ReviewPsychonomic bulletin & review2021
Perceptual variability: Implications for learning and generalization.
Review in Psychonomic bulletin & review, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 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.
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Who cites it
14 citing papers in PubMed.
- The next generation of fear generalization.Trends in cognitive sciences · 2026Review
- Computational protocol for hierarchical Bayesian modeling of perception and generalization in fear conditioning.STAR protocols · 2026Article
- Increased generalisation in trait anxiety is driven by aversive value transfer.Communications psychology · 2026Article
- Psychometric Considerations in Assessing Fear Generalization as a Predictor of Anxiety.Biological psychiatry global open science · 2025Article
- Article
- Task-Irrelevant Phase but not Contrast Variability Unlocks Generalization in Visual Perceptual Learning.Journal of cognitive enhancement : towards the integration of theory and practice · 2025Article
- Fear learning and generalization in youth with early-stage transdiagnostic psychiatric symptoms and the impact of acute exercise.Frontiers in psychiatry · 2025Article
- The representational instability in the generalization of fear learning.NPJ science of learning · 2024Article
- An infralimbic cortex neuronal ensemble encoded during learning attenuates fear generalization expression.bioRxiv : the preprint server for biology · 2024Article
- Anticipated variability increases generalization of predictive learning.NPJ science of learning · 2024Article
- Article
- Humans display interindividual differences in the latent mechanisms underlying fear generalization behaviour.Communications psychology · 2023Article
- Fear Extinction as a Psychologist Views It.Current topics in behavioral neurosciences · 2023Article
- When unsupervised training benefits category learning.Cognition · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
The generalization of learned behavior has been extensively investigated, but accounting for variance in generalized responding remains a challenge. Based on recent advances, we demonstrate that the inclusion of perceptual measures in generalization research may lead to a better understanding of both intra- and interindividual differences in generalization. We explore various ways through which perceptual variability can influence generalized responding. We investigate its impact on the ability to discriminate between stimuli and how similarity between stimuli may be variable, rather than fixed, because of it. Subsequently, we argue that perceptual variations can yield different learning experiences and that interindividual differences in generalized responding may be understood from this perspective. Finally, we point to the role of memory and decision-making within this context. Throughout this paper, we argue that accounting for perception in current generalization protocols will improve the precision of obtained generalization gradients and the ability to infer latent mechanisms. This can inspire future attempts to use generalization gradients as a (clinical) predictor or to relate them to individual traits and neural correlates and, ultimately, may lead to new theoretical and clinical insights.
Indexed as
Identifiers
32869189What 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.