Here we review recent findings that reveal the functional properties of extra-striate regions in the human visual cortex that are involved in the representation and perception of objects. We characterize both the invariant and non-invariant properties of these regions and we discuss the correlation between activation of these regions and recognition. Overall, these results indicate that the lateral occipital complex plays an important role in human object recognition.

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Several models have been proposed that attempt to explain how the brain identifies people by looking at their faces. However, to date, it is still not clear by which mechanism the brain successfully accomplishes the matching of two or more face images when di#erences in facial expression make the (local and global) appearance of these images di#erent from one another. There seems to be a consensus that faces are processed holistically rather than locally, but there is not yet consensus on whether information on facial expression is passed to the identification process to aid recognition of individuals or not. Models have been proposed that exploit each of these two views, and psychophysical data exist in favor of and against each view. In this article, we show how the experimental data of these two opposite views can be explained by incorporating a key process of motion estimation in the classical feedforward model of face processing. This new model will then lead us to hypothesize that to successfully match expression variant faces, it is convenient to use the information supplied by this motion estimation process within the matching task. We will show experimental results in favor of this hypothesis. Finally, we will show how we can also use the same motion estimator to recognize facial expressions.

FMRI investigation of working memory for faces in autism: Visual coding and underconnectivity with frontal areas

It has been proposed that actions are intrinsically linked to perception [James, W. (1890). Principles of psychology. New York, NY, USA: Holt; Jeannerod M. (1994). The representing brain – neural correlates of motor intention and imagery. Behavioural Brain Sciences, 17, 187–202; Prinz, W. (1997). Perception and action planning. European Journal of Cognitive Psychology, 9, 129–154]. The idea behind these theories is that observing, imagining or in any way representing an action excites the motor programs used to execute that same action. There is neurophysiological evidence that neurons in premotor cortex of monkeys respond both during movement execution and during the observation of goal-directed action (‘mirror neurons’). In humans, a proportion of the brain regions involved in executing actions are activated by the mere observation of action (the ‘mirror system’). In this paper, we briefly review recent empirical studies of the mirror system, and discuss studies demonstrating interference effects between observed and executed movements. This interference, which might be a form of ‘motor contagion’, seems to arise specifically from the observation of biological movements, whether or not these movements are goal-directed. We suggest that this crude motor contagion is the first step in a more sophisticated predictive system that allows us to infer goals from the observation of actions.

Computer-animated characters are common in popular culture and have begun to be used as experimental tools in social cognitive neurosciences. Here we investigated how appearance of these characters ’ influences perception of their actions. Subjects were presented with different characters animated either with motion data captured from human actors or by interpolating between poses (keyframes) designed by an animator, and were asked to categorize the motion as biological or artificial. The response bias towards ‘biological’, derived from the Signal Detection Theory, decreases with characters’ anthropomorphism, while sensitivity is only affected by the simplest rendering style, point-light displays. fMRI showed that the response bias correlates positively with activity in the mentalizing network including left temporoparietal junction and anterior cingulate cortex, and negatively with regions sustaining motor resonance. The absence of significant effect of the characters on the brain activity suggests individual differences in the neural responses to unfamiliar artificial agents. While computer-animated characters are invaluable tools to investigate the neural bases of social cognition, further research is required to better understand how factors such as anthropomorphism affect their perception, in order to optimize their appearance for entertainment, research or therapeutic purposes.

This book is about the interface between natural language and the sensorimotor system. It is obvious that there is an interface between language and sensorimotor cognition, because we can talk about what we see and do. The main proposal in the book is that the interface is more direct than is commonly assumed. To argue for this proposal I focus on a simple concrete episode—a man grabbing a cup—which can be reported in a simple transitive sentence (e.g. the English sentence The man grabbed a cup). In the first part of the book I present a detailed model of the sensorimotor processes involved in experiencing this episode, both as the agent bringing it about and as an observer watching it happen. The model draws on a large body of research in neuroscience and psychology. I also present a model of the syntactic structure of the associated transitive sentence, developed within the entirely separate discipline of theoretical linguistics. This latter model is a version of Chomsky’s ‘Minimalist ’ syntactic theory, which assumes that a sentence reporting the episode has the same underlying syntactic structure (called ‘logical form’) regardless of which language it is in. My main proposal is that these two independently motivated models are in fact closely

Neurophysiological measures can provide important information about the substrate of cognitive function in children, and most importantly, can give precise temporal information about ‘on-line’ brain function. These measures can be readily used in infants and children, and we present some examples of their application to understanding cognitive development. The most widely used of these techniques for developmental research is the method of event-related potentials (ERPs). In addition, the electroencephalogram (EEG) and the more rarely used invasive, intracranial investigations are also important to furthering our knowledge of how the brain–behaviour relations develop. The paper summarizes practical issues and presents some selected examples of experimental and clinical research.

The ability of humans to recognize faces provides an implicit benchmark for gauging the performance of automatic face recognition algorithms. In this chapter we review the factors that affect human accuracy. These factors can be classified into facial stucture constraints and viewing parameters. The former include factors such as face typicality, gender, and ethnicity. The latter include changes in illumination and viewpoint, as well as the perceptual complications introduced when we see faces and people in motion. The common thread of the chapter is that human experience and familiarity with faces can overcome many, if not all, of these challenges to face recognition. A goal of computional algorithms should be to emulate the ways in which humans acquire familiarity with faces. It may then be possible to apply these principles to the design of algorithms to meet the pressing challenges of face recognition in naturalistic viewing conditions.

Motion is the strongest visual appeal to attention [2], yet it is rarely used in the visualization of large-scale quantitative information. Motion is complex; it can vary across numerous dimensions, each of which is potentially an information-bearing element in the visualization. Which dimensions are used and how the data is mapped onto them are the key questions in using motion effectively. In this paper we present two interfaces that use motion as the primary visual element for representing data. These interfaces, Seascape and Volcano, use periodic animation loops to represent key social interaction features in online discussions. We propose that motion may be particularly well suited for representing data about behavior and actions, creating visualizations that intuitively depict different levels and types of activity. In this paper we describe the interfaces we have built and present the results of preliminary user studies.