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Human observers have optimal introspective access to perceptual processes even for visually masked stimuli

  1. Megan A K Peters Is a corresponding author
  2. Hakwan Lau
  1. University of California, Los Angeles, United States
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Cite as: eLife 2015;4:e09651 doi: 10.7554/eLife.09651
5 figures, 2 tables and 1 additional file

Figures

Stimuli and procedures for the 2IFC confidence-rating task.

(A) Targets consisted of oriented (45° left- or right-tilted from vertical) Gabor patches presented at multiple near-threshold contrast levels; masks consisted of bandpass-noise filtered random RGB values (see Materials and methods). (B) Each trial consists of two intervals of discrimination in which the target stimulus (T) was forward- and backward-masked (M). Gabor patch targets were presented only in target-present (TP) intervals; in target-absent (TA) intervals, the target was replaced with blank frames. Otherwise timings of stimuli were matched between the two intervals. (C) Experimental tasks. Experiment 1 required subjects to bet on which discrimination they felt more confident before they indicated their orientation discrimination choices (left or right tilt of the Gabor) sequentially for both intervals. Shown is an example trial in which TP is presented before TA; in the experiment this order varied randomly from trial to trial. In Experiment 2, subjects bet on the more confident interval after the discriminations, and feedback was given. (See Materials and methods for more details.)

https://doi.org/10.7554/eLife.09651.003
Schematic explanation of predictions of the experiments.

(A) A ‘Performance without Awareness’ pattern of behavior, in which subjects are able to discriminate the target above chance while betting on the target-present interval at chance. (B) A ‘Performance > Awareness’ pattern of behavior, in which subjects are less able to bet on their discrimination decisions than they are able to correctly discriminate the target. In both (A) and (B), the diagonal dashed line indicates where rate of betting on the target-present interval equals objective discrimination performance.

https://doi.org/10.7554/eLife.09651.004
Group-level results of behavioral experiments (rows 1 and 2), presented in comparison to the predictions of the Bayesian ideal observer model (row 3; see Materials and methods - Computational Model).

In both experiments, human observers displayed no evidence of Performance without Awareness, but appeared to demonstrate Performance > Awareness (panels A and D). However, the ideal observer model also demonstrated such behavior (panel G), indicating that it is not suboptimal at all but arises from the 2IFC nature of the confidence task (see Bayesian Ideal Observer Model results section and Figure 2 caption for explanation). Horizontal gray lines in panels A, D, and G indicate chance-level betting (50%) on the target-present (TP) interval. Panels B, E, and H show rising Type 2 hit rate (‘HR’; when subjects bet on a correct orientation discrimination choice) but relatively flat Type 2 false alarm rate (‘FAR’; when subjects bet on an incorrect orientation discrimination choice), and panels C, F, and I show higher orientation discrimination accuracy when the target-present (TP) interval is bet on; these patterns suggest that human subjects and the Bayesian ideal observer were rating confidence via assessing their probability of correctly discriminating orientation, rather than target presence versus absence only. The model demonstrates good explanatory power for the data across all participants (mean proportion of variance accounted for by the model, R2 = 0.565). Error bars for behavioral data indicate the standard error of the mean across subjects with data in each bin.

https://doi.org/10.7554/eLife.09651.005
Illustration of the Bayesian ideal observer’s 2-dimensional representation space, following standard 2-dimensional signal detection theory (King and Dehaene, 2014; Macmillan and Creelman, 2004).

(a) Distributions Sleft and Sright lie on orthogonal axes cleft and cright representing left- and right-tilted targets, respectively, and the noise distribution lies at the origin. On each simulated trial, the model ‘sees’ two samples, one drawn from a source distribution Si to represent the target-present interval (dTP) and the other from the noise distribution to represent the target-absent interval (dTA). It marginalizes across all contrast evidence levels to guess the orientations of both samples according to the posterior probabilities of left- and right-tilted sources. Then, it compares the posterior probabilities of the chosen orientations in each interval to select the interval with higher confidence (p(correct)) (see Materials and methods - Bayesian ideal observer model).

https://doi.org/10.7554/eLife.09651.007
Illustration of increasing values for σd on the appearance of Performance without Awareness behavior, used to evaluate the possibility that human participants may have exhibited Performance without Awareness.

Increasing σd values resulted in increasingly poor R2 values (see Results), indicating that the ideal observer (which displays no performance without awareness) produces the best fit to human data.

https://doi.org/10.7554/eLife.09651.009

Tables

Table 1

Individual values, means, standard deviations, and p-values for t-tests showing that Performance > Awareness occurs across both experiments. Results from Experiment 2 show that the pattern does not change with different question order or feedback.

https://doi.org/10.7554/eLife.09651.006
ExptSubjectp(choose TP interval) at p(correct) = 0.75
11AVT0.676
2AM0.714
3JDK0.716
4SH0.682
5MM0.684
6AC0.685
7MR0.674
8MK0.658
9RA0.619
21AVT0.666
2AM0.713
3JDK0.746
Mean (σ)0.686 (0.033)
t(11)6.718
p0.00003
Table 2

R2 values quantifying goodness of fit for ideal observer (σd = 0) and three alternative decisional noise magnitudes (σd >0) which cause increasing degrees of Performance without Awareness. Decisional noise greater than 0 – i.e., increased level of Performance without Awareness – causes a drop in goodness of fit between model and human data. See Methods and Appendix 4 for more details.

https://doi.org/10.7554/eLife.09651.008
ExptSubjectDecisional noise σd
0 (Ideal observer)0.10.20.3
110.4650.4590.4560.447
20.5800.5780.5650.544
30.4700.4640.4480.428
40.3960.3920.3810.363
50.6490.6550.6450.628
60.4800.4730.4580.434
70.4530.4520.4440.427
80.6020.5950.5830.563
90.5030.5090.5120.504
210.6240.6240.6220.612
20.7830.7800.7750.766
30.7770.7780.7670.753
Mean R2 (σ)0.565 (0.126)0.563
(0.128)
0.555
(0.129)
0.539 (0.131)

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