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Cognitive Science: Integrative Theory of Cognition, Cognitivism and Computationalism
Published in Harald Maurer, Cognitive Science, 2021
This chapter introduces the basic concepts, statements, subject area and methods of cognitive science (chap. 1.1, 1.3 and 1.5), including a list of cognitive science's subdisciplines (chap. 1.2). This will provide the background needed to adequately address the two main alternative approaches to a theory of cognition in cognitive science: the classical symbol theory (chap. 3.1) and the connectionist theory (chap. 3.2). The basic assumption is that a theory of neurocognition - in its structural core - can best be described by mathematical and logical computation operations. Finally, the goal of this introductory book in connectionist cognitive science is to present a comprehensive and appropriate theory of neurocognition in modern (system-theoretical) connectionism (chap. 1.4). This means that this book primarily takes account of empirical evidence from the cognitive neurosciences (chap. 4) and the theoretical concepts of neuroinformatics and computational neuroscience (chaps. 6-10), and is oriented towards them.
ENTRIES A–Z
Published in Philip Winn, Dictionary of Biological Psychology, 2003
Theorists interested in producing formal models of the associative learning process have been much concerned with effects like those just described. Of the models still current, the most important and influential is that first proposed in 1972 by R.A.Rescorla and A.R. Wagner—the RESCORLA-WAGNER THEORY. The essence of their model is that a stimulus already signalled by some event will lose its effectiveness and thus its ability to enter into new associations. This simple principle allows the model to accommodate the importance of the predictive relationship between the stimuli being associated. It is a principle that has been taken up by others and has gained wide currency among cognitive psychologists whose interest lies in developing connectionist models of learning, perception and language (see CONNECTIONISM).
Machine intelligence for radiation science: summary of the Radiation Research Society 67th annual meeting symposium
Published in International Journal of Radiation Biology, 2023
Lydia J. Wilson, Frederico C. Kiffer, Daniel C. Berrios, Abigail Bryce-Atkinson, Sylvain V. Costes, Olivier Gevaert, Bruno F. E. Matarèse, Jack Miller, Pritam Mukherjee, Kristen Peach, Paul N. Schofield, Luke T. Slater, Britta Langen
In order to capture deep semantic information from a graph such as an ontology we use embeddings that can be considered as compressed numeric representations of the structure of the graph. More easily, this can be thought of as taking multiple random walks between the nodes (terms) in the ontology graph along its edges (relationships). These walks are done many times from different starting points to capture the structure of the ontology graph, and can then be used as input to a neural network that learns a numeric representation (a vector) for each node or term. Each subject, say a person or a tree, that is annotated with multiple ontology terms then becomes annotated with multiple vectors which capture the whole depth of the relationships within the ontology, including axioms. This is a sparse representation of the knowledge in the ontology that is suitable for deep-learning applications. These approaches have been applied, among others, to rare-disease diagnosis (Decherchi et al. 2021) and gene-disease association (Smaili et al. 2020; Chen et al. 2021). Other novel approaches seek to use ontologies as a semantic framework for neuro-symbolic integration of data into neural networks (Althubaiti et al. 2019). This approach combines the connectionism of neural networks with high-level symbolic representations of a problem that are human-readable.
Conscious intelligence is overrated: The normative unconscious and hypnosis
Published in American Journal of Clinical Hypnosis, 2022
Joel Weinberger, Mathew Brigante, Kevin Nissen
A second major cognitive neuroscience model is connectionism. The most prominent connectionist model is parallel distributed processing (PDP – McClelland & Rumelhart, 1986). Unlike massive modularity, PDP posits small, non-specialized units distributed throughout the mind/brain. None of these units is unique to a specific representation or function. In fact, each unit can be and is used for many different representations and functions. When two or more units are activated together, the likelihood of their firing together again is increased. In this way, implicit learning (cf. Rogers & McClelland, 2014) is built into the system. Particularly relevant to hypnosis are two characteristics of PDP. There is little to no place for conscious functioning in this model (Cleeremans, 2014; Weinberger & Stoycheva, 2020). Further, memories are not copies of experiences; they are constructed with whatever cues and influences are available at the time. These cues and influences can result in modifications and even distortions of memory. In the PDP model, there is no real distinction between an accurate memory and a plausible (and maybe even implausible) reconstruction. The same holds for any experience (cf. Weinberger & Stoycheva, 2020).
Geert-Jan Rutten. The Broca-Wernicke Doctrine: A Historical and Clinical Perspective on the Localization of Language Functions. Heidelberg: Springer International, 2017. 306 + xvii pp. 73 b/w illustrations, 38 illustrations in color. $159.00 (hardback). ISBN 978-3-319-54632-2.
Published in Journal of the History of the Neurosciences, 2018
The second chapter is based largely on a reading of Wernicke’s 1874 paper, and here the author does a disservice to Meynert. Rutten quotes Wernicke’s famous acknowledgment of Meynert, but the latter’s 1866 paper, in which Meynert preceded Wernicke in describing the semiology and localization of “Wernicke’s” aphasia, is barely mentioned (Whitaker & Etlinger, 1993). The author otherwise reviews material that has been well covered elsewhere, most notably by Gertrude Eggert in her brilliant discussion of Wernicke’s thinking, published with her translation of Wernicke’s papers on aphasia (Eggert, 1977). Another chapter, titled “Neo-connectionism, Neurodynamics and Large-Scale Networks,” deals mostly with Geschwind’s interpretation of Wernicke.