Neural dynamics underlying the acquisition of distinct auditory category structures.

Despite the multidimensional and temporally fleeting nature of auditory signals we quickly learn to assign novel sounds to behaviorally relevant categories. The neural systems underlying the learning and representation of novel auditory categories are far from understood. Current models argue for a rigid specialization of hierarchically organized core regions that are fine-tuned to extracting and mapping relevant auditory dimensions to meaningful categories.

The impact of training methodology and representation on rule-based categorization: An fMRI study.

Hélie, Shamloo, & Ell (2017) showed that regular classification learning instructions (A/B) promote between-category knowledge in rule-based categorization whereas conceptual learning instructions (YES/NO) promote learning within-category knowledge with the same categories. Here we explore how these tasks affect brain activity using fMRI. Participants learned two sets of two categories.

Learning and generalization of within-category representations in a rule-based category structure.

The task requirements during the course of category learning are critical for promoting within-category representations (e.g., correlational structure of the categories).

The impact of training methodology and category structure on the formation of new categories from existing knowledge.

Categorization decisions are made thousands of times every day, and a typical adult knows tens of thousands of categories. It is thus relatively rare that adults learn new categories without somehow reorganizing pre-existing knowledge. Yet, most perceptual categorization research has investigated the ability to learn new categories without considering they relation to existing knowledge.

The effect of training methodology on knowledge representation in categorization.

Category representations can be broadly classified as containing within-category information or between-category information. Although such representational differences can have a profound impact on decision-making, relatively little is known about the factors contributing to the development and generalizability of different types of category representations. These issues are addressed by investigating the impact of training methodology and category structures using a traditional empirical approach as well as the novel adaptation of computational modeling techniques from the machine learning literature.

The impact of category structure and training methodology on learning and generalizing within-category representations.

When interacting with categories, representations focused on within-category relationships are often learned, but the conditions promoting within-category representations and their generalizability are unclear. We report the results of three experiments investigating the impact of category structure and training methodology on the learning and generalization of within-category representations (i.e.

Trial-by-trial identification of categorization strategy using iterative decision-bound modeling.

Identifying the strategy that participants use in laboratory experiments is crucial in interpreting the results of behavioral experiments. This article introduces a new modeling procedure called iterative decision-bound modeling (iDBM), which iteratively fits decision-bound models to the trial-by-trial responses generated from single participants in perceptual categorization experiments. The goals of iDBM are to identify: (1) all response strategies used by a participant, (2) changes in response strategy, and (3) the trial number at which each change occurs.

One Giant Leap for Categorizers: One Small Step for Categorization Theory.

We explore humans' rule-based category learning using analytic approaches that highlight their psychological transitions during learning. These approaches confirm that humans show qualitatively sudden psychological transitions during rule learning. These transitions contribute to the theoretical literature contrasting single vs.

Criterion learning in rule-based categorization: simulation of neural mechanism and new data.

In perceptual categorization, rule selection consists of selecting one or several stimulus-dimensions to be used to categorize the stimuli (e.g., categorize lines according to their length).

Learning robust cortico-cortical associations with the basal ganglia: an integrative review.

This article focuses on the interaction between the basal ganglia (BG) and prefrontal cortex (PFC). The BG are a group of nuclei at the base of the forebrain that are highly connected with cortex. A century of research suggests that the role of the BG is not exclusively motor, and that the BG also play an important role in learning and memory.

Is pressure stressful? The impact of pressure on the stress response and category learning.

We examined the basic question of whether pressure is stressful. We proposed that when examining the role of stress or pressure in cognitive performance, it is important to consider the type of pressure, the stress response, and the aspect of cognition assessed. In Experiment 1, outcome pressure was not experienced as stressful but did lead to impaired performance on a rule-based (RB) category-learning task, but not on a more procedural information-integration (II) task.

Targeted training of the decision rule benefits rule-guided behavior in Parkinson's disease.

The impact of Parkinson's disease (PD) on rule-guided behavior has received considerable attention in cognitive neuroscience. The majority of research has used PD as a model of dysfunction in frontostriatal networks, but very few attempts have been made to investigate the possibility of adapting common experimental techniques in an effort to identify the conditions that are most likely to facilitate successful performance. The present study investigated a targeted training paradigm designed to facilitate rule learning and application using rule-based categorization as a model task.

Unsupervised category learning with integral-dimension stimuli.

Despite the recent surge in research on unsupervised category learning, the majority of studies have focused on unconstrained tasks in which no instructions are provided about the underlying category structure. Relatively little research has focused on constrained tasks in which the goal is to learn predefined stimulus clusters in the absence of feedback. The few studies that have addressed this issue have focused almost exclusively on stimuli for which it is relatively easy to attend selectively to the component dimensions (i.

