cade (Rensink, O'Regan, & Clark, 1997), or is otherwise masked or hidden from view at the time of the change (e.g., Simons & Levin, 1998). This "change blindness" effect is striking because it seemingly undermines a long-standing assumption in vision science that the visual system constructs a complete and integrated representation of the visual world across glimpses. Furthermore, the effect has been taken to call into question the intuition that perceptual experience directly reflects the nature of the underlying visual representation; instead, change blindness appears to indicate that our experience of a complete and detailed visual world is based on what is in fact a sparse and incomplete visual representation (Dennett, 1991). Recent theoretical treatments of scene perception based on the change blindness effect have converged on two assumptions concerning visual representation. First, all forms of visual representation of a scene element are assumed to be lost once attention is wi

The authors identify 2 major types of statistical data from which semantic representations can be learned. These are denoted as experiential data and distributional data. Experiential data are derived by way of experience with the physical world and comprise the sensory-motor data obtained through sense receptors. Distributional data, by contrast, describe the statistical distribution of words across spoken and written language. The authors claim that experiential and distributional data represent distinct data types and that each is a nontrivial source of semantic information. Their theoretical proposal is that human semantic representations are derived from an optimal statistical combination of these 2 data types. Using a Bayesian probabilistic model, they demonstrate how word meanings can be learned by treating experiential and distributional data as a single joint distribution and learning the statistical structure that underlies it. The semantic representations that are learned in this manner are measurably more realistic—as verified by comparison to a set of human-based measures of semantic representation—than those available from either data type individually or from both sources independently. This is not a result of merely using quantitatively more data, but rather it is because experiential and distributional data are qualitatively distinct, yet intercorrelated, types of data. The semantic representations that are learned are based on statistical structures that exist both within and between the experiential and distributional data types.

& Recent evidence has suggested that the human occipitotemporal region comprises several subregions, each sensitive to a distinct processing level of visual words. To further explore the functional architecture of visual word recognition, we employed a subliminal priming method with functional magnetic resonance imaging (fMRI) during semantic judgments of words presented in two different Japanese scripts, Kanji and Kana. Each target word was preceded by a subliminal presentation of either the same or a different word, and in the same or a different script. Behaviorally, word repetition produced significant priming regardless of whether the words were presented in the same or different script. At the neural level, this cross-script priming was associated with repetition suppression in the left inferior temporal cortex

The authors investigated how human adults encode and remember parts of multielement scenes composed of recursively embedded visual shape combinations. The authors found that shape combinations that are parts of larger configurations are less well remembered than shape combinations of the same kind that are not embedded. Combined with basic mechanisms of statistical learning, this embeddedness constraint enables the development of complex new features for acquiring internal representations efficiently without being computationally intractable. The resulting representations also encode parts and wholes by chunking the visual input into components according to the statistical coherence of their constituents. These results suggest that a bootstrapping approach of constrained statistical learning offers a unified framework for investigating the formation of different internal representations in pattern and scene perception.

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A basic problem of visual perception is how human beings recognize objects after spatial transformations. Three central classes of findings have to be accounted for: (a) Recognition performance varies systematically with orientation, size, and position; (b) recognition latencies are sequentially additive, suggesting analogue transformation processes; and (c) orientation and size congruency effects indicate that recognition involves the adjustment of a reference frame. All 3 classes of findings can be explained by a transformational framework of recognition: Recognition is achieved by an analogue transformation of a perceptual coordinate system that aligns memory and input representations. Coordinate transformations can be implemented neurocomputationally by gain (amplitude) modulation and may be regarded as a general processing principle of the visual cortex.

This article reviews functional neuroimaging studies of priming, a behavioural change associated with the repeated processing of a stimulus. Using the haemodynamic techniques of functional magnetic resonance imaging (fMRI) and positron emission tomography (PET), priming-related effects have been observed in numerous regions of the human brain, with the specific regions depending on the type of stimulus and the manner in which it is processed. The most common finding is a decreased haemodynamic response for primed versus unprimed stimuli, though priming-related response increases have been observed. Attempts have been made to relate these effects to a form of implicit or "unconscious" memory. The priming-related decrease has also been used as a tool to map the brain regions associated with different stages of stimulus-processing, a method claimed to offer superior spatial resolution. This decrease has a potential analogue in the stimulus repetition effects measured with single-cell recording in the non-human primate. The paradigms reviewed include word-stem completion, masked priming, repetition priming of visual objects and semantic priming. An attempt is made to relate the findings within a "component process" framework, and the relationship between behavioural, haemodynamic and neurophysiological data is discussed. Interpretation of the findings is not always clear-cut, however, given potential confounding factors such as explicit memory, and several recommendations are made for future neuroimaging studies of priming.

We propose a conceptual framework for artificial object recognition systems based on findings from neurophysiological and neuropsychological research on the visual system in primate cortex. We identify some essential questions, which have to be addressed in the course of designing object recognition systems. As answers, we review some major aspects of biological object recognition, which are then translated into the technical field of computer vision. The key suggestions are the use of incremental and view-based approaches together with the ability of online feature selection and the interconnection of object-views to form an overall object representation. The effectiveness of the computational approach is estimated by testing a possible realization in various tasks and conditions explicitly designed to allow for a direct comparison with the biological counterpart. The results exhibit excellent performance with regard to recognition accuracy, the creation of sparse models and the selection of appropriate features.

ABSTRACT—Sensory adaptation and visual aftereffects have long given insight into the neural codes underlying basic dimensions of visual perception. Recently discovered perceptual adaptation effects for complex shapes like faces can offer similar insight into high-level visual representations. In the experiments reported here, we demonstrated first that face adaptation transfers across a substantial change in viewpoint and that this transfer occurs via processes unlikely to be specific to faces. Next, we probed the visual codes underlying face recognition using face morphs that varied selectively in reflectance or shape. Adaptation to these morphs affected the perception of ‘‘opposite’ ’ faces both from the same viewpoint and from a different viewpoint. These results are consistent with highlevel face representations that pool local shape and reflectance patterns into configurations that specify facial appearance over a range of three-dimensional viewpoints. These findings have implications for computational models of face recognition and for competing neural theories of face and object recognition. How does the brain encode the complex structure of human faces? A complete answer to this question includes both the feature dimensions underlying the neural encoding of faces and the visual information preserved in these codes. Adaptation has been used traditionally as a tool for probing the way basic visual dimensions such as color, motion, and orientation are encoded