A model of subjective report and objective discrimination as categorical decisions in a vast representational space — TEI-tagged view

King, Dehaene · 2014 · neuroscience · doi:10.1098/rstb.2013.0204 · full text at the source · TEI xml · tagged edition · index · concepts · authors
Where this record's text came from. faceted 2026-08 by reading the locally held text (html); every evidence string verified as an exact substring of that text before merging. Method is recorded as simulation because the primary object is a formalised decision model with simulated decision geometries, but the paper also reports an original behavioural psychophysics experiment (17 analysed subjects, morphed digits at perceptual threshold with a 10-point visibility scale) that tests one model prediction; a single-method tag cannot carry both. The paper also announces further supporting results in 'a companion paper in preparation', which is unverifiable from this text. 2026-08-13 and 2026-08-15 schema-0.3 passes: every quote verified as an exact substring of the staged text and anchored at merge time; concept terms verified present; every edge endpoint an existing node.
34 quotations on this page contain a reference number where the paper printed a superscript. This journal style prints citations as raised numerals — parameters12,13 — and the extraction flattened them into the line, so the sentence reads parameters 12 , 13 . The digits are citation markers and not part of what the author wrote. The quotes keep them for the same reason they keep every other extraction fault: a quote is reproduced exactly or it proves nothing.
Other fields define these differently the same word, defined another way in another discipline
attention 36 fields · confidence 5 fields · standard deviation 3 fields · task 2 fields · cost 2 fields · discrimination 2 fields · and 7 more →
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representational space input vector2 classes response classes task13 decision boundaries criterion5 posterior probability4 prior probability3 priors likelihood4 cost2 evidence8 stimulus strength4 signal detection2 ideal observer2 sequential sampling non-commutativity visibility discrimination12 identification5 detection8 confidence8 second-order2 subjective report2 seen unseen subliminal5 blindsight2 hysteresis3 attention29 variance54 sigmoid2 nonlinear4 all-or-none categorical perception2 conscious perception5 unconscious6 morph digit mask2 staircase2 global workspace3 simulation51

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Cross-disciplinary analysis
content-tags & facets: phenomenon · claim-type · cross-domain-transfer · mechanism · claim-relation · replication-status · limitation — grounded in the full text
phenomenondissociation between subjective visibility report and objective discrimination (subliminal perception)— “Subliminal perception studies have shown that one can objectively discriminate a stimulus without subjectively perceiving it.”
claim-typemechanism— “We show how a minimalist framework based on Signal Detection Theory and Bayesian inference can account for this dissociation, by describing subjective and objective tasks with similar decision-theoretic mechanisms.”
method — evidence— “In the following simulations, we use a series of computational simplifications.”
object of study — evidence— “We show how a minimalist framework based on Signal Detection Theory and Bayesian inference can account for this dissociation, by describing subjective and objective tasks with similar decision-theoretic mechanisms. Each of these tasks relies on distinct response classes, and therefore distinct priors and decision boundaries. As a result, they…
research question — evidence— “Here, building upon earlier proposals [ 8 , 12 ], we explore a simple theoretical idea: objective and subjective tasks rely on the same inference principles, but they differ in the nature and size of the decision space.”
