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A hallmark of human social behavior is the effortless ability to relate one's own actions to that of the interaction partner, e.g., when stretching out one's arms to catch a tripping child. What are the behavioral properties of the neural substrates that support this indispensable human skill? Here we examined the processes underlying the ability to relate actions to each other, namely the recognition of spatiotemporal contingencies between actions (e.g., a "giving" that is followed by a "taking"). We used a behavioral adaptation paradigm to examine the response properties of perceptual mechanisms at a behavioral level. In contrast to the common view that action-sensitive units are primarily selective for one action (i.e., primary action, e.g., 'throwing"), we demonstrate that these processes also exhibit sensitivity to a matching contingent action (e.g., "catching"). Control experiments demonstrate that the sensitivity of action recognition processes to contingent actions cannot be explained by lower-level visual features or amodal semantic adaptation. Moreover, we show that action recognition processes are sensitive only to contingent actions, but not to noncontingent actions, demonstrating their selective sensitivity to contingent actions. Our findings show the selective coding mechanism for action contingencies by action-sensitive processes and demonstrate how the representations of individual actions in social interactions can be linked in a unified representation. [ABSTRACT FROM AUTHOR]

One of the main functions of vision is to estimate the 3D shape of objects in our environment. Many different visual cues, such as stereopsis, motion parallax, and shading, are thought to be involved. One important cue that remains poorly understood comes from surface texture markings. When a textured surface is slanted in 3D relative to the observer, the surface patterns appear compressed in the retinal image, providing potentially important information about 3D shape. What is not known, however, is how the brain actually measures this information from the retinal image. Here, we explain how the key information could be extracted by populations of cells tuned to different orientations and spatial frequencies, like those found in the primary visual cortex. To test this theory, we created stimuli that selectively stimulate such cell populations, by "smearing" (filtering) images of 2D random noise into specific oriented patterns. We find that the resulting patterns appear vividly 3D, and that increasing the strength of the orientation signals progressively increases the sense of 3D shape, even though the filtering we apply is physically inconsistent with what would occur with a real object. This finding suggests we have isolated key mechanisms used by the brain to estimate shape from texture. Crucially, we also find that adapting the visual system's orientation detectors to orthogonal patterns causes unoriented random noise to look like a specific 3D shape. Together these findings demonstrate a crucial role of orientation detectors in the perception of 3D shape. [ABSTRACT FROM AUTHOR]

Our ability to return to the start of a route recently performed in darkness is thought to reflect path integration of motion-related in- formation. Here we provide evidence that motion-related interocep- tive representations (proprioceptive, vestibular, and motor efference copy) combine with visual representations to form a single multi- modal representation guiding navigation. We used immersive virtual reality to decouple visual inputfrom motion-related interoception by manipulating the rotation ortranslation gain of the visual projection. First, participants walked an outbound path with both visual and interoceptive input, and returned to the start in darkness, demon- strating the influences of both visual and interoceptive information in a virtual reality environment. Next, participants adapted to visual rotation gains in the virtual environment, and then performed the path integration task entirely in darkness. Our findings were accu- rately predicted bya quantitative model in which visual and intero- ceptive inputs combine into a single multimodal representation guiding navigation, and are incompatible with a model of separate visual and interoceptive influences on action (in which path integra- tion in darkness must rely solely on interoceptive representations). Overall, our findings suggest that a combined multimodal represen- tation guides large-scale navigation, consistent with a role for visual imagery or a cognitive map. [ABSTRACT FROM AUTHOR]

Determining the approach of a moving object is a vital survival skill that depends on the brain combining information about lateral translation and motion-in-depth. Given the importance of sensing motion for obstacle avoidance, it is surprising that humans make errors, reporting an object will miss them when it is on a collision course with their head. Here we provide evidence that biases observed when participants estimate movement in depth result from the brain's use of a "prior" favoring slow velocity. We formulate a Bayesian model for computing 3D motion using independently estimated parameters for the shape of the visual system's slow velocity prior. We demonstrate the success of this model in accounting for human behavior in separate experiments that assess both sensitivity and bias in 3D motion estimation. Our results show that a surprising perceptual error in 3D motion perception reflects the importance of prior probabilities when estimating environmental properties. [ABSTRACT FROM AUTHOR]

In this article, the authors discuss the development of visual abilities in infants. They mention that according to studies visual abilities develop in a stereotyped order and visual learning is partially driven by simple action events. The infants perceive simple action events by local analysis of motion flow patterns especially by detecting events in which visual motion flows.