Instance theory predicts categorization decisions in the absence of categorical structure: A computational analysis of artificial grammar learning without a grammar.
Theories of categorization have historically focused on the stimulus characteristics to which people are sensitive. Artificial grammar learning (AGL) provides a clear example of this phenomenon, with theorists debating between knowledge of rules, fragments, whole strings, and so on as the basis of c...
| Publicado en: | Memory & Cognition Vol. 52; no. 1; pp. 132 - 146 |
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| Autores principales: | , |
| Formato: | Artículo |
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Springer Nature
Jan2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=174971691&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 174971691 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 0090502X MEG jtl: Memory & Cognition issn: 0090502X maglogo: N pubinfo: dt: Jan2024 vid: 52 iid: 1 pid: 237 pub: Springer Nature artinfo: ui: 174971691 10.3758/s13421-023-01449-9 ppf: 132 ppct: 14 formats: fmt: – @attributes: type: T – @attributes: type: P size: 786KB tig: atl: Instance theory predicts categorization decisions in the absence of categorical structure: A computational analysis of artificial grammar learning without a grammar. aug: au: Curtis, E. T. Lebek, I. affil: https://ror.org/02y3prs94 Booth University College, 447 Webb Place, R3B 2P2, Winnipeg, MB, Canada su: Memory Comparative grammar Learning Research funding Descriptive statistics Data analysis software sug: subj: Memory Comparative grammar Learning Research funding Descriptive statistics Data analysis software keyword: Artificial grammar learning Categorization Computational models Artificial grammar learning Categorization Computational models ab: Theories of categorization have historically focused on the stimulus characteristics to which people are sensitive. Artificial grammar learning (AGL) provides a clear example of this phenomenon, with theorists debating between knowledge of rules, fragments, whole strings, and so on as the basis of categorization decisions (i.e., stimulus-driven explanations). We argue that this focus loses sight of the more important question of how participants make categorization decisions on a mechanistic level (i.e., process-driven explanations). To address the problem, we derived predictions from an instance-based model of human memory in a pseudo-AGL task in which all study and test strings were generated randomly, a task that stimulus-driven explanations of AGL would have difficulty accommodating. We conducted a standard AGL experiment with human participants using the same strings. The model's predictions corresponded to participants' decisions well, even in the absence of any experimenter-generated structure and regardless of whether test stimuli contained any incidental structure. We argue that theories of categorization ought to continue shifting towards the goal of modeling categorization at the level of cognitive processes rather than primarily attempting to identify the stimulus characteristics to which participants are drawn. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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