contribution — evidence— “Furthermore, the model provides a set of original predictions on the nonlinear profiles of discrimination performance obtained at various levels of visibility. We successfully test one such prediction in a novel experiment: when varying continuously the degree of perceptual ambiguity between two visual symbols presented at perceptual…
transferfrom Bayesian statistics: the MAP rule, which supplies the decision boundaries instead of their being assumed— “This MAP criterion results in the segregation of representational space into distinct regions separated by sharp decision boundaries (importantly, the placement of these boundaries does not constitute an additional hypothesis of the model, but derives directly from the hypothesis that decisions are based on a MAP criterion).”transfers[0]
transferfrom signal detection theory: likelihood distributions and a decision rule, moved from one axis into a multi-dimensional space— “Indeed, although SDT is not limited to a single dimension, it is often depicted as a binary problem with two Gaussian distributions plotted along a single axis. We argue that this classic diagram fails to capture the interaction between multiple features, classes and tasks, whereas a two-dimensional depiction fulfils these requirements”transfers[1]
transferfrom quantum mechanics: non-commuting projections, offered as a way to model why the order of two questions changes the answers— “Busemeyer et al. [ 69 ] have proposed accounting for the latter phenomenon with a computational principle inspired from quantum mechanics, according to which each successive judgement alters the input vector by projecting it onto a subspace defined by the task. As projections are not commutative, the order of successive questions can change…transfers[2]
transferfrom categorical perception research: within-category compression and between-category separation as the definition to be met— “According to Harnad's definition [ 53 ], categorical perception is defined by ‘within-category compression and between-category separation’. In a companion paper in preparation, we will present additional evidence that the conscious experience of our morphs follows Harnad's definition of categorical perception [ 53 ]. First…transfers[3]
mechanismObjective and subjective tasks apply the same maximum-a-posteriori inference to the same input vector, but over different response classes; the different class sets carry different priors and hence different decision boundaries, so the two tasks can return conflicting answers.— “Each of these tasks relies on distinct response classes, and therefore distinct priors and decision boundaries.”mechanisms[0]
mechanismInternal noise makes some threshold trials yield shorter input vectors; short vectors both fall in the 'absent' region and sit nearer the X/Y discrimination boundary, so trials called unseen are automatically the ones discriminated worse.— “As a consequence of this variability, sensory inputs reported as unseen are associated with a shorter input vector and are therefore closer to the X / Y discrimination border than samples reported as seen”mechanisms[1]
mechanismEvidence weak enough to be classified as unseen necessarily lies near the origin of the representational space, where the forced-choice psychometric curve is shallow, producing linear (rather than all-or-none) unconscious response profiles.— “According to the present model, the reason why unconscious responses tend to be linearly related to stimulus evidence is simple: when perceptual evidence is low enough to be categorized as unseen, the evidence necessarily lies close to the origin of the multi-dimensional space and therefore leads to shallow (though above-chance) forced-choice…mechanisms[2]
mechanismeach task imposes its own set of response classes on the same continuous vector space, so two tasks can partition it differently and return different answers— “Our third assumption is that each behavioural task imposes, in a top-down manner, a categorical structure of classes to this continuous vector space (e.g. ‘click left for faces and right for non-faces’). Performing the task consists in identifying, on every trial, the class in which the input vector falls. Formally, this is a statistical…mechanisms[3]
mechanismthe distance between the two stimulus classes can exceed their distance from the absent class, which is why discrimination survives where detection does not— “The geometry of the two-dimensional model reveals why discrimination performance (i.e. d’ of X / Y discrimination) can be higher than detection sensitivity (i.e. d’ of absent/not-absent judgement): the distance separating the X and Y vectors is larger than that separating them from the absent class. In the two-dimensional case, discrimination…mechanisms[4]
mechanisminternal variability alone makes half of physically identical threshold stimuli fall nearer the absent class and be reported unseen, and those samples sit closer to the discrimination border— “Even when the stimuli are physically identical, internal variability can explain why approximately 50% of them are reported as unseen (those which are most similar to the absent class). As a consequence of this variability, sensory inputs reported as unseen are associated with a shorter input vector and are therefore closer to the X / Y…mechanisms[5]
mechanismthe variance of the representation is what turns the visibility sigmoid from stepwise to nearly linear— “Interestingly, although the fraction of seen responses is always a sigmoid, its slope may vary from a stepwise ‘all-or-none’ pattern to a shallow and near-linear function. The parameter driving this change in sigmoid slope is the variance in representational space.”mechanisms[6]
