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. 2014 Mar;25(3):824-31.
doi: 10.1177/0956797613513810. Epub 2014 Jan 16.

The attentional blink reveals the probabilistic nature of discrete conscious perception

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The attentional blink reveals the probabilistic nature of discrete conscious perception

Christopher L Asplund et al. Psychol Sci. 2014 Mar.

Abstract

Attention and awareness are two tightly coupled processes that have been the subject of the same enduring debate: Are they allocated in a discrete or in a graded fashion? Using the attentional blink paradigm and mixture-modeling analysis, we show that awareness arises at central stages of information processing in an all-or-none manner. Manipulating the temporal delay between two targets affected subjects' likelihood of consciously perceiving the second target, but did not affect the precision of its representation. Furthermore, these results held across stimulus categories and paradigms, and they were dependent on attention having been allocated to the first target. The findings distinguish the fundamental contributions of attention and awareness at central stages of visual cognition: Conscious perception emerges in a quantal manner, with attention serving to modulate the probability that representations reach awareness.

Keywords: attention; cognitive processes; consciousness.

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Figures

Figure 1
Figure 1
Experimental paradigm of the color AB task (Experiment 1). A) Subjects reported the color of two square targets (Black or white for T1, any of the 180 equiluminant colors for T2). B) At the conclusion of each trial, subjects reported T2’s color via a color wheel before making their T1 response.
Figure 2
Figure 2
Results of the color AB task (Experiment 1). A) T1 results showing no effect of lag. B) Probability (Pe) of T2 encoding. C) Precision (σ) of T2 encoding, where lower σ values (standard deviation of report error) correspond to better precision. Error bars reflect standard error of the mean (SEM).
Figure 3
Figure 3
Mixture model example. Response error distributions aggregated across all participants for lags 2 and 8. Each distribution was modeled (black line) as a mixture of a distribution for target responses (circular Gaussian) and one for random guesses (uniform).
Figure 4
Figure 4
Face AB experimental design (Experiment 2). A) Two masked faces were sequentially presented either with a short (200 ms) or long (800 ms) SOA. B) Subjects reported T2’s identity via a face wheel.
Figure 5
Figure 5
Face AB experiment results. A) T1 results showing only a small effect of SOA (F1,15 = 16.22, p = 0.001). B) Probability (Pe) and C) precision (σ) of T2 report for the 200 ms and 800 ms SOAs. Faces were presented for 100 ms (gray dashed lines) or 200 ms (black solid lines). Error bars reflect SEM.

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