In the 1980s, psychologists made an unexpected discovery while working with individuals who had become blind after sustaining damage to areas of the brain required for vision. These individuals could respond correctly to questions about the shape and location of objects in their visual field, even though they could no longer see the objects. This phenomenon became known as 'blindsight', and it is regarded as a classic example of perception in the absence of conscious awareness.

Many researchers who study consciousness believe that everyone is capable of subliminal or unconscious perception: that is, of detecting and processing stimuli without being consciously aware of them. However, studies investigating this phenomenon have produced contradictory results. Peters and Lau have now tested unconscious perception directly, using a recently developed method that overcomes some of the problems faced by previous studies.

Human volunteers took part in several trials, in which they were shown two images. Each image was ‘masked’ to prevent the volunteers from consciously registering them. After each image was shown, the volunteers had to state whether a patch of gray and white stripes in the masked image was tilted to the left or to the right. However, one of the two images did not include a gray and white patch. After seeing both images in a trial, the volunteers also had to indicate which of their answers they were most confident about.

If the volunteers could perceive the patches without being consciously aware of doing so, their response should show two features. The volunteers should correctly state the tilt direction of the stripes more often than would be expected if they were guessing at random. However, they should also feel no more confident in their responses for the images that did feature a striped patch than for the ‘no patch’ ones. Peters and Lau found no such evidence of unconscious perception.

Nevertheless, the volunteers were consistently better at correctly stating the direction the stripes were tilted in than their confidence ratings would suggest. Does this indicate some degree of perception without awareness? Peters and Lau argue that it does not, because a computer model designed to perform the task showed a similar level of performance to the volunteers.

These findings suggest that previous reports of unconscious perception may have been contaminated by the problems that Peters and Lau controlled for, and that perhaps unconscious perception doesn’t occur in people without brain damage. Researchers will now need to do more studies using similar approaches to determine whether observers without brain damage can truly experience unconscious perception, and how such unconscious perception might be represented in the brain.

Above-chance performance without awareness in perceptual discrimination tasks is a strong form of unconscious perception. In these demonstrations (e.g., blindsight: Weiskrantz, 1986) the subjective threshold for awareness (when a stimulus is consciously ‘seen’) seems well above the objective threshold for forced-choice discrimination (when a stimulus can be correctly identified): subjects can discriminate a target above chance performance, yet report no awareness of the target. Many researchers believe normal, healthy subjects can also directly discriminate near-threshold, low-intensity targets without subjective awareness (e.g., Boyer et al., 2005; Charles et al., 2013; Merikle et al., 2001; but see Snodgrass et al., 2004 for an opposing view).

We conducted an informal survey to confirm this popular belief, which also revealed that many believe convincing evidence for this phenomenon is lacking. We asked survey participants three key questions: (1) "Do you believe in subliminal perception?" (2) "Do you believe that the subjective threshold for awareness is above the objective discrimination threshold?" and (3) "If ‘yes’, do you believe this has been convincingly demonstrated in the literature?" Most respondents reported believing that subliminal processing exists (94%), but also that they did not believe it had been convincingly demonstrated in the literature (64%). These belief patterns were shown even among those who reported having published on subliminal or unconscious perception (94% and 61%, respectively). See Appendix 1 for full text of questions and detailed survey results.

A primary culprit in this controversy is the problem of criterion bias: an observer’s report of ‘unseen’ doesn’t necessarily imply complete lack of awareness, only that the stimulus’ strength fell below some arbitrary boundary for reporting ‘seen’ (Eriksen, 1960; Hannula et al., 2005; Lloyd et al., 2013; Merikle et al., 2001). Unfortunately, most methods of studying unconscious perception suffer from this ‘criterion problem’ (e.g., Charles et al., 2013; Jachs et al., 2015; Ramsøy and Overgaard, 2004). With such methods, one could argue that reports of ‘unawareness’ may only mean some stimuli are relatively hard to perceive compared to those that are clearly visible.