mechanismraising the prior for one stimulus lowers the visibility threshold for that stimulus alone, which is offered as the mechanism behind hysteresis effects— “Similarly, Melloni et al. [ 29 ] recently used a hysteresis paradigm in which letters were embedded in white noise. Across a series of trials, the identity of the letter was fixed while its signal-to-noise ratio gradually increased and then gradually decreased. Subjects reported seeing the letter better in the descending than in the ascending…mechanisms[7]
mechanismif attention changes the variance of the input, it can raise or lower visibility depending on how many trials are already reported unseen— “Importantly, however, if P (absent) is high, so that most trials are reported as unseen, then increasing the stimulus variance still diminishes discrimination performance, but may paradoxically increase visibility ratings. This is because with higher variance, a greater number of samples fall outside the region responded as unseen and thus…mechanisms[8]
builds-on earlier proposals [8, 12] (including Lau's decision-theoretic account)— “Here, building upon earlier proposals [ 8 , 12 ], we explore a simple theoretical idea: objective and subjective tasks rely on the same inference principles, but they differ in the nature and size of the decision space.”claim_relations[0]
extends de Gardelle et al. [47]— “Our results extend a previous study by de Gardelle et al. [ 47 ], which examined the amount of…claim_relations[1]
contradicts Rahnev et al. [26]claim_relations[2]
critiques accounts on which conscious perception is qualitatively different - more integrated, or dependent on higher-order metacognitive representations
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claim_relations[3]
explains-away the reading of linear unconscious and categorical conscious priming as two distinct processes
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claim_relations[4]
critiques the methodological position that subjective reports are unreliable and objective sensitivity measures should be preferred
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claim_relations[5]
qualifies Lau and colleagues' designs that equate discrimination across seen and unseen trials, which do so at the cost of physical stimulus differences
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claim_relations[6]
contradicts Rahnev and colleagues' account of attention as inducing a conservative visibility bias through inter-trial variance
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claim_relations[7]
builds-on signal detection theory, extended from its usual one-dimensional depiction to multiple dimensions
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claim_relations[8]
extends de Gardelle and colleagues' morphed-face priming result, replicated here with digits and an explicit visibility scale
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claim_relations[9]
senseTHE SAME INPUT UNDER TWO CLASS STRUCTURES GIVES TWO ANSWERS, AND BOTH ARE CORRECT: each task imposes, top-down, its own categorical structure on one continuous space, and distinct response classes may therefore lead to different conclusions
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senses[0]
senseso a dissociation between what is discriminated and what is reported needs no second process - it is what one inference looks like when the question changes
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senses[1]
senseTHE BOUNDARIES ARE DERIVED, NOT POSITED: their placement is stated not to be an additional hypothesis but a consequence of choosing the most probable class
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senses[2]
senseA WIDELY USED TERM WHOSE ORDINARY FORM IS WRONG: criterion suggests a single scalar value, and in this framework it is a set of task-specific boundaries dividing a space, which move when the task or the priors move
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senses[3]
senseTHE BIAS IS THE SIGNAL: a shift in visibility criterion reflects the subject's own priors and costs and is argued not to be an experimental confound to be discarded
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senses[4]
senseTHE CONTROL CREATES THE CONFOUND IT WAS MEANT TO REMOVE: equating discrimination by masking or by attention introduces physical differences between the visible and invisible stimuli, so the contrast may reflect the stimulus rather than the visibility
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senses[5]
senseAND THE PAPER SAYS ITS OWN ARGUMENTS ARE RETROSPECTIVE before running a new experiment - the accommodation of six existing findings is not offered as the test
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senses[6]
sensethe target set is declared first: six empirical findings that any account should reproduce, listed before the model is built
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senses[7]
senseAND THE ORDER OF THE QUESTIONS MAY CHANGE THE ANSWERS: successive judgements may project the vector onto a task-defined subspace, and projections do not commute
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senses[8]
sensean explanation is removed rather than added: a linear-then-categorical pattern previously read as two processes is reproduced by one, with the extra kind declared unnecessary
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senses[9]
