From Connectionism to Deep Learning: Discussions on the Validation of Cognitive Theories
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Based on a critical review and analysis of the field of artificial intelligence, this article argues that connectionist theories of cognition can be validated through the design of artificial cognitive models using contemporary deep learning techniques. It proposes that models developed in accordance with the principles of the connectionist paradigm in cognitive science can provide a reliable and empirically grounded representation of cognition. The article further contends that current computational technology is sufficiently advanced to emulate the conditions inherent to the connectionist approach and the neurophysiological foundations of perceptual cognitive processes. As an illustrative case, the study examines visual depth perception, arguing that these autonomous models incorporating learning and adaptation mechanisms can function as experimental tools for supporting or challenging cognitive theory. Based on evidence reviewed in the literature, the article concludes that the development of such models is well suited to capturing the functional regularities of cognition and constitutes a robust experimental platform for evaluating connectionist postulates.
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