F. Gregory Ashby

A neuropsychological theory of positive affect and its influence on cognition.

Positive affect systematically influences performance on many cognitive tasks. A new neuropsychological theory is proposed that accounts for many of these effects by assuming that positive affect is associated with increased brain dopamine levels. The theory predicts or accounts for influences of positive affect on olfaction, the consolidation of long-term (i.e., episodic) memories, working memory, and creative problem solving. For example, the theory assumes that creative problem solving is improved, in part, because increased dopamine release in the anterior cingulate improves cognitive flexibility and facilitates the selection of cognitive perspective.

A neuropsychological theory of multiple systems in category learning

A neuropsychological theory is proposed that assumes category learning is a competition between separate verbal and implicit (i.e., procedural-learning-based) categorization systems. The theory assumes that the caudate nucleus is an important component of the implicit system and that the anterior cingulate and prefrontal cortices are critical to the verbal system. In addition to making predictions for normal human adults, the theory makes specific predictions for children, elderly people, and patients suffering from Parkinsons disease, Huntingtons disease, major depression, amnesia, or lesions of the prefrontal cortex. Two separate formal descriptions of the theory are also provided. One describes trial-by-trial learning, and the other describes global dynamics. The theory is tested on published neuropsychological data and on category learning data with normal adults.

Varieties of perceptual independence.

Several varieties of perceptual independence are investigated. These include sampling independence, dimensional orthogonality, stimulus separability and integrality, and performance parity. A general multivariate perceptual theory is developed, and a precise definition of perceptual independence is offered. Each of these related concepts is then examined within the framework of this theory, and their theoretical interrelationships are explicated. It is shown that none of the concepts are equivalent to perceptual independence but that if separability holds, then sampling independence is equivalent to perceptual independence. Several simple tests of separability are suggested that can be applied to the same data as sampling independence. Dimensional orthogonality is shown to test for independence only if some strong distributional assumptions are made about the perceptual effects of stimuli. Reaction time and information-based performance parity criteria are examined. The potential for empirically testing each of these concepts is discussed.

Decision Rules in the Perception and Categorization of Multidimensional Stimuli

This article examines decision processes in the perception and categorization of stimuli constructed from one or more components. First, a general perceptual theory is used to formally characterize large classes of existing decision models according to the type of decision boundary they predict in a multidimensional perceptual space. A new experimental paradigm is developed that makes it possible to accurately estimate a subjects decision boundary in a categorization task. Three experiments using this paradigm are reported. Three conclusions stand out: (a) Subjects adopted deterministic decision rules, that is, for a given location in the perceptual space, most subjects always gave the same response; (b) subjects used decision rules that were nearly optimal; and (c) the only constraint on the type of decision bound that subjects used was the amount of cognitive capacity it required to implement. Subjects were not constrained to make independent decisions on each component or to attend to the distance to each prototype.

Comparing decision bound and exemplar models of categorization

The performance of a decision bound model of categorization (Ashby, J992a; Ashby & Maddox, in press) is compared with the performance of two exemplar models. The first is the generalized context model (e.g., Nosofsky, 1986, 1992) and the second is a recently proposed deterministic exemplar model (Ashby & Maddox, in press), which contains the generalized context model as a special case. When the exemplars from each category were normally distributed and the optimal decision bound was linear, the deterministic exemplar model and the decision bound model provided roughly equivalent accounts of the data. When the optimal decision bound was nonlinear, the decision bound model provided a more accurate account of the data than did either exemplar model. When applied to categorization data collected by Nosofsky (1986, 1989), in which the category exemplars are not normally distributed, the decision bound model provided excellent accounts of the data, in many cases significantly outperforming the exemplar models. The decision bound model was found to be especially successful when(1) single subject analyses were performed, (2) each subject was given relatively extensive training, and (3) the subjects performance was characterized by complex suboptimalities. These results support the hypothesis that the decision bound is of fundamental importance in predicting asymptotic categorization performance and that the decision bound models provide a viable alternative to the currently popular exemplar models of categorization.

Cortical and basal ganglia contributions to habit learning and automaticity

In the 20th century it was thought that novel behaviors are mediated primarily in cortex and that the development of automaticity is a process of transferring control to subcortical structures. However, evidence supports the view that subcortical structures, such as the striatum, make significant contributions to initial learning. More recently, there has been increasing evidence that neurons in the associative striatum are selectively activated during early learning, whereas those in the sensorimotor striatum are more active after automaticity has developed. At the same time, other recent reports indicate that automatic behaviors are striatum- and dopamine-independent, and might be mediated entirely within cortex. Resolving this apparent conflict should be a major goal of future research.

The effects of concurrent task interference on category learning: Evidence for multiple category learning systems

Participants learned simple and complex category structures under typical single-task conditions and when performing a simultaneous numerical Stroop task. In the simple categorization tasks, each set of contrasting categories was separated by a unidimensional explicit rule, whereas the complex tasks required integrating information from three stimulus dimensions and resulted in implicit rules that were difficult to verbalize. The concurrent Stroop task dramatically impaired learning of the simple explicit rules, but did not significantly delay learning of the complex implicit rules. These results support the hypothesis that category learning is mediated by multiple learning systems.

Delayed feedback effects on rule-based and information-integration category learning.

The effect of immediate versus delayed feedback on rule-based and information-integration category learning was investigated. Accuracy rates were examined to isolate global performance deficits, and model-based analyses were performed to identify the types of response strategies used by observers. Feedback delay had no effect on the accuracy of responding or on the distribution of best fitting models in the rule-based category-learning task. However, delayed feedback led to less accurate responding in the information-integration category-learning task. Model-based analyses indicated that the decline in accuracy with delayed feedback was due to an increase in the use of rule-based strategies to solve the information-integration task. These results provide support for a multiple-systems approach to category learning and argue against the validity of single-system approaches.

The neurobiology of human category learning

Categorization is among the most important skills that any organism can possess. Recent advances in cognitive neuroscience have led to new insights about the neural basis of category learning. Perhaps most important is the finding that many different, widely separated neural structures appear to participate in category learning, but to varying degrees that depend on category structure. In particular, different brain regions are implicated according to whether the category-learning task involves explicit rules, prototype distortion or information integration.

A neurobiological theory of automaticity in perceptual categorization.

A biologically detailed computational model is described of how categorization judgments become automatic in tasks that depend on procedural learning. The model assumes 2 neural pathways from sensory association cortex to the premotor area that mediates response selection. A longer and slower path projects to the premotor area via the striatum, globus pallidus, and thalamus. A faster, purely cortical path projects directly to the premotor area. The model assumes that the subcortical path has greater neural plasticity because of a dopamine-mediated learning signal from the substantia nigra. In contrast, the cortical-cortical path learns more slowly via (dopamine independent) Hebbian learning. Because of its greater plasticity, early performance is dominated by the subcortical path, but the development of automaticity is characterized by a transfer of control to the faster cortical-cortical projection. The model, called SPEED (Subcortical Pathways Enable Expertise Development), includes differential equations that describe activation in the relevant brain areas and difference equations that describe the 2- and 3-factor learning. A variety of simulations are described, showing that the model accounts for some classic single-cell recording and behavioral results.