東華大學圖書館 |

Structured Semantic Knowledge Mediates Variability in Episodic Memory.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Structured Semantic Knowledge Mediates Variability in Episodic Memory./
作者:	Wang, Shao-Fang.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	173 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-03, Section: A.
Contained By:	Dissertations Abstracts International83-03A.
標題:	Behavior. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28671258
ISBN:	9798544200604

Structured Semantic Knowledge Mediates Variability in Episodic Memory.
Wang, Shao-Fang.

Structured Semantic Knowledge Mediates Variability in Episodic Memory. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 173 p.

Source: Dissertations Abstracts International, Volume: 83-03, Section: A.

Thesis (Ph.D.)--Stanford University, 2021.

This item must not be sold to any third party vendors.

This thesis builds on previous empirical evidence and theoretical work to further investigate the variability in recognition memory behavior. Specifically, the global matching models provide mechanistic explanations of how similarities of memory representations contribute to the memory signals that guide recognition memory decisions. In addition, it is commonly believed that long-term semantic knowledge has an impact on the recognition memory related to that knowledge, and the relationship between semantic knowledge and recognition memory is mediated, in part, by interactions between the hippocampus (important for aspects of recognition memory) and distributed cortical regions (important for semantic knowledge). While this framework has garnered empirical and theoretical support, the detailed mechanisms underlying the influence of semantic knowledge on recognition memory remains unclear. My research explores how similarity among semantic knowledge influences the gradient of the memory signals that give rise to false and accurate recognition judgements. I ask 1) whether it is possible to systematical-ly modulate false and accurate recognition decisions with objective similarity measurements of semantic knowledge, and 2) what is the computation that translates similarity of semantic knowledge to recognition memory signals. I answer these questions with two experiments: Experiment 1 investigated how model-based semantic similarity measurements modulate false and accurate memory behavior by leveraging tools from computational models (i.e., Natural Language Processing (NLP)) to systematically quantify se-mantic similarity of the stimuli. Building on the findings from Experiment 1, Experiment 2 used the model-based semantic similarity measurements to further examine the computation that transforms semantic similarity to recognition memory signals.In Experiment 1, I used NLP-derived measurements of semantic similarity to generate word lists with varying degrees of semantic similarities as experimental stimuli. These word lists were used in a memory task to allow for precise control of the semantic similarities. The primary findings from Experiment 1 demonstrated that recognition memory behavior is modulated by semantic similarities. More specifically, NLP-derived semantic similarity predicted both false recognition memory to lures and accurate recognition memory to old words: false and accurate recognition judgments increased as a function of NLP-derived semantic similarity. The results from Experiment 1 highlight the fundamental role global similarity computations play in generating recognition memory signals. In addition, our demonstration of the relationship between semantic similarity structure and recognition memory is largely compatible with the findings reported in the literature. Building on the findings in Experiment 1, one outstanding question is how se-mantic similarities are transformed to memory strength. To answer this question, in Experiment 2, we expanded our present findings to investigate how the dynamic of within-list distribution of semantic similarity affected recognition memory behavior.In Experiment 2, building on the results from Experiment 1, I explored the nature of the transformation between semantic similarities and recognition memory signals. In-spired by computational models of recognition memory, there could be a linear or a non-linear transformation between semantic similarities and recognition memory strengths. To differentiate between these two possibilities, I examined how skewed and uniform distributions of semantic similarities affect recognition memory. The main findings from Experiment 2 showed that there was no significant difference between skewed and uniform distribution of semantic similarities on false and accurate recognition memory. The null results indicate that encountering a few semantically highly similar items (i.e., the skewed condition) is not enough to significantly increase false memory for similar lures beyond a linear similarity computation (i.e., the uniform condition). One possible explanation for our null findings is that the memory representations used in our study are defined only by the semantic features of the stimuli; however, the proposed memory representations in global matching models and the neural representations in the fMRI studies both consist of various features beyond the semantic features. Another possible explanation for the null findings is that the process of extracting word embeddings from word co-occurrences already encompasses nonlinear transformations of the semantic space. Building on our findings, a potential research direction is to further explore the computation between NLP-derived semantic similarity and memory signals with more complex models.

