Transformation priming helps to disambiguate sudden changes of sensory inputs

A new 'CFS tracking' paradigm reveals uniform suppression depth regardless of target complexity or salience

When the eyes view separate and incompatible images, the brain suppresses one image and promotes the other into visual awareness. Periods of interocular suppression can be prolonged during continuous flash suppression (CFS) - when one eye views a static 'target' while the other views a complex dynamic stimulus. Measuring the time needed for a suppressed image to break CFS (bCFS) has been widely used to investigate unconscious processing, and the results have generated controversy regarding the scope of visual processing without awareness. Here, we address this controversy with a new 'CFS tracking' paradigm (tCFS) in which the suppressed monocular target steadily increases in contrast until breaking into awareness (as in bCFS) after which it decreases until it again disappears (reCFS), with this cycle continuing for many reversals. Unlike bCFS, tCFS provides a measure of suppression depth by quantifying the difference between breakthrough and suppression thresholds. tCFS confirms that (i) breakthrough thresholds indeed differ across target types (e.g. faces vs gratings, as bCFS has shown) - but (ii) suppression depth does not vary across target types. Once the breakthrough contrast is reached for a given stimulus, all stimuli require a strikingly uniform reduction in contrast to reach the corresponding suppression threshold. This uniform suppression depth points to a single mechanism of CFS suppression, one that likely occurs early in visual processing because suppression depth was not modulated by target salience or complexity. More fundamentally, it shows that variations in bCFS thresholds alone are insufficient for inferring whether the barrier to achieving awareness exerted by interocular suppression is weaker for some categories of visual stimuli compared to others.

Noise, multisensory integration, and previous response in perceptual disambiguation

Sensory information about the state of the world is generally ambiguous. Understanding how the nervous system resolves such ambiguities to infer the actual state of the world is a central quest for sensory neuroscience. However, the computational principles of perceptual disambiguation are still poorly understood: What drives perceptual decision-making between multiple equally valid solutions? Here we investigate how humans gather and combine sensory information–within and across modalities–to disambiguate motion perception in an ambiguous audiovisual display, where two moving stimuli could appear as either streaming through, or bouncing off each other. By combining psychophysical classification tasks with reverse correlation analyses, we identified the particular spatiotemporal stimulus patterns that elicit a stream or a bounce percept, respectively. From that, we developed and tested a computational model for uni- and multi-sensory perceptual disambiguation that tightly replicates human performance. Specifically, disambiguation relies on knowledge of prototypical bouncing events that contain characteristic patterns of motion energy in the dynamic visual display. Next, the visual information is linearly integrated with auditory cues and prior knowledge about the history of recent perceptual interpretations. What is more, we demonstrate that perceptual decision-making with ambiguous displays is systematically driven by noise, whose random patterns not only promote alternation, but also provide signal-like information that biases perception in highly predictable fashion.

Sensory information is generally ambiguous, and a single sensory modality most often cannot provide enough information to univocally specify the actual state of the world. A primary task for the brain is therefore to resolve perceptual ambiguity. Here we use a dynamic audiovisual ambiguous display embedded in noise to investigate the computational mechanisms of perceptual disambiguation. Results demonstrate that the brain first extracts visual information for perceptual disambiguation through motion detectors. Such information is next combined with auditory information–and memory of recent perceptual history–through weighted averaging to determine the final percept. This study revealed the particular spatiotemporal stimulus patterns that elicit a stream or a bounce percept, respectively, and it demonstrates that perceptual disambiguation is majorly affected by noise, whose random spatiotemporal patterns provide signal-like information that bias perception in a very systematic fashion.