To avoid this criterion problem, several groups (Kolb and Braun, 1995; Kunimoto et al., 2001) sought to identify conditions in which confidence was uncorrelated with accuracy, which they argued would indicate no subjective awareness of the target. Unfortunately, some of these efforts were not replicable (Morgan et al., 1997; Robichaud and Stelmach, 2003). Others revealed that estimating the correspondence between confidence and accuracy requires mathematical considerations more complicated than originally envisaged (Evans and Azzopardi, 2007; Galvin et al., 2003; Maniscalco and Lau, 2012). Importantly, the conceptual link between metacognitive sensitivity (i.e., correlation between confidence and accuracy) and conscious awareness is itself controversial (Charles et al., 2013; Fleming and Lau, 2014; Jachs et al., 2015).

Here, we employ a recently-developed confidence-rating method to address this problem (Barthelmé et al., 2009; de Gardelle and Mamassian, 2014). Subjects discriminated two stimulus intervals, only one of which contained a target, and indicated confidence in their decisions using a 2-interval forced-choice procedure (2IFC), that is, indicating which of the two discrimination decisions they felt more confident in. This approach has several advantages. First, 2IFC tasks depend little on response bias compared to multi-point confidence-rating scales. Maintaining the criteria for extensive confidence scales may also be demanding, leading subjects to respond somewhat randomly in conditions of vague awareness and thereby producing the negative result Kolb and Braun (1995) observed (Morgan et al., 1997). Second, the interpretation of 2IFC confidence-rating in this context is straightforward: ‘Performance without Awareness’ would mean subjects can perform the target discrimination yet fail to place bets appropriately to distinguish this performance from discrimination of a blank stimulus (which guarantees chance performance). That is, following psychophysics traditions (Kolb and Braun, 1995; Peirce and Jastrow, 1884), if a certain above-chance discrimination seems introspectively no different from a random guess based on no stimulus at all (as reflected by betting behavior), we interpret the discrimination to be unconscious. Here, we explored whether such Performance without Awareness occurs in normal observers in two behavioral experiments, and compared these results to predictions of a Bayesian ideal observer.

Blindsight (Weiskrantz, 1986) is the intriguing demonstration of Performance without Awareness in neurological patients. Despite widely held beliefs by experts, here we found no evidence that it occurs in normal observers. Importantly, although the measured psychophysical threshold (75%) for awareness seemed to be above the objective discrimination threshold, computational analysis revealed that the actual effective thresholds are essentially the same; people’s subjective ratings are close to ideal, given their objective performance levels. This challenges longstanding beliefs regarding the nature of subjective versus objective thresholds in perceptual studies (Merikle et al., 2001; see survey results in Appendix 1).

Our findings cannot rule out all forms of unconscious perception, such as subliminal priming, in which the evidence for unconscious processing is typically indirect benefits in reaction times (Hannula et al., 2005). However, our findings bear upon those studies, too. Traditionally, interpreting such effects as unconscious required that the relevant stimuli yield zero sensitivity in a direct task (d’ = 0). Recently, many have relaxed this requirement and considered subjectively reported lack of awareness as sufficient (Pessiglione et al., 2009; Soto et al., 2011), presumably because we (wrongly) believed that certain stimuli might surpass the objective threshold while still being below the subjective one. One may also argue that while objective threshold requirements are rigorous, the valid and meaningful measure is the subjective threshold (Charles et al., 2013; Merikle et al., 2001). Our results suggest this reasoning is flawed. If a stimulus surpasses the objective threshold, there is likely conscious experience; subjects likely report lack of awareness because they interpret the response options in relative terms in the context of stimuli of various strengths. This undermines claims that higher-cognitive phenomena – e.g. working memory, error detection, or motivation – can really operate unconsciously, if assessed with reference to subjective rather than objective thresholds (Charles et al., 2013; Pessiglione et al., 2009; Soto et al., 2011).

Although the 2IFC confidence-rating procedure bypasses the response bias problem, interpreting the subjective vs. objective function is non-trivial: to determine whether participants’ Performance > Awareness behavior was optimal required detailed computational analysis. An alternative approach, which may be simpler, would be to compare the objective and subjective functions between task conditions, in a rationale similar to Lau and Passingham (2006).

