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R M Nosofsky

Publications and source records attributed to R M Nosofsky.

At least 19 recordsLinked to original sources
PubMed1995

Investigations of exemplar and decision bound models in large, ill-defined category structures.

Experiments involving large-size, ill-defined categories were conducted to distinguish between the predictions of an exemplar model and linear and quadratic decision bound models. In conditions in which the optimal classification boundary was of a more complex form than the quadratic model, the exemplar model provided significantly better accounts of study participants' data than did the decision bound models, even in situations in which a linear bound would have yielded nearly optimal performance. The results suggest that participants are not predisposed or constrained to use linear or quadratic decision bounds for classifying multidimensional perceptual stimuli and that exemplar models may provide a parsimonious process-level account of the complex types of decision bounds used by experiment participants. The results also suggest some limitations on the complexity of the decision bounds that can be learned, in contrast to the predictions of the exemplar model.

Adult↗
PubMed1995

Recognition memory for exceptions to the category rule.

Experiments were conducted to demonstrate the utility of a rule-plus-exception model for extending current exemplar-based views of categorization and recognition memory. According to the model, exemplars that are exceptions to category rules have a special status in memory relative to other old items. In each of 4 experiments, participants first learned to categorize items organized into 2 ill-defined categories and then made old-new recognition judgments. Although the categories afforded no perfect rules, a variety of imperfect rules could be formed combined with memorization of exceptions to those rules. In each experiment, superior recognition of exceptions to imperfect logical rules was found. In addition, participants demonstrated better memory for old exemplars than new ones. A mixed model, which assumed a combination of rule-plus-exception processing and residual exemplar storage, provided good quantitative accounts of the data.

Humans↗
PubMed1994

Rule-plus-exception model of classification learning.

The authors propose a rule-plus-exception model (RULEX) of classification learning. According to RULEX, people learn to classify objects by forming simple logical rules and remembering occasional exceptions to those rules. Because the learning process in RULEX is stochastic, the model predicts that individual Ss will vary greatly in the particular rules that are formed and the exceptions that are stored. Averaged classification data are presumed to represent mixtures of these highly idiosyncratic rules and exceptions. RULEX accounts for numerous fundamental classification phenomena, including prototype and specific exemplar effects, sensitivity to correlational information, difficulty of learning linearly separable versus nonlinearly separable categories, selective attention effects, and difficulty of learning concepts with rules of differing complexity. RULEX also predicts distributions of generalization patterns observed at the individual subject level.

Adult↗
PubMed1994

Seven plus or minus two: a commentary on capacity limitations.

Miller's classic 1956 article is best known today for its discussion of capacity limitations in short-term memory, but the bulk of the article dealt with capacity limitations in absolute judgment tasks and the relation of such limitations to information theory. Many of the puzzles of absolute judgment first raised by Miller remain a puzzle today. The authors review some of the literature directed toward this issue and discuss a few models that attempt to elucidate the phenomena. Since 1956 there has been an enormous research effort aimed at understanding the mechanisms and limitations of short-term memory, resulting in considerable progress. The authors briefly discuss some of these advances. The authors conclude, as Miller did, by noting the probable lack of connection between the limitations observed in these 2 areas of inquiry.

Attention↗
PubMed1994

Comparing models of rule-based classification learning: a replication and extension of Shepard, Hovland, and Jenkins (1961).

We partially replicate and extend Shepard, Hovland, and Jenkins's (1961) classic study of task difficulty for learning six fundamental types of rule-based categorization problems. Our main results mirrored those of Shepard et al., with the ordering of task difficulty being the same as in the original study. A much richer data set was collected, however, which enabled the generation of block-by-block learning curves suitable for quantitative fitting. Four current computational models of classification learning were fitted to the learning data: ALCOVE (Kruschke, 1992), the rational model (Anderson, 1991), the configural-cue model (Gluck & Bower, 1988b), and an extended version of the configural-cue model with dimensionalized, adaptive learning rate mechanisms. Although all of the models captured important qualitative aspects of the learning data, ALCOVE provided the best overall quantitative fit. The results suggest the need to incorporate some form of selective attention to dimensions in category-learning models based on stimulus generalization and cue conditioning.

