CANDID - A Neurodynamical Model of Idea Generation

Bibliographic Details
Main Author:	Iyer, Laxmi R
Language:	English
Published:	University of Cincinnati / OhioLINK 2012
Subjects:	Computer Science ideation cognitive control working memory neural networks semantic cognition computational neuroscience
Online Access:	http://rave.ohiolink.edu/etdc/view?acc_num=ucin1326828617

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spelling	ndltd-OhioLink-oai-etd.ohiolink.edu-ucin13268286172021-08-03T06:15:19Z CANDID - A Neurodynamical Model of Idea Generation Iyer, Laxmi R Computer Science ideation cognitive control working memory neural networks semantic cognition computational neuroscience <p>Idea generation is a central cognitive activity in humans, and studying the mechanismsof idea generation is important both to understand the creative process betterand to produce applications that mimic human creativity. The goal of this research is toexplore the neural basis of idea generation in individuals through computational connectionistmodeling, and to use the resulting framework to study broader aspects of higherlevel cognition. The product of this is a model called Context-Adaptive NeuroDynamicalIDeation (CANDID). While there have been other models of ideation, CANDIDattempts to incorporate known information about the actual structures and processes ofthe brain - at least at an abstract level.</p><p>Following widely accepted theories of ideation, the model postulates that ideasare conceptual combinations, and that the combinations arise naturally from the dynamicsof the neurocognitive system under the influence of contextual information.Concepts, which constitute the fundamental semantic elements of the model, are representedin the system in two ways: 1) amodally via the activity of neural units in aConcept Network (CN); and 2) in terms of their sensory, functional and abstract attributes,or features, which are encoded in a neural network termed the Feature Layer(FL). Concepts are grouped together into categories based on their functional and/orattribute similarity. The categories are represented as distributed patterns of neural activityin the Dynamic Selector Network (DSN), which confines the ideational dynamicsto a context-appropriate cognitive space through a dynamic biasing mechanism – termed“neurocognitive spotlights” due to its usage. The system receives information about thecontext as input, which interacts with the intrinsic dynamics of the DSN-CN-FL idea generation system to generate an itinerant sequence of ideas. These are evaluated by acritic, which models both internal and external evaluative processes. Based on its evaluation,the critic generates a reward signal, which feeds back to the generation system toimprove ideation by reinforcing connections and modulating the dynamics.</p><p>The proposed mechanism for the generation of ideas involves three concurrentand interacting processes: 1) Selecting a context-specific subspace of the overall conceptspace within which ideas will be sought; 2) Searching productively through thissubspace via itinerant neural dynamics; and 3) Modulating and reconfiguring the searchprocess through learning based on evaluative feedback. The system is driven by a contextinput representing the context and/or goal of ideation, which activates appropriatecategories in the DSN, biasing the associated concepts in the CN to create a context-specificsearch space. Itinerant dynamics in the biased CN generate a productive searchpath to produce ideas, which are evaluated by the critic.</p><p>The research in this thesis makes two main contributions: 1) The first comprehensive,biologically plausible neural model of context-dependent ideation - and thinkingin general; and 2) A neurodynamical model for constructing context-specific cognitivespaces through the spotlight mechanism. In addition to these, the work also addressesother important issues such as the neural representation of semantic knowledge, theemergence of ideas as metastable attractors, and the formation of category representation.</p> 2012-04-19 English text University of Cincinnati / OhioLINK http://rave.ohiolink.edu/etdc/view?acc_num=ucin1326828617 http://rave.ohiolink.edu/etdc/view?acc_num=ucin1326828617 unrestricted This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws.
collection	NDLTD
language	English
sources	NDLTD
topic	Computer Science ideation cognitive control working memory neural networks semantic cognition computational neuroscience
spellingShingle	Computer Science ideation cognitive control working memory neural networks semantic cognition computational neuroscience Iyer, Laxmi R CANDID - A Neurodynamical Model of Idea Generation
author	Iyer, Laxmi R
author_facet	Iyer, Laxmi R
author_sort	Iyer, Laxmi R
title	CANDID - A Neurodynamical Model of Idea Generation
title_short	CANDID - A Neurodynamical Model of Idea Generation
title_full	CANDID - A Neurodynamical Model of Idea Generation
title_fullStr	CANDID - A Neurodynamical Model of Idea Generation
title_full_unstemmed	CANDID - A Neurodynamical Model of Idea Generation
title_sort	candid - a neurodynamical model of idea generation
publisher	University of Cincinnati / OhioLINK
publishDate	2012
url	http://rave.ohiolink.edu/etdc/view?acc_num=ucin1326828617
work_keys_str_mv	AT iyerlaxmir candidaneurodynamicalmodelofideageneration
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CANDID - A Neurodynamical Model of Idea Generation

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