Evaluation of a 'bias-free' measure of awareness
Abstract
The derivation of a reliable, subjective measure of awareness that is not contaminated by observers' response bias is a problem that has long occupied researchers. Kunimoto et al. (2001) proposed a measure of awareness (a') which apparently meets this criterion: a' is derived from confidence ratings and is based on the intuition that confidence should reflect awareness. The aim of this paper is to explore the validity of this measure. Some calculations suggested that, contrary to Kunimoto et al.'s intention, a' can vary as a result of changes in response bias affecting the relative proportions of high- and low-confidence responses. This was not evident in the results of Kunimoto et al.'s original experiments because their method may have artificially 'clamped' observers' response bias close to zero. A predicted consequence of allowing response bias to vary freely is that it can result in a' varying from negative, through zero, to positive values, for a given value of discriminability (d'). We tested whether such variations are likely to occur in practice by employing Kunimoto et al.'s paradigm with various modifications, notably the removal of constraints upon the proportions of low- and high-confidence responses, in a visual discrimination task. As predicted, a' varied with response bias in all participants. Similar results were found when a' was calculated from pre-existing data obtained from a patient with blindsight: a' varied through a range of positive results without approaching zero, which is inconsistent with his well-documented lack of awareness. A second experiment showed how response bias could be manipulated to yield elevated values of a'. On the basis of these findings we conclude that Kunimoto's measure is not as impervious to response bias as was originally assumed.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Evans and Azzopardi (2007) showed using simulation data that the type 2 d′ measure proposed by Kunimoto et al. (2001) is confounded by changes in metacognitive bias.
“Using simulation data, Evans and Azzopardi (2007) showed that this leads to the type 2 d′ measure proposed by Kunimoto et al. (2001) being confounded by changes in metacognitive bias.”
From How to measure metacognition (2014) · cites this paper as Evans and Azzopardi (2007)
No citation lists for this paper
No citation lists for this paper yet — checked again daily.
References 18 · Cited by 68
- Authors
- Azzopardi, P., & Evans, S.
- Affiliations
- University of Oxford (GB)
- Venue
- Spatial Vision
- Publisher
- Walter de Gruyter GmbH
- Published
- 2007
- Volume
- 20
- Issue
- 1-2
- Pages
- 61-77
- ISSN
- 0169-1015 (ISSN-L)
- 1568-5683
- OpenAlex
- W2063664495
- Cited by
- 68
- References
- 18
- Citations by year
- 2025: 6 · 2024: 3 · 2023: 7 · 2022: 3 · 2021: 1 · 2020: 3 · 2019: 3 · 2018: 1 · 2017: 7 · 2016: 3 · 2015: 6 · 2014: 6 · 2013: 4 · 2012: 4
- FWCI
- 1.16
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Spatial Vision (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Philosophy and Theoretical Science
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory Processes and Influences
- Keywords
- awareness, response bias, confidence ratings, visual discrimination, blindsight, discriminability
- MeSH terms
- Awareness, Humans, Models, Theoretical, Photic Stimulation, Visual Perception, Bias
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review