Sensitivity to visual features in inattentional blindness

eLife Assessment

This study presents valuable findings to the field interested in inattentional blindness (IB), the phenomenon that participants fail to notice salient stimuli when their attention is directed elsewhere. This study reveals that participants who indicate no awareness of unexpected stimuli through yes/no questions ("did you notice anything unusual?"), may still show above-chance sensitivity to specific properties of these stimuli through follow-up forced-choice questions (e.g., regarding its location or color). By introducing absent trials where no IB stimulus is presented, the authors show that this is because participants are generally conservative and biased to report not noticing in inattentional blindness experiments. The evidence supporting these conclusions is convincing, the samples sizes are large and the analysis protocol is novel.

https://doi.org/10.7554/eLife.100337.3.sa0

Significance of the findings:

Valuable: Findings that have theoretical or practical implications for a subfield

Landmark
Fundamental
Important
Valuable
Useful

Strength of evidence:

Convincing: Appropriate and validated methodology in line with current state-of-the-art

Exceptional
Compelling
Convincing
Solid
Incomplete
Inadequate

During the peer-review process the editor and reviewers write an eLife Assessment that summarises the significance of the findings reported in the article (on a scale ranging from landmark to useful) and the strength of the evidence (on a scale ranging from exceptional to inadequate). Learn more about eLife Assessments

Abstract
Introduction
Results
Discussion
Materials and methods
Appendix 1
Data availability
References
Article and author information
Metrics

Abstract

The relation between attention, perception, and awareness is among the most fundamental problems in the science of the mind. One of the most striking and well-known phenomena bearing on this question is inattentional blindness (IB). In IB, naive observers fail to report clearly visible stimuli when their attention is otherwise engaged—famously missing a gorilla parading before their eyes. IB carries tremendous significance, both as evidence that awareness requires attention and as a tool in seeking the neural correlates of consciousness. However, such implications rest on a notoriously biased measure: asking participants whether they noticed anything unusual (and interpreting negative answers as reflecting a complete lack of perception). Here, in the largest ever set of IB studies, we show that, as a group, inattentionally blind participants can successfully report the location, color, and shape of stimuli they deny noticing, demonstrating that perceptual information remains accessible in IB. By introducing absent trials, we further show that observers are collectively biased to report not noticing in IB—essentially ‘playing it safe’ in reporting their sensitivity. These data provide the strongest evidence to date of significant residual visual sensitivity in IB. They also challenge the use of inattentional blindness to argue that awareness requires attention.

Introduction

One of the most pervasive and compelling intuitions about perception is that we see what is right in front of us: If a large enough stimulus were to appear directly before our eyes (with good lighting, a well-functioning sensory apparatus, no special camouflage, etc.), we would see it and could easily report features such as its color, shape, and location. However, this seemingly secure assumption is challenged by perhaps the best-known result in contemporary perception science: The phenomenon of inattentional blindness (IB). In IB, engaging in an attentionally demanding task (e.g. judging the relative lengths of two briefly presented lines, or counting basketball passes) causes observers to miss large, highly visible, but unexpected stimuli appearing right before their eyes (e.g. a high-contrast novel shape, or a parading gorilla; Figure 1). Whereas in ordinary circumstances these stimuli would be extremely salient and easily noticed, under conditions of inattention we seemingly do not see them at all.

Figure 1

Download asset Open asset

Influential inattentional blindness paradigms and their results.

(**Left**) In a representative study from Mack and Rock, 1998, subjects fixated in the center of the circle and reported which arm of a centrally presented cross was longer. On the critical trial, an unexpected shape appeared in the near periphery at the same time as the cross and subjects were asked whether they noticed anything unusual. (**Center**) In Simons and Chabris, 1999, subjects counted the number of basketball passes made by individuals wearing white shirts. (Images drawn from Simons and Chabris, 1999, reprinted with permission from Dan Simons.) On the critical trial, a woman in a gorilla suit paraded through the display for 5 s, and subjects were asked whether they noticed anything unusual. (**Right**) In Wood and Simons, 2017 (based on Most et al., 2001), subjects fixated on the central blue square and reported the number of times the white or checkerboard squares bounced off the walls of the display. On the critical trial, a black circle entered from the right and crossed the display for several seconds, and subjects were asked whether they noticed anything that did not appear on previous trials. In all three experiments (and many others in this literature), a considerable proportion of subjects reported not noticing these unexpected stimuli.

IB is an extremely robust phenomenon, demonstrated in a stunning variety of laboratory and real-world contexts across half a century of research (Neisser and Becklen, 1975; Moore and Egeth, 1997; Mack and Rock, 1998; Simons and Chabris, 1999; Most et al., 2005; Drew et al., 2013; Murphy and Greene, 2016). Although the proportion of subjects demonstrating IB (the ‘IB rate’) varies widely depending on the protocol, it can be remarkably high (e.g. ~50% in the case of the famous gorilla, and over 80% in some of Mack & Rock’s studies; for reviews see Simons, 2000; De Brigard and Prinz, 2010; Jensen et al., 2011; de P Nobre et al., 2020; Redlich et al., 2021).

IB is also an extremely significant phenomenon. According to the ‘consensus view’ (Noah and Mangun, 2020; see also Mack and Rock, 1998; Dehaene et al., 2006; Cohen et al., 2012; Prinz, 2012; Prinz, 2015; though see Koch and Tsuchiya, 2007; Maier and Tsuchiya, 2021), subjects undergoing IB ‘have no awareness at all of the stimulus object’ (Rock et al., 1992), such that ‘one can have one’s eyes focused on an object or event … without seeing it at all’ (Carruthers, 2015). This interpretation has endowed IB with numerous theoretical and practical implications. First, IB is a key piece of evidence for the more general claim that awareness requires attention (or, conversely, that ‘without attention, conscious perception cannot occur’; Dehaene et al., 2006), and so has been used to support various leading theories of consciousness on which attention plays a critical role, such as global neuronal workspace theory (Dehaene and Naccache, 2001), attended intermediate representation (AIR) theory (Prinz, 2015), and attention schema theory (Graziano and Webb, 2015). Second, conceived as a tool to abolish awareness, IB is frequently used by neuroscientists to measure neural activity in the absence of consciousness (e.g. Rees et al., 1999; Pitts et al., 2014; Hutchinson, 2019), in the hope of isolating the neural correlates of consciousness. Third, IB challenges cherished assumptions about our ability to perceive the world around us—inspiring substantial theoretical and philosophical debate (e.g. O’Regan and Noë, 2001; Lamme, 2003; Block, 2011; Cohen et al., 2011) and rightly contributing to its status as one of the few results in perception science that has captured public interest and imagination (e.g. Chabris and Simons, 2011; Cloud, 2010; Murphy, 2017).

Crucially, however, this interpretation of IB and the many implications that follow from it rest on a measure that psychophysics has long recognized to be problematic: simply asking participants whether they noticed anything unusual. In IB studies, awareness of the unexpected stimulus (the novel shape, the parading gorilla, etc.) is retroactively probed with a yes/no question, standardly, ‘Did you notice anything unusual on the last trial which wasn’t there on previous trials?’. Any subject who answers ‘no’ is assumed not to have any awareness of the unexpected stimulus.

However, yes/no questions of this sort are inherently and notoriously subject to bias, because they require observers to set a criterion in order to decide whether they have enough evidence to answer ‘yes’ or instead answer ‘no’ (Dulany, 2001; cf. Eriksen, 1960; Holender, 1986; Irvine, 2012). Under the framework of Signal Detection Theory (SDT; Tanner and Swets, 1954; Green and Swets, 1966), observers who are asked to determine whether a signal is or is not present must in setting such a criterion consider tradeoffs between the various possible outcomes of their decision (i.e. the relative costs of hits, misses, false alarms, and correct rejections). Indeed, in an IB task (where the signal in question is the unexpected stimulus), observers may have reason to be conservative in their criterion-setting—that is to adopt a high standard for saying ‘yes’ (and answer ‘no’ if that standard isn’t met). For example, observers might be under-confident whether they saw anything (or whether what they saw counted as unusual); this might lead them to respond ‘no’ out of an excess of caution. Subjects might doubt that they could identify the unexpected stimulus if asked, and so respond ‘no’ to avoid having to do so. Subjects may also worry that if they report noticing the unexpected object, the experimenters will take that to mean they weren’t engaged in the task they had been given (e.g. judging which cross arm was longer or counting the passes; Dulany, 2001). On any of these possibilities, subjects who say they did not notice the critical stimulus may well have had some awareness of it, but simply underreported it given the constraints of traditional IB questioning.

Evidence for this alternative hypothesis would have dramatic consequences for the dominant interpretation of IB and the implications that follow from it: It would overturn the view that IB reflects a total failure to perceive and so challenge the appeal to IB in arguments that attention is required for awareness. Remarkably, however, the hypothesis that subjects in inattentional blindness tasks actually see more than they say (but respond otherwise because they are conservative in reporting their awareness) remains empirically unsettled, and the powerful tools of SDT unexploited in relation to IB.

A handful of prior studies have explored the possibility that inattentionally blind subjects may retain some visual sensitivity to features of IB stimuli (e.g. Schnuerch et al., 2016; see also Kreitz et al., 2020; de P Nobre et al., 2020). However, a recent meta-analysis of this literature (de P Nobre et al., 2022) argues that such work is problematic along a number of dimensions, including underpowered samples and evidence of publication bias that, when corrected for, eliminates effects revealed by earlier approaches, concluding ‘that more evidence, particularly from well-powered pre-registered experiments, is needed before solid conclusions can be drawn regarding implicit processing during inattentional blindness’ (de P Nobre et al., 2022).

Here, five experiments provide this critical test for the first time. We achieve this by making four key modifications to classic IB paradigms. First, we add follow-up questions to the classic yes/no methodology to explicitly probe awareness of features of the IB stimulus (i.e. we don't just ask whether subjects noticed anything, but also query features of the object they said they didn’t notice). Although the use of follow-up questions is not new, our approach overcomes several problems with their past use in the literature (see Discussion for much more on this point). Second, we include ‘absent trials’ in which some subjects are shown no additional stimulus on the critical trial but are still asked whether they noticed anything unusual. This procedure provides a false alarm rate to test—for the first time, to our knowledge—whether subjects are conservative in reporting their awareness in the ways hypothesized above. Third, we leverage online data collection to massively increase sample size (running 25,000 subjects, each experiencing just a single critical trial), in order to make possible the crucial signal-detection analyses that separate bias from sensitivity. Finally, to overcome the fact that in IB each subject necessarily only contributes one trial (since following a critical trial, stimuli are no longer unexpected), we introduce a novel analytic approach by applying signal detection models to a ‘super subject’.

Altogether this approach reveals that as a group subjects can report above-chance the features of stimuli (color, shape, and location) that they had all claimed not to notice under traditional yes/no questioning, and that this underreporting of awareness is well-modeled in terms of a conservative criterion. In place of the dominant interpretation that IB abolishes perception and awareness, the present results indicate that significant residual sensitivity remains in IB, and are even consistent with an alternative picture on which inattention instead degrades awareness. More generally, our findings motivate an approach to perception and awareness which treats them as graded as opposed to all-or-nothing, which we further discuss below.

Results

Above-chance sensitivity to inattentional blindness stimuli: location

Experiment 1 modified the canonical inattentional blindness task used by Mack and Rock, 1998; Figure 2a. On three trials, subjects were presented with a cross randomly assigned on each trial to be directly above or below a central fixation point (for 200 ms), and their task was to report which arm of the cross (horizontal or vertical) was longer. The fourth, critical trial, proceeded in the same way, but with the addition of an unexpected red line appearing in the periphery simultaneous with the cross. After again reporting which arm of the cross was longer, subjects were asked the standard question used to measure inattentional blindness: ‘Did you notice anything unusual on the last trial which wasn’t there on previous trials?’ (yes/no). In line with established findings on IB, a considerable proportion of subjects (28.6%) responded ‘no’ they didn’t notice anything unusual (we refer to these subjects across all our experiments as ‘non-noticers’).

Figure 2

Download asset Open asset

Stimuli, procedure and results for Experiment 1.

(a) Schematic trial sequence for Experiment 1. On Trials 1–3 subjects were presented with a cross above or below fixation for 200 ms and judged which arm was longer. On Trial 4, an unexpected red line appeared in the periphery simultaneous with the cross. After reporting which cross-arm was longer, subjects were asked, ‘Did you notice anything unusual on the last trial which wasn’t there on previous trials?’ (yes/no), followed by a 2afc question concerning the location of the line (left/right). (b) Percentage of subjects who report noticing or not noticing the extra red line. 29% of subjects were ‘non-noticers’. (c) Performance on the 2afc question (left/right location of the line), considering only those subjects who reported not noticing anything unusual. Remarkably, both %-correct and d′ were significantly above chance among the group of subjects who met traditional criteria for inattentional blindness. In other words, collectively, subjects who answered ‘no’ demonstrated sensitivity to the location of the stimulus they all had just claimed not to have noticed. Error bars are 95% CIs.

Following the standard IB question, our modification included an additional question: ‘In fact, the last trial you just saw contained one extra element—a vertical red line on either the left or the right side of the box. What side do you think it appeared on?’ (left or right). Far more subjects were able to correctly locate the stimulus (87.4%) than said they noticed it (71.4%), raising the possibility that non-noticers—that is those who demonstrated inattentional blindness—might have performed significantly better than chance. This is precisely what we found: 63.6% of non-noticers answered the forced-choice question correctly (significantly different from the 50% correct expected by chance in a two-sided binomial probability test, 95% CI = [0.54, 0.73], BF10 = 9.9). In other words, nearly two-thirds of subjects who had just claimed not to have noticed any additional stimulus were then able to correctly report its location.

Critically, this result also holds using d′, an unbiased measure of sensitivity (d′_2afc = 0.51, 95% CI = [0.16, 0.85]; see Box 1 on SDT), which we use in reporting all following results concerning sensitivity in non-noticers. An important novelty of our strategy is that it derives these statistics in relation to a ‘super subject’ whose responses are comprised of individual subjects’ responses in their single critical trials. Note that all analyses reported here relate to this super subject as opposed to individual subjects; see Discussion for more on the assumptions behind this analytic approach.

Box 1

Signal detection theory and inattentional blindness.