Adult↗
PubMed1992

Similarity, identification, and categorization: comment on Ashby and Lee (1991)

Ashby and Lee (1991) tested various models derived from the general recognition theory (GRT; Ashby & Perrin, 1988; Ashby & Townsend, 1986) on their ability to predict and interrelate similarity, categorization, and identification performance. This commentary (a) argues that contrary to Ashby and Lee's suggestion, the likelihood-based GRT cannot generally predict categorization from identification without incorporating selective attention, (b) argues that the categorization rule in the likelihood-based GRT is extremely close in spirit to Nosofsky's (1986) exemplar-based similarity model, (c) reports new model-based analyses that call into question Ashby and Lee's interpretation of their identification-confusion data, (d) raises questions about the identification and similarity models tested by Ashby and Lee, and (e) criticizes Ashby and Lee's methods of fitting and evaluating the various models.

Attention↗
PubMed1992

Similarity-scaling studies of dot-pattern classification and recognition.

Classification performance in the dot-pattern, prototype-distortion paradigm (e.g., Posner & Keele, 1968) was modeled within a multidimensional scaling (MDS) framework. MDS solutions were derived for sets of dot patterns that were generated from prototypes. These MDS solutions were then used in conjunction with exemplar, prototype, and combined models to predict classification and recognition performance. Across 3 experiments, an MDS-based exemplar model accounted for the effects of several fundamental learning variables, including level of distortion of the patterns, category size, delay of transfer phase, and item frequency. Most important, the model quantitatively predicted classification probabilities for individual dot patterns in the sets, not simply general trends of performance. There was little evidence for the existence of a prototype-abstraction process that operated above and beyond pure exemplar-based generalization.

Adult↗
PubMed1992

Combining exemplar-based category representations and connectionist learning rules.

Adaptive network and exemplar-similarity models were compared on their ability to predict category learning and transfer data. An exemplar-based network (Kruschke, 1990a, 1990b, 1992) that combines key aspects of both modeling approaches was also tested. The exemplar-based network incorporates an exemplar-based category representation in which exemplars become associated to categories through the same error-driven, interactive learning rules that are assumed in standard adaptive networks. Experiment 1, which partially replicated and extended the probabilistic classification learning paradigm of Gluck and Bower (1988a), demonstrated the importance of an error-driven learning rule. Experiment 2, which extended the classification learning paradigm of Medin and Schaffer (1978) that discriminated between exemplar and prototype models, demonstrated the importance of an exemplar-based category representation. Only the exemplar-based network accounted for all the major qualitative phenomena; it also achieved good quantitative predictions of the learning and transfer data in both experiments.

Concept Formation↗
PubMed1991

Tests of an exemplar model for relating perceptual classification and recognition memory.

Experiments were conducted in which Ss made classification, recognition, and similarity judgments for 34 schematic faces. A multidimensional scaling (MDS) solution for the faces was derived on the basis of the similarity judgments. This MDS solution was then used in conjunction with an exemplar-similarity model to accurately predict Ss' classification and recognition judgments. Evidence was provided that Ss allocated attention to the psychological dimensions differentially for classification and recognition. The distribution of attention came close to the ideal-observer distribution for classification, and some tendencies in that direction were observed for recognition. Evidence was also provided for interactive effects of individual exemplar frequencies and similarities on classification and recognition, in accord with the predictions of the exemplar model. Unexpectedly, however, the frequency effects appeared to be larger for classification than for recognition.

Adult↗
PubMed1991

Typicality in logically defined categories: exemplar-similarity versus rule instantiation.

A rule-instantiation model and a similarity-to-exemplars model were contrasted in terms of their predictions of typicality judgments and speeded classifications for members of logically defined categories. In Experiment 1, subjects learned a unidimensional rule based on the size of objects. It was assumed that items that maximally instantiated the rule were those farthest from the category boundary that separated small and large stimuli. In Experiment 2, subjects learned a disjunctive rule of the form "x or y or both". It was assumed that items that maximally instantiated the rule were those with both positive values (x and y). In both experiments, the frequency with which different exemplars were presented during classification learning was manipulated across conditions. These frequency manipulations exerted a major impact on subjects' postacquisition goodness-of-example judgments, and they also influenced reaction times in a speeded classification task. The results could not be predicted solely on the basis of the degree to which the rules were instantiated. The goodness judgments were predicted fairly well by a mixed exemplar model involving both relative-similarity and absolute-similarity components. It was concluded that even for logically defined concepts, stored exemplars may form a major component of the category representation.

Attention↗
PubMed1989

Rules and exemplars in categorization, identification, and recognition.