Although we found no evidence of ‘blindsight’ in normal observers, our study lays out the logic of what would be required to demonstrate it unequivocally. For example, it has recently been argued that TMS-induced ‘blindsight’ (Boyer et al., 2005) is contaminated by criterion bias (Lloyd et al., 2013). 2IFC confidence-rating may help resolve such issues without invoking theoretically complicated problems concerning signal detection theory (e.g., Heeks and Azzopardi, 2015). Thus, despite their negative nature, our findings may beget fruitful lines of inquiry to address which stimuli, procedures, or brain stimulation techniques can selectively impair subjective conscious experience, beyond impacting sheer objective processing sensitivity.

This work was supported by the National Institute of Health (US) to HL (grant number R01NS088628). We thank Brian Maniscalco, Dobromir Rahnev, Hongjing Lu, and Zili Liu for helpful comments.

Appendix 2—Figure 1

Download asset Open asset

Appendix 2—Figure 2

Download asset Open asset

Appendix 2—Figure 3

Download asset Open asset

We also found that subjects were overall unbiased in their choices of left- versus right-tilted Gabor patches, justifying use of percent correct rather than the signal detection theory metric d’. We calculated the mean criterion used by each subject across all levels of contrast according to signal detection theory, assuming equal variance for distributions representing left- versus right-tilted stimuli, i.e.

where HR refers to the hit rate (i.e., the participant said ‘left’ when a left-tilted Gabor was presented) and FAR refers to the false alarm rate (i.e., the participant said ‘left’ when a right-tilted Gabor was presented) (Macmillan and Creelman, 2004). Individual subjects’ criterion values are presented in Appendix 2–Table 1. A two-tailed t-test (treating each subject as an independent sample) revealed these values to not be significantly different from 0 (Appendix 2–Table 1).

We ran a control study to assess whether the lack of Performance without Awareness observed in Experiments 1 and 2 may have occurred because subjects have an unconscious ‘hunch’ of confidence despite having absolutely no phenomenal experience of the stimulus. Three observers (all male, ages 19-32, all right-handed) gave written informed consent to participate. One observer had participated in Experiments 1 and 2; the other two were naive. All procedures were identical to Experiment 2, with two exceptions: (1) observers were not told that one interval was blank; and (2) observers were asked to indicate which interval was ‘more visible’ rather than which discrimination they believed they were more likely to get correct. Thus, observers engaged in a task more akin to a 2IFC detection task rather than a metacognitive judgment.

Despite these manipulations, the results of the control experiment closely mirror the results of Experiments 1 and 2. Subjects were able to detect the target in the TP interval above chance as soon as they were able to discriminate the target above chance, and individual subjects’ data closely resembled the group data (Figure S3). Note that the computational model described in the main text does not apply to these data, as participants were doing 2IFC detectability discrimination rather than a 2IFC metacognitive judgment. This means that even though participants’ data lie near the identity line of equal orientation percent correct and percent betting on the TP interval (dashed diagonal line) – above the behavior predicted by the confidence model – this should not be taken to mean they are displaying any sort of ‘supra-optimal’ behavior, as the tasks are fundamentally different. The position of responses on this graph for this task will depend on the precise relationship between the detectability and discriminability of the stimulus for each individual subject (Thomas et al., 1982; Thomas, 1987).

Appendix 3—Figure 1

Download asset Open asset

For completeness, we detail two other models considered here.

Alternative model 1: heuristic – hierarchical inference

To evaluate whether the behavioral pattern predicted by the Bayesian ideal observer is idiosyncratic or robust to different decision strategies, we explored an alternative model which does not ‘ignore’ (marginalize over) contrast as a secondary or nuisance variable. Instead, this Bayesian heuristic observer engages in hierarchical inference: it first estimates the most likely contrast to have produced the current stimulus as a secondary or hidden variable, then uses it to inform the decision about the primary variable of interest, that is, the most likely orientation of the stimulus (Yuille et al., 1996).