In Signal Detection Theory (SDT; Green and Swets, 1966; Macmillan and Creelman, 2005), perceptual decisions are based on statistically variable sensory evidence (S + N) due to a stimulus or signal (S), if any, together with omnipresent noise (N). Deciding whether a stimulus was present or not (yes/no detection) involves deciding whether the evidence received is a sample from S + N or from N alone. To make this decision, an observer must set a criterion, a level of sensory evidence sufficient for a positive response. This criterion is flexible and can be adjusted in accord with the payoffs associated with different outcomes such as hits (saying ‘yes’ when a stimulus is present) and false alarms (saying ‘yes’ when no stimulus is present). SDT uses observed hit and false alarm rates to distinguish two distinct aspects of an observer’s performance: their sensitivity and bias. In simple models, N and S + N distributions are treated as equal variance Gaussians. The sensitivity of an observer is then measured as the standardized distance between the means of the N and S + N distributions. Intuitively, if the two distributions entirely overlap, an observer is entirely insensitive to the presence of a stimulus, whereas the greater the separation, the greater the sensitivity. To calculate this measure of sensitivity, we subtract the z-transform of the false alarm rate from the z-transform of the hit rate: d' = z(H) - z(FA). Critically, this measure is independent of the location of a subject’s criterion and so offers an objective, bias-free measure of their sensitivity. The bias of an observer is their tendency to prefer a particular response independent of the actual presence of a stimulus. This can be measured by the location of their criterion with respect to the midpoint of the N and S + N distributions. Intuitively, a criterion at the midpoint shows no preference for ‘yes’ responses over ‘no’ responses independent of the actual presence of a stimulus, whereas a conservative criterion reflects a preference for ‘no’ responses, and a liberal criterion a preference for ‘yes’ responses. We can calculate a standardized measure of bias, as follows: c = -½[z(H) + z(FA)]. The traditional question in IB studies—e.g. ‘Did you notice anything unusual on the last trial which wasn’t there on previous trials?’—can be treated as a yes/no detection question. By including absent trials, where no stimulus was presented and yet we ask this question anyway, we determined hit and false alarm rates across critical trials after applying the Hautus, 1995 correction of adding 0.5 to every cell in the decision matrix to avoid infinite values which would arise if any cell were zero. For example, in our Exp. 5, 7.2% of subjects who were shown no IB stimulus nonetheless responded ‘yes’—yielding a corrected false alarm rate of 0.073. On the other hand, 71% of subjects who were shown a stimulus responded ‘yes’—yielding a corrected hit rate of 0.71. Using the calculations described, we could separate the sensitivity of the corresponding super-subject from their bias. This revealed a significant conservative bias (c = 0.45), indicating a preference to deny noticing an unexpected stimulus, independent of its actual presence.

Box 1—figure 1

Download asset Open asset

Calculations of hit rate and false alarm rate for Exp. 5, revealing a significant conservative bias (c = 0.45).

Above-chance sensitivity to inattentional blindness stimuli: color

Experiment 2 repeated the design of Experiment 1, except that the additional line could be either red or blue, and the question about the line’s location was replaced with a one-interval forced-response question about the line’s color. This experiment yielded similar results to Experiment 1: Only 72.3% of subjects shown an additional stimulus said they had noticed something, yet 81% were able to indicate the additional line’s color correctly. And again, as a group, non-noticers demonstrated above chance sensitivity, this time to color (d′ = 0.38, 95% CI = [0.03, 0.73]). (See Appendix for reason to think that this may actually underestimate subjects’ true performance, which may be nearly double this figure.) Note that the unbiased nature of this measure is especially critical here since subjects displayed a significant bias in favor of responding ‘blue’ (c = 0.67, 95% CI = [0.49, 0.84]). In other words, consistent with the results of Experiment 1, as a group, subjects who had just claimed not to have noticed an additional stimulus were able to correctly report its color at rates well above chance. This pair of initial results shows that even subjects who answer ‘no’ under traditional questioning can, as a group, still correctly report various features of the stimuli they just reported not having noticed, indicating significant group-level sensitivity to visual features. Moreover, these results are even consistent with an alternative hypothesis about IB, namely that as a group, subjects who would traditionally be classified as inattentionally blind are in fact at least partially aware of the stimuli they deny noticing.

Conservative reporting of visual awareness

Our results raise a natural question: Why, if subjects could succeed at our forced-response questions as a group, did they all individually claim not to have noticed anything? Experiment 2 made an additional modification precisely to address this question: the introduction of ‘absent’ trials in which no additional line was shown but subjects were still asked the yes/no and one-interval forced-response questions. Absent trials provide an additional source of information about subjects’ biases, by revealing how often they respond ‘yes’ without any stimulus present (i.e. their false alarm rate). This allows for the computation of response bias (c) in relation to the crucial IB question (‘Did you notice anything unusual on the last trial which wasn’t there on previous trials?’; again, see Box 1), which to our knowledge is unique in this literature. This analysis revealed evidence for the second aspect of our alternative hypothesis: Not only do subjects collectively have residual sensitivity to unnoticed IB stimuli, they are, as a group, conservative in reporting their awareness (c = 0.31, 95% CI = [0.22, 0.41]; d′ = 1.81, 95% CI = [1.63, 1.99]; note that statistics here are for all subjects). In turn, this raises the possibility that subjects may retain awareness of unreported IB stimuli corresponding to their residual visual sensitivity but that this awareness is systematically underreported.

Above-chance sensitivity in high-confidence non-noticers

Although the previous two studies are consistent with the hypothesis that ‘inattentionally blind’ subjects retain at least partial awareness of unattended stimuli (and are conservative in reporting that awareness), it is possible that these results were driven by a subset of subjects, with other subjects remaining truly blind to the IB stimulus (i.e. having no sensitivity at all to its features). This might arise if above-chance sensitivity were restricted to subjects who were under-confident in responding ‘no’ when asked whether they had noticed anything unusual. In that case, subjects who confidently answered ‘no’ (i.e. felt certain they didn’t notice any additional stimulus) might, as a group, fail to perform above chance on the subsequent discrimination task. Experiment 3 addressed this possibility directly, by (a) adding confidence ratings to the standard yes/no question, and (b) dramatically increasing the sample size, so as to separately analyze the performance of high- and low-confidence subjects. The task proceeded in the same way as Experiment 1, except that after the yes/no question about noticing anything unusual, subjects were asked to rate their confidence in their answer, on a four-point scale from 0 to 3 (0 = Not at all confident; 3 = Highly confident); finally, subjects were then asked the left/right discrimination question (and gave their confidence in that answer as well, although this was less crucial to our hypothesis—see Appendix for details). As shown in Figure 3e, answers to these questions ran the full spectrum of responses, with subjects expressing varying degrees of confidence in ‘yes’ and ‘no’ responses to the IB question. Of particular relevance was the group of ‘high-confidence non-noticers’—that is, subjects who said ‘no’ (they didn’t notice anything unusual), and then rated their confidence in that answer as ‘3’ (highly confident). Remarkably, even this group of subjects (N=204) collectively demonstrated significantly above-chance sensitivity to the location of the IB stimulus (d′_2afc = 0.34; 95% CI = [0.08, 0.60]). Further, as is evident from Figure 3e, subjects’ confidence in their yes/no response predicted accuracy for that group on the discrimination task, suggesting that subjects may have graded awareness of unattended stimuli in IB tasks (see Appendix for details).

Figure 3

Download asset Open asset

Stimuli, procedure and results for Experiments 2 and 3.

(a) Schematic trial sequence for Experiment 2. On Trials 1–3 subjects were presented with a cross above or below fixation for 200 ms and judged which arm was longer. On Trial 4, for 2/3 of subjects, an unexpected line appeared in the periphery simultaneous with the cross. This line could be either blue or red and on the left or right. For 1/3 of subjects no additional line was shown. After reporting which cross-arm was longer, all subjects were asked, ‘Did you notice anything unusual on the last trial which wasn’t there on previous trials?’ (yes/no), followed by a one-interval forced-response question concerning the color of the line (red/blue). (b) Performance on the one-interval forced-response question about the unexpected line’s color amongst subjects who were shown a line and who reported not noticing anything unusual (N = 234; 27.73% of subjects). As a group, subjects who answered ‘no’ demonstrated sensitivity to the color of the stimulus they had just claimed not to have noticed. (c) Schematic trial sequence for Experiment 3. Trials 1–4 were identical to Experiment 1, except subjects were asked additional questions about their confidence following both yes/no and 2afc questions. (d) Performance on the 2afc question in Experiment 3, considering only subjects who reported not noticing anything unusual (N = 1634; 30.85% of subjects). Replicating the finding of Experiment 1, as a group, subjects who answered ‘no’ demonstrated sensitivity to the location of the stimulus they had just claimed not to have noticed. (e) Performance on the 2afc question in Experiment 3 for all subjects, broken down by confidence in their response to the yes/no question whether they had noticed anything unusual (N in each bin as follows: No-3 = 204; No-2 = 601; No-1 = 640; No-0 = 189; Yes-0 = 25; Yes-1 = 189; Yes-2 = 771; Yes-3 = 2677). Remarkably, even subjects who were highly confident that they had not noticed anything unusual were collectively significantly above chance. Error bars are 95% CIs.

Generalizing to dynamic inattentional blindness

Experiments 1–3 suggest that, collectively, subjects underreport their perception of a brief (200 ms) IB stimulus. However, in classic studies of dynamic inattentional blindness (Simons and Chabris, 1999; Most et al., 2005; Ward and Scholl, 2015), the unexpected stimulus remains in view for an extended period. Here, it is tempting to think that subjects will not be conservative in reporting that they noticed an IB stimulus given they have many seconds to build confidence in what they saw. Experiment 4 tested this possibility by modifying a traditional sustained inattentional blindness paradigm in which the IB stimulus remains on screen for 5 s. Finding the same pattern of above-chance sensitivity and conservative response bias in this very different paradigm would be striking evidence that, quite generally, there is residual sensitivity to visual features in inattentional blindness, and lend further support to our alternative hypothesis.

In Experiment 4, subjects’ primary task was to count how often squares of a particular color (black or white) bounced off the perimeter of a gray rectangle (adapted from Wood and Simons, 2017, in turn based on Most et al., 2001; see Figure 4a). This task demands significant attention as each set of colored squares bounces an average of 28 times during a 17-s trial. For some subjects, on the third and critical trial, an additional shape (a triangle or circle, which was either black or white) entered the display (on the left or the right) and traversed the full height of the display in a straight path (from either top to bottom, or bottom to top), remaining on screen for 5 s before disappearing. When the trial ended, subjects reported how many times the squares of their assigned color bounced. They were then immediately asked a standard question used to measure inattentional blindness: ‘Did you notice something on the last trial that did not appear on previous trials?’ (yes/no). Following this, each subject answered three additional questions about the extra object that may or may not have appeared, in a random order: (1) What color was it? (black or white), (2) What shape was it? (circle or triangle), (3) What side was it on? (left or right).

Figure 4

Download asset Open asset

Stimuli, procedure and results for Experiment 5.

(a) Stimulus parameters and a schematic critical trial for Experiment 5, in which an unexpected object moved across the display for several seconds while subjects counted the number of times the white squares bounced off the display’s ‘walls’ (task adapted from Wood and Simons, 2017). The unexpected object varied in its shape, color, direction of motion, and side of the display. As in Experiment 4, subjects were then asked follow-questions about this stimulus’s color, shape and location. (b) As a group, subjects who reported not noticing the unexpected stimulus (N = 2339) still showed above-chance sensitivity to its color and shape (though not to its location), a pattern predicted in our pre-registration. Thus, sensitivity to IB stimuli arises even when the stimuli are visible for a sustained period (rather than appearing only briefly, as in Experiments 1–3). Error bars are 95% CIs.

Consistent with previous studies using dynamic stimuli (see Figure 1b and c), a majority of subjects—57.3%—demonstrated IB. Put another way, just 42.7% of subjects who were shown something additional on the critical trial were correct on the yes/no detection question. However, despite the attentionally demanding primary task, non-noticers collectively again demonstrated significant sensitivity to the features of the IB stimulus, choosing the correct color (d′ = 0.82, 95% CI = [0.61, 1.04]) and shape (d′ = 0.21, 95% CI = [0.01, 0.42]) significantly more often than would be predicted by chance. Given that all the objects were changing location constantly in Experiment 4, we did not pre-register a prediction about location discrimination being above-chance, and we did not find that non-noticers performed above chance on the left/right discrimination question (though we did observe a trend in this direction, d′_2afc = 0.07, 95% CI = [–0.08, 0.22]).

Just as in Experiment 2, subjects underreported their awareness of the IB stimulus on the traditional yes/no question, employing a decision criterion that was even more conservative than that measured in Experiment 2 (c = 0.85, 95% CI = [0.73, 0.97]; d′ = 1.33, 95% CI = [1.10, 1.57]; again, note that statistics here are for all subjects; see Figure 5c). The fact that subjects were collectively more reluctant to report their awareness of the IB stimulus in this experiment compared with Experiment 2 is surprising given that subjects had 5 full seconds to build confidence that there was a stimulus. However, given that the colors of the squares in the primary task were the same as the colors used for the IB stimuli, it is possible that estimates of criterion and color sensitivity in Experiment 4 were affected by an interaction effect due to enhancement of color-congruent stimuli and suppression of color-incongruent stimuli (cf. Wood and Simons, 2017). Specifically, the IB rate for color-incongruent stimuli was substantially higher than for color-congruent stimuli (86.57% vs 28.71%), suggesting suppression occurred. That is, a subject shown an unexpected black stimulus while attending to black squares was more likely to report noticing than a subject shown an unexpected white stimulus. This in turn may have inflated our estimate of color sensitivity if subjects correctly assumed that they would have been more likely to notice a color-congruent stimulus and biased their responses towards the incongruent color.

Figure 5

Download asset Open asset

Conservative criteria in Experiments 2, 4, and 5.

