Mechanisms Of Perceptual Learning Research Articles

Asymmetric Transfer of Auditory Perceptual Learning

Perceptual skills can improve dramatically even with minimal practice. A major and practical benefit of learning, however, is in transferring the improvement on the trained task to untrained tasks or stimuli, yet the mechanisms underlying this process are still poorly understood. Reduction of internal noise has been proposed as a mechanism of perceptual learning, and while we have evidence that frequency discrimination (FD) learning is due to a reduction of internal noise, the source of that noise was not determined. In this study, we examined whether reducing the noise associated with neural phase locking to tones can explain the observed improvement in behavioral thresholds. We compared FD training between two tone durations (15 and 100 ms) that straddled the temporal integration window of auditory nerve fibers upon which computational modeling of phase locking noise was based. Training on short tones resulted in improved FD on probe tests of both the long and short tones. Training on long tones resulted in improvement only on the long tones. Simulations of FD learning, based on the computational model and on signal detection theory, were compared with the behavioral FD data. We found that improved fidelity of phase locking accurately predicted transfer of learning from short to long tones, but also predicted transfer from long to short tones. The observed lack of transfer from long to short tones suggests the involvement of a second mechanism. Training may have increased the temporal integration window which could not transfer because integration time for the short tone is limited by its duration. Current learning models assume complex relationships between neural populations that represent the trained stimuli. In contrast, we propose that training-induced enhancement of the signal-to-noise ratio offers a parsimonious explanation of learning and transfer that easily accounts for asymmetric transfer of learning.

Crossmodal Perceptual Learning and Sensory Substitution

A sensory substitution device for blind persons aims to provide the missing visual input by converting images into a form that another modality can perceive, such as sound. Here I will discuss the perceptual learning and attentional mechanisms necessary for interpreting sounds produced by a device (The vOICe) in a visuospatial manner. Although some aspects of the conversion, such as relating vertical location to pitch, rely on natural crossmodal mappings, the extensive training required suggests that synthetic mappings are required to generalize perceptual learning to new objects and environments, and ultimately to experience visual qualia. Here I will discuss the effects of the conversion and training on perception and attention that demonstrate the synthetic nature of learning the crossmodal mapping. Sensorimotor experience may be required to facilitate learning, develop expertise, and to develop a form of synthetic synaesthesia.

Functions and Mechanisms of Perceptual Learning

Functions and Mechanisms of Perceptual Learning

Neural Mechanisms of Human Perceptual Learning: Electrophysiological Evidence for a Two-Stage Process

BackgroundHumans and other animals change the way they perceive the world due to experience. This process has been labeled as perceptual learning, and implies that adult nervous systems can adaptively modify the way in which they process sensory stimulation. However, the mechanisms by which the brain modifies this capacity have not been sufficiently analyzed.Methodology/Principal FindingsWe studied the neural mechanisms of human perceptual learning by combining electroencephalographic (EEG) recordings of brain activity and the assessment of psychophysical performance during training in a visual search task. All participants improved their perceptual performance as reflected by an increase in sensitivity (d') and a decrease in reaction time. The EEG signal was acquired throughout the entire experiment revealing amplitude increments, specific and unspecific to the trained stimulus, in event-related potential (ERP) components N2pc and P3 respectively. P3 unspecific modification can be related to context or task-based learning, while N2pc may be reflecting a more specific attentional-related boosting of target detection. Moreover, bell and U-shaped profiles of oscillatory brain activity in gamma (30–60 Hz) and alpha (8–14 Hz) frequency bands may suggest the existence of two phases for learning acquisition, which can be understood as distinctive optimization mechanisms in stimulus processing.Conclusions/SignificanceWe conclude that there are reorganizations in several neural processes that contribute differently to perceptual learning in a visual search task. We propose an integrative model of neural activity reorganization, whereby perceptual learning takes place as a two-stage phenomenon including perceptual, attentional and contextual processes.

