Neuroscience

Distinct neural mechanisms underlying perceptual and attentional impairments of conscious access

Samuel Noorman author has email address
Timo Stein
Johannes J Fahrenfort
Simon van Gaal

Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands
Amsterdam Brain and Cognition, University of Amsterdam, Amsterdam, The Netherlands
Department of Applied and Experimental Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands

https://doi.org/10.7554/eLife.97900.1

Open access
Copyright information

Figures and data

Experimental design and behavior.
(A) Perceptual vs. attentional blindness in the four-stage model. A stimulus with low bottom-up strength (masked) is thought to interrupt local recurrent processing in sensory areas while leaving feedforward processing largely intact, while inattention (induced by the attentional blink) is thought to interrupt global recurrent processing between frontoparietal areas and sensory areas, while leaving local recurrent processing within sensory areas largely intact. Reprinted from Dehaene et al. (2006) with permission from Elsevier. (B) Target stimulus set and decoding analyses. (C) Trial design. (D) Perceptual performance refers to participants’ ability to detect the Kanizsa illusion. Metacognition refers to participants’ ability to evaluate their own performance using confidence judgments. Both perceptual performance and metacognition are measured as the area under the receiver operating characteristic curve (AUC). Error bars are mean ± standard error of the mean. Individual data points are plotted using low contrast. Ns is not significant (P≥0.477, BF₀₁≥4.05). *P≤0.001.

Local contrast and illusory triangle decoding using first targets as training data.
(A) Local contrast decoding. (B) Illusory Kanizsa triangle decoding. For both features, covariance/class separability maps reflecting underlying neural sources are shown. Below these maps: mean decoding performance, area under the receiver operating characteristic curve (AUC), over time ± standard error of the mean (SEM). Thick lines differ from chance: P<0.05, cluster-based permutation test. (C) Normalized (Z-scored) AUC for every measure: mean decoding time windows and two types of behavior. Each measure is Z-scored separately. Perceptual performance refers to participants’ ability to detect the Kanizsa illusion. Metacognition refers to participants’ ability to evaluate their own performance using confidence judgments. See Figure S3 for the same analyses but then for off-diagonal decoding profiles. Error bars are mean ± SEM. Individual data points are plotted using low contrast. Ns is not significant (P≥0.166, BF₀₁≥2.07). *P≤0.002.

Separating collinearity and illusion-specific processes using the independent training dataset.
(A) Illusory triangle decoding, after training classifiers on the independent training set on either the non-illusory (collinearity-only, purple lines) or illusory triangle (collinearity-plus-illusion, green lines). For comparison, training and testing on local contrast is shown in light blue. Mean decoding performance, area under the receiver operating characteristic curve (AUC), over time ± standard error of the mean (SEM) is shown. Thick lines differ from chance: P<0.05, cluster-based permutation test. The highlighted time windows are 75-95, 140-190, 200-250, and 375-475 ms, corresponding to separate panels in (B), which shows normalized (Z-scored) mean AUC for every time window. Each window is Z-scored separately. Error bars are mean ± SEM. Individual data points are plotted using low contrast. Ns is not significant (P≥0.084, BF₀₁≥1.26). *P≤0.048.

Sign up for email alerts