Structural Basis of Semantic Memory ☆

Nastase, Samuel A.; Haxby, James V.

doi:10.1016/b978-0-12-809324-5.21073-0

Cited by 5 publications

(3 citation statements)

References 217 publications

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“…But the activation of modal representations seems to participate in language processing more than expected in a system formed by purely amodal symbols (e.g., Binder, Westbury, McKiernan, Possing, & Medler, 2005;Hauk, Johnsrude, & Pulvermüller, 2004;Zwaan & Yaxley, 2003;Nastase & Haxby, 2017;Davis & Yee, 2021;Vergallito, Petilli, Cattaneo, & Marelli, 2019). Thus, there is also a need to formalize an embodiment process to deal with the classical claims of the symbol grounding problem (Harnad, 1990;Searle, 1980), as some amodal representations should be grounded to avoid the Chinese Room Argument (Searle, 1980).…”

Section: Introductionmentioning

confidence: 99%

“…Different studies have shown that this is a plausible mechanism for the generation of emotional (Hollis et al., 2017; Martínez‐Huertas et al., 2021) and perceptual (Günther et al., 2020; Günther et al., 2020) responses. Psychobiological evidence has been found for this proposal (Binder, 2016; Nastase & Haxby, 2016) and neural network architectures have been successfully used to computationally model it (Günther et al., 2020; Hoffman et al., 2018; Howell et al., 2005; Martínez‐Huertas et al., 2020, 2021). Neural network models provide a useful framework to predict word emotionality from amodal dimensions because, first, they are valid learning models (Quinlan, 2003) and, second, they learn what the most relevant indicators to predict are, and propagate emotions by activating/inhibiting responses through backpropagation.…”

Section: Introductionmentioning

confidence: 99%

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Emotional Valence Precedes Semantic Maturation of Words: A Longitudinal Computational Study of Early Verbal Emotional Anchoring

2021

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We present a longitudinal computational study on the connection between emotional and amodal word representations from a developmental perspective. In this study, children's and adult word representations were generated using the latent semantic analysis (LSA) vector space model and Word Maturity methodology. Some children's word representations were used to set a mapping function between amodal and emotional word representations with a neural network model using ratings from 9-year-old children. The neural network was trained and validated in the child semantic space. Then, the resulting neural network was tested with adult word representations using ratings from an adult data set. Samples of 1210 and 5315 words were used in the child and the adult semantic spaces, respectively. Results suggested that the emotional valence of words can be predicted from amodal vector representations even at the child stage, and accurate emotional propagation was found in the adult word vector representations. In this way, different propagative processes were observed in the adult semantic space. These findings highlight a potential mechanism for early verbal emotional anchoring. Moreover, different multiple linear regression and mixed-effect models revealed moderation effects for the performance of the longitudinal computational model. First, words with early maturation and subsequent semantic definition promoted emotional propagation. Second, an interaction effect between age of acquisition and abstractness was found to explain model performance. The theoretical and methodological implications are discussed.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Emotional Valence Precedes Semantic Maturation of Words: A Longitudinal Computational Study of Early Verbal Emotional Anchoring

2021

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“…Moreover, semantic relatedness can help facilitate memory in the face of increased interference [11][12][13] and promote integration of to-be-learned materials to later improve memory [14][15][16] . Conversely, episodic learning can selectively reshape pre-existing semantic information, for instance by increasing discriminability of within-category items by expanding representations of to-be-learned information and minimizing those of irrelevant information 17,18 .…”

Section: Introductionmentioning

confidence: 99%

Episodic learning-induced representational change is both influenced by and re-sculpts semantic memory

Walsh¹,

Rissman²

2023

Preprint

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While semantic and episodic memory have been shown to influence each other, uncertainty remains as to how this interplay occurs. We introduce a novel behavioral representational similarity analysis approach to index how semantic space is re-sculpted by episodic learning. Eighty participants learned word pairs that varied in semantic relatedness and whose learning was bolstered via either testing or restudying. Next-day recall was superior for semantically related pairs, but there was a larger benefit of testing for unrelated pairs. Critically, representations of cue words in semantically related pairs were drawn asymmetrically towards their targets, while cue and target representations in unrelated pairs were drawn together symmetrically. Testing additionally repelled semantic representations away from normative semantic space moreso than restudying. Taken together, we show that episodic learning can induce distortions of semantic space to improve later recall by making cues more predictive of targets, reducing interference from potential lures, and establishing elaborative links within pairs.

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Bridging the theoretical gap between semantic representation models without the pressure of a ranking: some lessons learnt from LSA

2019

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Structural Basis of Semantic Memory ☆

Cited by 5 publications

References 217 publications

Emotional Valence Precedes Semantic Maturation of Words: A Longitudinal Computational Study of Early Verbal Emotional Anchoring

Emotional Valence Precedes Semantic Maturation of Words: A Longitudinal Computational Study of Early Verbal Emotional Anchoring

Episodic learning-induced representational change is both influenced by and re-sculpts semantic memory

Bridging the theoretical gap between semantic representation models without the pressure of a ranking: some lessons learnt from LSA

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