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Deep dyslexia is an acquired reading disorder marked by the occurrence of semantic errors (e.g., reading RIVER as "ocean"). In addition, patients exhibit a number of other symptoms, including visual and morphological effects in their errors, a part-of-speech effect, and an advantage for concrete over abstract words. Deep dyslexia poses a distinct challenge for cognitive neuropsychology because there is little understanding of why such a variety of symptoms should co-occur in virtually all known patients. Hinton and Shallice (1991) replicated the co-occurrence of visual and semantic errors by lesioning a recurrent connectionist network trained to map from orthography to semantics. While the success of their simulations is encouraging, there is little understanding of what underlying principles are responsible for them. In this paper we evaluate and, where possible, improve on the most important design decisions made by Hinton and Shallice, relating to the task, the network architecture, the training procedure, and the testing procedure. We identify four properties of networks that underly their ability to reproduce the deep dyslexic symptom-complex: distributed orthographic and semantic representations, gradient descent learning, attractors for word meanings, and greater richness of concrete vs. abstract semantics. The first three of these are general connectionist principles and the last is based on earlier theorizing. Taken together, the results demonstrate the usefulness of a connectionist approach to understanding deep dyslexia in particular, and the viability of connectionist neuropsychology in general.

A connectionist model of argument strength is proposed that applies to categorical arguments involving natural categories and predicates about which subjects have few prior beliefs. An example is robins have sesamoid bones, therefore falcons have sesamoid bones. The model is based on the hypotheses that argument strength (i) increases with the overlap between features of the combined premise categories and features of the conclusion category; and (ii) decreases with the amount of prior knowledge about the conclusion category. The model assumes a two-stage process. First, premises are encoded by connecting the features of premise categories to the predicate. Second, conclusions are tested by examining the degree of activation of the predicate upon presentation of the features of the conclusion category. The model accounts for 13 qualitative phenomena and shows close quantitative fits to several sets of argument-strength ratings. Feature-based induction 3 3<E-2> One way we learn about ...

A theory of perception and attention that emphasizes the relational nature o { perceptual invariants is developed within the context of auditory pattern research. The theory is divided into two parts. The first part, addresses world pattern structure; the second describes interaction of organisms with pattern structure. Tn the former, world patterns arc subjectively represented as nested relations within a multidimensional space defined by pilch, loudncss, and time. But dependency of these defining dimensions means that a pattern's lime scale determines the serial integrity of its pitch/loudness structure. Second, the theory proposes a time scale for living things that is manifest in graded perceptual rhythms. These rhythms can be synchronized to corresponding nested time zones within world pattern structure. Related assumptions about the deployment of physical energy across time zones and cognitive locations of perceptual rhythms lead to a simple, but general, attentional theory. Theoretical support, found in research with tone patterns, speech, and sequences of noise is died in a final section. Beyond this focal research, the theory offers a general framework for understanding diverse phenomena thai range from speech perception and aphasia to sleep, growth, and time eslimation. The ideas presented in this paper are the foundation for a new way to understand human perception and memory. They are developed within the context of research with auditory patterns, but the general approach has implications for man} ' other areas of inquiry. Nevertheless, their most immediate relevance is to research and theory in speech and auditory pattern perception. The specific problems which give rise to this theory arc briefly outlined in the next section. This article was prepared with the support of National Science Foundation Grant UMS74-21492.

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We examine how investor sentiment affects the cross-section of stock returns. Theory predicts that a broad wave of sentiment will disproportionately affect stocks whose valuations are highly subjective and are difficult to arbitrage. We test this prediction by studying how the cross-section of subsequent stock returns varies with proxies for beginning-of-period investor sentiment. When sentiment is low, subsequent returns are relatively high on smaller stocks, high volatility stocks, unprofitable stocks, non-dividend-paying stocks, extreme-growth stocks, and distressed stocks, consistent with an initial underpricing of these stocks. When sentiment is high, on the other hand, these patterns attenuate or fully reverse. The results are consistent with predictions and appear unlikely to reflect an alternative explanation based on compensation for systematic risk.

concepts such as weather terms, proper names and emotional terms all segregate into their own meaning spaces. One advantage of representing meaning with vectors such as these is that, since each vector element is a symbol in the input stream (typically another word); all words have as their "features" other words. This translates into the ability to have a vector representation for abstract concepts as easily as one can have a representation for more basic concepts (Burgess & Lund, 1997b). This is important, if not absolutely crucial, when developing a memory model that purports to be general in nature. The other major aspect of categorization that the HAL model can address is the grammatical nature of word meaning. A clear categorization of nouns, prepositions, and Visual inspection of the MDS presentations in this paper all appear to show a robust separation of the various word groups. However, it is important to determine if these categorizations are clearly distinguished in the high-dimensional space. Our approach to this is to use an analysis of variance that compares the intragroup distances to the intergroup distances. This is accomplished by calculating all combinations of item-pair distances within a group and comparing them to all combinations of item-pair distances in the other groups. In all MDS presentations shown in this paper, these analyses were computed, and all differences discussed were reliable. verbs can be seen in Figure 2c. The generalizability of the HAL model to capture grammatical meaning as well as more traditional semantic characteristics of words is an important feature of the model (Burgess, 1998; Burgess & Lund, 1997a) and was part of our motivation to refer to the high-dimensional space as a context space rather than a semantic space. T...

this article. We also thank Denise Hatton, Tisha Baldwin, Joshua Nathan, Helen Sullivan, and Julia Wenzlaff for collecting data. Some of the stimulus materials used in Experiments 1 and 2 are adapted from the stimulus materials used in Rehder and Hastie (1997) and we thank them for inspiring many of the features and objects used in these studies. This project was supported by a National Science Foundation Grant (NSF-SBR 9515085) and a National Institute of Mental Health Grant (RO1 MH57737) given to Woo-kyoung Ahn, a National Science Foundation Graduate Fellowship to Nancy Kim, and a National Institute of Mental Health Postdoctoral Fellowship (MH10888-01A1) to Mary Lassaline

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Theories of categorization have typically focused on the internal structure of categories. This paper is concerned with the external structure of categories. In particular , it is suggested that many categories specify the relational role that is played by category members. To support this claim, the paper distinguishes between traditional feature-based categories, relational categories (which specify a relational structure) and role-governed categories (which specify that an item plays a particular role within a relational structure). After discussing the relationship among these types of categories, the implications of this view for the study of category learning and category use are discussed.