(a) Critical trials from Experiments 2, 4, and 5, showing present trials in which an IB stimulus was presented to subjects (2/3 subjects in Experiment 2, 3/4 in Experiments 4 and 5), and absent trials in which no IB stimulus was presented (1/3 subjects in Experiment 2, 1/4 in Experiments 4 and 5). On all trials, subjects were asked a standard IB question, e.g. ‘Did you notice anything unusual on the last trial which wasn’t there on previous trials?’ (yes/no). (b) Left: Decision matrix for this yes/no question, indicating the four possible stimulus/response pairings: hits corresponding to ‘yes’ responses on present trials; false alarms corresponding to ‘yes’ responses on absent trials; misses corresponding to ‘no’ responses on present trials (i.e. inattentional blindness); and correct rejections corresponding to ‘no’ responses on absent trials. Right: Formula for calculating criterion or response bias from hit rate (H), that is proportion of present trials in which subjects responded ‘yes’, and false alarm rate (FA), that is proportion of absent trials in which subjects responded ‘yes’. (c) Criteria for Experiments 2 (N = 844), 4 (N = 977), and 5 (N = 8069), showing that in each case subjects exhibited a significantly conservative bias, that is a tendency to say ‘no’ when asked if they noticed anything unusual, independent of the actual presence of a stimulus. This suggests that, collectively, subjects in inattentional blindness experiments may systematically underreport their awareness of unexpected stimuli across different paradigms. Error bars are 95% CIs.

To address this, Experiment 5 repeated the design of Experiment 4, with two key differences: (1) Every subject was assigned to attend to the white squares in the primary task, (2) the IB stimuli were either orange or green—two colors that as a pair produced equal IB rates in pilot testing. Moreover, by selecting such highly salient colors (which differ dramatically from any other stimulus on the display), these parameters served as an especially strong test of our broader hypothesis. Remarkably, even under these conditions (and with the concern about color congruency effects mitigated), the pattern of results matched that of Experiment 4: Subjects collectively performed above-chance in discriminations of both color (d′ = 0.12, 95% CI = [0.02, 0.23]) and shape (d′ = 0.23, 95% CI = [0.13, 0.33]), but not location (d′_2afc = 0.03, 95% CI = [–0.04, 0.11]) (see Figure 4b). Sensitivity to the color of the IB stimulus was lower than in Experiment 4, consistent with our prediction that color sensitivity in that experiment was inflated by a color-congruence bias; however, it was still significantly above chance, indicating that color-congruence does not fully account for the pattern of results. That subjects, as a group, could still correctly report the color and shape of the IB stimulus despite the attentionally demanding primary task is compelling evidence that perception of IB stimuli is not completely abolished by inattention. Further, we again found that subjects were collectively biased to respond ‘no’ to the traditional yes/no IB question (c = 0.45, 95% CI = [0.41, 0.49]; d′ = 2.01, 95% CI = [1.94, 2.09]; statistics here are for all subjects; see Figure 5c). Thus, even when an unexpected stimulus remains on screen for many seconds, subjects are hesitant to report noticing anything unusual.

Discussion

Inattentional blindness captures both scholarly interest and popular imagination because of its striking and counterintuitive implication that we may fail to see what is right before our eyes, simply because our attention is otherwise engaged. Its influence is both wide and deep: It apparently provides a dramatic demonstration of the limits of visual perception, serves as a tool to reveal the neural correlates of consciousness, and even motivates theories of consciousness holding that awareness requires attention. These and still other implications arise from the ‘consensus’ interpretation of IB, according to which inattention completely abolishes perception of the unexpected stimulus: ‘one can have one’s eyes focused on an object or event … without seeing it at all’ (Carruthers, 2015, emphasis added). Yet this interpretation rests on a crucial and untested assumption: that observers who say they didn’t notice a stimulus (i.e. answer ‘no’ under traditional yes/no questioning) in fact didn’t see it.

The present work puts this crucial assumption to the test, yielding results that point to a very different pattern than the consensus interpretation: Across five pre-registered experiments totaling over 25,000 subjects, we found that groups of observers could successfully report a variety of features of unattended stimuli, even when they all individually claimed not to have noticed those stimuli. Furthermore, our approach revealed that subjects are conservatively biased in reporting their awareness, in ways that not only explain our results (i.e. provide an account of how and why subjects who claimed not to have seen something could still report its features) but also recast the large and influential body of literature that has taken answers to yes/no questions in IB paradigms at face value.

Design and analytical approach

Our experiments all employed designs and protocols closely modeled on canonical IB studies. In Experiments 1–3, we studied IB using a cross task closely modeled on Mack and Rock’s classic studies (Mack and Rock, 1998). In Experiments 4–5, we studied IB using a dynamic task closely modeled on Wood and Simons, 2017 (itself adapted from influential work by Most et al., 2001; Most et al., 2005; Ward and Scholl, 2015). In all cases, we used the standard yes/no question from previous experiments to determine IB rates. These choices were deliberate: Our aim was to interrogate the canonical interpretation of a large and long-standing tradition of experimental work, and so we sought to cleave as closely as possible to the experiments which inspired and have been subject to this interpretation. Our results do not reflect idiosyncratic design choices but rather speak to the central paradigms in the literature.

Our approach also eschews the use of divided and full attention trials. Divided attention trials (trials after the critical trial in which the subject knows that an unexpected stimulus might occur) and full attention trials (where in addition the subject is told to ignore the primary task, and instead look out for unexpected objects) are often used to exclude from analysis subjects who do not report seeing the unexpected stimulus on these trials (see, e.g. Most et al., 2001). However, this practice is controversial and has been argued to ‘lead researchers to understate the pervasiveness of inattentional blindness’ (White et al., 2018). Because our aim was to offer an especially stringent test of sensitivity in inattentional blindness, we opted not to use any such exclusions. Since the use of such exclusions would tend to inflate the biases we are concerned with here, the fact that we found evidence of residual sensitivity despite not excluding subjects in this way is all the more telling.

To assess objective sensitivity and bias in IB, we adopted a novel analytical approach, applying signal detection models to a ‘super subject’ whose responses are comprised of individual subjects’ responses in their single critical trials. This strategy involves various assumptions which future work should explore. Nonetheless, we believe any violations of such assumptions will not affect our main results or, where they might, lead only to our underestimating residual sensitivity and response bias. First, in calculating d′, it is standardly assumed that a subject’s criterion is stable throughout a given experiment. This assumption may be more likely to be violated with respect to a ‘super subject’. However, its violation will not affect calculations of d′ in 2afc tasks which are criterion free (Exps. 1, 3 and location results in Exps. 4 and 5). Moreover, if criterion instability is present, its effect will be to reduce estimated sensitivity in one-interval forced-response tasks (Exp. 2, and shape and color results in Exps. 4 & 5; see, Azzopardi and Cowey, 2001). Our approach can thus be seen as offering a conservative estimate of residual sensitivity. (Indeed, given the essentially retrospective nature of IB judgments, our estimates should in any case be considered conservative since signal available at the time of stimulus presentation may have been lost by the time of judgment.) Second, in calculating d′ and c, it is standardly assumed that signal and noise distributions are equal variance Gaussians. There is theoretical reason to think this assumption is robust with respect to 2afc tasks (Macmillan and Creelman, 2005), and in general in relation to the Gaussian nature of the distributions (Green and Swets, 1966; Pastore et al., 2003; Wixted, 2020). However, empirically, equal variance might not hold in one-interval tasks. Violation of this equal variance assumption could lead to under- or over-estimation of d′ and c. However, any such under- or over-estimation would be slight and unlikely to affect our main results. For example, if the relative variance of the signal-plus-noise distribution as compared to the noise distribution (𝜎) is 1.25, then c for the yes/no task in Experiment 5 (illustrated in Box 1) would be 0.443 and d′ would be 1.898, whereas, if 𝜎 = 0.75 then c would instead be 0.438 and d′ would be 2.118. In either case, our main results would be qualitatively unchanged.

Our super subject analysis raises the question of how to interpret the responses of individual subjects. Even though our experiments revealed that subjects who denied noticing any unusual stimulus could collectively report its features above chance, it was also the case that some subjects denied noticing any unusual stimulus and then also went on to incorrectly answer the follow-up questions about its features. Indeed, this has also been true in other studies that include follow-ups (e.g. Most et al., 2001; Most et al., 2005; Cohen et al., 2011; Cohen et al., 2020). Are these individual subjects truly inattentionally blind? Intriguingly, even this seemingly cautious conclusion does not follow from that pattern of performance, for several reasons. First, and straightforwardly, many such follow-ups are not 2afc questions but rather yes/no questions themselves (including our own questions about color and shape, as well as many similar questions used in previous work); since such questions are biased (e.g. subjects may tend to favor responding that an object was blue rather than red, perhaps making assumptions about the visibility of each color), any individual incorrect answer to such a question may reflect this sort of bias as opposed to the total absence of color signal. Second, even in an unbiased 2afc task, an observer may have significant information from the stimulus (perhaps, well above an unbiased single-interval detection criterion) but still decide incorrectly because of high noise from the other spatial interval; in such a case, it is far from clear that the subject should be treated as blind. Third, there will inevitably be subjects who fail to correctly report an unnoticed object’s features because they failed to see it in the first place — not due to inattention, but rather due to more ordinary failures such as happening to look away from the display at the key moment, sneezing or blinking just as the unexpected stimulus appears, being interrupted by one’s child or pet or smoke alarm, and so on; such subjects would be ‘blind’ to the stimulus, of course, but not inattentionally blind. Fourth, any series of follow-up questions, including ours, inevitably probes only some limited set of features at the exclusion of others; thus, subjects may have been aware of some feature of the stimulus other than the features explicitly probed (e.g. the orientation of an unexpected line rather than its color). Fifth, many processes intervene between being (or not being) sensitive to a stimulus and generating a response to a follow-up question; subjects will occasionally press the wrong button by mistake, or rush through the questions without reading carefully, or forget what they saw (and thus guess); although such mishaps will tend to break in favor of incorrect answers just as often as correct answers, they make it so that any individual error (or success) is difficult to interpret on its own (and in ways that testify to the value of the group-level approach that we favor here). Sixth, and perhaps most generally, taking correct and incorrect answers to place subjects neatly into two categories — those who saw the stimulus and those who did not — reflects a binary approach to perception and awareness that we suggest should be resisted. Indeed, an aspect of our contribution here, discussed further below, is to encourage conceiving perception and awareness as coming in degrees, in line with a SDT framework. On this view, perception and awareness are most helpfully characterized in terms of continuous statistics such as d′ rather than more traditional but problematic measures such as the proportion of correct or incorrect responses.

Relation to previous work

Our work was motivated by concerns that the traditional interpretation of IB relies on assessing perception and awareness simply by asking participants whether they noticed anything unusual. As discussed, such yes/no questions are notoriously subject to bias, which may lead subjects to answer ‘no’ even when they do have a degree of perception or awareness, due to factors such as under-confidence. More recent studies of IB have attempted to improve on simple yes/no questioning through the use of various follow-up questions. However, although these improved methods undoubtedly have their merits, none of them resolves the concerns that motivated our investigations. This is for three fundamental reasons.

First, follow-up questions are often used not to exclude subjects from the IB group but to include subjects. For example, Most et al., 2001 treated as inattentionally blind not only subjects who denied noticing the unexpected object but also subjects who claimed they did notice the object but were subsequently unable to describe it. Similarly, Pitts et al., 2012 asked subjects to rate their confidence in their initial yes/no response from 1 = least confident to 5 = most confident, and used these ratings to include in the IB group those who rated their confidence in seeing at 3 or less. Counting such subjects as inattentionally blind can be problematic. There is a large gap between being under confident that one saw something and being completely blind to it; failure to describe a feature (e.g. color, shape) does not imply a complete lack of information concerning that feature; and even if a subject did lack all information concerning some feature of an object, this would not imply a complete failure to see the object.

Second, most follow-up questions remain subject to response bias in just the same way as the original yes/no awareness question. For example, Cohen et al., 2020 (see similarly: Cohen et al., 2011; Simons and Chabris, 1999; Most et al., 2001; Most et al., 2005; Drew et al., 2013; Memmert, 2014) use a series of follow-up probes: (1) ‘Did you notice anything strange or different about that last trial?’ (2) ‘If I were to tell you that we did something odd on the last trial, would you have a guess as to what we did?’ (3) ‘If I were to tell you we did something different in the second half of the last trial, would you have a guess as to what we did?’ (4) ‘Did you notice anything different about the colors in the last scene?’ These questions are intrinsically subject to response bias just by being yes/no questions, but in this case, they may be especially susceptible since subjects may be reluctant to ‘take back’ their earlier answers, leaving them all the more conservative to avoid any perceived inconsistency. (This may also explain the remarkable consistency in such responses reported in, e.g. Simons and Chabris, 1999, despite the very different wording across the questions asked.) It is also important to recognize that whereas 2afc questions are criterion free (in that they naturally have an unbiased decision rule), this is not generally true of nafc nor delayed n-alternative match to sample designs. Performance in such tasks thus requires SDT analysis – which itself may be problematic if the decision space is not properly understood or requires making substantial assumptions about observer strategy.

Third, and finally, many follow-up questions are insufficiently sensitive (especially with small sample sizes). For instance, Todd et al., 2005 used a 12-alternative match-to-sample task (see similarly: Fougnie and Marois, 2007; Devue et al., 2009). Most et al., 2005 asked an open-response follow-up: ‘If you did see something on the last trial that had not been present during the first two trials, what color was it? If you did not see something, please guess.’ These questions are more difficult and to that extent less sensitive than binary forced-response/2afc questions of the sort we use in our own studies – a difference which may be critical in uncovering degraded perceptual sensitivity. For all these reasons, we believe our novel approach of using 2afc or forced-response questions combined with signal detection analysis is an important improvement on prior methods.

Previous studies of the related phenomenon of change blindness have investigated whether subjects who fail to detect changes nonetheless perform above chance in discrimination tasks concerning the changed object (Mitroff et al., 2002; Mitroff et al., 2004; Hollingworth and Henderson, 2002). However, only a handful of prior studies have explored the possibility that inattentionally blind subjects outperform chance in reporting or responding to features of IB stimuli (e.g. Schnuerch et al., 2016; see also Kreitz et al., 2020; de P Nobre et al., 2020). Moreover, a recent meta-analysis of this literature (de P Nobre et al., 2022) concluded that such work is problematic along a number of dimensions, including underpowered samples and evidence of publication bias that, when corrected for, eliminates effects revealed by earlier approaches. (These concerns hold in addition to our own worries about biased measures of performance.) The authors of this meta-analysis conclude with the following recommendation for future work: ‘We suggest that more evidence, particularly from well-powered pre-registered experiments, is needed before solid conclusions can be drawn regarding implicit processing during inattentional blindness’ (de P Nobre et al., 2022). We see the present set of high-powered pre-registered studies as providing precisely this evidence, in ways that advance our understanding of IB considerably.