Learning to discriminate face views

Although visual feature leaning has been well studied, we still know little about the mechanisms of perceptual learning of complex object. Here, human perceptual learning in discrimination of in-depth orientation of face view was studied using psychophysics, EEG and fMRI. We trained subjects to discriminate face orientations around a face view (i.e. 30°) over eight daily sessions, which resulted in a significant improvement in sensitivity to the face view orientation. This improved sensitivity was highly specific to the trained orientation and persisted up to six months. Different from perceptual learning of simple visual features, this orientation-specific learning effect could completely transfer across changes in face size, visual field and face identity. A complete transfer also occurred between two partial face images that were mutually exclusive but constituted a complete face. However, the transfer of the learning effect between upright and inverted faces and between a face and a paperclip object was very weak. Before and after training, we measured EEG and fMRI BOLD signals responding to both the trained and the untrained face views. Analyses of ERPs and induced gamma activity showed that face view discrimination training led to a larger reduction of N170 latency at the left occipital-temporal area and a concurrent larger decrease of induced gamma activity at the left frontal area with the trained face view, compared with the untrained ones. BOLD signal amplitude and MVPA analyses showed that, in face-selective cortical areas, training did not lead to a significant amplitude change, but induced a more reliable spatial pattern of neural activity in the left FFA. These results suggest that the visual system had learned how to compute face orientation from face configural information more accurately and that a large amount of plastic changes took place at a level of higher visual processing where size-, location-, and identity-invariant face views are represented. The learning might be implemented through neuronal tuning curve sharpening and internal noise removing.

Learning to Discriminate Face Views

Although perceptual learning of simple visual features has been studied extensively and intensively for many years, we still know little about the mechanisms of perceptual learning of complex object recognition. In a series of seven experiments, human perceptual learning in discrimination of in-depth orientation of face view was studied using psychophysical methods. We trained subjects to discriminate face orientations around a face view (i.e., 30°) over eight daily sessions, which resulted in a significant improvement in sensitivity to the face view orientation. This improved sensitivity was highly specific to the trained orientation and persisted up to 6 mo. Different from perceptual learning of simple visual features, this orientation-specific learning effect could completely transfer across changes in face size, visual field, and face identity. A complete transfer also occurred between two partial face images that were mutually exclusive but constituted a complete face. However, the transfer of the learning effect between upright and inverted faces and between a face and a paperclip object was very weak. These results shed light on the mechanisms of the perceptual learning of face view discrimination. They suggest that the visual system had learned how to compute face orientation from face configural information more accurately and that a large amount of plastic changes took place at a level of higher visual processing where size-, location-, and identity-invariant face views are represented.

Neural mechanisms of multisensory perceptual learning

Neural mechanisms of multisensory perceptual learning

Perceptual learning in human primary visual cortex

Repeated practice of perceptual tasks often produces marked improvements in behavioral performance. While such perceptual learning is found across a variety of visual tasks, physiological evidence of corresponding neural changes in humans has remained elusive. We hypothesized that practice of a task thought to be mediated by early visual mechanisms (i.e., simple contrast-detection) would produce measurable changes in human primary visual cortex (V1). Here were used fMRI to measure the effects of perceptual learning on activity in V1. Subjects spent more than twenty days (>10,000 trials) practicing detection of sinusoidal gratings. In order to quantify the specificity of learning, fMRI measurements and behavioral thresholds for contrast-detection and orientation-discrimination were collected before and after training for several eccentricities and grating orientations. We found that V1 responses for practiced gratings were reliably greater than those responses collected prior to training. Critically, the specificity of changes in V1 responses mirrored the specificity of changes in perceptual thresholds; both the changes in detection thresholds and V1 responses were orientation, and spatial-location specific. While these results are consistent with several models of the neural mechanisms of perceptual learning, the simplest explanation of our data may be a bottom-up gain change produced by a re-weighting of synaptic connections.

ERP evidence for distinct mechanisms of fast and slow visual perceptual learning

Perceptual learning (PL) occurs not only within the first training session but also between sessions. Once acquired, the learning effects can last for a long time. By examining the time course of learning-associated ERP changes, this study explores whether fast and slow visual PL contribute to long-term preservation. Subjects first participated in a visual task for three training sessions, and were then given one test session six months later. ERP results showed that fast learning effects, as reflected by the decrement of posterior N1 and increment of posterior P2 within session 1, were preserved in session 3 but not in the test session. However, slow learning effects, as reflected by the increment of posterior N1 and decrement of frontal P170 between sessions 1 and 3, were retained completely in the test session. This study indicates that PL induces different changes in the human adult brain during and after active training, and only the delayed changes of brain activity can be preserved for a long period of six months.