controltwo subjects excluded for failing to converge to a stable threshold — held — rules out a threshold estimate that never settled, which would put an unmeasurable visibility level into the group analysis
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controls[0]
differs inRahnev et al. [26] — what drives the visibility rating: Rahnev has attention inducing a bias BY CHANGING THE INTER-TRIAL VARIANCE OF THE STIMULUS, where this model predicts AN INTERACTION BETWEEN THE VARIANCE AND INITIAL VISIBILITY THRESHOLD
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claim_relations[2]
differs inaccounts on which conscious perception is qualitatively different - more integrated, or dependent on higher-order metacognitive representations — quantitative against qualitative: CONSCIOUS PROCESSES ARE QUANTITATIVELY MORE COMPLEX, INTEGRATED AND DIFFERENTIATED (Tononi and Edelman) against CONSCIOUS PERCEPTION IS QUALITATIVELY DIFFERENT FROM UNCONSCIOUS PERCEPTION, AS IT RELIES ON HIGHER ORDER METACOGNITIVE REPRESENTATIONS (Lau, Rosenthal)
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claim_relations[3]
differs inthe methodological position that subjective reports are unreliable and objective sensitivity measures should be preferred — whether one measure reduces to the other: WE SHOW THAT SUBJECTIVE REPORTS CANNOT BE REDUCED TO OBJECTIVE MEASURES, against the position that OBJECTIVE MEASURES, FOR EXAMPLE DETECTION SENSITIVITY (d'), SHOULD BE FAVOURED
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claim_relations[5]
differs inLau and colleagues' designs that equate discrimination across seen and unseen trials, which do so at the cost of physical stimulus differences — what the design holds constant and what it lets vary: DISCRIMINATION PERFORMANCE IS EQUATED AT THE EXPENSE OF INTRODUCING PHYSICAL DIFFERENCES between the conditions, since masking and attention BOTH MAY CHANGE THE SIGNAL-TO-NOISE RATIO OF THE INCOMING EVIDENCE
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claim_relations[6]
differs inRahnev and colleagues' account of attention as inducing a conservative visibility bias through inter-trial variance — the same parameter as item 2: variance alone against AN INTERACTION BETWEEN THE VARIANCE AND INITIAL VISIBILITY THRESHOLD
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claim_relations[7]
audittask —causes→ classes — Audit 2026-09-28: DOUBTFUL - read against its span and kept, but the span says each task imposes a categorical structure of classes; causes is loose.
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concept_edges[0]
notetask —causes→ classes — THE WHOLE ARGUMENT: each task imposes, top-down, its own categorical structure on a continuous space, and distinct response classes may therefore lead to different answers about the same input
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concept_edges[0]
auditresponse classes —part-of→ task — Audit 2026-09-28: DOUBTFUL - read against its span and kept, but confidence judgements add response classes to a task; part-of is loose.
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concept_edges[1]
noteresponse classes —part-of→ task — and a confidence judgement is just another set of classes, so there is one confidence per task rather than one confidence
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concept_edges[1]
noteidentification —operationalised-as→ posterior probability — three tasks over the same three classes: identification across all of them, discrimination with the absent class excluded, visibility as absent against not-absent
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concept_edges[6]
auditinput vector —distinct-from→ sequential sampling — Audit 2026-09-28: DOUBTFUL - read against its full span and kept, but the model assumes a SINGLE input vector, which sequential sampling models supersede; distinct-from is loose.
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concept_edges[11]
noteinput vector —distinct-from→ sequential sampling — a single point where the newer models use a trajectory, which is why this model says nothing about reaction times
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concept_edges[11]
notediscrimination —contrasted-with→ detection — the distance between the two stimuli exceeds their distance from the absent class - in two dimensions by a factor of root two - so one can be significant when the other is not
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concept_edges[14]
auditunseen —distinct-from→ seen — Audit 2026-09-28: DOUBTFUL - read against its span and kept, but the span explains why some identical stimuli are reported unseen; distinct-from is loose.
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concept_edges[15]
noteunseen —distinct-from→ seen — internal variability alone splits physically identical threshold trials, and the unseen half necessarily sits closer to the discrimination border
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concept_edges[15]
auditvariance —causes→ sigmoid — Audit 2026-09-28: DOUBTFUL - read against its span and kept, but the span names a parameter driving the sigmoid's slope; causes 'sigmoid' is loose.
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concept_edges[17]
notevariance —causes→ sigmoid — the parameter that turns the visibility function from stepwise to nearly linear is the variance in representational space, not a change of process
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concept_edges[17]
auditattention —causes→ visibility — Audit 2026-09-28: DOUBTFUL - read against its span and kept, but the span predicts how attention affects visibility ratings; causes states a prediction.
