Verbal Working Memory, Long-Term Knowledge, and Statistical Learning

Saito, Satoru; Nakayama, Masataka; Tanida, Yuki

doi:10.1177/0963721420920383

Cited by 8 publications

(9 citation statements)

References 40 publications

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“…By contrast, unified representations of entire array configurations are unique, and these traces gain strength for repeated arrays, outcompeting traces of the unique arrays. This conclusion converges with insights from studies implying that individual item-position associations are not sufficient to explain the Hebb effect (Cumming et al, 2003; Fastame et al, 2005; Saito et al, 2020).…”

Section: Discussionsupporting

confidence: 85%

Promoting visual long-term memories: When do we learn from repetitions of visuospatial arrays?

Souza¹,

Oberauer²

2022

Journal of Experimental Psychology: General

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Repeated exposure is assumed to promote long-term learning. This is demonstrated by the so-called “Hebb-effect”: when short lists of verbal or spatial materials are presented sequentially for an immediate serial recall test, recall improves with list repetition. This repetition benefit, however, is not ubiquitous. Previous studies found little or no performance improvement for repetitions of visuospatial arrays (e.g., arrays of colored squares). Across eight experiments with college students and Prolific samples, we investigated which factors promote visuospatial learning by testing all combinations of variables distinguishing between visual-array tasks (brief + simultaneous presentation + a single recognition test) and tasks showing the Hebb effect (slow + sequential presentation + recall test probing all items). Participants profited from repetitions when all items were tested with a recall procedure, but not if the test consisted of recognition. Hence, the key to promote long-term learning is to recall all of the memorized information over the short-term.

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Section: Discussionsupporting

confidence: 85%

Promoting visual long-term memories: When do we learn from repetitions of visuospatial arrays?

Souza¹,

Oberauer²

2022

Journal of Experimental Psychology: General

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“…This means, in the words of Shimi and Logie (2019), being aware of the contents input to a domain-specific cache, thereby making the episodic long-term memory accessible. We found that performance based on location–item frequency statistics and whole list (array) statistics were similar across the visuospatial and verbal item domain (see also Majerus et al, 2004, Majerus & Oberauer, 2020; Saito et al, 2020). Musfeld et al (2022) observed that learning in the visuospatial domain was slower than in the verbal domain.…”

Section: Discussionmentioning

confidence: 73%

“…Thus, location-item association learning could be accumulated throughout the experiment. The arrangement of this learning schedule was based on the prediction that position-item association learning would be slower than whole-list learning (see Saito et al, 2020, for discussions on this issue). Note that their Hebb lists were always constructed with high-frequency position items.…”

Section: Experiments 1: Simultaneous Presentation With Whole-probe Re...mentioning

confidence: 99%

Sequential processing facilitates Hebb repetition learning in visuospatial domains.

Ueda¹,

Huang²,

Shen³

et al. 2023

Journal of Experimental Psychology: General

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Exposure to the same information improves auditory/verbal short-term memory performance, but such improvement is not always observed in visual short-term memory. In this study, we demonstrate that sequential processing makes visuospatial repetition learning efficient in a paradigm that employs a similar design previously used for an auditory/verbal domain. When we presented sets of color patches simultaneously in Experiments 1-4, recall accuracy did not increase with repetition; however, once color patches were presented sequentially in Experiment 5, accuracy did increase rapidly with repetition, even when participants engaged in articulatory suppression. Moreover, these learning dynamics matched those in Experiment 6, which used verbal materials. These findings suggest that (a) sequential focus on each item facilitates a repetition learning effect, indicating a temporal bottleneck is involved early in this process and (b) repetition learning is mechanistically similar across sensory modalities even though these modalities differently specialize in processing spatial or temporal information. Public Significance StatementWe acquire knowledge of the world by learning the regularities of what occurs repeatedly. Such learning has been shown to occur in a variety of modalities, including visual and verbal, from early ages, even if one is not aware of the regularity. However, it has also been noted that the learning effect is small or absent in some situations. As the majority of evidence for regularity learning is associated with temporal sequence processing, it is possible that a temporal bottleneck exists during the early stages of the learning. This study provides insight into learning mechanisms that operate in the visuospatial domain and presents avenues for further investigation of the interaction between short-term and long-term memory.

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“…An important property of verbal working memory is that the quality of phonological 1 representations in working memory depends on long‐term knowledge of the statistical distribution of phonological information in the linguistic environment (e.g., Gupta & Tisdale, 2009a; 2009b; Hartley & Houghton, 1996; for a collection of articles from this field, see Thorn & Page, 2008; for a review, see Saito, Nakayama, & Tanida, 2020). The effect of phonotactic frequency is an example of a phenomenon that reflects long‐term serial order knowledge in the phonemic domain.…”

Section: Introductionmentioning

confidence: 99%

Predicting the Structure of a Lexical Environment from Properties of Verbal Working Memory

Tanida

Saito

2022

Cognitive Science

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We analyzed a Japanese lexical database to investigate the structure of the lexical environment based on the hypothesis that the lexical environment is optimized for the functioning of verbal working memory. Our prediction was that, as a consequence of the cultural transmission of language, low-imageable meanings tend to be represented by frequent phonological patterns in the current vocabulary rather than infrequent phonological patterns. This prediction was based on two findings of previous laboratory studies on verbal working memory. (1) The quality of phonological (phonemic and accent) representations in verbal working memory depends on phonological regularity knowledge; therefore, short-term phonological representations are less robust for words with infrequent phonological patterns. (2) Phonological representations are underpinned by contributions from semantic knowledge; therefore, phonological representations of highly imageable words are more robust than those for lowimageable words. Our database analyses show that nouns with less imageable meanings tend to be associated with more frequent phonological patterns in Japanese vocabulary. This lexical structure can maintain the quality of phonological representations in verbal working memory through contributions of semantic and phonological regularity knowledge. Larger semantic contributions compensate for the less robust phonological representations of infrequent phonological forms. The quality of phonological representations is preserved by phonological regularity knowledge when larger semantic contributions are not expected.

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Verbal Working Memory, Long-Term Knowledge, and Statistical Learning

Cited by 8 publications

References 40 publications

Promoting visual long-term memories: When do we learn from repetitions of visuospatial arrays?

Promoting visual long-term memories: When do we learn from repetitions of visuospatial arrays?

Sequential processing facilitates Hebb repetition learning in visuospatial domains.

Predicting the Structure of a Lexical Environment from Properties of Verbal Working Memory

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