ReviewPsychonomic bulletin & review2019
Using experiential optimization to build lexical representations.
Review in Psychonomic bulletin & review, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- An embedded computational framework of memory: The critical role of representations in veridical and false recall predictions.Psychonomic bulletin & review · 2025Review
- Contextual dynamics in lexical encoding across the ageing spectrum: A simulation study.Quarterly journal of experimental psychology (2006) · 2023Article
- A Large-Scale Semantic Analysis of Verbal Fluency Across the Aging Spectrum: Data From the Canadian Longitudinal Study on Aging.The journals of gerontology. Series B, Psychological sciences and social sciences · 2020Article
- Semantic diversity in paired-associate learning: Further evidence for the information accumulation perspective of cognitive aging.Psychonomic bulletin & review · 2020Article
- Mining a Crowdsourced Dictionary to Understand Consistency and Preference in Word Meanings.Frontiers in psychology · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
To account for natural variability in cognitive processing, it is standard practice to optimize a model's parameters by fitting it to behavioral data. Although most language-related theories acknowledge a large role for experience in language processing, variability reflecting that knowledge is usually ignored when evaluating a model's fit to representative data. We fit language-based behavioral data using experiential optimization, a method that optimizes the materials that a model is given while retaining the learning and processing mechanisms of standard practice. Rather than using default materials, experiential optimization selects the optimal linguistic sources to create a memory representation that maximizes task performance. We demonstrate performance on multiple benchmark tasks by optimizing the experience on which a model's representation is based.
Indexed as
Identifiers
29968206What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.