Our results also shed new light on evidence that inattentionally blind subjects process the unexpected stimuli they deny noticing. For example, in the electrophysiology literature, unexpected line patterns have been found to elicit the same Nd1 ERP component in both noticers and inattentionally blind subjects (Pitts et al., 2012). Likewise, preserved neural signatures of scene segmentation and perceptual inference (e.g. Kanizsa figures) have been found in inattentionally blind subjects (see respectively, Scholte et al., 2006; Vandenbroucke et al., 2014), although interestingly not differential responses to meaningful words versus meaningless letter strings (Rees et al., 1999). Similarly, behavioral studies show that unattended stimuli can influence the accuracy and speed of inattentionally blind subjects’ judgments in a primary task (e.g. Moore and Egeth, 1997; replicated in Wood and Simons, 2019; Pugnaghi et al., 2020). Although some researchers have interpreted these results as implying a kind of subliminal processing of IB stimuli, our results just as easily raise an alternative (and perhaps more straightforward) explanation: that inattentionally blind subjects may retain a degree of awareness of these stimuli after all.

We acknowledge that above-chance performance in our experiments could be taken to reflect unconscious representations in a manner akin to orthodox interpretations of blindsight (Weiskrantz et al., 1974; Kolb and Braun, 1995; although see Phillips, 2021b; Morgan et al., 1997; and, for more general skepticism, Newell and Shanks, 2023). However, in our view, explicit voluntary judgments of stimulus features (especially in neurotypical subjects) constitute prima facie evidence of conscious processing, and should be interpreted that way unless there is some compelling reason to favor an alternative (Snodgrass, 2002; Balsdon and Azzopardi, 2015; Heeks and Azzopardi, 2015; Phillips, 2021a). As a result, although we acknowledge the possibility that our data reflect unconscious processing, we think there are compelling reasons to interpret our results in terms of residual conscious vision in IB (although we note that this claim is tentative and secondary to our primary finding).

Evidence that inattentionally blind subjects process and are sensitive to the unexpected stimuli they deny noticing has been used to support so-called inattentional amnesia accounts of IB—the traditional rival to the orthodox interpretation of IB. On inattentional amnesia accounts, unattended objects and features are consciously perceived but not encoded so as to be available for later explicit report (Wolfe, 1999; Moore, 2001; though see Ward and Scholl, 2015; Hirschhorn et al., 2024). Our results are consistent with some degree of inattentional amnesia, and likewise, with what Block, 2001 calls inattentional inaccessibility. (For a fuller discussion of these alternative hypotheses and IB more generally, see Wu, 2014.) However, our findings suggest that inattentional amnesia cannot be the whole story, since they reveal that some features of unexpected objects are available for later explicit report even in the group of subjects who deny noticing anything unusual.

Visual awareness as graded

A further upshot of our findings is that they lend support to a more graded perspective on IB (in particular) and both perception and visual awareness (in general). This stands in contrast to the two interpretations that have dominated discussion of IB, both of which adopt a binary perspective. On the orthodox interpretation, inattention abolishes all perception and awareness; on the rival inattentional amnesia account, inattention abolishes all explicit encoding. Our data suggest the need to move beyond such binaries (cf. Cohen et al., 2023): Inattention degrades but does not eliminate perception and awareness – and likewise explicit encoding. This more nuanced approach has some kinship with what Simons, 2000 calls inattentional agnosia. On this account, subjects who report not noticing may have some awareness of the unexpected object but fail to ‘encode the properties necessary to register that the item was something new, different, or noteworthy’ (Most et al., 2005). Although aligned with the spirit of our view, this account still does not fully capture our results, since in our studies the group of non-noticing subjects could explicitly report features which would ordinarily suffice to mark the unexpected object as new or different (e.g. color, shape). Nonetheless, we agree that unattended stimuli are encoded in a partial or degraded way. Here, we see a variety of promising options for future work to investigate. One is that unattended stimuli are only encoded as part of ensemble representations or summary scene statistics (Rosenholtz, 2011; Cohen et al., 2016). Another is that only certain basic ‘low-level’ or ‘preattentive’ features (see Wolfe and Utochkin, 2019 for discussion) can enter awareness without attention. A final possibility consistent with the present data is that observers can in principle perceive individual objects and higher level features under inattention but that the precision of the corresponding representations is severely reduced. Our central aim here is to provide evidence that there is residual perceptual sensitivity to visual features for subjects who would ordinarily be classified as inattentionally blind. Further work is needed to characterize the exact nature of this perception, and the awareness (if any) which corresponds to it.

Conclusion

Taken together, and after decades of inconclusive findings, the results of our five studies offer the strongest evidence so far of significant residual visual sensitivity across a range of visual features in IB. In other words, as a group, the inattentionally blind enjoy at least some degraded or partial sensitivity to the location, color, and shape of stimuli which they report not noticing. Together with our finding that subjects collectively exhibit a systematically conservative bias in reporting their awareness, our results also call into question the orthodox interpretation of IB on which inattention entirely abolishes awareness, suggesting that a reconceptualization of inattentional blindness may be required. Indeed, perhaps ironically, inattentional blindness if anything provides evidence that awareness of certain features survives inattention. Our results highlight the critical value of assessing response bias and including objective measures of sensitivity in studying inattentional blindness and visual awareness. They also point to a broader rethinking of perception and consciousness as graded, rather than binary, phenomena.

Materials and methods

Open science practices

Request a detailed protocol

All sample sizes, exclusion criteria, analyses, and key experimental parameters reported here have been pre-registered. Data, analyses, stimuli, and pre-registrations are publicly available at https://osf.io/fcrhu/. Readers can also experience all experiments for themselves at https://perceptionresearch.org/ib/.

Experiment 1: above-chance sensitivity to the location of unnoticed stimuli

Participants

500 adults were recruited from the online platform Prolific (for validation of the reliability of this subject pool, see Peer et al., 2017), with participation limited to US subjects. As described in our pre-registration, we reached this number by running batches of 100 subjects until a target number of 100 non-noticers (i.e. subjects answering ‘no’ to the yes/no question about whether they noticed the unexpected stimulus) was reached. After excluding subjects who incorrectly reported which arm of the cross was longer on any of Trials 1–3 and those who failed to provide a complete dataset, or failed a test for color vision (Ishihara color plate; see data archive), 374 subjects were included in the analysis. (All these exclusion criteria were pre-registered.) This experiment and all others reported here were approved by the Homewood Institutional Review Board of Johns Hopkins University (protocol number: HIRB00005762). All subjects provided informed consent and were compensated financially for their participation.

Stimuli and procedure

Request a detailed protocol

As shown in Figure 2, Experiment 1 contained four trials, with the fourth trial differing from the first three in several ways. All trials took place in a display with dimensions 600 px x 600 px. Due to the nature of online experiments, we cannot be sure of the exact size or distance of stimuli as subjects actually experienced them (and so we give these figures in pixels); however, any differences in subjects’ monitors and/or display setups would have been constant across all trials of the experiment.

On Trials 1–3, a fixation circle appeared in the center of the display; subjects pressed the spacebar when they were ready to begin the trial. The keypress was followed by a 1500 ms delay, after which a cross formed by two thin black lines (3 px thickness; 200 px x 140 px dimensions) appeared for 200 ms either 150 px above or below the fixation circle. The location of the cross (above or below), as well as its aspect ratio (vertical-longer or horizontal-longer) was randomly chosen on each of these trials. The cross then disappeared, followed by a 500-ms blank interval. Subjects were then asked which of the cross’s arms was longer (horizontal or vertical).

The fourth, critical trial proceeded the same way, except that simultaneous with the cross, a vertical red line (200 px long; 3 px thick; RGB(147,0,0)) appeared on one side of the display (10 px from the boundary of the display), also for 200 ms. Subjects were then asked the same cross arm length question as before.

Following this, two additional questions were asked, in the following order, each on its own display (such that subjects only saw the second question after answering the first).

Question 1
Did you notice anything unusual on the last trial which wasn't there on previous trials? (yes / no)
Question 2
In fact, the last trial you just saw contained one extra element — a vertical red line on either the left or the right side of the box. What side do you think it appeared on? If you don't know, or don't think it appeared on either side, take your best guess. (left / right)

For all forced-choice and forced-response questions asked in all experiments reported here, subjects indicated their answers by clicking a radio button next to the text corresponding to their answer, and then submitted their responses by clicking a separate ‘Submit’ button, a design aimed at eliminating motor-error responses.

Analysis and results

Request a detailed protocol

As reported in the main text, 28.6% (107/374) of subjects responded ‘no’ to Question 1; we refer to these subjects as ‘non-noticers’. These subjects are those who demonstrate inattentional blindness by conventional standards. However, 63.6% of non-noticers answered Question 2 correctly (the 2afc location task). This proportion was compared to chance responding (50%) with both frequentist and Bayesian null hypothesis tests, using the binom.test and proportionBF functions, respectively, from the BayesFactor package in R (Ihaka and Gentleman, 1996; Morey et al., 2015; all arguments used were defaults), which yielded a 95% CI = [0.54, 0.73], and a BF10 = 9.9.

SDT analyses began by calculating the number of hits, false alarms, present trials, and absent trials, and then applying the log-linear correction of adding 0.5 to all cells of the decision matrix (Hautus, 1995; see also Box 1). This is a standard correction to prevent an infinite d′ in the event that either hits or false alarms are zero (this correction was applied to all experiments, although neither hits nor false alarms were ever zero), and simulations have shown that if anything, this correction underestimates d′ (Hautus, 1995).

The SDT measure of sensitivity (d′) for non-noticers’ performance on the 2afc location task was calculated as follows:

d_{2 a f c}^{'} = \frac{1}{\sqrt{2}} [z (H) - z (F A)]

For our analysis, we (arbitrarily) considered trials where a stimulus was presented on the left to be ‘present’ trials, and trials where a stimulus was presented on the right to be ‘absent’ trials (d′ will be identical regardless of which trial type is considered present/absent; c will be the same value with the opposite sign). With a hit rate and false alarm rate of 72.64% and 45.54% respectively (after log-linear adjustment), the resulting d′_2afc = 0.51. Note that d′ for this experiment was adjusted downward by a factor of $1 / \sqrt{2}$ because 2afc tasks are theoretically easier than yes/no or forced-response tasks (Macmillan and Creelman, 2005). This formula for d′_2afc (as well as those for d′ and criterion, described below) assumes equal variance for signal and noise distributions (a standard assumption in SDT), but the analysis code provided allows for the calculation of SDT statistics when the variance of signal and noise distributions is unequal.

Because each subject in the experiment contributes just one trial, the signal detection metrics were calculated at the group level, and the standard error for each SDT calculation was estimated using methods described by Macmillan and Creelman, 2005, pgs. 325–328; see also Kadlec, 1999. We estimated the variance for d′ in this 2afc task using methods first described by Gourevitch and Galanter, 1967, and re-described in Macmillan and Creelman, 2005, equations 13.5 and 13.7. First, equation 13.5 demonstrates how ϕ (a function which converts z-scores into probabilities) can be computed for the hit rate and false alarm rates:

ϕ (H) = \frac{1}{\sqrt{2 π}} e^{- \frac{1}{2} z {(H)}^{2}}

ϕ (F A) = \frac{1}{\sqrt{2 π}} e^{- \frac{1}{2} z {(F A)}^{2}}

With ϕ(H) and ϕ(FA) computed, we then estimated the variance of d′ in this 2afc task using equation 13.7:

var (d_{2 a f c}^{'}) = \frac{H (1 - H)}{2 N_{2} {[ϕ (H)]}^{2}} + \frac{F A (1 - F A)}{2 N_{1} {[ϕ (F A)]}^{2}}

where N₂ is the number of present trials, and N₁ is the number of absent trials. (For information about how the variance is affected by sample size using different methods and in different tasks, see Macmillan and Creelman, 2005, Tables 13.2 and 13.3; our sample sizes are more than sufficient to expect this variance estimation to be accurate to the hundredth decimal place).

Finally, we computed a confidence interval around d′_2afc using standard methods: The result is 95% CI = [0.16, 0.85], suggesting performance in the non-noticing group was significantly above chance.

Experiment 2: above-chance sensitivity to the color of unnoticed stimuli

Participants

1700 adults were recruited from Prolific, collected in batches of 100 subjects until a target number of 100 non-noticers was reached. After excluding subjects who incorrectly reported which arm of the cross was longer on any of Trials 1–3 and those who failed to provide a complete dataset, or failed a test for color vision, 1261 subjects were included in the analysis.

Stimuli and procedure

Request a detailed protocol

The fourth, critical trial proceeded the same way as Experiment 1, except that the extra vertical line that appeared simultaneous with the cross was either red (RGB(147,0,0)) or blue (RGB(0,0,136)), with the color and location of the line randomized across subjects.

Following the presentation of Trial 4, subjects were asked which cross arm was longer, and then the same traditional IB question as in Experiment 1 (Question 1), followed by:

Question 2
The last trial you just saw contained one extra element — a vertical line on one side of the box. What color was the extra line? If you don’t know, or don’t think any line appeared, take your best guess. (red / blue)

To reduce uncertainty about what color ‘red’ and ‘blue’ referred to, the text for each color option was printed in the red and blue color used for the IB stimuli in the different conditions (RGB(147,0,0), RGB(0,0,136)).

Analyses and results

Request a detailed protocol

As reported in the main text, 27.7% of subjects shown an additional stimulus responded ‘no’ to Question 1 (i.e. demonstrated inattentional blindness by conventional standards). However, 58.5% of these non-noticers answered correctly on Question 2 (95% CI = [51.95%, 64.93%]; BF10 = 4.54).

In Experiment 2, the follow-up color discrimination was a one-interval forced-response design, and so for signal detection analyses, sensitivity was calculated without the 1/√2 adjustment, such that d′ = z(H) - z(FA), resulting in d′ = 0.38. We estimated the variance of d′ for this one-interval forced-response task using similar methods to those described for Experiment 1, with one minor change to the variance equation to account for this being a forced-response task (equation 13.4 in Macmillan and Creelman, 2005):

var (d^{'}) = \frac{H (1 - H)}{N_{2} {[ϕ (H)]}^{2}} + \frac{F A (1 - F A)}{N_{1} {[ϕ (F A)]}^{2}}

The only difference between equation 13.4 and equation 13.7 is that the latter includes a factor of 2 in the denominator of both terms, which accounts for 2afc tasks theoretically being easier than yes/no tasks.

