Prototype-distortion category learning: a two-phase learning process across a distributed network

An adaptive linear filter model of procedural category learning

We use a feature-based association model to fit grouped and individual level category learning and transfer data. The model assumes that people use corrective feedback to learn individual feature to categorization-criterion correlations and combine those correlations additively to produce classifications. The model is an Adaptive Linear Filter (ALF) with logistic output function and Least Mean Squares learning algorithm. Categorization probabilities are computed by a logistic function. Our data span over 31 published data sets. Both at grouped and individual level analysis levels, the model performs remarkably well, accounting for large amounts of available variances. When fitted to grouped data, it outperforms alternative models. When fitted to individual level data, it is able to capture learning and transfer performance with high explained variances. Notably, the model achieves its fits with a very minimal number of free parameters. We discuss the ALF's advantages as a model of procedural categorization, in terms of its simplicity, its ability to capture empirical trends and its ability to solve challenges to other associative models. In particular, we discuss why the model is not equivalent to a prototype model, as previously thought.

Neuroimaging of reading intervention: a systematic review and activation likelihood estimate meta-analysis

A growing number of studies examine instructional training and brain activity. The purpose of this paper is to review the literature regarding neuroimaging of reading intervention, with a particular focus on reading difficulties (RD). To locate relevant studies, searches of peer-reviewed literature were conducted using electronic databases to search for studies from the imaging modalities of fMRI and MEG (including MSI) that explored reading intervention. Of the 96 identified studies, 22 met the inclusion criteria for descriptive analysis. A subset of these (8 fMRI experiments with post-intervention data) was subjected to activation likelihood estimate (ALE) meta-analysis to investigate differences in functional activation following reading intervention. Findings from the literature review suggest differences in functional activation of numerous brain regions associated with reading intervention, including bilateral inferior frontal, superior temporal, middle temporal, middle frontal, superior frontal, and postcentral gyri, as well as bilateral occipital cortex, inferior parietal lobules, thalami, and insulae. Findings from the meta-analysis indicate change in functional activation following reading intervention in the left thalamus, right insula/inferior frontal, left inferior frontal, right posterior cingulate, and left middle occipital gyri. Though these findings should be interpreted with caution due to the small number of studies and the disparate methodologies used, this paper is an effort to synthesize across studies and to guide future exploration of neuroimaging and reading intervention.

Activation in the neural network responsible for categorization and recognition reflects parameter changes

According to various influential formal models of cognition, perceptual categorization and old-new recognition recruit the same memory system. By contrast, the prevailing view in the cognitive neuroscience literature is that separate neural systems mediate perceptual categorization and recognition. A direct form of evidence is that separate brain regions are activated when observers engage in categorization and recognition tasks involving the same types of stimuli. However, even if the same memory-based comparison processes underlie categorization and recognition, one would not expect to see identical patterns of brain activity across the tasks; the reason is that observers would adjust parameter settings (e.g., vary criterion settings) across the tasks to satisfy the different task goals. In this fMRI study, we conducted categorization and recognition tasks in which stimulus conditions were held constant, and in which observers were induced to vary hypothesized parameter settings across conditions. A formal exemplar model was fitted to the data to track the changes in parameters to help interpret the fMRI results. We observed systematic effects of changes in parameters on patterns of brain activity, which were interpretable in terms of differing forms of evidence accumulation that resulted from the changed parameter settings. After controlling for stimulus and parameter-related differences, we found little evidence that categorization and recognition recruit separate memory systems.

Assessing the neural basis of uncertainty in perceptual category learning through varying levels of distortion

