Year: 2025
Type: preprint
Abstract: A central challenge in consciousness research is to dissociate perception from awareness usingrigorous, bias-free methods. Traditional approaches often suffer from two key limitations: thecriterion biases in reporting perceptual (un)awareness, and the criterion content fallacy, whereawareness measures do not capture the specific information required to perform the perceptualtask. These problems are compounded by the widespread use of single-object paradigms, whichtypically involve presenting isolated stimuli near threshold and suppressing them with maskingtechniques. These constraints result in a low signal-to-noise ratio that severely limits sensitivity todetect effects, and offer low ecological validity. Here we introduce a novel paradigm based onensemble perception that overcomes these long-standing limitations. Experiment 1 used a bias-freetwo-interval forced-choice task in which observers had to discriminate the predominant category inensembles comprising animate and inanimate items and also detect which interval contained thetask-relevant features Experiment 2 extended these findings with a single-interval design, addressingconcerns about potential detection inefficiencies. Multiple control analyses across experimentsdemonstrate robust unconscious ensemble perception: observers reliably discriminatedensemble-level properties even when detection of the task-relevant features was at chance.Additionally, Bayesian ideal observer modeling anchors these findings within a computationalaccount of unconscious perception. Together, our findings demonstrate that perceptual processingof complex visual summaries can occur without conscious awareness - a phenomenon we termensemble blindsight. The work offers a new framework for studying visual consciousness beyondthe limits of traditional single-target paradigms.
Authors: Patxi Elosegi, Pietro Amerio, Ning Mei, Marta Valdazo, Nirmitee Mulay, Roberto Santana, Axel Cleeremans, David Soto
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: gold
OpenAlex ID: W4414552992
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