Upon seeing a sample d, this observer first makes a guess ${\hat{c}}_i$ at the most probable c to have generated the current data point d, along both the c_left and c_right dimensions. It does so via maximizing the likelihood of each data point d given the possible source distributions centered at all possible contrast values c along the axes:

${\hat{c}}_{left} = \underset{c_{left}}{argmax } p \left(\overline{)d}{\hat{S}}_{c_{left}}\right)$

and

(A5-1) ${\hat{c}}_{right} = \underset{c_{right}}{argmax } p \left(\overline{)d}{\hat{S}}_{c_{right}}\right)$

where ${\hat{S}}_{c_{left}}~N\left(\left[c_{left},0\right],\sum \right)$ and ${\hat{S}}_{c_{right}}~N\left(\left[0,c_{right}\right],\sum \right)$. This can easily be calculated by a projection of each $d=\left[d_{left},d_{right}\right]$ onto the c_left and c_right axes, that is ${\hat{c}}_{left} = d_{left}$ and ${\hat{c}}_{right} = d_{right}$ (Appendix 5—Figure 1). In other words, the model decomposes the stimulus in each interval into the most likely 'left tilt contrast' (c_left) and 'right tilt contrast' (c_right) to have given rise to what the model is 'seeing'. Values for ${\hat{c}}_{left}$and ${\hat{c}}_{right}$ are not constrained to be positive; negative values represent contrast evidence for a left or right tilt that falls below the 'medium gray' of the 'nothing' distribution. Note that one could also assess this for values of θ other than 90º.

Appendix 5—Figure 1

Download asset Open asset

Once the most likely values for ${\hat{c}}_{left}$and ${\hat{c}}_{right}$ have been obtained, the observer refers to these new ‘best guess’ source distributions as ${\hat{S}}_{{\hat{c}}_{left}}$and ${\hat{S}}_{{\hat{c}}_{right}}$ (Appendix 5—Figure 1). The Bayesian heuristic observer determines the posterior probability of each stimulus orientation source distribution ${\stackrel{⏜}{S}}_{{\stackrel{⏜}{c}}_i}$for each data sample d via Bayes’ Rule,

(A5-2) $p\left(\overline{){\hat{S}}_{{\hat{c}}_I}}d\right) = \frac{p\left(\overline{)d}{\hat{S}}_{{\hat{c}}_I}\right)p\left({\hat{S}}_{{\hat{c}}_I}\right)}{p\left(d\right)}$

with each orientation ${\hat{S}}_{{\hat{c}}_I}$ having equal prior probability of 0.5. The observer then makes its orientation decision via

(A5-3) ${\hat{S}}_{chosen} = \underset{i}{\arg \max } p\left(\overline{){\hat{S}}_{{\hat{c}}_I}}d\right)$

To determine which choice the observer is more confident in, the model refers to the magnitude of the posterior probabilities of each ${\hat{S}}_{chosen}$ in each interval as a measure of the probability of having made a correct orientation discrimination choice, i.e. $p\left(correct\right) = p\left(\hat{S}\overline{){}_{chosen}}d\right)$. Then the observer compares these posterior probabilities for the Target Present (TP) and Target Absent (TA) intervals by computing the decision variable D via

(A5-4) $D = \log \left(\frac{p\left(\hat{S}\overline{){}_{chosen,TP}}{d}_{TP}\right)}{p\left(\overline{){\hat{S}}_{chosen,TA}}{d}_{TA}\right)}\right)$

As before, if this decision variable is greater than 0, the observer selects the TP interval; if it is smaller than 0, the observer selects the TA interval as being more confident. This model produces the same behavior as the Bayesian ideal observer described in the main text (Appendix 5—Figure 2, top row).

Alternative model 2: heuristic – likelihood comparison

Barthelmé et al., 2009 used a variant of the two-by-two forced-choice paradigm (Nachmias and Weber, 1975), in which two threshold-level stimuli are simultaneously rather than sequentially presented. The observer is asked to choose which stimulus he would be more confident discriminating, and then to discriminate that stimulus. Importantly, in their task, templates are provided on-screen, such that observers may match the stimulus to a provided template rather than a remembered or inferred stimulus dimension.

In that study, the authors compare the performance of a series of models in predicting discrimination and confidence decision, including a Bayesian posterior for each template given the stimulus, and a Bayesian likelihood of each stimulus given each template. The authors report that the likelihood model fits their data better than the posterior model, and conclude that the likelihood method of evaluating confidence describes how human observers judge confidence (despite it being suboptimal). Here, we evaluate the performance of a similar likelihood-only model, defining the decision variable with reference to the most likely contrast level to have given rise to the current sample, i.e.