Perceptual learning

Perceptual learning

What do we rely on: our hands or our eyes? - Perceptual and motor factors of implicit skill learning

Event Abstract Back to Event What do we rely on: our hands or our eyes? - Perceptual and motor factors of implicit skill learning Emese Hallgato1, Karolina Janacsek1, Tímea Sándor1, Zsuzsa Londe2 and Dezso Németh1* 1 University of Szeged, Institute of Psychology, Hungary 2 University of Southern California, American Language Institute, United States Implicit skill learning is crucial for adapting to the environment and to evaluate events; therefore represents an important aspect of life from infancy to old age. Implicit sequence learning has been investigated for decades, but there is no agreement about how this kind of learning relies on motor and perceptual factors, and what their proportions are. The goals of the present study were to determine whether motor or perceptual cues provide better prompts to sequence learning and to remove the possibility of oculomotor learning during the task. We used a modified version of the probabilistic ASRT (Alternating Serial Reaction Time) task, the AS-RT Race, which allowed the separation of motor and perceptual factors. Our results demonstrated that motor and perceptual factors influenced skill learning to a similar extent. Using the AS-RT Race task, more detailed studies can be conducted in cognitive neuroscience for various pathologies affecting implicit skill learning and the underlying mechanisms of motor and perceptual learning. Conference: IBRO International Workshop 2010, Pécs, Hungary, 21 Jan - 23 Jan, 2010. Presentation Type: Poster Presentation Topic: Sensory and motor systems Citation: Hallgato E, Janacsek K, Sándor T, Londe Z and Németh D (2010). What do we rely on: our hands or our eyes? - Perceptual and motor factors of implicit skill learning. Front. Neurosci. Conference Abstract: IBRO International Workshop 2010. doi: 10.3389/conf.fnins.2010.10.00252 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 05 May 2010; Published Online: 05 May 2010. * Correspondence: Dezso Németh, University of Szeged, Institute of Psychology, Szeged, Hungary, nemethd@gmail.com Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Emese Hallgato Karolina Janacsek Tímea Sándor Zsuzsa Londe Dezso Németh Google Emese Hallgato Karolina Janacsek Tímea Sándor Zsuzsa Londe Dezso Németh Google Scholar Emese Hallgato Karolina Janacsek Tímea Sándor Zsuzsa Londe Dezso Németh PubMed Emese Hallgato Karolina Janacsek Tímea Sándor Zsuzsa Londe Dezso Németh Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Models for the mechanisms of perceptual learning: linking predictions for brain and behavior

