title: The relative contribution of noise and adaptation to competition during tri-stable motion perception.
abstract: Animals exploit antagonistic interactions for sensory processing and these can cause oscillations between competing states. Ambiguous sensory inputs yield such perceptual multistability. Despite numerous empirical studies using binocular rivalry or plaid pattern motion, the driving mechanisms behind the spontaneous transitions between alternatives remain unclear. In the current work, we used a tristable barber pole motion stimulus combining empirical and modeling approaches to elucidate the contributions of noise and adaptation to underlying competition. We first robustly characterized the coupling between perceptual reports of transitions and continuously recorded eye direction, identifying a critical window of 480 ms before button presses, within which both measures were most strongly correlated. Second, we identified a novel nonmonotonic relationship between stimulus contrast and average perceptual switching rate with an initially rising rate before a gentle reduction at higher contrasts. A neural fields model of the underlying dynamics introduced in previous theoretical work and incorporating noise and adaptation mechanisms was adapted, extended, and empirically validated. Noise and adaptation contributions were confirmed to dominate at the lower and higher contrasts, respectively. Model simulations, with two free parameters controlling adaptation dynamics and direction thresholds, captured the measured mean transition rates for participants. We verified the shift from noise-dominated toward adaptation-driven in both the eye direction distributions and intertransition duration statistics. This work combines modeling and empirical evidence to demonstrate the signal-strength-dependent interplay between noise and adaptation during tristability. We propose that the findings generalize beyond the barber pole stimulus case to ambiguous perception in continuous feature spaces.
authors.affiliation: Institut de Neurosciences de la Timone, UMR 7289 CNRS & Aix-Marseille Université, Marseille, FrancePsychology and Interdisciplinary Neurosciences Research Group, Faculty of Science and Technology, Bournemouth University, UKameso@bournemouth.ac.uk.
authors.forename: Andrew Isaac
authors.initials: AI
authors.lastname: Meso
authors.affiliation: Team Mathneuro, Université Côte d'Azur, INRIA Research Center, Sophia Antipolis, FranceCenter for Neural Science, New York University, New York, NYjames.rankin@gmail.com.
authors.forename: James
authors.initials: J
authors.lastname: Rankin
authors.affiliation: Team Mathneuro, Université Côte d'Azur, INRIA Research Center, Sophia Antipolis, Franceolivier.faugeras@inria.fr.
authors.forename: Olivier
authors.initials: O
authors.lastname: Faugeras
authors.affiliation: Team Biovision, Université Côte d'Azur, INRIA Research Center, Sophia Antipolis, Francepierre.kornprobst@inria.fr.
authors.forename: Pierre
authors.initials: P
authors.lastname: Kornprobst
authors.affiliation: Institut de Neurosciences de la Timone, UMR 7289 CNRS & Aix-Marseille Université, Marseille, Franceguillaume.masson@univ-amu.fr.
authors.forename: Guillaume S
authors.initials: GS
authors.lastname: Masson
mesh_terms: D000222:Adaptation, Physiological / Q000502:physiology*; D005260:Female; D006801:Humans; D008297:Male; D008962:Models, Theoretical; D009038:Motion; D009039:Motion Perception / Q000502:physiology*; D009622:Noise; D010775:Photic Stimulation