Biomedical subjects

John K Kruschke

Publications and source records attributed to John K Kruschke.

8 recordsLinked to original sources

PubMed2006

Locally Bayesian learning with applications to retrospective revaluation and highlighting.

A scheme is described for locally Bayesian parameter updating in models structured as successions of component functions. The essential idea is to back-propagate the target data to interior modules, such that an interior component's target is the input to the next component that maximizes the probability of the next component's target. Each layer then does locally Bayesian learning. The approach assumes online trial-by-trial learning. The resulting parameter updating is not globally Bayesian but can better capture human behavior. The approach is implemented for an associative learning model that first maps inputs to attentionally filtered inputs and then maps attentionally filtered inputs to outputs. The Bayesian updating allows the associative model to exhibit retrospective revaluation effects such as backward blocking and unovershadowing, which have been challenging for associative learning models. The back-propagation of target values to attention allows the model to show trial-order effects, including highlighting and differences in magnitude of forward and backward blocking, which have been challenging for Bayesian learning models.

Attention↗

PubMed2006

Attention and salience in associative blocking.

The associative learning effect called blocking has previously been found in many cue-competition paradigms where all cues are of equal salience. Previous research by Hall, Mackintosh, Goodall,and dal Martello (1977) found that, in animals, salient cues were less likely to be blocked. Crucially, they also found that when the to-be-blocked cue was highly salient, the blocking cue would lose some control over responding. The present article extends these findings to humans and suggests that shifts in attention can explain the apparent loss of control by the previously learned cue. A connectionist model that implements attentional learning is shown to fit the main trends in the data. Model comparisons suggest that mere forgetting, implemented as weight decay, cannot explain the results.

Association Learning↗

PubMed2005

Eye gaze and individual differences consistent with learned attention in associative blocking and highlighting.

The associative learning effects called blocking and highlighting have previously been explained by covert learned attention, but evidence for learned attention has been indirect, via models of response choice. The present research reports results from eye tracking consistent with the attentional hypothesis: Gaze duration is diminished for blocked cues and augmented for highlighted cues. If degree of attentional learning varies across individuals but is relatively stable within individuals, then the magnitude of blocking and highlighting should covary across individuals. This predicted correlation is obtained for both choice and eye gaze. A connectionist model that implements attentional learning is shown to fit the data and account for individual differences by variation in its attentional parameters.

Adolescent↗

PubMed2005

Category representation for classification and feature inference.

This research's purpose was to contrast the representations resulting from learning of the same categories by either classifying instances or inferring instance features. Prior inference learning research, particularly T. Yamauchi and A. B. Markman (1998), has suggested that feature inference learning fosters prototype representation, whereas classification learning encourages exemplar representation. Experiment 1 supported this hypothesis. Averaged and individual participant data from transfer after inference training were better fit by a prototype than by an exemplar model. However, Experiment 2, with contrasting inference learning conditions, indicated that the prototype model was mimicking a set of label-based bidirectional rules, as determined by the inference learning task demands in Experiment 1. Only the set of rules model accounted for all the inference learning conditions in these experiments.

Humans↗

PubMed2004

Population of linear experts: knowledge partitioning and function learning.

Knowledge partitioning is a theoretical construct holding that knowledge is not always integrated and homogeneous but may be separated into independent parcels containing mutually contradictory information. Knowledge partitioning has been observed in research on expertise, categorization, and function learning. This article presents a theory of function learning (the population of linear experts model--POLE) that assumes people partition their knowledge whenever they are presented with a complex task. The authors show that POLE is a general model of function learning that accommodates both benchmark results and recent data on knowledge partitioning. POLE also makes the counterintuitive prediction that a person's distribution of responses to repeated test stimuli should be multimodal. The authors report 3 experiments that support this prediction.

Analysis of Variance↗

PubMed2002

Assessing clinically relevant perceptual organization with multidimensional scaling techniques.

Multidimensional scaling (MDS) techniques provide a promising measurement strategy for characterizing individual differences in cognitive processing, which many clinical theories associate with the development, maintenance, and treatment of psychopathology. The authors describe the use of deterministic and probabilistic MDS techniques for investigating numerous aspects of perceptual organization, such as dimensional attention, perceptual correlation, within-attribute organization, and perceptual variability. Additionally, they discuss how formal quantitative models can be used, in conjunction with MDS-derived representations of individual differences in perceptual organization, to test theories about the role of cognitive processing in clinically relevant phenomena. They include applied examples from their work in the areas of eating disorders and sexual coercion.

Coercion↗

PubMed2002

Rule-based extrapolation in perceptual categorization.

Erickson and Kruschke (1998) provided a demonstration that in certain situations people will classify novel stimuli according to an extrapolated rule, even when the most similar training exemplar is an exception to the rule. This result challenged exemplar models. Nosofsky and Johansen (2000) have called this finding into question by offering an exemplar-based explanation for those data based on the perceptual features of the stimuli. Here, we describe the results of a new experiment that yields results similar to those found previously without the questionable perceptual features: Participants who learn to classify all the training stimuli have patterns of generalization that indicate a combination of rule and exemplar representation. ATRIUM, a hybrid rule and exemplar model (Erickson & Kruschke, 1998), is shown to account for these data much better than ALCOVE, an exemplar model (Kruschke, 1992). Moreover, four alternate exemplar explanations, including one suggested by Nosofsky and Johansen, cannot account for our new findings.

Cognition↗

PubMed2002

Single-system models and interference in category learning: commentary on Waldron and Ashby (2001).

In a recent article, Waldron and Ashby (2001) observed that performing a concurrent task caused greater interference in learning a simple one-dimensional categorization rule than in learning a complex three-dimensional one. They argued that this result was incompatible with all existing single-system models of category learning but was as predicted by the multiple-system COVIS model (Ashby, Alfonso-Reese, Turken, & Waldron, 1998). In contrast to Waldron and Ashby's argument, we demonstrate that the single-system ALCOVE model (Kruschke, 1992) naturally predicts the result by assuming that its selective-attention learning process is disrupted by the concurrent task.

Attention↗