores usando aprendizaje Hebbiano y la aleatorizacion de los estados neuronales una vez que la red ha reforzada su configuración actual.
*** SECOND CALL FOR PAPERS ***
EXTENDED DEADLINE: *August 31st, 2017*
Third Avant Conference
Trends in Interdisciplinary Studies
UNDERSTANDING SOCIAL COGNITION
October 20-22 2017
Maria Curie-Skłodowska University
Within the social sciences, it is widely accepted that groups of people exhibit social properties and dynamics that emerge from, but cannot be reductively identified with the actions and properties of individual members. Nevertheless, psychology and cognitive science have only reluctantly embraced the idea that something similar might happen in the domain of mind and cognition.