Event Abstract Back to Event Models for the mechanisms of perceptual learning: linking predictions for brain and behavior Michael Wenger1* and Von Der H. Rebecca2 1 The Pennsylvania State University , Department of Psychology, United States 2 The Pennsylvania State University , United States Standard indicators of the acquisition of visual perceptual expertise include systematic reductions in detection and identification thresholds, along with decreases in mean response times (RTs). Two additional patterns have emerged in recent studies of perceptual learning for gray-scale contrast: systematic increases in false alarm rates in detection (but not identification), and systematic increases in the ability to adapt to variations in perceptual workload (capacity, as measured at the level of the hazard function of the RT distribution). The present effort is an initial step in developing a modeling approach capable of accounting for these behavioral results—with a specific focus on changes in capacity in the present effort—while simultaneously predicting patterns of scalp-level EEG. The approach is intended to allow for the representation of multiple competing hypotheses for the neural mechanisms responsible for these observable variables (i.e., placing the alternative hypotheses on a "level playing field"), and for the ability to systematically relate these hypotheses to formal models for perceptual behavior. The neural modeling approach uses populations of discrete-time integrate-and-fire neurons, connected as networks. The architecture is based on the known circuitry of early visual areas as well as known connectivity into and out of early visual areas. The architecture is shown to be capable of instantiating a set of prominent competing hypotheses for neural mechanisms (Gilbert, Sigman, & Crist, 2001): changes in cortical recruitment, sharpening of feature-specific tuning curves, changes in synaptic weightings, changes in within-region synchrony, and changes in across-region coherence, in both feed-forward and feed-back relations. In addition, it is shown that under reasonable simplifying assumptions, the models are also capable of making predictions for both observable response behavior and scalp-level EEG. Analysis of the computational models for the set of contrasting hypotheses reveals that (a) although all of the hypotheses are capable of accounting for some of the standard empirical regularities, at least one (the cortical recruitment hypothesis) is shown to be unable to account for all those empirical regularities, ruling it out as a general explanation for the neural mechanisms; and (b) many of the standard measures of EEG (e.g., peak values of early negative and positive components) are unable to distinguish among the competing hypotheses; and (c) measures of synchrony across and within regions do offer potential for testing among competing hypotheses. Conference: Computational and Systems Neuroscience 2010, Salt Lake City, UT, United States, 25 Feb - 2 Mar, 2010. Presentation Type: Poster Presentation Topic: Poster session I Citation: Wenger M and Rebecca VH (2010). Models for the mechanisms of perceptual learning: linking predictions for brain and behavior. Front. Neurosci. Conference Abstract: Computational and Systems Neuroscience 2010. doi: 10.3389/conf.fnins.2010.03.00157 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 02 Mar 2010; Published Online: 02 Mar 2010. * Correspondence: Michael Wenger, The Pennsylvania State University, Department of Psychology, University Park, PA, United States, mjw19@psu.edu Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Michael Wenger Von Der H Rebecca Google Michael Wenger Von Der H Rebecca Google Scholar Michael Wenger Von Der H Rebecca PubMed Michael Wenger Von Der H Rebecca Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Task difficulty modulates electrophysiological correlates of perceptual learning

To investigate the influence of task difficulty on the neural mechanisms of visual perceptual learning, we recorded event-related potentials (ERPs) from human adults while they were trained with either an easy or a difficult orientation discrimination task. The discrimination thresholds of participants trained with the difficult task decreased after training, but thresholds of participants with the easy task did not. More importantly, although posterior N1 reduction and anterior P2 decrement were obtained in both conditions, only the difficult task training showed an enhancement in posterior P1, an increase in N2 and P3, and a broader scalp distribution of posterior N1 attenuation. These differences indicated that the difficult task training affected the early visual processing stage, the later ERP components, and the broader visual cortical regions, respectively. The results in the present study provide direct electrophysiological evidence for task difficulty modulation of perceptual learning-related neuroplasticity.

Perceptual learning of cochlear implant simulated speech: Cognitive findings and clinical implications.

Adaptation to the acoustic world after cochlear implantation requires significant adjustment to degraded auditory signals. The cognitive mechanisms underlying the mapping of such signals onto preexisting internal representations (postlingually deafened individuals), or the formation of novel internal representations (prelingually deafened individuals), are not well understood. Therefore, understanding the mechanisms of perceptual learning is critical to providing efficient training and (re)habilitation for new cochlear implant users. The advent of noise and sinewave vocoders to model the output of a cochlear implant speech processor has increased the tools available to investigate perceptual learning of speech in normal-hearing listeners. A fundamental question is whether training should focus exclusively on speech perception (synthetic approach), or whether training on extralinguistic or nonspeech auditory information (analytic approach) promotes more robust perceptual learning and generalization to novel materials and signals. In this talk, I will present data from two recent studies on the perceptual learning of sinewave-vocoded speech by normal-hearing participants that focused on different levels of linguistic/extralinguistic and nonspeech acoustic information. I will discuss the implications these data have for understanding perceptual learning and the cognitive mechanisms that mediate speech perception.

