Psychology

Abstract: A key to understanding visual cognition is to determine when, how, and with what information the human brain distinguishes between visual categories. So far, the dynamics of information processing for categorization of visual stimuli has not been elucidated. By using an ecologically important categorization task (seven expressions of emotion), we demonstrate, in three human observers, that an early brain event (the N170 Event Related Potential, occurring 170 ms after stimulus onset integrates visual information specific to each expression, according to a pattern. Specifically, starting 50 ms prior to the ERP peak, facial information tends to be integrated from the eyes downward in the face. This integration stops, and the ERP peaks, when the information diagnostic for judging a particular expression has been integrated (e.g., the eyes in fear, the corners of the nose in disgust, or the mouth in happiness). Consequently, the duration of information integration from the eyes down determines the latency of the N170 for each expression (e.g., with â??fearâ?? being faster than â??disgust,â?? itself faster than â??happyâ??). For the first time in visual categorization, we relate the dynamics of an important brain event to the dynamics of a precise information-processing function.

Abstract: A challenging issue in relating brain function to perception and cognition concerns the functional interpretation of brain responses. For example, while there is agreement that the N170 component of event-related potentials is sensitive to face processing, there is considerable debate about whether its response reflects a structural encoder for faces, a feature (e.g. eye) detector, or something else. We introduce a principled approach to determine the stimulus features driving brain responses. Our analysis on two observers resolving different face categorisation tasks (gender and expressive or not) reveal that the N170 responds to the eyes within a face irrespective of task demands. This suggests a new methodology to attribute function to different components of the neural system for perceiving complex stimuli.

Abstract: Everyday, people flexibly perform different categorizations of common faces, objects and scenes. Intuition and scattered evidence suggest that these categorizations require the use of different visual information from the input. However, there is no unifying method, based on the categorization performance of subjects, that can isolate the information used. To this end, we developed Bubbles, a general technique that can assign the credit of human categorization performance to specific visual information. To illustrate the technique, we applied Bubbles on three categorization tasks (gender, expressive or not and identity) on the same set of faces, with human and ideal observers to compare the features they used.

Abstract: The perceptual features people extract from objects depend on how they typically categorize them. It is now commonly acknowledged that the human perceiver can interact with the objects of his or her world at different hierarchically organized levels of categorization. People who have learned to categorize an object as general or specific may therefore perceive different features in this object. We report two experiments that examined the hypothesis that the nature of categorization (general vs. specific) can influence the perceived properties of an identical distal object.

Abstract: According to one productive and influential approach to cognition, categorization, object recognition, and higher level cognitive processes operate on a set of fixed features, which are the output of lower level perceptual processes. In many situations, however, it is the higher level cognitive process being executed that influences the lower level features that are created. Rather than viewing the repertoire of features as being fixed by low-level processes, we present a theory in which people create features to subserve the representation and categorization of objects. Two types of category learning should be distinguished. Fixed space category learning occurs when new categorizations are representable with the available feature set. Flexible space category learning occurs when new categorizations cannot be represented with the features available. Whether fixed or flexible, learning depends on the featural contrasts and similarities between the new category to be represented and the individuals existing concepts. Fixed feature approaches face one of two problems with tasks that call for new features: If the fixed features are fairly high level and directly useful for categorization, then they will not be flexible enough to represent all objects that might be relevant for a new task. If the fixed features are small, subsymbolic fragments (such as pixels), then regularities at the level of the functional features required to accomplish categorizations will not be captured by these primitives. We present evidence of flexible perceptual changes arising from category learning and theoretical arguments for the importance of this flexibility. We describe conditions that promote feature creation and argue against interpreting them in terms of fixed features. Finally, we discuss the implications of functional features for object categorization, conceptual development, chunking, constructive induction, and formal models of dimensionality reduction.