1. The emergence of all-or-none retrieval of chunks in verbal serial recall.
- Author
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Shafaghat Ardebili, Amirhossein, Liu, Yang S., and Caplan, Jeremy B.
- Subjects
MEMORY ,SEMANTICS ,SPEECH perception ,PHONOLOGICAL awareness ,VERBAL behavior ,RESEARCH funding ,MEMORY disorders - Abstract
People often subdivide a list into smaller pieces, called chunks. Some theories of serial recall assume memories are stored hierarchically, with all-or-none retrieval of chunks, but most mathematical models avoid hierarchical assumptions. Johnson (Journal of Verbal Learning and Verbal Behavior, 8(6), 725–731, 1969) found steep drops in errors following correct recalls (transitional-error probabilities) within putative chunks during multi-trial letter-list learning, and viewed this as evidence for all-or-none retrieval. Here we test whether all-or-none retrieval occurs in lists studied only once. In serial recall of six-word lists (Experiment 1), transitional-error probabilities were inconsistent with all-or-none retrieval, both when participants were instructed to subdivide and when temporal grouping induced subdivision. Curiously, the same analysis of previous temporally grouped nine-letter lists produced compelling evidence for all-or-none retrieval, which may result from recoding rather than the formation of chunks. In Experiment 2, participants were pre-trained on three-word chunks. For nine-word lists constructed from those trained chunks, transitional-error probabilities exhibited more pronounced evidence of all-or-none retrieval. Nearly all effects reversed with post-cued backward recall, suggesting mechanisms that play out over the course of recall rather than encoding of the list. In sum, subdivided lists do not result in hierarchical memories after a single study trial, although they may emerge in lists formed from chunks that are previously learned as such. This suggests a continuous transition from non-hierarchical subdivision of lists to all-or-none retrieval over the course of chunk formation. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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