The procedure for significance testing comparing d′ to chance (d′ = 0) was identical to Experiment 1 Methods, and the two-sided frequentist binomial probability test yielded a 95% confidence interval of [0.03, 0.73]. As stated in the main text, subjects demonstrated a significant bias to respond ‘blue’, which we measured by calculating subjects’ criterion on the red/blue question as:

c = - \frac{1}{2} [z (H) + z (F A)]

resulting in a positive (conservative) criterion value of c = 0.67. The 95% confidence interval around the criterion estimate is [0.49, 0.84]. Since this interval does not contain zero, this represents a statistically significant bias. For a more intuitive understanding of the bias, 78.42% of subjects shown no IB stimulus guessed that the stimulus was blue.

Lastly, an important contribution of this work is the inclusion of absent trials, which enable us to compute response bias (c) for the traditional IB question, ‘Did you notice anything unusual on the last trial which wasn’t there on previous trials?’ As predicted, we found that, as a group, subjects were conservative in reporting their awareness of the IB stimulus (c = 0.31, 95% CI = [0.22, 0.41]), suggesting that subjects in inattentional blindness experiments may systematically underreport their awareness of unexpected stimuli.

Experiment 3: above-chance sensitivity even in highly confident non-noticers

Participants

7000 subjects were recruited from Prolific, and data were collected in batches until we reached 200 non-noticers who reported being ‘highly confident’ in their yes/no response. After excluding duplicate data files, subjects who incorrectly reported which arm of the cross was longer on any of Trials 1–3, those who failed to provide a complete dataset, or failed a test for color vision, 5296 subjects were included in the analysis.

Stimuli and procedure

Request a detailed protocol

This experiment explored whether sensitivity to the visual features of an IB stimulus varies as a function of subjects’ confidence in their responses to the traditional IB question (i.e. whether or not they noticed anything unusual on the last trial), and more specifically whether even highly confident non-noticers would show such sensitivity. Experiment 3 was identical to Experiment 1, except that after subjects answered the traditional yes/no question (which was the same as Question 1 in Experiment 1) and the follow-up left/right question (which was the same as Question 2 in Experiment 2), they also rated their confidence in each of those responses:

Question 1
Did you notice anything unusual on the last trial which wasn't there on previous trials? (yes / no)
Question 2 (Confidence rating for Question 1)
How confident are you in your answer? (Four-point scale: 0 = Not at all confident – 3 = Highly confident)
Question 3
The last trial you just saw contained one extra element - a vertical red line on either the left or the right side of the box. What side do you think it appeared on? If you don't know, or don't think it appeared on either side, just take your best guess. (left / right)
Question 4 (confidence rating for Question 3; not shown in Figure 3)
How confident are you in your answer? (Four-point scale: 0 = Not at all confident – 3 = Highly confident)

Analyses and results

Request a detailed protocol

As reported in the main text, 30.85% of subjects shown an additional stimulus responded ‘no’ to Question 1 (i.e. demonstrated inattentional blindness by conventional standards).

As with Experiments 1 and 2, we were interested in whether subjects who responded ‘no’ to the traditional IB question would nevertheless perform above-chance on subsequent discrimination questions about the IB stimulus. Beyond our general interest in all subjects who answered ‘no’, we were most interested in whether the non-noticers who reported high confidence in their answer still performed above-chance on the 2afc question. We compared the performance of those observers to chance (d′_2afc = 0) using the methods described above, and found that even the most confident non-noticers—that is those who reported being ‘highly confident’ that they did not notice anything unusual, rating their confidence at 3 on a scale from 0 to 3—collectively demonstrated significantly above-chance sensitivity to the location of the IB stimulus: d′_2afc = 0.34; 95% CI = [0.08, 0.60]. Importantly, this group of subjects is minimally powered, with just 204 subjects, adding force to the argument that there is meaningful sensitivity amongst highly confident non-noticers. The bin of second-most interest is that of the moderately confident non-noticers (those responding with a confidence rating of 2 on a scale from 0 to 3). Sensitivity in this group was above zero (d′_2afc = 0.05), but not significantly so (95% CI = [–0.10, 0.20]). Figure 3e depicts the uncorrected results of null hypothesis significance tests comparing the d′_2afc estimate for each confidence rating bin to chance (d′_2afc = 0), but essentially the same pattern of significant results is obtained when the Holm-Bonferroni correction for multiple comparisons is applied (p = 0.034 for the No-3 bin, and p = 0.09 for the Yes-0 bin–although note this bin only contains 25 subjects).

Experiment 4: above-chance sensitivity in a sustained inattentional blindness task

Participants

1500 subjects were recruited from Prolific, and data were collected in batches until we reached 100 non-noticers who answered the color discrimination question first, 100 non-noticers who answered the shape discrimination question first, and 100 non-noticers who answered the location discrimination question first (more on these three discrimination questions below).

After exclusions, 1278 subjects including 417 non-noticers remained in the analysis for Experiment 4. The pre-registered exclusion criteria for this experiment were the same as those reported by Wood and Simons, 2017. Subjects were excluded if: (i) their reported bounces for either of the first two trials erred by more than 50% in either direction from the true number of bounces of their attended objects on that trial, (ii) they failed to contribute a complete dataset, (iii) they reported problems with experimental playback, such as stuttering, freezing or another issue specified in a free-response, or (iv) an observer managed to submit or run the study twice (evidenced by two files sharing the same Prolific ID), in which case we excluded their second run from the analysis.

Stimuli and procedure

Request a detailed protocol

In Experiment 4, subjects participated in a sustained inattentional blindness task modified slightly from experimental code published by Wood and Simons, 2017. All subjects completed three trials of a dynamic, multiple object tracking task containing black squares and white squares. At the beginning of the experiment, each subject was told whether they should attend to the black or white squares. At the beginning of each trial, subjects were instructed to fixate on a small blue square (11 px x 11 px; RGB(0,0,255)) in the center of a gray (RGB(127,127,127)) rectangle (666 px x 546 px), and were told that their task was to count the number of times the squares of their attended color bounced off of the walls of the gray rectangle. Each trial lasted approximately 17 s, and each subset of black/white squares bounced an average of 28 times. After the trial ended, subjects were asked to report how many times the squares of their attended color bounced off of the walls of the rectangle. Of the 222 excluded subjects, 185 were excluded for bounce reports that erred by more than 50% in either direction of the actual number of bounces.

The critical trial was Trial 3. In this trial, 3/4 of subjects (Present condition) were shown an unexpected shape (a circle or a triangle, which was either black or white). This unexpected object entered the display on either the left or the right 5 s after the trial began, and moved either upward or downward until it exited the other side of the display (thus, there were 2 color x 2 shape x 2 side x 2 motion direction options for IB stimuli, randomly chosen for each subject). For 1/4 of subjects, no additional object was shown on Trial 3 (Absent condition).

Regardless of condition, at the end of the critical trial, subjects were again asked to report the number of bounces, followed by four additional questions:

Question 1
Did you notice something on the last trial that did not appear on previous trials? (yes/no)
After answering Question 1 (the standard IB question), subjects were told (again regardless of condition): An extra object may have appeared on that last trial. If you saw it, please tell us its color, shape, and whether it appeared on the left or the right side of this gray box. If you didn't see the extra object, please guess. We'll ask about [whichever discrimination question was randomly chosen to be asked first] first.
Questions 2–4 (presented in random order for each subject)
The new object was… (black/white)
The new object was a… (circle/triangle)
The new object was on the… (left/right)

Note that Question 1 (the traditional IB question) differs slightly in Experiments 4 and 5 from the question wording in Experiments 1–3 because we aimed to cleave as closely as possible to Wood and Simons, 2017 and other inattentional blindness experiments using similar paradigms.

Analyses and results

Request a detailed protocol

As reported in the main text, 57.32% of subjects shown an additional stimulus on the critical trial answered ‘no’ to the traditional IB question (Question 1). For absent trials, 6.31% of subjects answered ‘yes’ to the traditional IB question when no additional stimulus appeared; this is the false alarm rate, which can be used (along with the hit rate) to estimate subjects’ bias in responding to the traditional IB question. In Experiment 4, as in Experiment 2, we found that, as a group, subjects answered the traditional IB question using a conservative criterion (c = 0.85, 95% CI = [0.73, 0.97]), suggesting they may be underreporting their awareness of IB stimuli.

Experiment 4 asked subjects to perform three discrimination tasks following their response to Question 1, with the order randomized for each subject. The color and shape discriminations are one-interval forced-response tasks, and so d′ was calculated for these two questions without the 2afc correction. We found that non-noticers collectively performed significantly above chance on both the color (d′ = 0.82, 95% CI = [0.61, 1.04]) and shape (d′ = 0.21, 95% CI = [0.005, 0.42]) discriminations. In addition to the issue identified below, the fact that this result was marginal further motivated Experiment 5, in which we substantially increased the number of subjects recruited in order to reduce the size of the confidence intervals around d′ estimates of discrimination sensitivity by 50%. For the location discrimination (left/right), the 2afc correction to d′ was applied, and non-noticers’ performance was trending but was not above-chance (d′_2afc = 0.07, 95% CI = [–0.08, 0.22]).

Because the order of the discrimination questions varied by subject, we pre-registered an analysis specifying that we would also derive sensitivity and bias for subjects who answered a given discrimination question first. With these subsets of non-noticers (roughly one-third the original sample size), confidence intervals are much larger, and although color discrimination remained significantly above-chance (d′ = 0.78, 95% CI = [0.41, 1.16]; N=189), shape discrimination was no longer significant (d′ = 0.15, 95% CI = [–0.20, 0.50]; N=195) and location (d′_2afc = -0.17, 95% CI = [–0.44, 0.10]; N=176) was again not significant.

Finally, in a pre-registered analysis breaking down the inattentional blindness rate by the color of the attended stimuli, we found that subjects were roughly 3 x more likely to report noticing a color-congruent than a color-incongruent IB stimulus (86.57% vs 28.71%). This interacted with subjects’ responses on the color discrimination task, with subjects shown an IB stimulus demonstrating a significant bias to say that the stimulus that appeared was the opposite color to the colored squares they attended (84.25% of subjects who attended to white squares answered black; 75.09% of subjects who attended to black squares answered white). In order to get a better estimate of sensitivity to color in this task, we pilot tested a new pair of IB stimulus colors in Experiment 5 to equate the guessing rate on absent trials.

Experiment 5: replicating above-chance sensitivity in a sustained inattentional blindness task

Participants

To ensure a large enough sample without overlap with previous experiments using this paradigm, Experiment 5 recruited Prolific subjects not only from the USA but also from Canada, the United Kingdom, and Australia. In order to reduce the size of the confidence intervals around the sensitivity estimates relative to Experiment 4, we collected data until we reached at least 2200 subjects who reported not noticing the unexpected stimulus. Exclusion criteria were identical to Experiment 4. After exclusions, 10,830 subjects in total, including 2339 non-noticers, were included in the analysis. (To our knowledge, this made Experiment 5 the largest single inattentional blindness sample ever collected.)

Stimuli and procedure

Request a detailed protocol

Experiment 5 repeated the design of Experiment 4, with two key differences: (1) Every subject was assigned to attend to the white squares in the primary task, (2) the IB stimuli were either orange or green—two colors that as a pair produced equal IB rates in pilot testing. Again, subjects were asked the traditional IB question (yes/no) followed by three discrimination questions (color, shape, and location) in random order for each subject. The change to the colors of the IB stimuli of course meant that the color question and options were changed to read:

The new object was… (color 1/color 2; with the text printed in the given color)

Analyses and results

Request a detailed protocol

As reported in the main text, with the congruency effects mitigated and despite the highly salient color of the orange/green IB stimuli, the pattern of results matched that of Experiment 4: Analysis of responses to the traditional yes/no question once again revealed that subjects were collectively biased to respond ‘no’ (c = 0.45, 95% CI = [0.41, 0.49]), with the hit rate (subjects in the Present condition who responded ‘yes’) being 71.01% (100% - the IB rate) and the false alarm rate (subjects in the Absent condition who responded ‘yes’) being 7.24% (see a more detailed breakdown of the SDT analysis for this experiment in Box 1). Thus, even when a highly salient, moving stimulus entered the display suddenly and remained on screen for multiple seconds, subjects were hesitant to report noticing it.

Non-noticers’ performance on the three discrimination questions was also consistent with the pattern of results in Experiment 4, with non-noticers collectively performing significantly above-chance in discriminating both color (d′ = 0.12, 95% CI = [0.02, 0.23]) and shape (d′ = 0.23, 95% CI = [0.13, 0.33]), but not location (d′_2afc = 0.03, 95% CI = [–0.04, 0.11]). Biases in the discrimination question responses among subjects shown no IB stimulus on the critical trial (the Absent condition) were minimal, confirming that the congruency effect (interaction between the IB rate and subjects’ choices on the color discrimination question) was mitigated in Experiment 5: For subjects shown no unexpected stimulus on the critical trial (the Absent condition), 52.95% guessed that the extra object had been orange, and 47.05% guessed that it had been green. On the shape question, 51.43% of subjects guessed that the extra object had been a triangle, and 48.57% guessed that it had been a circle. Finally, on the location question, 54.87% of subjects guessed that the extra object had been on the left side of the display, and 45.13% guessed that it had been on the right side of the display.

Appendix 1

Supplementary data and analyses

Experiment 1

We pre-registered that we would re-analyze the data for subjects who indicated that they were unfamiliar with IB (49.2%) vs. subjects who reported some familiarity (50.8%) with IB, and report any important differences. The IB rate was 32.07% (59/184) for subjects who were unfamiliar with IB, and 25.26% (48/190) for subjects who were familiar with IB. All trends in the data were the same as reported in the main text (e.g. above-chance performance of non-noticers in the left/right question), though some fail to be statistically significant since the corresponding sample sizes are reduced by approximately 50% and we collected data from the minimum number of non-noticers required to sufficiently power a binomial probability test on accuracy in the left/right question.