(A5-5) $D = log\left(\frac{p\left(\overline{)d_{TP}}{\hat{S}}_{chosen,TP}\right)}{p\left(\overline{)d_{TA}}{\hat{S}}_{chosen,TA}\right)}\right)$

This decision variable is evaluated as described for Equation A5-5 in the main text. This model failed to produce behavior consistent with human observers’ responses (Appendix 5—Figure 2, bottom row).

Appendix 5—Figure 2

Download asset Open asset

1. 1. Barthelmé S
   2. Mamassian P

   (2010) Flexible mechanisms underlie the evaluation of visual confidence

   Proceedings of the National Academy of Sciences of the United States of America 107:20834–20839.

   https://doi.org/10.1073/pnas.1007704107

   • Google Scholar
2. 1. Barthelmé S
   2. Mamassian P
   3. Kording K

   (2009) Evaluation of objective uncertainty in the visual system

   PLoS Computational Biology 5:e1000504.

   https://doi.org/10.1371/journal.pcbi.1000504.s001

   • Google Scholar
3. 1. Barthelmé S
   2. Mamassian P

   (2009) Evaluation of objective uncertainty in the visual system

   PLoS Computational Biology 5:e1000504.

   https://doi.org/10.1371/journal.pcbi.1000504

   • Google Scholar
4. 1. Boyer JL
   2. Harrison S
   3. Ro T

   (2005) Unconscious processing of orientation and color without primary visual cortex

   Proceedings of the National Academy of Sciences of the United States of America 102:16875–16879.

   https://doi.org/10.1073/pnas.0505332102

   • Google Scholar
5. 1. Charles L
   2. van Opstal F
   3. Marti S
   4. Dehaene S

   (2013) Distinct brain mechanisms for conscious versus subliminal error detection

   NeuroImage 73:80–94.

   https://doi.org/10.1016/j.neuroimage.2013.01.054

   • Google Scholar
6. 1. de Gardelle V
   2. Mamassian P

   (2014) Does confidence use a common currency across two visual tasks?

   Psychological Science 25:1286–1288.

   https://doi.org/10.1177/0956797614528956

   • Google Scholar
7. 1. Dorfman DD
   2. Alf E

   (1969) Maximum-likelihood estimation of parameters of signal-detection theory and determination of confidence intervals—rating-method data

   Journal of Mathematical Psychology 6:487–496.

   https://doi.org/10.1016/0022-2496(69)90019-4

   • Google Scholar
8. 1. Dosher BA
   2. Lu Z-L

   (1998) Perceptual learning reflects external noise filtering and internal noise reduction through channel reweighting

   Proceedings of the National Academy of Sciences of the United States of America 95:13988–13993.

   https://doi.org/10.1073/pnas.95.23.13988

   • Google Scholar
9. 1. Eriksen CW

   (1960) Discrimination and learning without awareness: a methodological survey and evaluation

   Psychological Review 67:279–300.

   https://doi.org/10.1037/h0041622

   • Google Scholar
10. 1. Evans S
    2. Azzopardi P

    (2007) Evaluation of a 'bias-free' measure of awareness

    Spatial Vision 20:61–77.

    https://doi.org/10.1163/156856807779369742

    • Google Scholar
11. 1. Galvin SJ
    2. Podd JV
    3. Drga V
    4. Whitmore J

    (2003) Type 2 tasks in the theory of signal detectability: discrimination between correct and incorrect decisions

    Psychonomic Bulletin & Review 10:843–876.

    https://doi.org/10.3758/BF03196546

    • Google Scholar
12. Book

    1. Green DM
    2. Swets JA

    (1966)

    Signal Detection Theory and Psychophysics

    New York: John Wiley & Sons, Inc.

