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Using semantic space models to quant...

Matthews, Sean Christopher.

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Using semantic space models to quantify semantic memory dysfunction in individuals with schizophrenia.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Using semantic space models to quantify semantic memory dysfunction in individuals with schizophrenia./
Author:	Matthews, Sean Christopher.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2015,
Description:	171 p.
Notes:	Source: Dissertation Abstracts International, Volume: 77-05(E), Section: B.
Contained By:	Dissertation Abstracts International77-05B(E).
Subject:	Cognitive psychology. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3746429
ISBN:	9781339398242

Using semantic space models to quantify semantic memory dysfunction in individuals with schizophrenia.
Matthews, Sean Christopher.

Using semantic space models to quantify semantic memory dysfunction in individuals with schizophrenia. - Ann Arbor : ProQuest Dissertations & Theses, 2015 - 171 p.

Source: Dissertation Abstracts International, Volume: 77-05(E), Section: B.

Thesis (Ph.D.)--Indiana University, 2015.

Abnormalities in semantic memory have been observed in individuals with schizophrenia using a wide variety of memory tasks. Analyses of results in many of these tasks either rely upon representations of semantic structure constructed by researchers (e.g. Troyer et al., 1997) or representations derived from patients that may reflect confounds associated with the task used to construct the representation. Semantic space models such as LSA and BEAGLE avoid these issues, learning semantic representations from patterns of co-occurrence and word order in large text corpora. In this dissertation, representations learned by these models were utilized to model behavior in two semantic memory tasks in order to characterize semantic memory dysfunction in individuals with schizophrenia. A hierarchical Bayesian instantiation of the drift diffusion model (Ratcliff, 1978; Wiecki 2013) was used to model a lexical decision task.

ISBN: 9781339398242Subjects--Topical Terms:

523881
Cognitive psychology.

Using semantic space models to quantify semantic memory dysfunction in individuals with schizophrenia.
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245 1 0 $a Using semantic space models to quantify semantic memory dysfunction in individuals with schizophrenia.
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300 $a 171 p.
500 $a Source: Dissertation Abstracts International, Volume: 77-05(E), Section: B.
500 $a Advisers: Peter M. Todd; Brian F. O'Donnell.
502 $a Thesis (Ph.D.)--Indiana University, 2015.
520 $a Abnormalities in semantic memory have been observed in individuals with schizophrenia using a wide variety of memory tasks. Analyses of results in many of these tasks either rely upon representations of semantic structure constructed by researchers (e.g. Troyer et al., 1997) or representations derived from patients that may reflect confounds associated with the task used to construct the representation. Semantic space models such as LSA and BEAGLE avoid these issues, learning semantic representations from patterns of co-occurrence and word order in large text corpora. In this dissertation, representations learned by these models were utilized to model behavior in two semantic memory tasks in order to characterize semantic memory dysfunction in individuals with schizophrenia. A hierarchical Bayesian instantiation of the drift diffusion model (Ratcliff, 1978; Wiecki 2013) was used to model a lexical decision task.
520 $a Results were consistent with an account of decreases in the rate of information accumulation in semantic memory in addition to general slowing of reaction times in individuals with schizophrenia, as opposed to the hyperpriming frequently observed in this population. Verbal fluency tasks were also modeled using semantic representations learned by BEAGLE and LSA, and models of memory retrieval related to SAM (Raaijmakers & Shiffrin, 1981) and ACT-R (Anderson, 1993). While the paths taken through semantic space by non-psychiatric controls were best modeled with the use of a dynamic retrieval cue that switched between word frequency for global search and semantic similarity for local search, static cue models that incorporate both word frequency and semantic similarity were a better fit for individuals with schizophrenia. Furthermore, predictions regarding behavior on the semantic fluency task derived from optimal foraging theory were found to hold equally well for controls and patients. Patterns of results across both experiments indicate semantic dysfunction beyond general slowing in this population consistent with semantic inhibition.
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