Efficient categorizations of complex visual stimuli require effective encodings of their distinctive properties. However, the question remains of how processes of object and scene categorization use the information associated with different perceptual spatial scales. The psychophysics of scale perception suggests that recognition uses coarse blobs before fine scale edges, because the former is perceptually available before the latter. Although possible, this perceptually determined scenario neglects the nature of the task the recognition system must solve. If different spatial scales transmit different information about the input, an identical scene might be flexibly encoded and perceived at the scale that optimizes information for the considered task—i.e., the diagnostic scale. This paper tests the hypothesis that scale diagnosticity can determine scale selection for recognition. Experiment 1 tested whether coarse and fine spatial scales were both available at the onset of scene categorization. The second experiment tested that the selection of one scale could change depending on the diagnostic information present at this scale. The third and fourth experiments investigated whether scalespecific cues were independently processed, or whether they perceptually cooperated in the recognition of the input scene. Results suggest that a mandatory low-level registration of multiple spatial scales promotes flexible scene encodings, perceptions, and categorizations.

Three theories currently compete to explain the conceptual deficits that result from brain damage: sensory-functional theory, domain-specific theory, and conceptual structure theory. We argue that all three theories capture important aspects of conceptual deficits, and offer different insights into their origins. Conceptual topography theory (CTT) integrates these insights, beginning with A. R. Damasio’s (1989) convergence zone theory and elaborating it with the similarity-in-topography (SIT) principle. According to CTT, feature maps in sensory-motor systems represent the features of a category’s exemplars. A hierarchical system of convergence zones then conjoins these features to form both property and category representations. According to the SIT principle, the proximity of two conjunctive neurons in a convergence zone increases with the similarity of the features they conjoin. As a result, conjunctive neurons become topographically organised into local regions that represent properties and categories. Depending on the level and location of a lesion in this system, a wide variety of deficits is possible. Consistent with the literature, these deficits range from the loss of a single category to the loss of multiple categories that share sensory-motor properties.

Previous research has shown that objects that are grouped together in the same category become more similar to each other and that objects that are grouped in different categories become increasingly dissimilar, as measured by similarity ratings and psychophysical discriminations. These ndings are consistent with two theories of the inuence of concept learning on similarity. By a Strategic Judgment Bias account, the categories associated with objects are explicitly used as cues for determining similarity, and objects that are categorized together are judged to be more similar because similarity is not only a function of the objects themselves, but also the objects' category labels. By a Changed Object Description account, category learning alters the description of the objects themselves, emphasizing properties that are relevant for categorization. A new method for distinguishing between these accounts is introduced which measures the difference between the similarity ratings of categorized objects to a neutral object. The results indicate both strategic biases based on category labels and genuine representational change, with the strategic bias affecting mostly objects belonging to different categories and the representational change affecting mostly objects belonging to the same category.

Twelve participants were trained to be experts at identifying a set of `Greebles', novel objects that, like faces, all share a common spatial configuration. Tests comparing expert with novice performance revealed: (1) a surprising mix of generalizability and specificity in expert object recognition processes; and (2) that expertise is a multi-faceted phenomenon, neither adequately described by a single term nor adequately assessed by a single task. Greeble recognition by a simple neural-network model is also evaluated, and the model is found to account surprisingly well for both generalization and individuation using a single set of processes and representations. 1998 Elsevier Science Ltd. All rights reserved. Keywords: Configural encoding; Face recognition; Neural networks; Object categorization; Perceptual expertise 1. Introduction Are the mechanisms used by perceivers as they become increasingly familiar with an object class the same as those used by perceivers when they first en...

this paper, we propose the short-cut rule, which states that, other things being equal, human vision prefers to use the shortest possible cuts to parse silhouettes. We motivate this rule, and the well-known Petters rule for modal completion, by the principle of transversality. We present five psychophysical experiments that test the short-cut rule, show that it successfully predicts part cuts which connect boundary points given by the minima rule, and show that it can also create new boundary points

Five experiments explored the question of whether new perceptual units can be developed if they are diagnostic for a category learning task, and if so, what are the constraints on this unitization process? During category learning, participants were required to attend either a single component or a conjunction of five components to correctly categorize an object. Evidence consistent with unitization was found in that the conjunctive task became much easier with practice, and this improvement was not found for the single component task, or for conjunctive tasks in which the components could not be unitized. Influences of

ositive side, though, the second person might have some advantage over the first person in learning how to shift gears, because the second person would not have to overcome negative transfer from experience with automatic transmissions. As another example, imagine that you are an explorer visiting a remote island, with the purpose of writing a book about the people that you see there. You bring to this island many forms of prior knowledge that will guide you in learning about these new people. For example, based on your experiences in other places, you would expect to see males and females, younger and older people, shy people and arrogant people. You would also have certain hypotheses at a more abstract level, for example, that the clothes that someone wears may be related to the person's age and gender. (Goodman, 1955, referred to such abstract hypotheses as overhypotheses.) In a way, these biases due to previous knowledge might seem to be undesirable. After all, wouldn't be it be be

egorized for the community's nonlinguistic purposes or, to use his term, for the level of ##############. As Brown points out, the level of usual utility changes according to the demands of the linguistic community and this is especially true for expert populations. So, for example, while it is quite acceptable for most of us to refer to the object outside our office window as a "bird," if we were among a group of bird watchers, it would be important to specify whether the object was a "whitethroated " or "white-crown sparrow." Generally, experts prefer to identify objects in their domain of expertise more specifically than novices do. While few would argue that experts identify objects in their domain at a more specific level than novices, a separate question is whether experts initially recognize objects at this more specific level. In Section I of the chapter, we define object expertise as the ability to quickly and accurately recognize objects at specific or subordina

modifies the perception of faces in rapid visual presentations

Previous research has shown that background knowledge affects the ease of concept learning, but little research has examined its effects on speeded categorization of instances after the category is well learned. Subjects in 4 experiments first learned novel categories. At test, they categorized a new set of novel stimuli that were either consistent or inconsistent with background knowledge given about the categories. Background knowledge affected catego-rization responses in an untimed task, with usual reaction time instructions, with a response deadline, or when the stimuli were presented for 50 ms followed by a mask. Three other experiments using a part-detection task showed that subjects were more likely to notice missing parts that were critical than noncritical according to background knowledge. The mechanisms by which background knowledge affects categorization and part detection are discussed. Human categorization is a cognitive proceSs in which people decide whether an instance is a member of a cate-gory by comparing the instance with their conceptual rep-resentations. Categorization research in the 1970s and early