Experiment 2

While data were being collected for Batch 5 of Experiment 2, we noticed some strange behavior on Prolific — for example, Prolific web pages outside of the study not properly loading, or loading very slowly. Similar behavior was observed by several other labs, who posted about their unusual experiences at this time in online discussions. We noticed these anomalies before looking at the data for Batch 5; however, when we did examine the results from this batch, they too were unusual in that, unlike the other six batches, non-noticing subjects in Batch 5 produced a negative d′ value on the red/blue question (d′ = -0.51), seeming to systematically report the opposite of the color they were shown. We did not feel that these were strong enough grounds to exclude these data (especially since these were not specified as exclusion criteria in our pre-registration). However, we note that, without Batch 5, the d′ for all non-noticers (collapsing across the other batches) was nearly double the estimate we report in the main text (d′ = 0.38 with Batch 5 included, vs. d′ = 0.64 with Batch 5 excluded). We suspect that d′ = 0.64 is likely closer to the ‘true’ sensitivity in this task; however, out of an abundance of caution, and out of respect for new standards in our field, we report the more conservative figure.

We re-analyzed the data for Experiment 2 looking only at subjects who indicated that they were either unfamiliar or familiar with IB. 52.74% of subjects reported being familiar with IB in general, and the IB rates for subjects who reported being familiar with IB were comparable to those of subjects who reported being unfamiliar with IB (IB for unfamiliar subjects = 27.92%; IB for familiar subjects = 25%). As with Experiment 1, we found that all trends in the data were the same. In particular, d′ remained significantly above-chance for both familiar and unfamiliar non-noticers on the red/blue question, despite the sample size being approximately halved.

Experiment 3

As with Experiments 1 and 2, we re-analyzed the data looking only at subjects who reported being familiar or unfamiliar with IB. 56.9% of subjects indicated that they were at least somewhat familiar with IB, with 28% of those subjects failing to report the IB stimulus, compared with 35% of subjects who said that they were unfamiliar. The overall trends for both familiar and unfamiliar noticers and non-noticers were the same, with all four groups performing significantly above-chance on the left/right question. While the pattern of results is also the same when separating noticers and non-noticers into different confidence rating bins, not all comparisons to chance are statistically significant as these analyses are now underpowered.

In case there were not enough subjects in a given confidence bin to compare each bin individually to chance, we also pre-registered an analysis collapsing confidence into high-confidence (a confidence rating of 2 or 3) and low-confidence (a confidence rating of 0 or 1) groups for noticers and non-noticers. With the data collapsed into just two groups, accuracy (percent correct) and sensitivity (d′_2afc) were significantly above-chance for low-confidence non-noticers (60.31% correct, 95% CI = [56.89%, 63.66%]; d′_2afc = 0.515, p < .0001), low-confidence noticers (83.18%, 95% CI = [77.48%, 87.93%]; d′_2afc = 1.44, p < .0001), and high-confidence noticers (97.22%, 95% CI = [96.61%, 97.74%]; d′_2afc = 2.75, p < .0001). Performance for high-confidence non-noticers was marginally significant by the preferred measure of sensitivity (d′_2afc = 0.12, p = .059), but was not significantly above-chance by percent correct measures (51.18%, 95% CI = [47.67%, 54.69%]) — perhaps further validating the choice of unbiased sensitivity estimates in tasks such as these. We remind readers that, when not collapsed in this way, the very highest-confidence non-noticers (i.e. those who answered ‘3’ after responding that they didn’t notice anything) performed significantly above-chance (d′_2afc = 0.34; 95% CI = [0.08, 0.60]).

It is apparent from the pattern of data depicted in Figure 3e that a participant’s confidence in their response is useful for predicting whether they will go on to be correct on the follow-up left/right question (over and above their yes/no response alone). To quantify the impact of both yes/no response and degree of confidence in that response, we performed a logistic regression analysis predicting accuracy on the left/right question from each of these predictors, as well as their interaction. Both predictor variables were centered on zero so that they would be interpretable in the presence of an interaction term. Yes/no response was contrast coded (‘yes’ as -1, ‘no’ as 1). We treated confidence rating (on a four-point scale) as a continuous numeric variable, which enabled us to test whether confidence rating had the predicted (and preregistered) linear effect on left/right accuracy. (Treating confidence ratings in this way assumes that psychological differences in confidence rating levels are equal, which of course may not be the case.) Importantly, we reversed the sign of the confidence ratings for ‘no’ responders (e.g. 3 to -3, 2 to -2, etc.), because we predicted that (in line with SDT) subjects who responded ‘no’ and were highly confident in that response should perform the worst on the left/right question, and subjects who responded ‘yes’ and were highly confident in this response should perform the best on the left-right question. In other words, the linear relationship between accuracy and confidence in a ‘no’ response should be in the opposite direction of the relationship between accuracy and confidence in a ‘yes’ response. We then centered these confidence ratings on zero, separately for ‘yes’ and ‘no’ functions so that ratings for ‘yes’ and ‘no’ responses would range between -2 and 2.

The main effect of yes/no response was significant (β = -1.70, z = -27.94, p < .0001), as was the main effect of confidence rating (β = 0.71, z = 12.05, p < .0001). The interaction was also significant (β = -0.52, z = -8.88, p < .0001), suggesting that confidence had a different effect depending on whether a subject answered ‘yes’ or ‘no’ to the traditional IB question. Because the interaction was significant, we followed up with two additional models: One predicting accuracy on the left/right question from confidence ratings for ‘no’ responders, and another model predicting accuracy on the left/right question from confidence ratings for ‘yes’ responders. Unsurprisingly, the main effect of confidence was significant for ‘yes’ responders (β = 0.41, z = 8.00, p < .0001), but crucially, the main effect of confidence was also significant for ‘no’ responders (β = 0.18, z = 3.18, p < .01), further supporting our claim that there exist significant differences in visual sensitivity within the group of inattentionally blind subjects

Although our confidence scale was clearly labeled, and intuitively used ‘0’ to indicate the lowest level of confidence, it is possible that some subjects might have misconstrued the scale (e.g. inverting it by mistake). (We thank an anonymous reviewer for raising this issue.) To address this concern, we reasoned that any measurement error due to inverting or misconstruing the confidence scale should be symmetric. That is: If subjects are liable to invert the confidence scale, they should do so just as often when they answer ‘yes’ as when they answer ‘no’ – since the very same scale is being used in both cases. We thus explored evidence of measurement error in relation to the large number of high confidence ‘yes’ subjects (N = 2677), since this large sample provides a robust indicator as to whether subjects are generally liable to misconstrue the confidence scale. To search for evidence of measurement error, we looked for ‘inconsistent’ confidence ratings (i.e. high confidence ‘yes’ responses, followed by low confidence 2afc responses). Such a pattern of responding certainly need not reflect measurement error; it is, however, suggestive of self-correction following a misconstrual. Looking at the number of such high-confidence noticers who subsequently responded to the 2afc question with low confidence, we found that the number was extremely small. Only 28/2677 (1.05%) of high-confidence noticers subsequently gave the lowest level of confidence on the 2afc question, and only 63/2677 (2.35%) subjects gave either of the two lower levels of confidence. We conclude that any measurement error due to misunderstanding the confidence scale is likely extremely minimal.

Experiment 4

The pattern of results for percent correct measures of accuracy on each follow-up question (color, shape, and location discrimination) closely tracked the pattern reported in the main text for d'. As a group, non-noticers performed above-chance by percent correct measures on the color discrimination (65.36% correct, 95% CI = [61.25%, 69.30%]; BF10 = 3.47x10¹⁰). Non-noticers’ performance on the shape question was also marginally significant (54.12% correct, 95% CI = [49.88%, 58.30%]; BF10 = 0.68). Non-noticers’ performance on the location discrimination was not significantly above-chance (51.96% correct, 95% CI = [47.74%, 56.17%]; BF10 = 0.16).

Because the three follow-up questions in Experiment 4 were asked for each subject in randomized order, we pre-registered that we would analyze d' and accuracy for the first question a subject answered, and then we would analyze d' and accuracy collapsed across the order in which the question was asked. When restricted to only the first question, these analyses yielded very similar patterns of results to the analyses collapsing across question order, except that including only the first question that was asked cut each sample size by ~2/3, reducing power and thus limiting statistical significance. For the percent correct analysis including only the first question answered by each subject, color discrimination for non-noticers was significant (64.02% correct, 95% CI = [56.74%, 70.86%]; BF10 = 257.02), but shape was not (52.82% correct, 95% CI = [45.56%, 60.00%]; BF10 = 0.24), nor was location (46.60% correct, 95% CI = [39.05%, 54.25%]; BF10 = 0.27). For d', color discrimination was significant (d' = 0.78, 95% CI = [0.41, 1.16]), but shape was not (d' = 0.15, 95% CI = [-0.20, 0.50]), nor was location (d'_2afc = -0.17, 95% CI = [-0.44, 0.10]).

Because this experiment was our first to test individual subjects’ sensitivity on multiple follow-up questions about the IB stimulus, we performed an exploratory analysis on the probability that a noticer or non-noticer was correct on more than one follow-up question. We looked only at shape and color, since location discrimination was not above-chance for non-noticers. Interestingly, we found that 34.82% of non-noticers answered the color and shape question correctly, significantly different than would be expected by chance (34.82% vs. 25% chance, 95% CI = [30.88, 38.93]). Noticers, unsurprisingly, were overwhelmingly likely to answer both of these questions correctly (96.16% vs. 25% chance, 95% CI = [93.84%, 97.79%]). This avenue of analysis raises interesting questions for future work about the (in)dependence of visual features in memory under conditions of inattention.

For absent trials, we also calculated the percentage of subjects that chose either discrimination question option (white or black for color discrimination, circle or triangle for shape discrimination, and left or right for location discrimination) as a measure of bias in responding when no signal was present: For the color discrimination question, subjects chose black on 50.79% of trials, and white on 49.21% of trials. For the shape discrimination question, subjects chose circle on 53.33% of trials, and triangle on 46.67% of trials. For the location discrimination question, subjects chose left on 54.92% of trials, and right on 45.08% of trials. From this analysis alone, it would appear that subjects were relatively unbiased in their guesses when no IB stimulus was present. However, if we compare subjects’ guesses when they attended to black squares in the bounce-counting task vs. when they attended to white squares, it becomes apparent that the attended color had an impact on subjects’ responses to the color discrimination question: 79.68% of subjects shown no IB stimulus guessed that the IB stimulus that appeared was the opposite color of the squares they attended in the bounce-counting task.

For present trials, non-noticers were biased to respond that the unexpected stimulus that appeared was white, independent of what actually appeared (c = 0.16, 95% CI = [0.06, 0.27]), while non-noticers did not exhibit significant biases to choose circle over triangle (c = 0.05, 95% CI = [-0.05, 0.15]) or to respond left over right (c = -0.09, 95% CI = [-0.19, 0.01]). In addition, as with the absent trials, the attended color had an impact on non-noticers’ responses on the color discrimination question (but not the shape or location discrimination questions), though it was not nearly as strong as when no signal had been present: 53.39% of non-noticers chose the opposite color of the squares they attended in the bounce-counting task.

Finally, the direction that the IB stimulus traveled (upward or downward across the display) was the only parameter that varied between subjects that was not included in our follow-up questions. Because there are well-established asymmetries in detectability and discriminability of stimuli in the upper and lower half of the visual field (Levine and McAnany, 2005; Barbot et al., 2021), we re-analyzed IB rates, d' for the yes/no question, and d' for each follow-up question as a function of whether the IB stimulus moved upward or downward. The IB rate was 59.03% for upward-moving stimuli and 55.69% for downward-moving stimuli. The d' for the yes/no question was similar when only including upward-moving stimulus-present trials (d' = 1.29, 95% CI = [1.04, 1.54]) and when only including downward-moving stimulus-present trials (d' = 1.38, 95% CI = [1.13, 1.62]). For color discrimination, non-noticers’ d' was higher for downward- than upward-moving stimuli (d'_downward = 0.92, 95% CI = [0.62, 1.22]; d'_upward = 0.73, 95% CI = [0.43, 1.03]), though not significantly so. Likewise for shape discrimination, non-noticers’ d' was higher for downward- than upward-moving stimuli (d'_downward = 0.21, 95% CI = [-0.08, 0.50]; d'_upward = 0.12, 95% CI = [-0.17, 0.42]), although these do not differ significantly from one another, and neither d' was significantly above-chance on its own because the sample size was reduced by half. Location discrimination showed the opposite pattern, with d'_2afc for upward-moving stimuli being lower than for downward-moving stimuli (d'_downward = -0.003, 95% CI = [-0.21, 0.20]; d'_upward = 0.16, 95% CI = [-0.05, 0.36]), although again this was not a significant difference.

In Experiments 4 and 5, we asked subjects a different question about their familiarity with IB than we asked of subjects in Experiments 1–3. In Experiments 1–3, subjects were asked about their familiarity with IB in general — that is, ‘Have you heard of experiments where something (a gorilla, for example) appears unexpectedly when you were not paying attention?’ (yes/no). For Experiments 4 and 5, because we were informed that the researchers whose code we modified had tested subjects on Prolific using a nearly identical IB paradigm, we instead asked subjects about their familiarity with that dynamic IB paradigm specifically. Subjects were shown a video of the paradigm used in prior work, with the question, ‘Not including this one, how many studies like this have you participated in? Below is an example of another study you may (or may not) have seen. (0 / 1-2 / 3+)’. The overwhelming majority (94% in both cases) of subjects in both Experiment 4 (1200/1278) and Experiment 5 (10172/10830) responded ‘0’. Since the question was not aimed at IB in general, it is not clear that the same kind of IB familiarity analysis conducted in Experiments 1–3 is appropriate here; however, the same pattern of results obtains even considering only those subjects who responded ‘0’.

Experiment 5

As in Experiment 4, we pre-registered that we would analyze d' and accuracy for the first question a subject answered, in addition to those analyses collapsed across the order in which the questions were asked. This analysis is more robust than in Experiment 4 as the sample size was much larger. The pattern of results did not change when only the first question asked of each subject was included in the analysis: Non-noticers’ d' was above-chance for color discrimination (d' = 0.20, 95% CI = [0.02, 0.38]), and shape discrimination (d' = 0.32, 95% CI = [0.14, 0.50]), but not location discrimination (d'_2afc = 0.01, 95% CI = [-0.12, 0.13]).