ISBN: 9798544200604Subjects--Topical Terms:

532476
Behavior.

Structured Semantic Knowledge Mediates Variability in Episodic Memory.
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245 1 0 $a Structured Semantic Knowledge Mediates Variability in Episodic Memory.
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300 $a 173 p.
500 $a Source: Dissertations Abstracts International, Volume: 83-03, Section: A.
500 $a Advisor: Wagner, Anthony;Grill-Spector, Kalanit;McClelland, James.
502 $a Thesis (Ph.D.)--Stanford University, 2021.
506 $a This item must not be sold to any third party vendors.
520 $a This thesis builds on previous empirical evidence and theoretical work to further investigate the variability in recognition memory behavior. Specifically, the global matching models provide mechanistic explanations of how similarities of memory representations contribute to the memory signals that guide recognition memory decisions. In addition, it is commonly believed that long-term semantic knowledge has an impact on the recognition memory related to that knowledge, and the relationship between semantic knowledge and recognition memory is mediated, in part, by interactions between the hippocampus (important for aspects of recognition memory) and distributed cortical regions (important for semantic knowledge). While this framework has garnered empirical and theoretical support, the detailed mechanisms underlying the influence of semantic knowledge on recognition memory remains unclear. My research explores how similarity among semantic knowledge influences the gradient of the memory signals that give rise to false and accurate recognition judgements. I ask 1) whether it is possible to systematical-ly modulate false and accurate recognition decisions with objective similarity measurements of semantic knowledge, and 2) what is the computation that translates similarity of semantic knowledge to recognition memory signals. I answer these questions with two experiments: Experiment 1 investigated how model-based semantic similarity measurements modulate false and accurate memory behavior by leveraging tools from computational models (i.e., Natural Language Processing (NLP)) to systematically quantify se-mantic similarity of the stimuli. Building on the findings from Experiment 1, Experiment 2 used the model-based semantic similarity measurements to further examine the computation that transforms semantic similarity to recognition memory signals.In Experiment 1, I used NLP-derived measurements of semantic similarity to generate word lists with varying degrees of semantic similarities as experimental stimuli. These word lists were used in a memory task to allow for precise control of the semantic similarities. The primary findings from Experiment 1 demonstrated that recognition memory behavior is modulated by semantic similarities. More specifically, NLP-derived semantic similarity predicted both false recognition memory to lures and accurate recognition memory to old words: false and accurate recognition judgments increased as a function of NLP-derived semantic similarity. The results from Experiment 1 highlight the fundamental role global similarity computations play in generating recognition memory signals. In addition, our demonstration of the relationship between semantic similarity structure and recognition memory is largely compatible with the findings reported in the literature. Building on the findings in Experiment 1, one outstanding question is how se-mantic similarities are transformed to memory strength. To answer this question, in Experiment 2, we expanded our present findings to investigate how the dynamic of within-list distribution of semantic similarity affected recognition memory behavior.In Experiment 2, building on the results from Experiment 1, I explored the nature of the transformation between semantic similarities and recognition memory signals. In-spired by computational models of recognition memory, there could be a linear or a non-linear transformation between semantic similarities and recognition memory strengths. To differentiate between these two possibilities, I examined how skewed and uniform distributions of semantic similarities affect recognition memory. The main findings from Experiment 2 showed that there was no significant difference between skewed and uniform distribution of semantic similarities on false and accurate recognition memory. The null results indicate that encountering a few semantically highly similar items (i.e., the skewed condition) is not enough to significantly increase false memory for similar lures beyond a linear similarity computation (i.e., the uniform condition). One possible explanation for our null findings is that the memory representations used in our study are defined only by the semantic features of the stimuli; however, the proposed memory representations in global matching models and the neural representations in the fMRI studies both consist of various features beyond the semantic features. Another possible explanation for the null findings is that the process of extracting word embeddings from word co-occurrences already encompasses nonlinear transformations of the semantic space. Building on our findings, a potential research direction is to further explore the computation between NLP-derived semantic similarity and memory signals with more complex models.
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650 4 $a Knowledge. $3 872758
650 4 $a Recall. $3 3449079
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650 4 $a Linguistics. $3 524476
650 4 $a Language. $3 643551
650 4 $a Standard deviation. $3 3560390
650 4 $a Accuracy. $3 3559958
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