Acquisition Of Perceptual Expertise Research Articles

The Impact of Implicit and Explicit Training Methods on the Acquisition of Perceptual Expertise in Young Athletes

This study examined the effectiveness of implicit and explicit learning methods on the acquisition and retention of the decision-making skill in low and high complexity situations. 60 novice students were divided into explicit, implicit, and control group. Experimental groups followed 12 training courses. A pre-test, a post-test, and a retention test were used to evaluate the effectiveness of the instructional methods. All participants were evaluated in decision-making speed and accuracy in laboratory tests under simulated conditions. A three-way factorial ANOVA was conducted (3 Group X 2 Complexity X 3 Measurement periods) with repeated measurements on the last factor for the accuracy and reaction time. The analysis showed a significant improvement in decision-making accuracy, in low complexity for both experimental groups. In high complexity situations, the explicit method improved over time and was better than the implicit for decision accuracy. No differences were found among groups or measurements for the decision speed in either low or high complexity situations. It seems that in complex sport conditions, the use of explicit learning helps novices to develop decision-making accuracy more than the implicit learning method, since guided discovery may guide the participants to process contextual information from the sports environment more effectively.

Changes in Visual Scanning Strategies Accompany the Acquisition of Perceptual Expertise

Changes in Visual Scanning Strategies Accompany the Acquisition of Perceptual Expertise

How We Learn to Make Decisions: Rapid Propagation of Reinforcement Learning Prediction Errors in Humans

Our ability to make decisions is predicated upon our knowledge of the outcomes of the actions available to us. Reinforcement learning theory posits that actions followed by a reward or punishment acquire value through the computation of prediction errors-discrepancies between the predicted and the actual reward. A multitude of neuroimaging studies have demonstrated that rewards and punishments evoke neural responses that appear to reflect reinforcement learning prediction errors [e.g., Krigolson, O. E., Pierce, L. J., Holroyd, C. B., & Tanaka, J. W. Learning to become an expert: Reinforcement learning and the acquisition of perceptual expertise. Journal of Cognitive Neuroscience, 21, 1833-1840, 2009; Bayer, H. M., & Glimcher, P. W. Midbrain dopamine neurons encode a quantitative reward prediction error signal. Neuron, 47, 129-141, 2005; O'Doherty, J. P. Reward representations and reward-related learning in the human brain: Insights from neuroimaging. Current Opinion in Neurobiology, 14, 769-776, 2004; Holroyd, C. B., & Coles, M. G. H. The neural basis of human error processing: Reinforcement learning, dopamine, and the error-related negativity. Psychological Review, 109, 679-709, 2002]. Here, we used the brain ERP technique to demonstrate that not only do rewards elicit a neural response akin to a prediction error but also that this signal rapidly diminished and propagated to the time of choice presentation with learning. Specifically, in a simple, learnable gambling task, we show that novel rewards elicited a feedback error-related negativity that rapidly decreased in amplitude with learning. Furthermore, we demonstrate the existence of a reward positivity at choice presentation, a previously unreported ERP component that has a similar timing and topography as the feedback error-related negativity that increased in amplitude with learning. The pattern of results we observed mirrored the output of a computational model that we implemented to compute reward prediction errors and the changes in amplitude of these prediction errors at the time of choice presentation and reward delivery. Our results provide further support that the computations that underlie human learning and decision-making follow reinforcement learning principles.

Detecting anomalous features in complex stimuli: The role of structured comparison.

The ability to detect anomalies in perceived stimuli is critical to a broad range of cognitive tasks, yet acquiring this ability often requires lengthy practice. In this research, we asked whether findings from research on analogical comparison can be used to aid in the acquisition of perceptual expertise. Building on findings that comparison can facilitate the detection of differences, the present research addressed two questions: (1) Does having an alignable comparison standard improve performance on a difficult detection task? (2) Can such comparison experience improve subsequent detection performance on single anomalous targets? Across 3 experiments, university undergraduates were asked to find an anomalous bone in drawings of animal skeletons. Target items including an anomaly were presented either alone or with a correct standard. Furthermore, to evaluate the impact of ease of alignment, the correct standard was presented either mirror-reversed (low alignable) or regular (high alignable). Results showed increased accuracy when a comparison standard was present and further gains when the standard was more easily alignable. In Experiment 3, we used a between-subjects design to reveal that advance comparison (as opposed to single-item training) led to improved detection of anomalies in subsequent novel examples presented as isolated targets. We conclude that the availability of a standard and ease of alignment promote encoding and processing. Furthermore, comparison-based learning confers an ongoing advantage even without standards for comparison. Therefore, task performance in application areas requiring detection of nonobvious anomalies can be improved by providing alignable standards next to targets or in advance training.