The percent correct analysis collapsing across the order in which the question was asked follows the same pattern as the (preferred) d' measure, with non-noticers performing above-chance in discriminating the color (52.46%, 95% CI = [50.41%, 54.50%]) and the shape (54.51%, 95% CI = [52.47%, 56.54%]), but not the location (50.58%, 95% CI = [48.53%, 52.62%]) of the IB stimulus they failed to report noticing. The pattern of results was also the same when looking only at subjects who were asked a given question first, with non-noticers who were asked the color question first performing above-chance on the color question (53.84%, 95% CI = [50.27%, 57.37%], N = 782), and non-noticers who were asked the shape question first performing above-chance on the shape question (56.21%, 95% CI = [52.65%, 59.72%], N = 781). Accuracy was not above-chance for the location question among non-noticers who were asked the location question first (49.61%, 95% CI = [46.04%, 53.19%], N = 776).

Experiment 5 remedied the color-congruency effect discovered in Experiment 4 in two ways: (1) Every subject was assigned to attend to the white squares in the primary task, (2) the IB stimuli were either orange or green—two colors that as a pair produced equal IB rates in pilot testing. Even with more than 10,000 subjects in the sample after exclusions, orange and green stimuli produced remarkably similar IB rates (28.41% inattentional blindness for orange IB stimuli; 29.58% inattentional blindness for green stimuli). Further alleviating worries about an interaction between IB rate for stimuli of different colors and bias on the color discrimination question, subjects who were shown an IB stimulus did not demonstrate a significant bias to choose one color over the other (c = -0.03, 95% CI = [-0.08, 0.02]).

Again, in Experiment 5 the IB stimulus traveled either upward or downward across the display (a parameter that varied randomly between subjects), and we re-analyzed IB rates, d' for the yes/no question, and d' for each follow-up question as a function of whether the IB stimulus moved upward or downward. The IB rate was 32.46% for upward-moving stimuli and 25.53% for downward-moving stimuli, similar to the pattern observed in Experiment 4. The d' for the yes/no question was similar when only including upward-moving stimulus-present trials (d' = 1.91, 95% CI = [1.83, 1.99]) and when only including downward-moving stimulus-present trials (d' = 2.11, 95% CI = [2.03, 2.20]). Unlike in Experiment 4, on the color discrimination question, non-noticers’ d' was higher for upward- than for downward-moving stimuli (d'_downward = 0.08, 95% CI = [-0.08, 0.23]; d'_upward = 0.16, 95% CI = [0.02, 0.30]). Likewise for shape discrimination, non-noticers’ d' was slightly higher for upward- than downward-moving stimuli (d'_downward = 0.22, 95% CI = [0.07, 0.38]; d'_upward = 0.24, 95% CI = [0.10, 0.38]). Location discrimination again showed the opposite pattern, with d'_2afc for downward-moving stimuli being lower than for upward-moving stimuli (d'_downward = 0.09, 95% CI = [-0.02, 0.20]; d'_upward = -0.01, 95% CI = [-0.11, 0.09]).

Appendix 1—table 1

Raw Performance Data for Experiments 1–5.

Appendix 1—table 1 shows the following performance data for each of our five experiments: the total number of included subjects (stimulus present trials only) (Column 2); the number of subjects responding ‘yes’ to the relevant yes/no question, for example, ‘Did you notice anything unusual on the last trial which wasn’t there on previous trials?’ (Column 3); the number of subjects answering ‘yes’ and correctly answering the follow-up question(s) (Column 4); the percentage of subjects answering ‘yes’ and correctly answering the follow-up question(s) (Column 5); the number of subjects responding ‘no’ to the relevant yes/no question (Column 6); the number of subjects answering ‘no’ and correctly answering the follow-up question(s) (Column 7); and, the percentage of subjects answering ‘no’ and correctly answering the follow-up question(s) (Column 8).

Exp.	Total Ss	Total ‘yes’	Total ‘yes’ Ss correct on follow-up	Percent ‘yes’ Ss correct on follow-up	Total ‘no’	Total ‘no’ Ss correct on follow-up	Percent ‘no’ Ss correct on follow-up
1	374	267	Location: 259	Location: 97.0%	107	Location: 68	Location: 63.6%
2	844	610	Color: 549	Color: 90.0%	234	Color: 137	Color: 58.5%
3	5296	3662	Location: 3530	Location: 96.4%	1634	Location: 912	Location: 55.8%
4	977	417	Color: 405 Shape: 409 Location: 394	Color: 97.1% Shape: 98.1% Location: 94.5%	560	Color: 366 Shape: 303 Location: 291	Color: 65.4% Shape: 54.1% Location: 52.0%
5	8069	5730	Color: 5600 Shape: 5578 Location: 5391	Color: 97.7% Shape: 97.4% Location: 94.1%	2339	Color: 1227 Shape: 1275 Location: 1183	Color: 52.5% Shape: 54.5% Location: 50.6%

Appendix 1—table 2

Raw performance data for Experiment 3, by confidence bin.

Appendix 1—table 2 shows the following information for each group of subjects in Experiment 3, broken down by confidence in their response to the yes/no question whether they had noticed anything unusual (with confidence reported on a four-point scale from 0 = Not at all confident to 3 = Highly confident): the total number of included subjects (Column 2); the percentage of included subjects (Column 3); the number of subjects correct on the follow-up location discrimination question (Column 4); the percentage of subjects correct on the follow-up location discrimination question (Column 5).

Confidence Bin	Total Ss	Percent Ss	Total correct on Location	Percent correct on Location
Yes-3	2677	50.5%	2640	98.6%
Yes-2	771	14.6%	712	92.3%
Yes-1	189	3.6%	161	85.2%
Yes-0	25	0.5%	17	68.0%
No-0	189	3.6%	124	65.6%
No-1	640	12.1%	376	58.8%
No-2	601	11.3%	297	49.4%
No-3	204	3.9%	115	56.4%

Appendix 1—table 3

Confidence ratings for 2afc task in Experiment 3.

Appendix 1—table 3 shows the confidence ratings given by subjects with respect to their 2afc location discrimination judgment in Experiment 3. These are broken down, first into subjects who answered ‘yes’ or ‘no’ to the question whether they had noticed anything unusual, and then further by confidence in relation to the question whether they had noticed anything unusual. In all cases, confidence was reported on a four-point scale from 0 = Not at all confident to 3 = Highly confident.

Yes/No response and confidence level	2afc confidence = 0 (lowest)	2afc confidence = 1	2afc confidence = 2	2afc confidence = 3 (highest)
Yes (all confidence levels)	95 (2.59%)	163 (4.45%)	444 (12.12%)	2960 (80.83%)
No (all confidence levels)	812 (49.69%)	548 (33.54%)	212 (12.97%)	62 (3.79%)
Yes-3	28 (1.05%)	35 (1.31%)	136 (5.08%)	2478 (92.75%)
Yes-2	28 (3.63%)	86 (11.15%)	232 (30.09%)	425 (55.12%)
Yes-1	27 (14.29%)	40 (21.16%)	72 (38.1%)	50 (26.46%)
Yes-0	12 (48%)	2 (8%)	4 (16%)	7 (28%)
No-0	128 (67.72%)	31 (16.4%)	23 (12.17%)	7 (3.7%)
No-1	263 (41.09%)	254 (39.69%)	99 (15.47%)	24 (3.75%)
No-2	316 (52.58%)	206 (34.28%)	66 (10.98%)	13 (2.16%)
No-3	105 (51.47%)	57 (27.94%)	24 (11.76%)	18 (8.82%)

Data availability

All data, analyses, stimuli and pre-registrations are publicly available at https://osf.io/fcrhu/.

The following data sets were generated

(2024) Open Science Framework
ID osf.io/fcrhu. Sensitivity to visual features in inattentional blindness.

https://osf.io/fcrhu/

References

Book
1. Azzopardi P
2. Cowey A
(2001) Why is blindsight blind
In: Heywood CA, editors. Out of Mind: Varieties of Unconscious Processes. Oxford University Press. pp. 3–19.

https://doi.org/10.1093/oso/9780198506300.003.0001
- Google Scholar
1. Balsdon T
2. Azzopardi P
(2015) Absolute and relative blindsight
Consciousness and Cognition 32:79–91.

https://doi.org/10.1016/j.concog.2014.09.010
- PubMed
- Google Scholar
(2021) Asymmetries in visual acuity around the visual field
Journal of Vision 21:2.

https://doi.org/10.1167/jov.21.1.2
- PubMed
- Google Scholar
1. Block N
(2001) Paradox and cross purposes in recent work on consciousness
Cognition 79:197–219.

https://doi.org/10.1016/s0010-0277(00)00129-3
- PubMed
- Google Scholar
1. Block N
(2011) Perceptual consciousness overflows cognitive access
Trends in Cognitive Sciences 15:567–575.

https://doi.org/10.1016/j.tics.2011.11.001
- Google Scholar
Book
1. Carruthers P
(2015) The Centered Mind: What the Science of Working Memory Shows Us about the Nature of Human Thought
Oxford: OUP.

https://doi.org/10.1093/acprof:oso/9780198738824.001.0001
- Google Scholar
Book
1. Chabris C
2. Simons D
(2011)
The Invisible Gorilla: How Our Intuitions Deceive Us

Harmony.
- Google Scholar
Website
1. Cloud J
(2010) Human Perception and the Invisible Gorilla
Accessed July 12, 2010.

https://content.time.com/time/health/article/0,8599,2003097,00.html
(2011) Natural-scene perception requires attention
Psychological Science 22:1165–1172.

https://doi.org/10.1177/0956797611419168
- PubMed
- Google Scholar
(2012) The attentional requirements of consciousness
Trends in Cognitive Sciences 16:411–417.

https://doi.org/10.1016/j.tics.2012.06.013
- PubMed
- Google Scholar
(2016) What is the bandwidth of perceptual experience?
Trends in Cognitive Sciences 20:324–335.

https://doi.org/10.1016/j.tics.2016.03.006
- PubMed
- Google Scholar
(2020) The limits of color awareness during active, real-world vision
PNAS 117:13821–13827.

https://doi.org/10.1073/pnas.1922294117
- Google Scholar
(2023) Perceptual awareness occurs along a graded continuum: No evidence of all-or-none failures in continuous reproduction tasks
Psychological Science 34:1033–1047.

https://doi.org/10.1177/09567976231186798
- PubMed
- Google Scholar
1. De Brigard F
2. Prinz J
(2010) Attention and consciousness
Wiley Interdisciplinary Reviews. Cognitive Science 1:51–59.

https://doi.org/10.1002/wcs.27
- PubMed
- Google Scholar
1. Dehaene S
2. Naccache L
(2001) Towards a cognitive neuroscience of consciousness: basic evidence and a workspace framework
Cognition 79:1–37.

https://doi.org/10.1016/s0010-0277(00)00123-2
- PubMed
- Google Scholar
(2006) Conscious, preconscious, and subliminal processing: a testable taxonomy
Trends in Cognitive Sciences 10:204–211.

https://doi.org/10.1016/j.tics.2006.03.007
- Google Scholar
(2020) Implicit processing during inattentional blindness: a systematic review and meta-analysis
Neuroscience & Biobehavioral Reviews 119:355–375.

https://doi.org/10.1016/j.neubiorev.2020.10.005
- Google Scholar
(2022) Publication bias casts doubt on implicit processing in inattentional blindness
Neuroscience & Biobehavioral Reviews 140:104775.

https://doi.org/10.1016/j.neubiorev.2022.104775
- Google Scholar
1. Devue C
2. Laloyaux C
3. Feyers D
4. Theeuwes J
5. Brédart S
(2009) Do pictures of faces, and which ones, capture attention in the inattentional-blindness paradigm?
Perception 38:552–568.

https://doi.org/10.1068/p6049
- PubMed
- Google Scholar
1. Drew T
2. Võ MLH
3. Wolfe JM
(2013) The invisible gorilla strikes again: sustained inattentional blindness in expert observers
Psychological Science 24:1848–1853.

https://doi.org/10.1177/0956797613479386
- PubMed
- Google Scholar
1. Dulany DE
(2001)
Inattentional Awareness

Psyche: An Interdisciplinary Journal of Research on Consciousness 7:1–14.
- Google Scholar
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
- PubMed
- Google Scholar
1. Fougnie D
2. Marois R
(2007) Executive working memory load induces inattentional blindness
Psychonomic Bulletin & Review 14:142–147.

https://doi.org/10.3758/BF03194041
- Google Scholar
1. Gourevitch V
2. Galanter E
(1967) A significance test for one parameter isosensitivity functions
Psychometrika 32:25–33.

https://doi.org/10.1007/BF02289402
- PubMed
- Google Scholar
1. Graziano MSA
2. Webb TW
(2015) The attention schema theory: a mechanistic account of subjective awareness
Frontiers in Psychology 6:500.

https://doi.org/10.3389/fpsyg.2015.00500
- PubMed
- Google Scholar
Book
1. Green DM
2. Swets JA
(1966)
Signal Detection Theory and Psychophysics

New York: Wiley.
- Google Scholar
1. Hautus MJ
(1995) Corrections for extreme proportions and their biasing effects on estimated values of d′
Behavior Research Methods, Instruments, & Computers 27:46–51.

https://doi.org/10.3758/BF03203619
- Google Scholar
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
- PubMed
- Google Scholar
1. Hirschhorn R
2. Biderman D
3. Biderman N
4. Yaron I
5. Bennet R
6. Plotnik M
7. Mudrik L
(2024) Using virtual reality to induce multi-trial inattentional blindness despite trial-by-trial measures of awareness
Behavior Research Methods 56:3452–3468.

https://doi.org/10.3758/s13428-024-02401-8
- PubMed
- Google Scholar
1. Holender D
(1986) Semantic activation without conscious identification in dichotic listening, parafoveal vision, and visual masking: a survey and appraisal
Behavioral and Brain Sciences 9:1–23.

https://doi.org/10.1017/S0140525X00021269
- Google Scholar
1. Hollingworth A
2. Henderson JM
(2002) Accurate visual memory for previously attended objects in natural scenes
Journal of Experimental Psychology 28:113–136.

https://doi.org/10.1037//0096-1523.28.1.113-136
- Google Scholar
1. Hutchinson BT
(2019) Toward a theory of consciousness: a review of the neural correlates of inattentional blindness
Neuroscience & Biobehavioral Reviews 104:87–99.

https://doi.org/10.1016/j.neubiorev.2019.06.003
- Google Scholar
1. Ihaka R
2. Gentleman R
(1996) R: A language for data analysis and graphics
Journal of Computational and Graphical Statistics 5:299–314.