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concept_edges[19]
noteattention —causes→ visibility — and the disagreement with the rival account is stated as a testable difference rather than an interpretation
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concept_edges[19]
weightattention —causes→ visibility — 0.8
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concept_edges[19]
auditRETRACTED (AN ATTRIBUTED PROPOSAL, AND THE CONTAINMENT IS A CATEGORY CONFUSION) · global workspace —part-of→ conscious perception — Audit 2026-09-28: DOUBTFUL - read against its full span and kept, but these areas 'HAVE BEEN PROPOSED to form' a global workspace that contributes to conscious perception; part-of is loose.
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concept_edges[36]
noteRETRACTED (AN ATTRIBUTED PROPOSAL, AND THE CONTAINMENT IS A CATEGORY CONFUSION) · global workspace —part-of→ conscious perception — the neural sketch is left explicitly as future work, with prefrontal, anterior temporal and parietal areas as the categorical decision system
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concept_edges[36]
retractedRETRACTED (AN ATTRIBUTED PROPOSAL, AND THE CONTAINMENT IS A CATEGORY CONFUSION) · global workspace —part-of→ conscious perception — AN ATTRIBUTED PROPOSAL, AND THE CONTAINMENT IS A CATEGORY CONFUSION. The edge is 'global workspace --part-of--> conscious perception'. The span: 'These areas have been proposed to form A “GLOBAL WORKSPACE”…'. A workspace is a proposed ARCHITECTURE in which conscious information is held - it is not a PART of conscious perception, and the sentence attributes the proposal to others rather than asserting it.
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concept_edges[36]
weightRETRACTED (AN ATTRIBUTED PROPOSAL, AND THE CONTAINMENT IS A CATEGORY CONFUSION) · global workspace —part-of→ conscious perception — 0.8
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concept_edges[36]
outcome (stated by the tagger, no quotation)control — heldcontrols[0]
evidential statusmechanism — the paper's own words: can explain why
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mechanisms[5]
replicationuntested
no type for thisdiscrimination → visibility — A DEPENDENCE, AND A SAMENESS CLAIM UNDERNEATH IT: the model predicts that linear and nonlinear response profiles ARISE FROM THE SAME DECISION MECHANISM, and that the discrimination profile of physically identical stimuli becomes increasingly nonlinear AS VISIBILITY INCREASES. No type expresses 'these vary together because one mechanism produces both'.
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unhoused_relations[0]
limitationThe account of linear versus nonlinear visibility profiles rests on an internal-variance parameter that cannot be measured in existing experiments.
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limitations[0]
limitationThe model assumes a decision based on one static input vector rather than an accumulation of samples over time.
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limitations[1]
limitationMost of the model's coverage of the literature is post hoc rather than predictive.
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limitations[2]
limitationmost of the account is retrospective, which is why a new experiment was run
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limitations[3]
limitationthe internal variance that drives the central prediction cannot be determined from existing experiments, so that part of the account remains speculative
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limitations[4]
limitationsix computational simplifications are declared in order: the cost function neglected, priors fixed in a given context rather than continuously updated, Gaussian distributions assumed, subjects assumed to have an accurate estimate of stimulus distributions, the same input vector entering different tasks, and stimulus evidence treated as a single discrete point
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limitations[5]
limitationthe hypothesis that the input vector remains unchanged and identically available for a series of successive judgements may turn out to be simplistic, given temporal decay, an attentional cue presented after the stimulus, the task set imposed by the first task, and the order in which two questions are presented
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limitations[6]
limitationthe model assumes a single input vector where sequential sampling models assume an accumulation, and extending it that way would be needed to predict reaction times
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limitations[7]
limitationthe assumption that the decision system is fully informed of the stimulus distributions is called undoubtedly an idealization, with a dynamic model whose likelihood functions, priors and costs are learned and may therefore be ill estimated offered as the route to human deviations from optimality
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limitations[8]
limitationthe predicted exceptions to the all-or-none pattern, at high variance or low signal to noise, remain untested
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limitations[9]
Content tags
content-observations: finding · quantity · variable · definition · key-term — grounded in the full text
findingA single Bayesian/signal-detection decision framework, differing only in the response classes each task imposes, reproduces the standard dissociation between objective discrimination and subjective report.
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content_tags.findings[0]
findingThe decision model suffices to account for several classical characteristics of conscious and unconscious perception.
