Dynamic engagement of human motion detectors across space-time coordinates

Motion detection is a fundamental property of the visual system. The gold standard for studying and understanding this function is the motion energy model. This computational tool relies on spatiotemporally selective filters that capture the change in spatial position over time afforded by moving objects. Although the filters are defined in space-time, their human counterparts have never been studied in their native spatiotemporal space but rather in the corresponding frequency domain. When this frequency description is back-projected to spatiotemporal description, not all characteristics of the underlying process are retained, leaving open the possibility that important properties of human motion detection may have remained unexplored. We derived descriptors of motion detectors in native space-time, and discovered a large unexpected dynamic structure involving a >2× change in detector amplitude over the first ∼100 ms. This property is not predicted by the energy model, generalizes across the visual field, and is robust to adaptation; however, it is silenced by surround inhibition and is contrast dependent. We account for all results by extending the motion energy model to incorporate a small network that supports feedforward spread of activation along the motion trajectory via a simple gain-control circuit.

Semantic control of feature extraction from natural scenes

In the early stages of image analysis, visual cortex represents scenes as spatially organized maps of locally defined features (e.g., edge orientation). As image reconstruction unfolds and features are assembled into larger constructs, cortex attempts to recover semantic content for object recognition. It is conceivable that higher level representations may feed back onto early processes and retune their properties to align with the semantic structure projected by the scene; however, there is no clear evidence to either support or discard the applicability of this notion to the human visual system. Obtaining such evidence is challenging because low and higher level processes must be probed simultaneously within the same experimental paradigm. We developed a methodology that targets both levels of analysis by embedding low-level probes within natural scenes. Human observers were required to discriminate probe orientation while semantic interpretation of the scene was selectively disrupted via stimulus inversion or reversed playback. We characterized the orientation tuning properties of the perceptual process supporting probe discrimination; tuning was substantially reshaped by semantic manipulation, demonstrating that low-level feature detectors operate under partial control from higher level modules. The manner in which such control was exerted may be interpreted as a top-down predictive strategy whereby global semantic content guides and refines local image reconstruction. We exploit the novel information gained from data to develop mechanistic accounts of unexplained phenomena such as the classic face inversion effect.