The formation of new perceptual categories involves learning to extract that information from a wide range of often noisy sensory inputs, which is critical for selecting between a limited number of responses. To identify brain regions involved in visual classification learning under noisy conditions, we developed a task on the basis of the classical dot pattern prototype distortion task [M. I. Posner, Journal of Experimental Psychology, 68, 113-118, 1964]. Twenty-seven healthy young adults were required to assign distorted patterns of dots into one of two categories, each defined by its prototype. Categorization uncertainty was modulated parametrically by means of Shannon's entropy formula and set to the levels of 3, 7, and 8.5 bits/dot within subsets of the stimuli. Feedback was presented after each trial, and two parallel versions of the task were developed to contrast practiced and unpracticed performance within a single session. Using event-related fMRI, areas showing increasing activation with categorization uncertainty and decreasing activation with training were identified. Both networks largely overlapped and included areas involved in visuospatial processing (inferior temporal and posterior parietal areas), areas involved in cognitive processes requiring a high amount of cognitive control (posterior medial wall), and a cortico-striatal-thalamic loop through the body of the caudate nucleus. Activity in the medial prefrontal wall was increased when subjects received negative as compared with positive feedback, providing further evidence for its important role in mediating the error signal. This study characterizes the cortico-striatal network underlying the classification of distorted visual patterns that is directly related to decision uncertainty.

Changes in S1 neural responses during tactile discrimination learning

In freely moving rats that are actively performing a discrimination task, single-unit responses in primary somatosensory cortex (S1) are strikingly different from responses to comparable tactile stimuli in immobile rats. For example, in the active discrimination context prestimulus response modulations are common, responses are longer in duration and more likely to be inhibited. To determine whether these differences emerge as rats learned a whisker-dependent discrimination task, we recorded single-unit S1 activity while rats learned to discriminate aperture-widths using their whiskers. Even before discrimination training began, S1 responses in freely moving rats showed many of the signatures of active responses, such as increased duration of response and prestimulus response modulations. As rats subsequently learned the discrimination task, single unit responses changed: more cortical units responded to the stimuli, neuronal sensory responses grew in duration, and individual neurons better predicted aperture-width. In summary, the operant behavioral context changes S1 tactile responses even in the absence of tactile discrimination, whereas subsequent width discrimination learning refines the S1 representation of aperture-width.

An ERP analysis of recognition and categorization decisions in a prototype-distortion task

BACKGROUND

Theories of categorization make different predictions about the underlying processes used to represent categories. Episodic theories suggest that categories are represented in memory by storing previously encountered exemplars in memory. Prototype theories suggest that categories are represented in the form of a prototype independently of memory. A number of studies that show dissociations between categorization and recognition are often cited as evidence for the prototype account. These dissociations have compared recognition judgements made to one set of items to categorization judgements to a different set of items making a clear interpretation difficult. Instead of using different stimuli for different tests this experiment compares the processes by which participants make decisions about category membership in a prototype-distortion task and with recognition decisions about the same set of stimuli by examining the Event Related Potentials (ERPs) associated with them.

METHOD

Sixty-three participants were asked to make categorization or recognition decisions about stimuli that either formed an artificial category or that were category non-members. We examined the ERP components associated with both kinds of decision for pre-exposed and control participants.

CONCLUSION

In contrast to studies using different items we observed no behavioural differences between the two kinds of decision; participants were equally able to distinguish category members from non-members, regardless of whether they were performing a recognition or categorisation judgement. Interestingly, this did not interact with prior-exposure. However, the ERP data demonstrated that the early visual evoked response that discriminated category members from non-members was modulated by which judgement participants performed and whether they had been pre-exposed to category members. We conclude from this that any differences between categorization and recognition reflect differences in the information that participants focus on in the stimuli to make the judgements at test, rather than any differences in encoding or process.

Evidence of metacognitive control by humans and monkeys in a perceptual categorization task

Metacognition research has focused on the degree to which nonhuman primates share humans' capacity to monitor their cognitive processes. Convincing evidence now exists that monkeys can engage in metacognitive monitoring. By contrast, few studies have explored metacognitive control in monkeys, and the available evidence of metacognitive control supports multiple explanations. The current study addresses this situation by exploring the capacity of human participants and rhesus monkeys (Macaca mulatta) to adjust their study behavior in a perceptual categorization task. The study found that humans and monkeys increased their study for high-difficulty categories, suggesting that both share the capacity to exert metacognitive control. (PsycINFO Database Record (c) 2009 APA, all rights reserved).

