Psychology Dictionary of ArgumentsHome
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| Neural networks: Neural networks are computational models inspired by the human brain, designed to recognize patterns and solve complex problems. They consist of layers of interconnected nodes (analogous to neurons) that process input data and learn to perform tasks by adjusting the strength of connections based on feedback. Used extensively in machine learning, they enable applications like image recognition, language processing, and predictive analysis. See also Artificial Neural networks, Connectionism, Computer models, Computation, Artificial Intelligence, Machine learning._____________Annotation: The above characterizations of concepts are neither definitions nor exhausting presentations of problems related to them. Instead, they are intended to give a short introduction to the contributions below. – Lexicon of Arguments. | |||
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Denis Mareschal on Neural Networks - Dictionary of Arguments
Slater I 92 Neural networks/connectionism/Mareschal: Connectionist models are implemented computer simulations of “brain-style” learning and information processing (Rumelhart & McClelland, 1986)(1). Connectionist network models are made up of simple processing units (idealized neurons) interconnected via weighted communication lines (idealized Slater I 93 synapses). Units are often represented as circles and the weighted communication lines, as lines between these circles. Activation flows from unit to unit via these connection weights. The network’s global behavior is determined by the connection weights. As activation flows through the network, it is transformed by the set of connection weights between successive layers in network. Learning/connectionism/Marechal: (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). >Supervised learning, >Learning. Slater I 94 The key conclusion from this work 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. >Object permanence/Connectionsm, >Knowledge. 1. Rumelhart, D.E. and McClelland, J.L. 1986.Parallel distributed processing: explorations in the microstructure of cognition, vols. I and II. Cambridge, MA: MIT Press 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_____________Explanation of symbols: Roman numerals indicate the source, arabic numerals indicate the page number. The corresponding books are indicated on the right hand side. ((s)…): Comment by the sender of the contribution. Translations: Dictionary of Arguments The note [Concept/Author], [Author1]Vs[Author2] or [Author]Vs[term] resp. "problem:"/"solution:", "old:"/"new:" and "thesis:" is an addition from the Dictionary of Arguments. If a German edition is specified, the page numbers refer to this edition. |
Mareschal, Denis Slater I Alan M. Slater Paul C. Quinn Developmental Psychology. Revisiting the Classic Studies London 2012 |
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> Counter arguments in relation to Neural Networks
