Here is a technical report on a pilot study using the Enactive Torch sensory substitution interface, which involved several different kinds of analyses.
The Enactive Torch: Interactive embodied learning with a sensory substitution interface
Ximena González Grandón, Leonardo Zapata-Fonseca, Hector Gómez-Escobar, Guillermo Ortíz-Garin, Javier Flores, Ariel Sáenz-Burrola, and Tom Froese
Traditionally, the pedagogical design for teaching and learning practices has been characterized as a process during which an active expert supports passive learner for the accomplishment of a specific goal or task. Nowadays, however, the accessibility of information technologies and the understanding of the learner’s active role have caused that interactive, embodied and contextual learning perspectives have begun to gain room. Here, we contribute with a technical report of a pilot study based on the Enactive Torch, a tool for the scientific study of perception, which aimed to investigate the crucial role of embodied process in the generation of perceptual experience for sensory substitution. In using this technological scaffolding, a group of students, from various academic disciplines, have coordinated and conducted three projects using different methods, each of them analyzing quantitative and qualitative data recorded from the participants’ first- and third-person perspective. By means of this practical engagement, the students gained awareness of the transformative potential of technology and developed insights into the challenges of performing interdisciplinary research with their peers, in regard to embodied perception and cognition. The study, therefore, serves as a proof-of-concept for the Enactive Torch, as a technological scaffolding, that can facilitate the kind of interactive learning that students need to gain a deeper understanding of the complexity of human embodied cognition and its relationship with technology.
Special issue: Spotlight on 4E Cognition Research in Mexico
Adaptative Behavior, Volume: 26, Number: 5 (October 2018)
This issue is now available at: http://journals.sagepub.com/toc/adba/26/5
Table of contents (more…)
Guest Editor(s): Marcos Silva (Federal University of Alagoas) and Francicleber Ferreira
(Federal University of Ceará)
Special Issue Description:
Several contemporary philosophers have been developing tenets in pragmatism (broadly construed) to motivate it as an alternative philosophical foundation for a comprehensive understanding of cognition, opposed to a far-reaching representationalist tradition.
This long-established representationalist tradition in philosophy of mind and cognitive science defends that cognition is fundamentally content-involving. On the other side, some radical contenders advocate that cognition is neither basically representational nor does it involve, as in usual internalist views, processing or manipulating informational contents. They call attention to the importance of inherited and embodied practices and social interactions in order to understand relevant topics in perception, language and the nature of intentionality. They take seriously evolving biological systems and situated individuals interacting in communities over time as preconditions of our rationality, features often dismissed as not central in the representationalist and internalist tradition.
The classical cognitivist theory in cognitive science depicts perception as the result of information processing of sense data, which is transformed into a representation of the original information to be useful for the human mind. In the same vein, perceptual learning has been understood as an enrichment of sensations by representational mechanisms. In this view, the improvement in performance must be understood as the effect of a sophistication of computational algorithms entailing a better interpretation of sensory stimuli.
At the end of the 20th century, criticism against the cognitivist framework and its ideas of perception, cognition, and representation started to arise. Some of these arguments crystallized in alternative theories of cognition that offers an innovative way to understand perception and, consequently, perceptual learning.
The aim of this special issue is to document the theories and research that highlight a “4E cognition” approach to perceptual learning. The issue is focused on contributions from the current panorama of post-cognitivism with an emphasis on theories from the ecological, enactive and sensorimotor accounts.
The last couple of decades in cognitive science have seen an increasing interest in the philosophical and scientific study of embodied, embedded, extended, and enactive cognition – so-called “4E cognition.” By now theories of 4E cognition have matured and a lot of evidence has been collected, which consequently has reshaped our understanding of the relationship between an agent’s brain, body, and its material and sociocultural world. Despite their differences in emphasis, the various strands of 4E cognition research are united in proposing that an agent’s cognitive activity is bodily mediated, especially by the context-sensitive deployment of sensorimotor capacities.
While these interdisciplinary approaches have largely been developed in Europe, the United States, and Australia, other regions have also been influenced by this growing movement and have started to advance their own original contributions. The aim of this special issue is, therefore, to put a spotlight on 4E cognition research from one such region, Colombia. It intends to do so in two respects: first, to explore the current state and breadth of the field in Colombia; second, to critically examine questions and problems elicited by this Colombian research, focusing on open challenges, with the aim to articulate more precise arguments for and against key claims advanced by 4E cognition research.
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.
Rio de Janeiro, Brazil. 8-13 July, 2018
CALL FOR PAPERS
Evolutionary Robotics (ER) aims to apply evolutionary computation techniques to automatically design the control and/or hardware of both real and simulated autonomous robots. Its origins date back to the beginning of the nineties and since then it has been attracting the interest of many research centres all over the world.
ER techniques are mostly inspired by existing biological architectures and Darwin’s principle of selective reproduction of the fittest. Evolution has revealed that living creatures are able to accomplish complex tasks required for their survival, thus embodying cooperative, competitive and adaptive behaviours.
Having an intrinsic interdisciplinary character, ER has been employed towards the development of many fields of research, among which we can highlight neuroscience, cognitive science, evolutionary biology and robotics. Hence, the objective of this special session is to assemble a set of high-quality original contributions that reflect and advance the state-of-the-art in the area of Evolutionary Robotics, with an emphasis on the cross-fertilization between ER and the aforementioned research areas, ranging from theoretical analysis to real-life applications.
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.