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content_tags.findings[1]
findingGeometrically, the two-dimensional model implies that discrimination sensitivity exceeds detection sensitivity, which is why above-chance discrimination can coexist with chance-level detection.
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content_tags.findings[2]
findingIn a new experiment with morphed digits at threshold, subjects identified the digit above chance even at the lowest visibility rating.
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content_tags.findings[3]
findingThe steepness of the identification sigmoid rose systematically with reported visibility, as the model predicted.
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content_tags.findings[4]
findingUnseen trials produced a near-linear relation between morph level and response, whereas seen trials produced a near step-function, all-or-none pattern.
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content_tags.findings[5]
findingBecause one decision process yields both response profiles, no qualitative difference between conscious and unconscious processing need be postulated.
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content_tags.findings[6]
findingSIX EMPIRICAL FINDINGS ARE DECLARED IN ADVANCE as the set any account must reproduce, before the model is introduced
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content_tags.findings[7]
findingeach task is taken to impose its own class structure on one continuous space, so distinct response classes can give different answers about the same input
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content_tags.findings[8]
findingthe conscious percept is modelled as the most probable class among all classes, with negative classes such as I did not see anything included
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content_tags.findings[9]
findingTHE DECISION BOUNDARIES ARE DERIVED RATHER THAN POSITED - their placement is explicitly not an additional hypothesis
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content_tags.findings[10]
findingSIX SIMPLIFICATIONS ARE LISTED IN ORDER: no cost function, fixed priors, Gaussian distributions, a calibrated observer, a task-invariant input vector, and a single point rather than a trajectory
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content_tags.findings[11]
findinga degraded stimulus can fall in the unseen region and still be discriminated above chance when the choice excludes the absent class
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content_tags.findings[12]
findingand this is not a response bias - the simulation is Bayes-optimal and still produces the dissociation
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content_tags.findings[13]
findingthe geometry gives discrimination a root-two advantage over detection in two dimensions, so one can be significant while the other is not
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content_tags.findings[14]
findinginternal variability alone explains why about half of physically identical threshold trials are reported unseen, and why those trials discriminate worse
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content_tags.findings[15]
findingTHE TERM CRITERION IS RECONSTRUCTED: it suggests a single scalar value, and here it is a task-specific set of boundaries that move when the task or the priors move
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content_tags.findings[16]
findingthree distinct mechanisms are offered for equating discrimination while visibility varies, each with its own manipulation to test it
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content_tags.findings[17]
findingthe slope of the visibility sigmoid is driven by the variance of the representation, so all-or-none and graded patterns come from one process
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content_tags.findings[18]
findingand that part is marked speculative, because internal variance cannot be determined from existing experiments
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content_tags.findings[19]
findingin a hysteresis paradigm, subjects reported letters as more visible in the descending than in the ascending sequence, once they knew the letter's identity
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content_tags.findings[20]
findingand the account is immediately qualified, since subjects also learn the sequence structure and would expect more absent trials at its ends
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content_tags.findings[21]
findingwith a discriminating prediction attached: the same sequences should shift the bias but leave discrimination unchanged
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content_tags.findings[22]
findingattention modelled as a variance change can raise or lower visibility depending on how many trials are already reported unseen
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content_tags.findings[23]
findingwhich is stated as a testable disagreement with the rival account rather than an interpretation of it
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content_tags.findings[24]
findingTHE PAPER STATES THAT ITS ARGUMENTS SO FAR ARE RETROSPECTIVE and runs a new experiment for that reason
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content_tags.findings[25]
findingthe prediction under test is that identical stimuli give increasingly nonlinear discrimination as visibility rises
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content_tags.findings[26]
findingthe continuum is built by varying the contrast of a single bar between two digits, so one physical dimension carries two categories
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content_tags.findings[27]
findingstimuli were held at perceptual threshold so that a fixed stimulus produced both seen and unseen reports
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content_tags.findings[28]
findingcontrast was set per subject by staircase to about 70 percent accuracy, and two subjects who would not converge were excluded
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content_tags.findings[29]
findingthe visibility scale was validated against target-absent trials before being used
