ArticleJournal of vision2018
Decision-variable correlation.
Article in Journal of vision, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 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
16 citing papers in PubMed.
- Principles of Local and Global Grouping that Underlie Segmentation of Natural Texture Images.bioRxiv : the preprint server for biology · 2026Article
- Quantifying task-relevant representational similarity using decision variable correlation.ArXiv · 2026Article
- Quantifying Task-relevant Similarities in Representations Using Decision Variable Correlations.Advances in neural information processing systems · 2025Article
- Duration effects on detection cues in simultaneous masking: Analysis using decision variable correlationa).The Journal of the Acoustical Society of America · 2025Article
- Normal behavioral discrimination of envelope statistics in budgerigars with kainate-induced cochlear synaptopathy.Hearing research · 2024Article
- Article
- Distinguishing shadows from surface boundaries using local achromatic cues.PLoS computational biology · 2022Article
- Animal models of hidden hearing loss: Does auditory-nerve-fiber loss cause real-world listening difficulties?Molecular and cellular neurosciences · 2022Review
- Normal Tone-In-Noise Sensitivity in Trained Budgerigars despite Substantial Auditory-Nerve Injury: No Evidence of Hidden Hearing Loss.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2021Article
- Local reliability weighting explains identification of partially masked objects in natural images.Proceedings of the National Academy of Sciences of the United States of America · 2020Observational
- Identifying cues for tone-in-noise detection using decision variable correlation in the budgerigar (Melopsittacus undulatus).The Journal of the Acoustical Society of America · 2020Article
- Predicting the Partition of Behavioral Variability in Speed Perception with Naturalistic Stimuli.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2020Article
- Modeling second-order boundary perception: A machine learning approach.PLoS computational biology · 2019Article
- Linking V1 Activity to Behavior.Annual review of vision science · 2018Review
- Spatial summation in the human fovea: Do normal optical aberrations and fixational eye movements have an effect?Journal of vision · 2018Article
- Sensorineural Hearing Loss Diminishes Use of Temporal Envelope Cues: Evidence From Roving-Level Tone-in-Noise Detection.Ear and hearingArticle
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
An extension of the signal-detection theory framework is described and demonstrated for two-alternative identification tasks. The extended framework assumes that the subject and an arbitrary model (or two subjects, or the same subject on two occasions) are performing the same task with the same stimuli, and that on each trial they both compute values of a decision variable. Thus, their joint performance is described by six fundamental quantities: two levels of intrinsic discriminability (d'), two values of decision criterion, and two decision-variable correlations (DVCs), one for each of the two categories of stimuli. The framework should be widely applicable for testing models and characterizing individual differences in behavioral and neurophysiological studies of perception and cognition. We demonstrate the framework for the well-known task of detecting a Gaussian target in white noise. We find that (a) subjects' DVCs are approximately equal to the square root of their efficiency relative to ideal (in agreement with the prediction of a popular class of models), (b) between-subjects and within-subject (double-pass) DVCs increase with target contrast and are greater for target-present than target-absent trials (rejecting many models),
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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.