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Three experiments with 88 college-aged participants explored how unlabeled experiences--learning episodes in which people encounter objects without information about their category membership--influence beliefs about category structure. Participants performed a simple one-dimensional categorization task in a brief supervised learning phase, then made a large number of unsupervised categorization decisions about new items. In all three experiments, the unsupervised experience altered participants' implicit and explicit mental category boundaries, their explicit beliefs about the most representative members of each category, and even their memory for the items encountered during the supervised learning phase. These changes were influenced by both the range and frequency distribution of the unlabeled stimuli: mental category boundaries shifted toward the middle of the range and toward the trough of the bimodal distribution of unlabeled items, whereas beliefs about the most representative category members shifted toward the modes of the unlabeled distribution. One consequence of this shift in representations is a false-consensus effect (Experiment 3) where participants, despite receiving very disparate training experiences, show strong agreement in judgments about representativeness and boundary location following unsupervised category judgments. (Contains 5 figures.)

Patients with semantic dementia show a specific pattern of impairment on both verbal and non-verbal "pre-semantic" tasks, e.g., reading aloud, past tense generation, spelling to dictation, lexical decision, object decision, colour decision and delayed picture copying. All seven tasks are characterised by poorer performance for items that are atypical of the domain and "regularisation errors" (irregular/atypical items are produced as if they were domain-typical). The emergence of this pattern across diverse tasks in the same patients indicates that semantic memory plays a key role in all of these types of "pre-semantic" processing. However, this claim remains controversial because semantically impaired patients sometimes fail to show an influence of regularity. This study demonstrates that (a) the location of brain damage and (b) the underlying nature of the semantic deficit affect the likelihood of observing the expected relationship between poor comprehension and regularity effects. We compared the effect of multimodal semantic impairment in the context of semantic dementia and stroke aphasia on the seven "pre-semantic" tasks listed above. In all of these tasks, the semantic aphasia patients were less sensitive to typicality than the semantic dementia patients, even though the two groups obtained comparable scores on semantic tests. The semantic aphasia group also made fewer regularisation errors and many more unrelated and perseverative responses. We propose that these group differences reflect the different locus for the semantic impairment in the two conditions: patients with semantic dementia have degraded semantic representations, whereas semantic aphasia patients show deregulated semantic cognition with concomitant executive deficits. These findings suggest a reinterpretation of single-case studies of comprehension-impaired aphasic patients who fail to show the expected effect of regularity on "pre-semantic" tasks. Consequently, such cases do not demonstrate the independence of these tasks from semantic memory. (Contains 9 figures and 6 tables.)

People are generally faster and more accurate to name or categorize objects at the basic level (e.g., dog) relative to more general (animal) or specific (collie) levels, an effect replicated in Experiment 1 for categorization of object pictures. To some, this pattern suggests a dual-process mechanism, in which objects first activate basic-level categories directly and later engage more general or specific categories through the spread of activation in a processing hierarchy. This account is, however, challenged by data from Experiment 2 showing that neuropsychological patients with impairments of conceptual knowledge categorize more accurately at superordinate levels than at the basic level--suggesting that knowledge about an object's general nature does not depend on prior basic-level categorization. The authors consider how a parallel distributed processing theory of conceptual knowledge can reconcile the apparent discrepancy. This theory predicts that if healthy individuals are encouraged to make rapid categorization responses, the usual basic greater than general advantage should also reverse, a prediction tested and confirmed in Experiment 3. Implications for theories of visual object recognition are discussed.

As the articles in this issue attest, U-shaped curves in development have stimulated a wide spectrum of research across disparate task domains and age groups and have provoked a variety of ideas about their origins and theoretical significance. In the authors' view, the ubiquity of the general pattern suggests that U-shaped curves can arise from multiple factors, and that the various viewpoints represented herein may be useful for explaining some aspects of developmental change. In this spirit, the authors offer an additional way of thinking about such phenomena. Specifically, they suggest that U-shaped curves can arise within a domain-general learning mechanism as it slowly masters a domain characterized by statistical regularities and exceptions. This idea differs from those considered thus far, and may encompass many of the phenomena addressed by other views, three of which are outlined in this paper. (Contains 2 figures.)

Wernicke (1900, as cited in G. H. Eggert, 1977) suggested that semantic knowledge arises from the interaction of perceptual representations of objects and words. The authors present a parallel distributed processing implementation of this theory, in which semantic representations emerge from mechanisms that acquire the mappings between visual representations of objects and their verbal descriptions. To test the theory, they trained the model to associate names, verbal descriptions, and visual representations of objects. When its inputs and outputs are constructed to capture aspects of structure apparent in attribute-norming experiments, the model provides an intuitive account of semantic task performance. The authors then used the model to understand the structure of impaired performance in patients with selective and progressive impairments of conceptual knowledge. Data from 4 well-known semantic tasks revealed consistent patterns that find a ready explanation in the model. The relationship between the model and related theories of semantic representation is discussed.