Subjects learned to classify perceptual stimuli varying along continuous, separable dimensions into rule-described categories. The categories were designed to contrast the predictions of a selective-attention exemplar model and a simple rule-based model formalizing an economy-of-description view. Converging evidence about categorization strategies was obtained by also collecting identification and recognition data and by manipulating strategies via instructions. In free-strategy conditions, the exemplar model generally provided an accurate quantitative account of identification, categorization, and recognition performance, and it allowed for the interrelationship of these paradigms within a unified framework. Analyses of individual subject data also provided some evidence for the use of rules, but in general, the rules seemed to have a great deal in common with exemplar storage processes. Classification and recognition performance for subjects given explicit instructions to use specific rules contrasted dramatically with performance in the free-strategy conditions and could not be predicted by the exemplar model.

Adult↗
PubMed1989

Further tests of an exemplar-similarity approach to relating identification and categorization.

Further tests were provided of an exemplar-similarity model for relating the identification and categorization of separable-dimension stimuli (Nosofsky, 1986). On the basis of confusion errors in an identification paradigm, a multidimensional scaling (MDS) solution was derived for a set of 16 separable-dimension stimuli. This MDS solution was then used in conjunction with the exemplar-similarity model to accurately predict performance in four separate categorization paradigms with the same stimuli. A key to achieving the accurate quantitative fits was the assumption that a selective attention process systematically modifies similarities among exemplars across different category structures. The tests reported go well beyond earlier ones (Nosofsky, 1986) in demonstrating the generalizability and utility of the theoretical approach. Implications of the results for alternative quantitative models of classification performance, including Ashby and Perrin's (1988) general recognition theory, were also considered.

Adult↗
PubMed1987

Attention and learning processes in the identification and categorization of integral stimuli.

The relationship between subjects' identification and categorization learning of integral-dimension stimuli was studied within the framework of an exemplar-based generalization model. The model was used to predict subjects' learning in six different categorization conditions on the basis of data obtained in a single identification learning condition. A crucial assumption in the model is that because of selective attention to component dimensions, similarity relations may change in systematic ways across different experimental contexts. The theoretical analysis provided evidence that, at least under unspeeded conditions, selective attention may play a critical role in determining the identification-categorization relationship for integral stimuli. Evidence was also provided that similarity among exemplars decreased as a function of identification learning. Various alternative classification models, including prototype, multiple-prototype, average distance, and "value-on-dimensions" models, were unable to account for the results.

Attention↗
PubMed1986

Attention, similarity, and the identification-categorization relationship.

A unified quantitative approach to modeling subjects' identification and categorization of multidimensional perceptual stimuli is proposed and tested. Two subjects identified and categorized the same set of perceptually confusable stimuli varying on separable dimensions. The identification data were modeled using Shepard's (1957) multidimensional scaling-choice framework. This framework was then extended to model the subjects' categorization performance. The categorization model, which generalizes the context theory of classification developed by Medin and Schaffer (1978), assumes that subjects store category exemplars in memory. Classification decisions are based on the similarity of stimuli to the stored exemplars. It is assumed that the same multidimensional perceptual representation underlies performance in both the identification and categorization paradigms. However, because of the influence of selective attention, similarity relationships change systematically across the two paradigms. Some support was gained for the hypothesis that subjects distribute attention among component dimensions so as to optimize categorization performance. Evidence was also obtained that subjects may have augmented their category representations with inferred exemplars. Implications of the results for theories of multidimensional scaling and categorization are discussed.

Attention↗
PubMed1984

Choice, similarity, and the context theory of classification.

Medin and Schaffer's (1978) context theory of classification learning is interpreted in terms of Luce's (1963) choice theory and in terms of theoretical results obtained in multidimensional scaling theory. En route to this interpretation, quantitative relationships that may exist between identification and classification performance are investigated. It is suggested that the same basic choice processes may operate in the two paradigms but that the similarity parameters that determine performance change systematically according to the structure of the choice paradigm. In particular, when subjects are able to attend selectively to the component dimensions that compose the stimuli, the similarity parameters may tend toward what is optimal for maximizing performance.

Attention↗
PubMed1983

Information integration and the identification of stimulus noise and criterial noise in absolute judgment.

Two main classes of theories have been proposed regarding range effects in unidimensional absolute-identification tasks. One class posits that as range is increased, criterial noise increases but stimulus noise remains constant. Another class posits increasing stimulus noise but constant criterial noise. In this study, an effort is made to help decide this issue. Multiple observations are used in several absolute-identification tasks of varying range. A stimulus integration model is proposed in which averaging takes place over stimulus internal representations, thereby reducing stimulus variance; on the other hand, it is assumed that criterial variance is unaffected by the number of observations. The model allows one to identify the relative amounts of stimulus noise and criterial noise inherent in observers' recognition judgments. The model yields good fits to data in several experiments, and it is concluded that both stimulus noise and criterial noise increase as range in the absolute-identification task is increased.

Adolescent↗