https://doi.org/10.1080/10618600.1996.10474713
- Google Scholar
Book
1. Irvine E
(2012) Consciousness as A Scientific Concept: A Philosophy of Science Perspective
Springer Science & Business Media.

https://doi.org/10.1007/978-94-007-5173-6
- Google Scholar
1. Jensen MS
2. Yao R
3. Street WN
4. Simons DJ
(2011) Change blindness and inattentional blindness
Wiley Interdisciplinary Reviews. Cognitive Science 2:529–546.

https://doi.org/10.1002/wcs.130
- PubMed
- Google Scholar
1. Kadlec H
(1999) Statistical properties of d' and β estimates of signal detection theory
Psychological Methods 4:22–43.

https://doi.org/10.1037//1082-989X.4.1.22
- Google Scholar
1. Koch C
2. Tsuchiya N
(2007) Attention and consciousness: two distinct brain processes
Trends in Cognitive Sciences 11:16–22.

https://doi.org/10.1016/j.tics.2006.10.012
- Google Scholar
1. Kolb FC
2. Braun J
(1995) Blindsight in normal observers
Nature 377:336–338.

https://doi.org/10.1038/377336a0
- PubMed
- Google Scholar
(2020) Guessing right: Preconscious processing in inattentional blindness
Quarterly Journal of Experimental Psychology 73:1055–1065.

https://doi.org/10.1177/1747021820911324
- Google Scholar
1. Lamme VAF
(2003) Why visual attention and awareness are different
Trends in Cognitive Sciences 7:12–18.

https://doi.org/10.1016/S1364-6613(02)00013-X
- Google Scholar
1. Levine MW
2. McAnany JJ
(2005) The relative capabilities of the upper and lower visual hemifields
Vision Research 45:2820–2830.

https://doi.org/10.1016/j.visres.2005.04.001
- PubMed
- Google Scholar
Book
1. Mack A
2. Rock I
(1998) Inattentional Blindness
Cambridge, MA: MIT Press.

https://doi.org/10.7551/mitpress/3707.001.0001
- Google Scholar
Book
1. Macmillan NA
2. Creelman CD
(2005) Detection Theory: A User’s Guide
Cambridge University Press.

https://doi.org/10.4324/9781410611147
- Google Scholar
1. Maier A
2. Tsuchiya N
(2021) Growing evidence for separate neural mechanisms for attention and consciousness
Attention, Perception & Psychophysics 83:558–576.

https://doi.org/10.3758/s13414-020-02146-4
- PubMed
- Google Scholar
1. Memmert D
(2014) Inattentional blindness to unexpected events in 8–15-year-olds
Cognitive Development 32:103–109.

https://doi.org/10.1016/j.cogdev.2014.09.002
- Google Scholar
(2002)
The siren song of implicit change detection

Journal of Experimental Psychology. Human Perception and Performance 28:798–815.
- PubMed
- Google Scholar
(2004) Nothing compares 2 views: Change blindness can occur despite preserved access to the changed information
Perception & Psychophysics 66:1268–1281.

https://doi.org/10.3758/BF03194997
- Google Scholar
1. Moore CM
2. Egeth H
(1997) Perception without attention: evidence of grouping under conditions of inattention
Journal of Experimental Psychology. Human Perception and Performance 23:339–352.

https://doi.org/10.1037//0096-1523.23.2.339
- PubMed
- Google Scholar
1. Moore CM
(2001)
Inattentional blindness: Perception or memory and what does it matter

Psyche 7:178–194.
- Google Scholar
Software
1. Morey RD
2. Rouder JN
3. Jamil T
4. Morey MRD
(2015) Package ‘bayesfactor’, version 0.9.12-4.7
CRAN.

https://cran.r-project.org/package=BayesFactor
(1997) Blindsight in normal subjects?
Nature 385:401–402.

https://doi.org/10.1038/385401b0
- PubMed
- Google Scholar
1. Most SB
2. Simons DJ
3. Scholl BJ
4. Jimenez R
5. Clifford E
6. Chabris CF
(2001) How not to be seen: the contribution of similarity and selective ignoring to sustained inattentional blindness
Psychological Science 12:9–17.

https://doi.org/10.1111/1467-9280.00303
- PubMed
- Google Scholar
(2005) What you see is what you set: sustained inattentional blindness and the capture of awareness
Psychological Review 112:217–242.

https://doi.org/10.1037/0033-295X.112.1.217
- PubMed
- Google Scholar
1. Murphy G
2. Greene CM
(2016) Perceptual load induces inattentional blindness in drivers
Applied Cognitive Psychology 30:479–483.

https://doi.org/10.1002/acp.3216
- Google Scholar
Website
1. Murphy H
(2017) Why We Miss Objects That Are Right in Front of Us
Accessed October 6, 2017.

https://www.nytimes.com/interactive/2017/10/06/science/why-we-miss-things-in-front-of-us.html
1. Neisser U
2. Becklen R
(1975) Selective looking: Attending to visually specified events
Cognitive Psychology 7:480–494.

https://doi.org/10.1016/0010-0285(75)90019-5
- Google Scholar
Book
1. Newell BR
2. Shanks DR
(2023) Open Minded: Searching for Truth about the Unconscious Mind
MIT Press.

https://doi.org/10.7551/mitpress/14922.001.0001
- Google Scholar
1. Noah S
2. Mangun GR
(2020) Recent evidence that attention is necessary, but not sufficient, for conscious perception
Annals of the New York Academy of Sciences 1464:52–63.

https://doi.org/10.1111/nyas.14030
- PubMed
- Google Scholar
1. O’Regan JK
2. Noë A
(2001) A sensorimotor account of vision and visual consciousness
The Behavioral and Brain Sciences 24:939–973.

https://doi.org/10.1017/s0140525x01000115
- PubMed
- Google Scholar
(2003) “Nonparametric”A’ and other modern misconceptions about signal detection theory
Psychonomic Bulletin & Review 10:556–569.

https://doi.org/10.3758/BF03196517
- Google Scholar
(2017) Beyond the Turk: alternative platforms for crowdsourcing behavioral research
Journal of Experimental Social Psychology 70:153–163.

https://doi.org/10.1016/j.jesp.2017.01.006
- Google Scholar
1. Phillips I
(2021a)
Scepticism about unconscious perception is the default hypothesis

Journal of Consciousness Studies 28:186–205.
- Google Scholar
1. Phillips I
(2021b) Blindsight is qualitatively degraded conscious vision
Psychological Review 128:558–584.

https://doi.org/10.1037/rev0000254
- PubMed
- Google Scholar
(2012) Visual processing of contour patterns under conditions of inattentional blindness
Journal of Cognitive Neuroscience 24:287–303.

https://doi.org/10.1162/jocn_a_00111
- PubMed
- Google Scholar
(2014) Isolating neural correlates of conscious perception from neural correlates of reporting one’s perception
Frontiers in Psychology 5:1078.

https://doi.org/10.3389/fpsyg.2014.01078
- PubMed
- Google Scholar
Book
1. Prinz J
(2012) The Conscious Brain
Oxford University Press.

https://doi.org/10.1093/acprof:oso/9780195314595.001.0001
- Google Scholar
Book
1. Prinz JJ
(2015)
Unconscious perception

In: Matthen M, editors. Oxford Handbook of Philosophy of Perception. Oxford: OUP. pp. 371–389.
- Google Scholar
(2020) Loads of unconscious processing: the role of perceptual load in processing unattended stimuli during inattentional blindness
Attention, Perception & Psychophysics 82:2641–2651.

https://doi.org/10.3758/s13414-020-01982-8
- PubMed
- Google Scholar
(2021) A systematic overview of methods, their limitations, and their opportunities to investigate inattentional blindness
Applied Cognitive Psychology 35:136–147.

https://doi.org/10.1002/acp.3746
- Google Scholar
1. Rees G
2. Russell C
3. Frith CD
4. Driver J
(1999) Inattentional blindness versus inattentional amnesia for fixated but ignored words
Science 286:2504–2507.

https://doi.org/10.1126/science.286.5449.2504
- PubMed
- Google Scholar
1. Rock I
2. Linnett CM
3. Grant P
4. Mack A
(1992) Perception without attention: results of a new method
Cognitive Psychology 24:502–534.

https://doi.org/10.1016/0010-0285(92)90017-v
- PubMed
- Google Scholar
Conference
1. Rosenholtz R
(2011) What your visual system sees where you are not looking
Proceedings of SPIE (Human Vision and Electronic Imaging XVI). pp. 343–356.

https://doi.org/10.1117/12.876659
- Google Scholar
(2016) Not quite so blind: semantic processing despite inattentional blindness
Journal of Experimental Psychology. Human Perception and Performance 42:459–463.

https://doi.org/10.1037/xhp0000205
- PubMed
- Google Scholar
(2006) The influence of inattention on the neural correlates of scene segmentation
Brain Research 1076:106–115.

https://doi.org/10.1016/j.brainres.2005.10.051
- PubMed
- Google Scholar
1. Simons DJ
2. Chabris CF
(1999) Gorillas in our midst: sustained inattentional blindness for dynamic events
Perception 28:1059–1074.

https://doi.org/10.1068/p281059
- PubMed
- Google Scholar
1. Simons DJ
(2000) Attentional capture and inattentional blindness
Trends in Cognitive Sciences 4:147–155.

https://doi.org/10.1016/S1364-6613(00)01455-8
- Google Scholar
1. Snodgrass M
(2002) Disambiguating conscious and unconscious influences: do exclusion paradigms demonstrate unconscious perception?
The American Journal of Psychology 115:545–579.

https://doi.org/10.2307/1423527
- PubMed
- Google Scholar
1. Tanner WP
2. Swets JA
(1954) A decision-making theory of visual detection
Psychological Review 61:401–409.

https://doi.org/10.1037/h0058700
- Google Scholar
(2005) Visual short-term memory load suppresses temporo-parietal junction activity and induces inattentional blindness
Psychological Science 16:965–972.

https://doi.org/10.1111/j.1467-9280.2005.01645.x
- PubMed
- Google Scholar
(2014) Seeing without knowing: neural signatures of perceptual inference in the absence of report
Journal of Cognitive Neuroscience 26:955–969.

https://doi.org/10.1162/jocn_a_00530
- PubMed
- Google Scholar
1. Ward EJ
2. Scholl BJ
(2015) Inattentional blindness reflects limitations on perception, not memory: evidence from repeated failures of awareness
Psychonomic Bulletin & Review 22:722–727.

https://doi.org/10.3758/s13423-014-0745-8
- Google Scholar
(1974) Visual capacity in the hemianopic field following a restricted occipital ablation
Brain 97:709–728.

https://doi.org/10.1093/brain/97.1.709
- PubMed
- Google Scholar
(2018) Inattentional blindness on the full-attention trial: are we throwing out the baby with the bathwater?
Consciousness and Cognition 59:64–77.

https://doi.org/10.1016/j.concog.2017.10.002
- PubMed
- Google Scholar
1. Wixted JT
(2020) The forgotten history of signal detection theory
Journal of Experimental Psychology. Learning, Memory, and Cognition 46:201–233.

https://doi.org/10.1037/xlm0000732
- PubMed
- Google Scholar
Book
1. Wolfe JM
(1999)
Inattentional amnesia

In: Coltheart V, editors. Fleeting Memories: Cognition of Brief Visual Stimuli. The MIT Press. pp. 71–94.
- Google Scholar
1. Wolfe JM
2. Utochkin IS
(2019) What is a preattentive feature?
Current Opinion in Psychology 29:19–26.

https://doi.org/10.1016/j.copsyc.2018.11.005
- PubMed
- Google Scholar
1. Wood K
2. Simons DJ
(2017) Selective attention in inattentional blindness: selection is specific but suppression is not
Collabra 3:1–11.

https://doi.org/10.1525/collabra.90
- Google Scholar
1. Wood K
2. Simons DJ
(2019) Processing without noticing in inattentional blindness: a replication of Moore and Egeth (1997) and Mack and Rock (1998)
Attention, Perception, & Psychophysics 81:1–11.

https://doi.org/10.3758/s13414-018-1629-1
- Google Scholar
Book
1. Wu W
(2014)
Attention

Routledge.
- Google Scholar

Article and author information

Author details

Makaela Nartker

Department of Psychological & Brain Sciences, Johns Hopkins University, Baltimore, United States

Contribution
Conceptualization, Data curation, Software, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing – original draft, Writing – review and editing

Competing interests
No competing interests declared
Chaz Firestone
1. Department of Psychological & Brain Sciences, Johns Hopkins University, Baltimore, United States
2. Department of Philosophy, Johns Hopkins University, Baltimore, United States
Contribution
Conceptualization, Resources, Supervision, Funding acquisition, Validation, Visualization, Methodology, Writing – original draft, Project administration, Writing – review and editing

Competing interests
No competing interests declared

"This ORCID iD identifies the author of this article:" 0000-0002-1247-2422
Howard Egeth

Department of Psychological & Brain Sciences, Johns Hopkins University, Baltimore, United States

Contribution
Conceptualization, Supervision, Validation, Visualization, Methodology, Writing – original draft, Project administration, Writing – review and editing

Competing interests
No competing interests declared

"This ORCID iD identifies the author of this article:" 0000-0002-0147-0827
Ian Phillips
1. Department of Psychological & Brain Sciences, Johns Hopkins University, Baltimore, United States
2. Department of Philosophy, Johns Hopkins University, Baltimore, United States
Contribution
Conceptualization, Formal analysis, Supervision, Validation, Investigation, Visualization, Methodology, Writing – original draft, Project administration, Writing – review and editing

For correspondence
ianbphillips@jhu.edu

Competing interests
No competing interests declared

"This ORCID iD identifies the author of this article:" 0000-0003-2932-8045

Funding

National Science Foundation (BCS-2021053)

Chaz Firestone

Johns Hopkins University (Catalyst Award)

Chaz Firestone

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Ethics

All experiments reported were approved by the Homewood Institutional Review Board of Johns Hopkins University (protocol number: HIRB00005762). All subjects provided informed consent and were compensated financially for their participation.

Version history

Preprint posted: June 8, 2024
Sent for peer review: July 10, 2024
Reviewed Preprint version 1: October 4, 2024
Reviewed Preprint version 2: February 3, 2025
Version of Record published: May 19, 2025

Cite all versions

You can cite all versions using the DOI https://doi.org/10.7554/eLife.100337. This DOI represents all versions, and will always resolve to the latest one.

Copyright

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.