Unconscious Perception of Vernier Offsets

Abstract

The comparison between conscious and unconscious perception is a cornerstone of consciousness science. However, most studies reporting above-chance discrimination of unseen stimuli do not control for criterion biases when assessing awareness. We tested whether observers can discriminate subjectively invisible offsets of Vernier stimuli when visibility is probed using a bias-free task. To reduce visibility, stimuli were either backward masked or presented for very brief durations (1-3 milliseconds) using a modern-day Tachistoscope. We found some behavioral indicators of perception without awareness, and yet, no conclusive evidence thereof. To seek more decisive proof, we simulated a series of Bayesian observer models, including some that produce visibility judgements alongside type-1 judgements. Our data are best accounted for by observers with slightly suboptimal conscious access to sensory evidence. Overall, the stimuli and visibility manipulations employed here induced mild instances of blindsight-like behavior, making them attractive candidates for future investigation of this phenomenon.

Keywords: bias-free task; consciousness; observer model; tachistoscope; unconscious perception.

Figure 1.

Stimuli and 2-Interval Forced-Choice procedure for the unmasked (A) and masked (B) conditions. Targets consisted of two vertical lines displayed on a grey background, either with a horizontal offset to the left/right (offset-present interval) or without an offset (offset-absent interval). In the unmasked condition (A), stimuli were presented between 980 μs and 3000 μs. In the masked condition (B), visibility was determined by the inter-stimulus interval (ISI) ranging from 16.7 ms to 100 ms. After each interval, participants reported the direction of the offset. Then, they indicated in which interval the offset was more visible.

Figure 2.

Attributes of the Bayesian observer models. Our seven observers result from the various combinations of these attributes. Each model’s evidence space is set in a Cartesian space where the main axes represent evidence in favor of a leftward (d_LEFT) or rightward offset (d_RIGHT). Stimuli of different intensities are generated from bivariate Gaussian distributions (represented as concentric circles). Crosses represent bidimensional evidence samples. (A) We implemented two different structures of the evidence space. Some models (A-upper) represent the non-informative stimulus (in gray) at the origin of axes. Stimuli with more visible task-relevant features are represented further away from the origin. Other models (A-lower) represent the absence of a stimulus at the origin, while non-informative stimuli are produced by sources on the diagonal (in gray). Here, distance from the origin represents stimulus visibility, while distance from the diagonal represents visibility of the task-relevant feature. (B) We also implemented two different ways of accounting for the strength of the stimulus. Some models (B-upper) perform the orientation discrimination and interval selection on a set of potential signal sources at various levels of stimulus strengths, over which they then marginalize. Other observers (B-lower) proceed in a hierarchical fashion. First, they compute the most likely evidence strength of the stimulus (the highlighted signal source), then they perform the two main tasks. (C) Finally, we modeled the interval selection process in two ways. To aid visualization, we illustrate here the case of a hierarchical observer with the non-informative signal source placed on the diagonal. Some models (C-upper) judge the interval where the task-relevant feature was most visible by comparing their confidence in the orientation discrimination response across the two intervals. For each interval (OP and OA), our example model determined one most likely signal source. Darker shades mean higher confidence in the discrimination response. Otherwise, some models (C-lower) mimic a visibility judgment by choosing the interval in which the stimulus was the least likely to have been produced by a non-informative signal source (darker shade represents higher probability that the sample actually contained a task-relevant feature). OA = offset-absent interval. OP = offset-present interval.

Figure 3.

Behavioral group level results. Each data point represents performance for a single participant at a given ISI (A, C, E) or presentation speed (B, D, F). Panels A and B represent accuracy in OP (offset-present) interval selection as a function of orientation discrimination performance. Panels C and D show orientation discrimination conditioned on judging the offset as being more visible in the offset-present interval (OP chosen, blue) or not (OA chosen, red). Panels E and F show Type-2 Hits (blue) and False alarm rates (red). All panels represent orientation discrimination accuracy on the horizontal axis. Error bars represent cross-participant mean performances and standard errors. Darker shades of blue/red indicate conditions with longer ISI/duration.

Figure 4.

Ideal vs. noisy Observer models. For every model, mean BIC (panels A–B) and cross-validated log-likelihoods (CV-logL, panels C–D) are shown. Scores for both ideal (black) and noisy observers (blue) are shown. Error bars represent cross-participant standard errors. Data from the masking experiment (panels A and C) and from the Tachistoscope experiment (panels B and D) were fitted separately.

Figure 5.

Best-fitting Bayesian observers. The marginalizing confidence observer with the evidence space configuration shown in Figure 2A-lower (model 3) was the best at recapitulating data from the masking experiment. Panels A and B show its predictions relative to interval selection and Type-2 False Alarm rates. Behavior in the task with unmasked stimuli was best represented by the hierarchical visibility model (model 7, also simulated in the evidence space of Figure 2A-lower). Its predictions for interval selection (C) and Type-2 False-Alarm rates (D) are shown. Black line: ideal observer (σ_D = 0). Blue line: noisy observer with the closest σ_D to the mean best-fitting σ_D. Single dots represent performance for one participant at one ISI/duration (darker dots represent longer ISI/durations).