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Development and Disintegration of Conceptual Knowledge: A Parallel-Distributed Processing Approach Jay McClelland Department of Psychology and Center for.

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Presentation on theme: "Development and Disintegration of Conceptual Knowledge: A Parallel-Distributed Processing Approach Jay McClelland Department of Psychology and Center for."— Presentation transcript:

1 Development and Disintegration of Conceptual Knowledge: A Parallel-Distributed Processing Approach Jay McClelland Department of Psychology and Center for Mind, Brain, and Computation Stanford University

2 Some Phenomena in Development Progressive differentiation of concepts Overgeneralization in naming Illusory correlations

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4 Overgeneralization in Naming and “Illusory Correlations” Children often over-generalize object names, e.g. calling most animals “dog”. Rochel Gelman found that many children think that all animals have feet. –Even animals that look like small furry balls and don’t seem to have any feet at all.

5 Patients typically in 50s present with difficulty naming familiar objects. Intact executive functions, normal memory for unfamiliar stimuli (novel faces, random shapes), fluent speech, spared ability in abstract tests such as Raven’s matrices. Patients have increasingly severe deficits in tests of semantic knowledge both from words and pictures. Disease also progresses, from the temporal pole to encompass large parts of anterior and lateral temporal neocortex. language Semantic Dementia

6 Some Phenomena in Semantic Dementia Progressive loss of conceptual differentiation Overgeneralization of frequent names Illusory correlations

7 Picture naming and drawing in Sem. Demantia

8 Representation is a pattern of activation distributed over neurons within and across brain areas. Bidirectional propagation of activation underlies the ability to bring these representations to mind from given inputs. The knowledge underlying propagation of activation is in the connections. language Parallel Distributed Processing Approach to Semantic Cognition

9 A Principle of Learning and Representation Captured in PDP models Learning and representation are sensitive to coherent covariation of properties across experiences.

10 What is Coherent Covariation? The tendency of properties of objects to co- occur in clusters. e.g. –Has wings –Can fly –Is light Or –Has roots –Has rigid cell walls –Can grow tall

11 The Rumelhart Model

12 Target output for ‘robin can’ input

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15 ExperienceExperience Early Later Later Still

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17 Overgeneralization of Frequent Names to Similar Objects “dog” “goat” “tree”

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19 Severity of DementiaFraction of Neurons Destroyed omissionswithin categ. superord. Patient Data Simulation Results Errors in Naming for As a Function of Severity

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21 Ongoing Projects in the Lab Semantic Cognition: –Domain generality (Tim Rogers) –Bayesian vs. Connectionist Approaches –Relation of semantic and linguistic knowledge (Katia Dilkina) –Integration of semantic and episodic memory (Cynthia Hendersion) Explicit and implicit processes in causal reasoning and contingency learning (Daniel Sternberg) Where do biases and stereotypes come from? (Jeremy Glick) How do brain areas work together when we think, perceive and remember? (Cynthia Henderson)

22 Collaborations at Stanford Paul Thibadeau and Lera Boreditsky –How we share knowledge across content domains Analogy, metaphor, and cross-domain knowledge sharing Juan Gao (post-doc) and Bill Newsome –Dynamics of decision making; continuous representation of decision state Dharshan Kumaran (post-doc) and Anthony Wagner –Neural basis of new semantic learning You and {…} –???


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