Biomedical subjects

J K Kruschke

Publications and source records attributed to J K Kruschke.

10 recordsLinked to original sources

PubMed2001

The inverse base-rate effect is not explained by eliminative inference.

The inverse base-rate effect is a phenomenon in which people learn about some common and some rare outcomes and in subsequent testing people predict the rare outcome for particular sets of conflicting cues, contrary to normative predictions. P. Juslin, P. Wennerholm, and A. Winman suggested that the effect could be explained by eliminative inference, contrary to the attention-shifting explanation of J. K. Kruschke. The present article shows that the eliminative inference model exhibits ordinal discrepancies from previously published data and from data of 2 new experiments. A connectionist implementation of attentional theory fits the data well. The author concludes that people can use eliminative inference but that it cannot account for the inverse base-rate effect.

Adult↗

PubMed2001

Using cognitive science methods to assess the role of social information processing in sexually coercive behavior.

Seventy-four undergraduate men completed cognitive performance tasks assessing perceptual organization, classification, and category learning, as well as self-report measures relevant to sexual coercion. The stimuli were slides of Caucasian women who varied along affect and physical exposure (i.e., sensuality) dimensions. Data were analyzed using a weighted multidimensional scaling model, signal-detection theory analyses, and a connectionist learning model (RASHNL; J. K. Kruschke & M. K. Johansen, 1999). Individual differences in performance on the classification and category-learning tasks were congruent with individual differences in perceptual organization. Additionally, participants who showed relatively more attention to exposure than to affect were less sensitive to women's negative responses to unwanted sexual advances. Overall, the study demonstrates the feasibility and utility of cognitive science methods for studying information processing in psychopathology.

Adolescent↗

PubMed2000

The role of attention shifts in the categorization of continuous dimensioned stimuli.

Results of human category learning experiments, using stimulus dimensions with binary values, have implicated a rapidly acting mechanism of attention shifts. Theories of categorization desire that stimuli with binary, discrete and continuous valued dimensions should all be treated similarly. Theoretical analyses of attention shifting, however, have up to now only been developed for shifts between features, or shifts between entire dimensions, not shifts within dimensions. Here we present a model of how people learn to discriminate categories made up of stimuli with continuous-valued dimensions. The model uses rapid shifts in attention within stimulus dimensions to reduce errors during learning; the model generalizes J. K. Kruschke's (Psychological Review, 99, 22-44, 1992) ADIT model. In an experiment in category learning, subjects were trained to discriminate four bivariate normal distributions that are presented with differential base rates. The base-rate manipulation produces several qualitative effects, for which the model accounts very well. With attention shifting turned off, the model fails to account for some aspects of the data, suggesting that attentions shifts are an important mechanism in the model.

Association↗

PubMed2000

Blocking and backward blocking involve learned inattention.

Four experiments examine blocking of associative learning by human participants in a disease diagnosis procedure. The results indicate that after a cue is blocked, subsequent learning about the cue is attenuated. This attenuated learning after blocking is obtained for both standard blocking and for backward blocking. Attenuated learning after blocking cannot be accounted for by theories such as the Rescorla-Wagner model that rely on lack of learning about a redundant cue, nor can it be accounted for by extensions of the Rescorla-Wagner model designed to address backward blocking that encode absent cues with negative values. The results are predicted by the hypothesis that people learn not to attend to the blocked cue.

Adolescent↗

PubMed1999

A model of probabilistic category learning.

A new connectionist model (named RASHNL) accounts for many "irrational" phenomena found in nonmetric multiple-cue probability learning, wherein people learn to utilize a number of discrete-valued cues that are partially valid indicators of categorical outcomes. Phenomena accounted for include cue competition, effects of cue salience, utilization of configural information, decreased learning when information is introduced after a delay, and effects of base rates. Experiments 1 and 2 replicate previous experiments on cue competition and cue salience, and fits of the model provide parameter values for making qualitatively correct predictions for many other situations. The model also makes 2 new predictions, confirmed in Experiments 3 and 4. The model formalizes 3 explanatory principles: rapidly shifting attention with learned shifts, decreasing learning rates, and graded similarity in exemplar representation.

Cues↗

PubMed1998

Rules and exemplars in category learning.

Psychological theories of categorization generally focus on either rule- or exemplar-based explanations. We present 2 experiments that show evidence of both rule induction and exemplar encoding as well as a connectionist model, ATRIUM, that specifies a mechanism for combining rule- and exemplar-based representation. In 2 experiments participants learned to classify items, most of which followed a simple rule, although there were a few frequently occurring exceptions. Experiment 1 examined how people extrapolate beyond the range of training. Experiment 2 examined the effect of instance frequency on generalization. Categorization behavior was well described by the model, in which exemplar representation is used for both rule and exception processing. A key element in correctly modeling these results was capturing the interaction between the rule- and exemplar-based representations by using shifts of attention between rules and exemplars.

Attention↗

PubMed1997

Decision boundaries in one-dimensional categorization.

Decision-boundary theories of categorization are often difficult to distinguish from exemplar-based theories of categorization. The authors developed a version of the decision-boundary theory, called the single-cutoff model, that can be distinguished from the exemplar theory. The authors present 2 experiments that test this decision-boundary model. The results of both experiments point strongly to the absence of single cutoff in most participants, and no participant displayed use of the optimal boundary. The range of nonoptimal solutions shown by individual participants was accounted for by an exemplar-based adaptive-learning model. When combined with the results of previous research, this suggests that a comprehensive model of categorization must involve both rules and exemplars, and possibly other representations as well.

Decision Making↗

PubMed1996

Base rates in category learning.

Previous researchers have discovered perplexing inconsistencies in how people appear to utilize category base rates when making category judgments. In particular, D.L. Medin and S.M. Edelson (1988) found an inverse base-rate effect, in which participants tended to select a rare category when tested with a combination of conflicting cues, and M.A. Gluck and G.H. Bower (1988) reported apparent base-rate neglect, in which participants tended to select a rare category when tested with a single symptom for which objective diagnosticity was equal for all categories. This article suggests that common principles underlie both effects: First, base-rate information is learned and consistently applied to all training and testing cases. Second, the crucial effect of base rates is to cause frequent categories to be learned before rare categories so that the frequent categories are encoded by their typical features and the rare categories are encoded by their distinctive features. Four new experiments provide evidence consistent with those principles. The principles are formalized in a new connectionist model that can rapidly shift attention to distinctive features.

Decision Making↗

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↗

PubMed1992

ALCOVE: an exemplar-based connectionist model of category learning.

ALCOVE (attention learning covering map) is a connectionist model of category learning that incorporates an exemplar-based representation (Medin & Schaffer, 1978; Nosofsky, 1986) with error-driven learning (Gluck & Bower, 1988; Rumelhart, Hinton, & Williams, 1986). Alcove selectively attends to relevant stimulus dimensions, is sensitive to correlated dimensions, can account for a form of base-rate neglect, does not suffer catastrophic forgetting, and can exhibit 3-stage (U-shaped) learning of high-frequency exceptions to rules, whereas such effects are not easily accounted for by models using other combinations of representation and learning method.

Attention↗