Network changes in the transition from initial learning to well-practiced visual categorization

Visual categorization is a remarkable ability that allows us to effortlessly identify objects and efficiently respond to our environment. The neural mechanisms of how visual categories become well-established are largely unknown. Studies of initial category learning implicate a network of regions that include inferior temporal cortex (ITC), medial temporal lobe (MTL), basal ganglia (BG), premotor cortex (PMC) and prefrontal cortex (PFC). However, how these regions change with extended learning is poorly characterized. To understand the neural changes in the transition from initially learned to well-practiced categorization, we used functional MRI and compared brain activity and functional connectivity when subjects performed an initially learned categorization task (100 trials of training) and a well-practiced task (4250 trials of training). We demonstrate that a similar network is implicated for initially learned and well-practiced categorization. Additionally, connectivity analyses reveal an increased coordination between ITC, MTL, and PMC when making category judgments during the well-practiced task. These results suggest that category learning involves an increased coordination between a distributed network of regions supporting retrieval and representation of categories.

Dissociable prototype learning systems: evidence from brain imaging and behavior

The neural underpinnings of prototype learning are not well understood. A major source of confusion is that two versions of the prototype learning task have been used interchangeably in the literature; one where participants learn to categorize exemplars derived from two prototypes (A/B task), and one where participants learn to categorize exemplars derived from one prototype and noncategorical exemplars (A/non-A). We report results from an fMRI study of A/B and A/non-A prototype learning that allows for a direct contrast of the two learning methods. Accuracy in the two tasks did not correlate within subject despite equivalent average difficulty. The fMRI results revealed neural activation in a network of regions consistent with episodic memory retrieval for the A/B task while greater activation of a nondeclarative learning network was observed for the A/non-A task. The results demonstrate that learning in these two tasks is mediated by different neural systems and that recruitment of each system is dictated by the context of learning rather than the actual category structure.

Modifying the brain activation of poor readers during sentence comprehension with extended remedial instruction: a longitudinal study of neuroplasticity

This study used fMRI to longitudinally assess the impact of intensive remedial instruction on cortical activation among 5th grade poor readers during a sentence comprehension task. The children were tested at three time points: prior to remediation, after 100 h of intensive instruction, and 1 year after the instruction had ended. Changes in brain activation were also measured among 5th grade good readers at the same time points for comparison. The central finding was that prior to instruction, the poor readers had significantly less activation than good readers bilaterally in the parietal cortex. Immediately after instruction, poor readers made substantial gains in reading ability, and demonstrated significantly increased activation in the left angular gyrus and the left superior parietal lobule. Activation in these regions continued to increase among poor readers 1 year post-remediation, resulting in a normalization of the activation. These results are interpreted as reflecting changes in the processes involved in word-level and sentence-level assembly. Areas of overactivation were also found among poor readers in the medial frontal cortex, possibly indicating a more effortful and attentive guided reading strategy.

Event-related fMRI of category learning: Differences in classification and feedback networks

Eighteen healthy young adults underwent event-related (ER) functional magnetic resonance imaging (fMRI) of the brain while performing a visual category learning task. The specific category learning task required subjects to extract the rules that guide classification of quasi-random patterns of dots into categories. Following each classification choice, visual feedback was presented. The average hemodynamic response was calculated across the eighteen subjects to identify the separate networks associated with both classification and feedback. Random-effects analyses identified the different networks implicated during the classification and feedback phases of each trial. The regions included prefrontal cortex, frontal eye fields, supplementary motor and eye fields, thalamus, caudate, superior and inferior parietal lobules, and areas within visual cortex. The differences between classification and feedback were identified as (i) overall higher volumes and signal intensities during classification as compared to feedback, (ii) involvement of the thalamus and superior parietal regions during the classification phase of each trial, and (iii) differential involvement of the caudate head during feedback. The effects of learning were then evaluated for both classification and feedback. Early in learning, subjects showed increased activation in the hippocampal regions during classification and activation in the heads of the caudate nuclei during the corresponding feedback phases. The findings suggest that early stages of prototype-distortion learning are characterized by networks previously associated with strategies of explicit memory and hypothesis testing. However as learning progresses the networks change. This finding suggests that the cognitive strategies also change during prototype-distortion learning.