Barbara Anne Dosher - US grants

Collaborative Research: Mechanisms of Perceptual Learning

Adult humans can show large improvements in the performance of even the simplest perceptual tasks as the result of training and/or practice. These improvements have been observed in tasks in virtually every sensory modality. This project investigates the brain mechanisms of perceptual learning and the circumstances under which perceptual learning occurs. The findings will help us understand the adaptive nature of human behavior, and may generate new computational and training principles for performance optimization in particular task environments. Our research applies a powerful method for identifying and characterizing the mechanism of perceptual learning in visual tasks. The method adds systematically increasing amounts of external noise - random visual noise (similar to random TV noise) to the visual stimulus and observes the effect on a perceptual task as perceptual learning takes place under different training protocols. Performance in clear and in noisy visual task environments can be modeled quantitatively to identify three mechanisms of perceptual learning. Each mechanism has a "signature". (E.g., modification of the observer's perceptual template through training only affects performance at high levels of external visual noise, where the external noise is large enough to be the limiting factor. Experts can eliminate external noise more efficiently.) We investigate the mechanisms of perceptual learning in a wide variety of perceptual tasks including discrimination and identification or classification of both simple and complex visual patterns. Combining our methods with transfer manipulations that test for critical properties of learning will validate the measurements and provide information about the level of learning. The proposed work will improve our empirical and theoretical understanding of the nature of perceptual learning. Characterization of perceptual learning mechanisms is necessary to a full understanding of the adaptive nature of the adult human brain. Our theory and methods provide a basis for developing adaptive models of the human brain and may contribute to the development of efficient training procedures in applied settings.

DESCRIPTION: Large improvements in performing even the simplest perceptual tasks as a result of practice or training have been observed in adult humans in virtually every sensory modality. Identifying the mechanisms by which the adult brain achieves perceptual learning is an important question that will improve our understanding of neural development and may also allow the creation of adaptive systems that mimic human behavior. The investigators' research applies a powerful method for identifying and characterizing the mechanisms of perceptual learning in visual tasks due to fine-tuning of the perceptual template (external noise exclusion), stimulus enhancement, or internal noise suppression. The method adds systematically increasing amounts of external noise - random visual noise (similar to random TV noise) - to the visual stimulus and observes the effect on a perceptual task as perceptual learning takes place. The effects on task performance of adding external noise of the appropriate character (white Gaussian random noise) can be modeled quantitatively. The three mechanisms of perceptual learning yield three "signature" patterns of performance. (E.g., fine-tuning of the perceptual template only affects performance at high levels of external noise because the external noise is large enough to be the limiting factor in performance and it can be better eliminated by a better-tuned perceptual template.) The investigators develop and apply this method to characterize the mechanisms of perceptual learning in a wide variety of perceptual tasks including discrimination and identification or classification of both simple and complex visual patterns. Combining this method with transfer manipulations for stimulus feature, eye of origin, and scale invariance will provide information about the level of learning. The work improves the empirical and theoretical understanding of the nature of perceptual learning. Characterization of perceptual learning mechanisms is necessary to a full understanding of the adaptive nature of the adult human brain. The investigators' theory and methods provide a basis for developing adaptive models of the human brain, evaluate performance in high noise environments, and may contribute to the development of efficient training procedures in applied settings.

Project Summary/Abstract: Research in perceptual learning has demonstrated a remarkable ability of training or practice to enhance perception in the adult human. The last thirty years have yielded many important findings about how people learn, what limits transfer, how generalization can be improved, how to model learning, and the nature of visual plasticity. At the same time, learning and transfer have been measured at a relatively coarse scale that leads to relatively inaccurate measures of learning in individuals, which could be very important to choosing adapted training options. Related issues of estimation have also limited the types of training protocols that have been studied. The objective of this research is to use innovative new adaptive performance assessment (based on Bayesian principles) to provide unbiased and high precision estimates of learning in individuals. We also use computational neural network models to generate predictions about more complicated training regimens that are then tested experimentally. We develop a framework for searching among these predictions computationally to identify better (optimized) training methods. The long-term goal is to develop efficient new assessments of learning and transfer and the modeling techniques that may then be applied to improve clinical applications, rehabilitation, and perceptual expertise identified as key aspects of the NEI mission.

DESCRIPTION (provided by applicant): Attending to certain visual features or objects may have significant consequences for performance in some circumstances, but is not necessarily important in all tasks. Understanding the role of attention in the performance of visual tasks requires the characterization of the mechanisms of attention and a taxonomy of task conditions under which attention is important. We extend a powerful method and model for identifying and characterizing the effect of attention on perceptual performance. The method combines an external noise approach (adding visual noise similar to random TV noise) with different visual tasks similar to everyday or operator tasks involving complex displays to identify the mechanism(s) of attention in each task and to understand when attention is important to high performance. The approach provides a multi-dimensional characterization of task performance in terms of signal strength, external noise, and target similarity. We use a number of task manipulations to assay the role of attention, including spatial cuing, inhibition of return, visual search and visual short-term memory. The results from these empirical observations will be used to construct and test a taxonomy of visual attention. The goal is to develop an overarching system and theoretical structure to organize all the empirical observations about when and how and why attention can improve human performance. In addition, we evaluate whether and when training can eliminate attention demands. PUBLIC HEALTH RELEVANCE: Understanding when attention is a limiting factor in performance and whether training can eliminate attention demands will have theoretical and practical implications for understanding attention deficits in individuals exhibiting abnormalities of attention associated with mental health conditions. This analysis can additionally constrain process models of attention in normal observers and may suggest constraints on the relevant properties of neurological models of attention.