Statistical extraction affects visually guided action

The visual system summarizes average properties of ensembles of similar objects. We demonstrated an adaptation aftereffect of one such property, mean size, suggesting it is encoded along a single visual dimension (Corbett, et al., 2012), in a similar manner as basic stimulus properties like orientation and direction of motion. To further explore the fundamental nature of ensemble encoding, here we mapped the evolution of mean size adaptation over the course of visually guided grasping. Participants adapted to two sets of dots with different mean sizes. After adaptation, two test dots replaced the adapting sets. Participants first reached to one of these dots, and then judged whether it was larger or smaller than the opposite dot. Grip apertures were inversely dependent on the average dot size of the preceding adapting patch during the early phase of movements, and this aftereffect dissipated as reaches neared the target. Interestingly, perceptual judgements still showed a marked aftereffect, even though they were made after grasping was completed more-or-less veridically. This effect of mean size adaptation on early visually guided kinematics provides novel evidence that mean size is encoded fundamentally in both perception and action domains, and suggests that ensemble statistics not only influence our perceptions of individual objects but can also affect our physical interactions with the external environment.     

Linking patterns of infant eye movements to a neural network model of the ventral stream using representational similarity analysis

Little is known about the development of higher-level areas of visual cortex during infancy, and even less is known about how the development of visually guided behavior is related to the different levels of the cortical processing hierarchy. As a first step toward filling these gaps, we used representational similarity analysis (RSA) to assess links between gaze patterns and a neural network model that captures key properties of the ventral visual processing stream. We recorded the eye movements of 4- to 12-month-old infants (N = 54) as they viewed photographs of scenes. For each infant, we calculated the similarity of the gaze patterns for each pair of photographs. We also analyzed the images using a convolutional neural network model in which the successive layers correspond approximately to the sequence of areas along the ventral stream. For each layer of the network, we calculated the similarity of the activation patterns for each pair of photographs, which was then compared with the infant gaze data. We found that the network layers corresponding to lower-level areas of visual cortex accounted for gaze patterns better in younger infants than in older infants, whereas the network layers corresponding to higher-level areas of visual cortex accounted for gaze patterns better in older infants than in younger infants. Thus, between 4 and 12 months, gaze becomes increasingly controlled by more abstract, higher-level representations. These results also demonstrate the feasibility of using RSA to link infant gaze behavior to neural network models. A video abstract of this article can be viewed at https://youtu.be/K5mF2Rw98Is