    • Google Scholar
13. 1. Hannula DE
    2. Simons DJ
    3. Cohen NJ

    (2005) Imaging implicit perception: promise and pitfalls

    Nature Reviews. Neuroscience 6:247–255.

    https://doi.org/10.1038/nrn1630

    • Google Scholar
14. 1. Heeks F
    2. Azzopardi P

    (2015) Thresholds for detection and awareness of masked facial stimuli

    Consciousness and Cognition 32:68–78.

    https://doi.org/10.1016/j.concog.2014.09.009

    • Google Scholar
15. 1. Jachs B
    2. Blanco MJ
    3. Grantham-Hill S
    4. Soto D

    (2015) On the independence of visual awareness and metacognition: a signal detection theoretic analysis

    Journal of Experimental Psychology 41:269–276.

    https://doi.org/10.1037/xhp0000026

    • Google Scholar
16. 1. Kiani R
    2. Corthell L
    3. Shadlen MN

    (2014) Choice certainty is informed by both evidence and decision time

    Neuron 84:1329–1342.

    https://doi.org/10.1016/j.neuron.2014.12.015

    • Google Scholar
17. 1. King J-R
    2. Dehaene S

    (2014) A model of subjective report and objective discrimination as categorical decisions in a vast representational space

    Philosophical Transactions of the Royal Society B: Biological Sciences 369:20130204.

    https://doi.org/10.1098/rstb.2013.0204

    • Google Scholar
18. 1. Kolb FC
    2. Braun J

    (1995) Blindsight in normal observers

    Nature 377:336–338.

    https://doi.org/10.1038/377336a0

    • Google Scholar
19. 1. Kunimoto C
    2. Miller J
    3. Pashler H

    (2001) Confidence and accuracy of near-threshold discrimination responses

    Consciousness and Cognition 10:294–340.

    https://doi.org/10.1006/ccog.2000.0494

    • Google Scholar
20. 1. Lagarias JC
    2. Reeds JA
    3. Wright MH
    4. Wright PE

    (1998) Convergence properties of the nelder--mead simplex method in low dimensions

    SIAM Journal on Optimization 9:112–147.

    https://doi.org/10.1137/S1052623496303470

    • Google Scholar
21. 1. Lau HC
    2. Passingham RE

    (2006) Relative blindsight in normal observers and the neural correlate of visual consciousness

    Proceedings of the National Academy of Sciences of the United States of America 103:18763–18768.

    https://doi.org/10.1073/pnas.0607716103

    • Google Scholar
22. 1. Lloyd DA
    2. Abrahamyan A
    3. Harris JA
    4. Antal A

    (2013) Brain-stimulation induced blindsight: unconscious vision or response bias?

    PLoS One 8:e82828.

    https://doi.org/10.1371/journal.pone.0082828.g006

    • Google Scholar
23. Book

    1. Macmillan NA
    2. Creelman CD

    (2004)

    Detection Theory: A User’s Guide

    New York: Taylor & Francis.

    • Google Scholar
24. 1. Maniscalco B
    2. Lau H

    (2012) A signal detection theoretic approach for estimating metacognitive sensitivity from confidence ratings

    Consciousness and Cognition 21:422–430.

    https://doi.org/10.1016/j.concog.2011.09.021

    • Google Scholar
25. 1. Merikle P
    2. Smilek D
    3. Eastwood JD

    (2001) Perception without awareness: perspectives from cognitive psychology

    Cognition 79:115–134.

    https://doi.org/10.1016/S0010-0277(00)00126-8

    • Google Scholar
26. 1. Morgan MJ
    2. Mason AJS
    3. Solomon JA

    (1997) Blindsight in normal subjects?

    Nature 385:401–402.

    https://doi.org/10.1038/385401b0

    • Google Scholar
27. 1. Nachmias J
    2. Weber A

    (1975) Discrimination of simple and complex gratings

    Vision Research 15:217–223.

    https://doi.org/10.1016/0042-6989(75)90210-2

    • Google Scholar
28. 1. Nadaraya EA

    (1964) On estimating regression

    Theory of Probability & Its Applications 9:141–142.

    https://doi.org/10.1137/1109020

    • Google Scholar
29. 1. Nagelkerke NJD

    (1991) A note on a general definition of the coefficient of determination

    Biometrika 78:691–692.

    https://doi.org/10.1093/biomet/78.3.691

    • Google Scholar
30. 1. Peirce CS
    2. Jastrow J

    (1884)

    On small differences in sensation

    Memoirs of the National Academy of Sciences 3:.