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content_tags.findings[30]
findingTHE RESULT: choices tracked bar contrast at every visibility level, and the sigmoid steepened significantly as visibility rose
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content_tags.findings[31]
findingthree predictions verified - above chance throughout, rising with visibility, and increasingly nonlinear with visibility
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content_tags.findings[32]
findingAND NO QUALITATIVE DIFFERENCE HAD TO BE POSTULATED between conscious and unconscious processing to get it
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content_tags.findings[33]
findingan earlier result read as two processes - unconscious analogue against conscious discrete - is reproduced by one
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content_tags.findings[34]
findingconscious percepts are held to a borrowed definition of categorical perception, with three separate signatures promised in a companion paper
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content_tags.findings[35]
findingperception is framed as a massive classification problem, with a picture-naming task involving tens of thousands of classes
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content_tags.findings[36]
findingsubjective reports are argued not to be reducible to objective measures nor to second-order confidence
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content_tags.findings[37]
findingTHE BIAS IS THE SIGNAL: a criterion shift reflects the subject's own priors and costs and is argued not to be discarded as a confound
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content_tags.findings[38]
findingTHE CONTROL CREATES A CONFOUND: equating discrimination by masking or attention introduces physical differences between visible and invisible stimuli
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content_tags.findings[39]
findingthe predicted exceptions to the all-or-none pattern remain untested
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content_tags.findings[40]
findingthe assumption that one vector serves several successive tasks is supported by a four-guess experiment, and its limits are named
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content_tags.findings[41]
findingincluding decay, retro-cueing, task set and the order of the questions
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content_tags.findings[42]
findingAND PROJECTIONS DO NOT COMMUTE, so question order could change the answers - left open as a possible addition
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content_tags.findings[43]
findingthe single-vector assumption is contrasted with sequential sampling models, which is what would be needed for reaction times
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content_tags.findings[44]
findingthe ideal-observer assumption is named as an idealization, with learned and miscalibrated priors offered as the route to deviations from optimality
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content_tags.findings[45]
findingthe classic one-dimensional depiction of signal detection theory is argued to miss the interaction between features, classes and tasks
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content_tags.findings[46]
findingthe vast representational space is placed in posterior unimodal and multimodal sensory areas, whose role may be to augment the dimensionality of sensory inputs and turn decisions into linearly separable problems
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content_tags.findings[47]
findingwith the global workspace named as the candidate for the categorical decision system
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content_tags.findings[48]
quantityn = 17 — number of subjects contributing to the reported results (two of nineteen were excluded)
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content_tags.quantities[0]
quantity29% males, age: 25 ± 5 — composition of the volunteer sample recruited for the experiment
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content_tags.quantities[1]
quantity67.7% — identification accuracy on unambiguous targets, confirming the staircase hit its ~70% goal
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content_tags.quantities[2]
quantity36.7 versus 16.9% — proportion of trials given the lowest (0%) visibility rating on target-absent versus target-present trials
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content_tags.quantities[3]
quantity√2 — the factor by which discrimination sensitivity exceeds detection sensitivity in the two-dimensional version of the model
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content_tags.quantities[4]
quantity1000 trials divided into 25 blocks — total trial count per subject in the main experiment
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content_tags.quantities[5]
quantityr ²(15) = 0.79 — strength of the relation between reported visibility level and the steepness of the identification sigmoid
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content_tags.quantities[6]
quantity50% — the share of physically identical threshold trials reported unseen through internal variability alone
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content_tags.quantities[7]
quantity36.7 versus 16.9% — use of the zero-visibility response on target-absent against target-present trials, the check that the scale was used properly
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content_tags.quantities[8]
quantityr ²(15) = 0.79 — the increase in sigmoid slope with visibility rating - the tested prediction
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content_tags.quantities[9]
quantity70% — the accuracy the per-subject staircase targeted before the experiment began
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content_tags.quantities[10]
variableparameter λ (independent)
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content_tags.variables[0]
variableidentification performance (dependent)
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content_tags.variables[1]
variablesubjective visibility (dependent)
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content_tags.variables[2]