Modeling mechanisms of perceptual learning with augmented Hebbian re-weighting

Using the external noise plus training paradigm, we have consistently found that two independent mechanisms, stimulus enhancement and external noise exclusion, support perceptual learning in a range of tasks. Here, we show that re-weighting of stable early sensory representations through Hebbian learning ( Petrov et al., 2005, 2006) can generate performance patterns that parallel a large range of empirical data: (1) perceptual learning reduced contrast thresholds at all levels of external noise in peripheral orientation identification ( Dosher & Lu, 1998, 1999), (2) training with low noise exemplars transferred to performance in high noise, while training with exemplars embedded in high external noise transferred little to performance in low noise ( Dosher & Lu, 2005), and (3) pre-training in high external noise only reduced subsequent learning in high external noise, whereas pre-training in zero external noise left very little additional learning in all the external noise conditions ( Lu et al., 2006). In the augmented Hebbian re-weighting model (AHRM), perceptual learning strengthens or maintains the connections between the most closely tuned visual channels and a learned categorization structure, while it prunes or reduces inputs from task-irrelevant channels. Reducing the weights on irrelevant channels reduces the contributions of external noise and additive internal noise. Manifestation of stimulus enhancement or external noise exclusion depends on the initial state of internal noise and connection weights in the beginning of a learning task. Both mechanisms reflect re-weighting of stable early sensory representations.

Shared mechanisms of perceptual learning and decision making.

Perceptual decisions require the brain to weigh noisy evidence from sensory neurons to form categorical judgments that guide behavior. Here we review behavioral and neurophysiological findings suggesting that at least some forms of perceptual learning do not appear to affect the response properties of neurons that represent the sensory evidence. Instead, improved perceptual performance results from changes in how the sensory evidence is selected and weighed to form the decision. We discuss the implications of this idea for possible sites and mechanisms of training-induced improvements in perceptual processing in the brain.

Perceptual learning and adult cortical plasticity

The visual cortex retains the capacity for experience-dependent changes, or plasticity, of cortical function and cortical circuitry, throughout life. These changes constitute the mechanism of perceptual learning in normal visual experience and in recovery of function after CNS damage. Such plasticity can be seen at multiple stages in the visual pathway, including primary visual cortex. The manifestation of the functional changes associated with perceptual learning involve both long term modification of cortical circuits during the course of learning, and short term dynamics in the functional properties of cortical neurons. These dynamics are subject to top-down influences of attention, expectation and perceptual task. As a consequence, each cortical area is an adaptive processor, altering its function in accordance to immediate perceptual demands.

MECHANISMS OF PERCEPTUAL LEARNING.

What is learned in perceptual learning? How does perceptual learning change the perceptual system? We investigate these questions using a systems analysis of the perceptual system during the course of perceptual learning using psychophysical methods and models of the observer. Effects of perceptual learning on an observer's performance are characterized by external noise tests within the framework of noisy observer models. We find evidence that two independent mechanisms, external noise exclusion and stimulus enhancement support perceptual learning across a range of tasks. We suggest that both mechanisms may reflect re-weighting of stable early sensory representations.

Perceptual learning and acquired face familiarity: Evidence from inversion, use of internal features, and generalization between viewpoints

Pairs of similar faces were created from photographs of different people using morphing software. The ability of participants to discriminate between novel pairs of faces and between those to which they had received brief, unsupervised, exposure (5×2 s each) was assessed. In all experiments exposure improved discrimination performance. Overall, discrimination was better when the faces were upright, but exposure produced improved discrimination for both upright and inverted faces (Experiment 1). The improvement produced by exposure was selective to internal face features (Experiment 2) and was evident when there was a change in orientation (three-quarter to full face or vice versa) between exposure and test (Experiment 3). These findings indicate that perceptual learning observed following brief exposure to faces exhibit well-established hallmarks of familiar face processing (i.e., internal feature advantage and insensitivity to a change of viewpoint). Considered in combination with previous studies using the same type of stimuli (Mundy, Honey, & Dwyer, 2007), the current results imply that general perceptual learning mechanisms contribute to the acquisition of face familiarity.

Statistical frequency in perception affects children’s lexical production

Children’s early word production is influenced by the statistical frequency of speech sounds and combinations. Three experiments asked whether this production effect can be explained by a perceptual learning mechanism that is sensitive to word-token frequency and/or variability. Four-year-olds were exposed to nonwords that were either frequent (presented 10 times) or infrequent (presented once). When the frequent nonwords were spoken by the same talker, children showed no significant effect of perceptual frequency on production. When the frequent nonwords were spoken by different talkers, children produced them with fewer errors and shorter latencies. The results implicate token variability in perceptual learning.