    • Google Scholar
31. 1. Pessiglione M
    2. Schmidt L
    3. Draganski B
    4. Kalisch R
    5. Lau H
    6. Dolan RJ
    7. Frith CD

    (2007) How the brain translates money into force: a neuroimaging study of subliminal motivation

    Science 316:904–906.

    https://doi.org/10.1126/science.1140459

    • Google Scholar
32. 1. Ramsøy Thomas Zoëga
    2. Overgaard M

    (2004) Introspection and subliminal perception

    Phenomenology and the Cognitive Sciences 3:1–23.

    https://doi.org/10.1023/B:PHEN.0000041900.30172.e8

    • Google Scholar
33. 1. Robichaud L
    2. Stelmach LB

    (2003) Inducing blindsight in normal observers

    Psychonomic Bulletin & Review 10:206–209.

    https://doi.org/10.3758/BF03196486

    • Google Scholar
34. 1. Scott RB
    2. Dienes Z
    3. Barrett AB
    4. Bor D
    5. Seth AK
    6. A. K

    (2014) Blind insight: metacognitive discrimination despite chance task performance

    Psychological Science 25:2199–2208.

    https://doi.org/10.1177/0956797614553944

    • Google Scholar
35. 1. Shabel SJ
    2. Murphy RT
    3. Malinow R

    (2014) Negative learning bias is associated with risk aversion in a genetic animal model of depression

    Frontiers in Human Neuroscience 8:1–9.

    https://doi.org/10.3389/fnhum.2014.00001

    • Google Scholar
36. 1. Snodgrass M
    2. Bernat E
    3. Shevrin H

    (2004) Unconscious perception: a model-based approach to method and evidence

    Perception & Psychophysics 66:846–867.

    https://doi.org/10.3758/BF03194978

    • Google Scholar
37. 1. Soto D
    2. Mäntylä T
    3. Silvanto J

    (2011) Working memory without consciousness

    Current Biology 21:R912–R913.

    https://doi.org/10.1016/j.cub.2011.09.049

    • Google Scholar
38. 1. Sridharan D
    2. Steinmetz NA
    3. Moore T
    4. Knudsen EI

    (2014) Distinguishing bias from sensitivity effects in multialternative detection tasks

    Journal of Vision 14:16.

    https://doi.org/10.1167/14.9.16

    • Google Scholar
39. 1. Thomas JP
    2. Gille J
    3. Barker RA

    (1982) Simultaneous visual detection and identification: theory and data

    Journal of the Optical Society of America 72:1642–1651.

    https://doi.org/10.1364/JOSA.72.001642

    • Google Scholar
40. 1. Thomas JP

    (1987) Effect of eccentricity on the relationship between detection and identification

    Journal of the Optical Society of America A 4:1599–1605.

    https://doi.org/10.1364/JOSAA.4.001599

    • Google Scholar
41. 1. Watson AB
    2. Robson JG

    (1981) Discrimination at threshold: labelled detectors in human vision

    Vision Research 21:1115–1122.

    https://doi.org/10.1016/0042-6989(81)90014-6

    • Google Scholar
42. 1. Watson GS

    (1964)

    Smooth regression analysis

    Sankya: The Indian Journal of Statistics, Series A 26:359–372.

    • Google Scholar
43. Book

    1. Weiskrantz L

    (1986)

    Blindsight: A Case Study and Implications

    Oxford: Oxford University Press.

    • Google Scholar
44. 1. Yuille AL
    2. Bülthoff HH
    3. Knill DC
    4. Richards W

    (1996)

    Perception as bayesian inference

    123–162, Bayesian decision theory and psychophysics, Perception as bayesian inference, Cambridge, Cambridge University Press, 10.1017/CBO9780511984037.006.

    • Google Scholar

Author details

1. Megan A K Peters

   Department of Psychology, University of California, Los Angeles, Los Angeles, United States

   Contribution

   MAKP, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   For correspondence

   meganakpeters@ucla.edu

   Competing interests

   The authors declare that no competing interests exist.

2. Hakwan Lau

   1. Department of Psychology, University of California, Los Angeles, Los Angeles, United States
   2. Brain Research Institute, University of California, Los Angeles, Los Angeles, United States

   Contribution

   HL, Conception and design, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

• 6,195

  views

• 1,332

  downloads

• 100

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.