Biomedical subjects

R M Nosofsky

Publications and source records attributed to R M Nosofsky.

At least 19 recordsLinked to original sources

PubMed2001

Exemplar accounts of blending and distinctiveness effects in perceptual old-new recognition.

Recent findings from the perceptual old-new recognition literature indicate that observers have extremely high false-alarm rates to new items that are "blends" of old ones. In addition, evidence suggests that "distinctive" old items--that is, those located in isolated regions of the similarity space--are recognized with higher probability than are typical old items. Both types of phenomena challenge the predictions of global-familiarity exemplar models of perceptual old-new recognition, which posit that the probability that an observer judges an item as old is based on its summed similarity to previously presented exemplars. In the present research the authors pursued these blending and distinctiveness effects by testing paradigms in which similarity relations among objects are highly controlled and in which the variables of blending and distinctiveness are not confounded with other properties associated with the individual objects themselves. In contrast to previous results, the authors found effects of blending and distinctiveness that are compatible with the predictions of a pure summed-similarity exemplar model.

Adult↗

PubMed2001

Central tendencies, extreme points, and prototype enhancement effects in ill-defined perceptual categorization.

In three perceptual classification experiments involving ill-defined category structures, extreme prototype enhancement effects were observed in which prototypes were classified more accurately than other category instances. Such empirical findings can prove theoretically challenging to exemplar-based models of categorization if prototypes are psychological central tendencies of category instances. We found instead that category prototypes were sometimes better characterized as psychological extreme points relative to contrast categories. Extending a classic and widely cited study (Posner & Keele, 1968), participants learned categories created from distortions of dot patterns arranged in familiar shapes. Participants then made pairwise similarity judgments of the patterns. Multidimensional scaling (MDS) analyses of the similarity data revealed the prototypes to be psychological extreme points, not central tendencies. Evidence for extreme point representations was also found for novel prototype patterns displaying a symmetry structure and for prototypes of grid patterns used in recent studies by McLaren and colleagues (McLaren, Bennet, Guttman-Nahir, Kim, & Mackintosh, 1995). When used in combination with the derived MDS solutions, an exemplar-based model of categorization, the Generalized Context Model (Nosofsky, 1986), provided good fits to the observed categorization data in all three experiments.

Adult↗

PubMed2001

Category variability, exemplar similarity, and perceptual classification.

Experiments were conducted in which observers learned to classify simple perceptual stimuli into low-variability and high-variability categories. Similarities between objects were measured in independent psychological-scaling tasks. The results showed that observers classified transfer stimuli into the high-variability categories with greater probability than was predicted by a baseline version of an exemplar-similarity model. Qualitative evidence for the role of category variability on perceptual classification, which could not be explained in terms of the baseline exemplar-similarity model, was obtained as well. Possible accounts of the effects of category variability are considered in the General Discussion section.

Cognition↗

PubMed2001

A single-system interpretation of dissociations between recognition and categorization in a task involving object-like stimuli.

In previous research (Reed, Squire, Patalano, Smith, & Jonides, 1999), amnesic patients performed at near normal levels in a categorization task involving stimuli with discrete features, but showed impaired recall of the features. These results were taken as evidence of the existence of separate memory systems for categorization and recall/recognition. The present research addresses a single-system account of this dissociation. In Experiment 1, results closely matching the dissociation pattern were obtained when normal participants' classification and recognition performance was tested either immediately or after a week delay. In addition, formal modeling of the data suggested that participants use only a few of the dimensions in the categorization task, whereas they must use many dimensions in the cued-recall task. In Experiment 2, we found that many participants could perform the categorization task without any exposure to the training sequence. These results suggest that different memory demands across the two tasks may be responsible for the observed dissociation--separate memory systems are not necessarily involved.

Adult↗

PubMed2000

An exemplar-retrieval model of speeded same--different judgments.

R. M. Nosofsky and T. J. Palmeri's (1997) exemplar-based random-walk (EBRW) model of speeded classification is extended to account for speeded same--different judgments among integral-dimension stimuli. According to the model, an important component process of same--different judgments is that people store individual examples of experienced same and different pairs of objects in memory. These exemplar pairs are retrieved from memory on the basis of how similar they are to a currently presented pair of objects. The retrieved pairs drive a random-walk process for making same--different decisions. The EBRW predicts correctly that same responses are faster for objects lying in isolated than in dense regions of similarity space. The model also predicts correctly effects of same-identity versus same-category instructions and is sensitive to observers' past experiences with specific same and different pairs of objects.

Humans↗

PubMed2000

Exemplar representation without generalization? Comment on Smith and Minda's (2000) "Thirty categorization results in search of a model".