Learning to become an expert: reinforcement learning and the acquisition of perceptual expertise

Acquisition of perceptual skills is of vital importance as it enables identification of objects in the environment. With training and experience, it is possible to get specialized at recognizing a particular category of objects. The research on perceptual expertise has mostly contrasted performances and brain areas between experts and novices. The differences in perceptual capabilities between the experts and the novices rely on learning of implicit rules for categorization of objects1 and 2 maintaining the domain-specific information in the working memory. There is evidence that acquisition of expertise is associated with activations in ventral visual stream, especially in the fusiform gyrus, the lateral occipital cortex and inferior temporal cortex. doi : 10.5214/ans.0972.7531.1118307

Learning to become an expert: reinforcement learning and the acquisition of perceptual expertise: Journal of Cognitive Neuroscience 2009; 21(9): 1834-1841.

Learning to become an expert: reinforcement learning and the acquisition of perceptual expertise: Journal of Cognitive Neuroscience 2009; 21(9): 1834-1841.

Perceptual Expertise as a Shift From Strategic Interference to Automatic Holistic Processing

Holistic processing was initially characterized as a unique hallmark of face perception but later was argued to be a marker of general perceptual expertise. More recently, evidence for holistic processing—measured by interference from task-irrelevant parts—was obtained in novices, raising questions for its usefulness as a test of expertise. Indeed, recent studies use the same task to make opposite claims: One group of researchers found more interference in novices than experts for Chinese characters, while another found more interference in experts than novices with objects. Offering a resolution to this paradox, our work on the perception of musical notation suggests that expert and novice interference effects represent two ends of a continuum: Interference is initially strategic and contextual but becomes more automatic as holistic processing develops with the acquisition of perceptual expertise.

Reinforcement learning and the acquisition of perceptual expertise in ERPs

In a category learning task, people are initially unaware when they have committed an error and therefore, require corrective feedback to modify their category decisions. Once the categories are learned, however, external feedback is no longer necessary. Electrophysiologically, the two phases of category learning are indicated by different event-related brain potentials (ERPs): the feedback ERN that is elicited following the presentation of negative feedback and the response ERN that is generated following an incorrect response. In a study of perceptual categorization, participants were asked to discriminate between very similar families of novel geometric shapes (blobs). Participants who learned the perceptual categories (i.e., expert learners) demonstrated a shift from a feedback ERN to the response ERN. The expert learners also showed an enhanced N250 response to the blob families — a component that is thought to index subordinate level representations. For the experts, the buildup of the N250 component was correlated with the shift in the ERN. In contrast, participants who were unable to learn the object families (i.e., novice learners ) failed to show a shift in their feedback-to-response ERN nor did they show an increased N250. Collectively, these results suggest that accompanying the acquisition of the subordinate categories, there is a change from an external source of error monitoring to an internal source.

A Reevaluation of the Electrophysiological Correlates of Expert Object Processing

Subordinate-level object processing is regarded as a hallmark of perceptual expertise. However, the relative contribution of subordinate- and basic-level category experience in the acquisition of perceptual expertise has not been clearly delineated. In this study, participants learned to classify wading birds and owls at either the basic (e.g., wading bird, owl) or the subordinate (e.g., egret, snowy owl) level. After 6 days of training, behavioral results showed that subordinate-level but not basic-level training improved subordinate discrimination of trained exemplars, novel exemplars, and exemplars from novel species. Event-related potentials indicated that both basic- and subordinate-level training enhanced the early N170 component, but only subordinate-level training amplified the later N250 component. These results are consistent with models positing separate basic and subordinate learning mechanisms, and, contrary to perspectives attempting to explain visual expertise solely in terms of subordinate-level processing, suggest that expertise enhances neural responses of both basic and subordinate processing.

Feature learning during the acquisition of perceptual expertise

Does feature evolution stop once we have acquired sufficient features to perform a recognition task? With extended practice, novices may develop a more sophisticated feature space that allows them to perform more accurately or quickly. Our work on perceptual expertise indicates that feature learning and reorganization can continue even after an initial set of features is available to represent a novel class of objects.