The impact of category separation on unsupervised categorization.

Most previous research on unsupervised categorization has used unconstrained tasks in which no instructions are provided about the underlying category structure or in which the stimuli are not clustered into categories. Few studies have investigated constrained tasks in which the goal is to learn predefined stimulus clusters in the absence of feedback. These studies have generally reported good performance when the stimulus clusters could be separated by a one-dimensional rule.

When bad stress goes good: increased threat reactivity predicts improved category learning performance.

The way in which we respond to everyday stressors can have a profound impact on cognitive functioning. Maladaptive stress responses in particular are generally associated with impaired cognitive performance. We argue, however, that the cognitive system mediating task performance is also a critical determinant of the stress-cognition relationship.

Rule-based categorization deficits in focal basal ganglia lesion and Parkinson's disease patients.

Patients with basal ganglia (BG) pathology are consistently found to be impaired on rule-based category learning tasks in which learning is thought to depend upon the use of an explicit, hypothesis-guided strategy. The factors that influence this impairment remain unclear. Moreover, it remains unknown if the impairments observed in patients with degenerative disorders such as Parkinson's disease (PD) are also observed in those with focal BG lesions.

Criterial noise effects on rule-based category learning: the impact of delayed feedback.

Variability in the representation of the decision criterion is assumed in many category-learning models, yet few studies have directly examined its impact. On each trial, criterial noise should result in drift in the criterion and will negatively impact categorization accuracy, particularly in rule-based categorization tasks, where learning depends on the maintenance and manipulation of decision criteria. In three experiments, we tested this hypothesis and examined the impact of working memory on slowing the drift rate.

Cerebellar pathology does not impair performance on identification or categorization tasks.

In comparison to the basal ganglia, prefrontal cortex, and medial temporal lobes, the cerebellum has been absent from recent research on the neural substrates of categorization and identification, two prominent tasks in the learning and memory literature. To investigate the contribution of the cerebellum to these tasks, we tested patients with cerebellar pathology (seven with bilateral degeneration, six with unilateral lesions, and two with midline damage) on rule-based and information-integration categorization tasks and an identification task. In rule-based tasks, it is assumed that participants learn the categories through an explicit reasoning process.

The effects of category overlap on information-integration and rule-based category learning.

In three experiments, we investigated whether the amount of category overlap constrains the decision strategies used in category learning, and whether such constraints depend on the type of category structures used. Experiments 1 and 2 used a category-learning task requiring perceptual integration of information from multiple dimensions (an information-integration task) and Experiment 3 used a task requiring the application of an explicit strategy (a rule-based task). In the information-integration task, participants used perceptual-integration strategies at moderate levels of category overlap, but explicit strategies at extreme levels of overlap--even when such strategies were suboptimal.

Focal putamen lesions impair learning in rule-based, but not information-integration categorization tasks.

Previous research on the role of the basal ganglia in category learning has focused on patients with Parkinson's and Huntington's disease, neurodegenerative diseases frequently accompanied by additional cortical pathology. The goal of the present study was to extend this work to patients with basal ganglia lesions due to stroke, asking if similar changes in performance would be observed in patients with more focal pathology. Patients with basal ganglia lesions centered in the putamen (6 left side, 1 right side) were tested on rule-based and information-integration visual categorization tasks.

FROST: a distributed neurocomputational model of working memory maintenance.

Many studies suggest that the sustained activation underlying working memory (WM) maintenance is mediated by a distributed network that includes the prefrontal cortex and other structures (e.g., posterior parietal cortex, thalamus, globus pallidus, and the caudate nucleus).

Dynamical trajectories in category learning.

Category learning has traditionally been studied by examining how percentage correct changes with experience (i.e., in the form of learning curves).

Procedural learning in perceptual categorization.

In two experiments, observers learned two types of category structures: those in which perfect accuracy could be achieved via some explicit rule-based strategy and those in which perfect accuracy required integrating information from separate perceptual dimensions at some predecisional stage. At the end of training, some observers were required to switch their hands on the response keys, whereas the assignment of categories to response keys was switched for other observers. With the rule-based category structures, neither change in response instructions interfered with categorization accuracy.

Category learning deficits in Parkinson's disease.

Sixteen patients with Parkinson's disease (PD), 15 older controls (OCs), and 109 younger controls (YCs) were compared in 2 category-learning tasks. Participants attempted to assign colored geometric figures to 1 of 2 categories. In rule-based tasks, category membership was defined by an explicit rule that was easy to verbalize, whereas in information-integration tasks, there was no salient verbal rule and accuracy was maximized only if information from 3 stimulus components was integrated at some predecisional stage.