Presents evidence that although patients with semantic deficits can sometimes show good performance on tests or object decisions, this pattern applies when nonsee-objects do not respect the regularities of the domain. Patients with semantic dementia viewed line drawings of a real and chimeric animals side-by-side and were asked to decide which was real. Object-decision scores were highly correlated with other pictorial and verbal assessments of conceptual knowledge. (Author/VWL)

The commentaries reflect three core themes that pertain not just to our theory, but to the enterprise of connectionist modeling more generally. The first concerns the relationship between a cognitive theory and an implemented computer model. Specifically, how does one determine, when a model departs from the theory it exemplifies, whether the departure is a useful simplification or a critical flaw? We argue that the answer to this question depends partially upon the model's intended function, and we suggest that connectionist models have important functions beyond the commonly accepted goals of fitting data and making predictions. The second theme concerns perceived in-principle limitations of the connectionist approach to cognition, and the specific concerns these perceived limitations raise for our theory. We argue that the approach is not in fact limited in the ways our critics suggest. One common misconception, that connectionist models cannot address abstract or relational structure, is corrected through new simulations showing directly that such structure can be captured. The third theme concerns the relationship between parallel distributed processing (PDP) models and structured probabilistic approaches. In this case we argue that there the difference between the approaches is not merely one of levels. Our PDP approach differs from structured statistical approaches at all of Marr's levels, including the characterization of the goals of cognitive computations, and of the representations and algorithms used.

In this precis of our recent book, Semantic Cognition: A Parallel Distributed Processing Approach (Rogers & McClelland 2004), we present a parallel distributed processing theory of the acquisition, representation, and use of human semantic knowledge. The theory proposes that semantic abilities arise from the flow of activation among simple, neuron-like processing units, as governed by the strengths of interconnecting weights; and that acquisition of new semantic information involves the gradual adjustment of weights in the system in response to experience. These simple ideas explain a wide range of empirical phenomena from studies of categorization, lexical acquisition, and disordered semantic cognition. In this precis we focus on phenomena central to the reaction against similarity-based theories that arose in the 1980s and that subsequently motivated the 'theory- theory' approach to semantic knowledge. Specifically, we consider (1) how concepts differentiate in early development, (2) why some groupings of items seem to form 'good' or coherent categories while others do not, (3) why different properties seem central or important to different concepts, (4) why children and adults sometimes attest to beliefs that seem to contradict their direct experience, (5) how concepts reorganize between the ages of 4 and 10, and (6) the relationship between causal knowledge and semantic knowledge. The explanations our theory offers for these phenomena are illustrated with reference to a simple feed-forward connectionist model. The relationships between this simple model, the broader theory, and more general issues in cognitive science are discussed. Keywords: categorization; causal knowledge; concepts; connectionism; development; innateness; learning; semantics; memory; theory-theory.

People are generally faster and more accurate to name or categorize objects at the basic level (e.g., dog) relative to more general (animal) or specific (collie) levels, an effect replicated in Experiment 1 for categorization of object pictures. To some, this pattern suggests a dual-process mechanism, in which objects first activate basic-level categories directly and later engage more general or specific categories through the spread of activation in a processing hierarchy. This account is, however, challenged by data from Experiment 2 showing that neuropsychological patients with impairments of conceptual knowledge categorize more accurately at superordinate levels than at the basic level--suggesting that knowledge about an object's general nature does not depend on prior basic-level categorization. The authors consider how a parallel distributed processing theory of conceptual knowledge can reconcile the apparent discrepancy. This theory predicts that if healthy individuals are encouraged to make rapid categorization responses, the usual basic > general advantage should also reverse, a prediction tested and confirmed in Experiment 3. Implications for theories of visual object recognition are discussed. Keywords: semantic memory, categorization, object recognition, basic level, semantic dementia

Wernicke (1900, as cited in G. H. Eggert, 1977) suggested that semantic knowledge arises from the interaction of perceptual representations of objects and words. The authors present a parallel distributed processing implementation of this theory, in which semantic representations emerge from mechanisms that acquire the mappings between visual representations of objects and their verbal descriptions. To test the theory, they trained the model to associate names, verbal descriptions, and visual representations of objects. When its inputs and outputs are constructed to capture aspects of structure apparent in attribute-norming experiments, the model provides an intuitive account of semantic task performance. The authors then used the model to understand the structure of impaired performance in patients with selective and progressive impairments of conceptual knowledge. Data from 4 well-known semantic tasks revealed consistent patterns that find a ready explanation in the model. The relationship between the model and related theories of semantic representation is discussed.

The human cortex encodes information in complex networks that can be anatomically dispersed and variable in their microstructure across individuals. Using simulations with neural network models, we show that contemporary statistical methods for functional brain imaging--including univariate contrast, searchlight multivariate pattern classification, and whole-brain decoding with L1 or L2 regularization--each have critical and complementary blind spots under these conditions. We then introduce the sparse-overlapping-sets (SOS) LASSO--a whole-brain multivariate approach that exploits structured sparsity to find network-distributed information--and show in simulation that it captures the advantages of other approaches while avoiding their limitations. When applied to fMRI data to find neural responses that discriminate visually presented faces from other visual stimuli, each method yields a different result, but existing approaches all support the canonical view that face perception engages localized areas in posterior occipital and temporal regions. In contrast, SOS LASSO uncovers a network spanning all four lobes of the brain. The result cannot reflect spurious selection of out-of-system areas because decoding accuracy remains exceedingly high even when canonical face and place systems are removed from the dataset. When used to discriminate visual scenes from other stimuli, the same approach reveals a localized signal consistent with other methods--illustrating that SOS LASSO can detect both widely distributed and localized representational structure. Thus, structured sparsity can provide an unbiased method for testing claims of functional localization. For faces and possibly other domains, such decoding may reveal representations more widely distributed than previously suspected. [ABSTRACT FROM AUTHOR]