#connectionism

z. X "The Brain, Pt. 1" Quick steps through key concepts in neuroscience of great utility when reading our works on neurodivergence, consciousness, the critical brain, differential processing, etc

View On WordPress

Learning/connectionism/Mareschal/Kaufman: (i.e., adapting one’s behavior) is accomplished by tuning the connection weights until some stable behavior is obtained. Supervised networks adjust their weights until the output response (for a given input) matches a target response. That target can come from an active teacher, or passively through observing the environment, but it must come from outside the system. Unsupervised networks adjust their weights until some internal constraint is satisfied (e.g., maximally different inputs must have maximally different internal representations). Slater I 94 The key conclusion from this work (Munakata et al. 1997)(1) is the notion of the graded representation of knowledge. That is, rather than existing as an all-or-none concept, object permanence was acquired gradually. Consequently, the representations that underlay this concept existed in graded states, becoming ever more robust with age and experience, and supporting ever more complex disappearance events. 1. Munakata, Y., McClelland, J. L., Johnson, M. H., & Siegler, R. S. (1997). Rethinking infant knowledge: Toward an adaptive process account of successes and failures in object permanence tasks. Psychological Review, 104, 686–713. Denis Mareschal and Jordy Kaufman, „Object permanence in Infancy. Revisiting Baillargeon’s Drawbridge Experiment“ in: Alan M. Slater & Paul C. Quinn (eds.) 2012. Developmental Psychology. Revisiting the Classic Studies. London: Sage Publications. Pp. 93-94

View On WordPress

View On WordPress

With the rise of neuroscience, we are able to understand human behavior in a new way, one that does not rely on the abstract concepts of folk psychology. A more advanced model of the mind, connectionism, is an artificial intelligence model based on the biological workings of the brain. It focuses on the idea of behavior being an emergent process of interconnected, parallel, highly distributed neural networks comprised of nodes (e.g. neurons) with varying activation levels (e.g. action potentials). Beliefs and other constructs are therefore not necessary to understand the mind. There are no single unchanging structures of the mind that influence behavior (e.g. intentions, motivations, beliefs). Instead, the mind can be understood as being emergent from the parallel distributed processing of the brain. The relationship between activation in a system of nodes represents different cognitive capabilities. Connectionism can accurately incorporate things we see in advancements in neuroscience, such as neuroplasticity (the way the brain is able to regain functioning when a particular area is damaged). A connectionist model is explanatorily strong because it is essentially modeling its emergent phenomena as the biology of the brain.