Neural Responses to Binocularly In-phase and Anti-phase Stimuli
Poster Presentation: Sunday, May 18, 2025, 8:30 am – 12:30 pm, Pavilion
Session: Binocular Vision: Rivalry and bistability, stereopsis, models, neural mechanisms
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Bruno Richard1 (), Daniel Baker2; 1Rutgers University - Newark, 2University of York
Binocular vision fuses similar stimuli into a single percept, yet incompatible stimuli result in other experiences such as rivalry, lustre and diplopia. We measured neural responses to binocular stimuli with different phase relationships, intending to understand them using contemporary binocular models. Steady-State Visually Evoked Potentials (SSVEPs) were recorded from 15 observers in response to monocular and binocular stimulation at 3Hz, using either on-off or counterphase flicker. Across the eyes, binocular stimuli could be (i) in spatial and temporal phase, (ii) in temporal phase but spatial antiphase, (iii) in spatial phase but temporal antiphase, or (iv) in spatial and temporal antiphase (for counterphase flicker this is identical to condition(i)). Responses to monocular on-off flicker showed peaks at the fundamental frequency (3Hz) and its harmonics (integer multiples of 3Hz). In contrast, counterphase flicker produced responses only at twice the flicker frequency (6Hz) and its harmonics. Binocular in-phase stimulation resulted in a similar pattern of responses, consistent with ‘ocularity invariance’ – the observation that binocular and monocular stimuli appear equal at high contrasts. Changing the phase relationship modulated the harmonics pattern in complex ways: in particular, on-off flicker in temporal antiphase reduced the fundamental response, but there was no such effect for counterphase flicker. We modelled the data using a progression of binocular combination algorithms that increased in complexity from a simple linear sum to a two-stage binocular gain control model with parallel monocular and binocular phase-selective channels (the Lustre model; Georgeson et al., 2016). The most complex model (lustre) outperformed all other models in capturing the variance of our SSVEP data, although simpler phase-insensitive models performed similarly well in most experimental conditions. Simpler models struggled to capture the response magnitude to counterphase stimuli. Our findings suggest explaining neural responses to binocular stimuli with different phase relationships requires phase-selective channels.