J. D. Smith and J. P. Minda (2000) conducted a meta-analysis of 30 data sets reported in the classification literature that involved use of the "5-4" category structure introduced by D. L. Medin and M. M. Schaffer (1978). The meta-analysis was aimed at investigating exemplar and elaborated prototype models of categorization. In this commentary, the author argues that the meta-analysis is misleading because it includes many data sets from experimental designs that are inappropriate for distinguishing the models. Often, the designs involved manipulations in which the actual 5-4 structure was not, in reality, tested, voiding the predictions of the models. The commentary also clarifies various aspects of the workings of the exemplar-based context model. Finally, concerns are raised that the all-or-none exemplar processes that form part of Smith and Minda's (2000) elaborated prototype models are implausible and lacking in generality.

Humans↗

PubMed2000

Exemplar-based accounts of "multiple-system" phenomena in perceptual categorization.

We demonstrate that a wide variety of recently reported "rule-described" and "prototype-described" phenomena in perceptual classification, which have led to the development of a number of multiple-system models, can be given an alternative interpretation in terms of a single-system exemplar-similarity model. The phenomena include various rule- and prototype-described patterns of generalization, dissociations between categorization and similarity judgments, and dissociations between categorization and old-new recognition. The alternative exemplar-based interpretation relies on the idea that similarity is not an invariant relation but a context-dependent one. Similarity relations among exemplars change systematically because of selective attention to dimensions and because of changes in the level of sensitivity relating judged similarity to distance in psychological space. Adaptive learning principles may help explain the systematic influence of the selective attention process and of modulation in sensitivity settings on judged similarity.

Adult↗

PubMed1999

Effects of similarity and practice on speeded classification response times and accuracies: further tests of an exemplar-retrieval model.

Observers were tested in a perceptual category-learning experiment in which they were instructed to make classification decisions as rapidly as possible without making errors. Nosofsky and Palmeri's (1997b) exemplar-based random walk (EBRW) model of speeded classification was tested for its ability to fit the classification response times and accuracies. The authors demonstrated that the EBRW model provided good quantitative fits to the mean response times and accuracies associated with individual objects as a function of their locations in a multidimensional similarity space and as a function of practice in the task. Preliminary evidence was also obtained that stimulus-specific adjustments in the random walk response criteria may have occurred during the course of learning.

Adult↗

PubMed1997

An exemplar-based random walk model of speeded classification.

The authors propose and test an exemplar-based random walk model for predicting response times in tasks of speeded, multidimensional perceptual classification. The model combines elements of R. M. Nosofsky's (1986) generalized context model of categorization and G. D. Logan's (1988) instance-based model of automaticity. In the model, exemplars race among one another to be retrieved from memory, with rates determined by their similarity to test items. The retrieved exemplars provide incremental information that enters into a random walk process for making classification decisions. The model predicts correctly effects of within- and between-categories similarity, individual-object familiarity, and extended practice on classification response times. It also builds bridges between the domains of categorization and automaticity.

Concept Formation↗

PubMed1997

Comparing exemplar-retrieval and decision-bound models of speeded perceptual classification.

The authors compared the exemplar-based random-walk (EBRW) model of Nosofsky and Palmeri (1997) and the decision-bound model (DBM) of Ashby and Maddox (1994; Maddox & Ashby, 1996) on their ability to predict performance in Garner's (1974) speeded classification tasks. A key question was the extent to which the models could predict facilitation in the correlated task and interference in the filtering task, in situations involving integral-dimension stimuli. To obtain rigorous constraints for model evaluation, the goal was to fit the detailed structure of the response time (RT) distribution data associated with each individual stimulus in each task. Both models yielded reasonably good global quantitative fits to the RT distribution and accuracy data. However, the DBM failed to properly characterize the interference effects in the filtering task. Apparently, a fundamental limitation of the DBM is that it predicts that the fastest RTs in the filtering task should be faster than the fastest RTs in the control task, whereas the opposite pattern was observed in our data.

Decision Making↗

PubMed1996

Selective attention and the formation of linear decision boundaries.

Classification experiments were designed to compare the predictions of a linear decision bound model with those of an exemplar-similarity model incorporating an explicit selective attention mechanism. Linear boundaries could account for the data only in tasks involving separable dimension stimuli and where the boundary separating the categories was orthogonal to the psychological dimensions. Linear boundaries provided poor fits to the classification data in situations involving integral dimensions or when the boundary needed to be oriented in oblique directions in the space. The results were consistent with the selection-attention assumptions embodied in the exemplar model. It was argued that similar assumptions about selective attention need to be incorporated within decision bound models.

Adult↗

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↗