variabletarget contrast (control)
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content_tags.variables[3]
variablevisibility
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content_tags.variables[4]
variablediscrimination
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content_tags.variables[5]
variablevariance
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content_tags.variables[6]
variableprior probability
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content_tags.variables[7]
variableevidence
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content_tags.variables[8]
definitionvast representational space
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content_tags.definitions[0]
definitioncategorical structure of classes
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content_tags.definitions[1]
definitionforced-choice discrimination
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content_tags.definitions[2]
definitionvisibility judgement
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content_tags.definitions[3]
definitionsubliminal perception
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content_tags.definitions[4]
definitionrepresentational space
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content_tags.definitions[6]
definitioncriterion
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content_tags.definitions[7]
definitioncategorical perception
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content_tags.definitions[8]
definitionidentification
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content_tags.definitions[9]
definitionsequential sampling
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content_tags.definitions[10]
key-termsrepresentational space, input vector, classes, response classes, task, decision boundaries, criterion, posterior probability, prior probability, priors, likelihood, cost, evidence, stimulus strength, signal detection, ideal observer, sequential sampling, non-commutativity, visibility, discrimination, identification, detection, confidence, second-order, subjective report, seen, unseen, subliminal, blindsight, hysteresis, attention, variance, sigmoid, nonlinear, all-or-none, categorical perception, conscious perception, unconscious, morph, digit, mask, staircase, global workspace
Statistics — cross-field join keys
field-invariant statistical terms, used in their statistical sense (gated on a number / symbol / test). A p-value or a standard deviation means the SAME thing in physics, biology, psychology and sociology — so these are the strongest cross-field join keys. Grounded verbatim.
t-test t as written: t 16 = 5.00, p < 0.001
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content_tags.statistics[0]
standard deviation s.d. as written: s.d. = 14.1%
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content_tags.statistics[1]
model fitting (binomial / logistic psychometric fit) as written: fit a binomial distribution as a function of bar intensity
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content_tags.statistics[2]
correlation (coefficient of determination) r² as written: r ²(15) = 0.79, p = 0.004
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content_tags.statistics[3]
d’
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content_tags.statistics[4]
binomial
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content_tags.statistics[5]
t 16
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content_tags.statistics[6]
Second readings
a facet read a second time, independently, from its own sentence — and whether the two readings agree
claim typemechanism — a second reading reaching the same value; recorded: mechanism
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alternatives.claim_type
methoda Bayesian signal-detection model in a multi-dimensional feature space, simulated for six known findings, then tested behaviourally by morphing one bar between two digits at perceptual threshold and collecting identification and visibility on every trial — a second reading DIFFERING from the recorded value; recorded: simulation
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alternatives.method
phenomenonsubliminal perception: above-chance discrimination of a stimulus the subject reports not having seen — a second reading DIFFERING from the recorded value; recorded: dissociation between subjective visibility report and objective discrimination (subliminal perception)
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alternatives.phenomenon

When we have changed our mind. Some entries in the table begin with a word in capitals and the reason for it. EVIDENCE CORRECTED — the finding still stands, but we had quoted the wrong sentence from the paper, and the quotation has been replaced with the right one. RETRACTED — the entry itself was wrong; it stays on the page, marked, with the reason, rather than being deleted. WITHDRAWN AND REPLACED — the entry was wrong, and a corrected version of it appears in the entries below. Each mark carries its own reason, in brackets, because the reasons differ from one entry to the next. An entry we have WITHDRAWN is removed from this page and kept in the record, with its reason. A line reading Audit and a date is a later re-reading of the entry against its whole sentence: DOUBTFUL means it was kept, and the reason says what is loose about it. Quotation marks inside the tagger's sentence. Where a tag is the tagger's own sentence, a phrase in quotation marks inside it is borrowed from the paper — the tagger's wording around it is not. The paper's own sentence is the evidence quote for that row; on this page it is withheld with the article, and the article is at its publisher. Nothing inside quotation marks anywhere on this page is ours: if it is quoted, it is the paper's. Whose words a concept carries. A concept entry records a term the paper itself uses, so the term, the sense given for it, and its evidence are the paper's own words, quoted where the paper fixes them. This is the opposite of a sense entry in the tag table, which is the tagger's sentence about how the paper uses a term. Where a concept entry could not be grounded in the paper's words, the entry says so on its own row.

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