Specificity Of Perceptual Learning Research Articles

Optimality predicts transition to specificity in perceptual learning

In any given task, there are at least two broad classes of strategies that can be utilized to select actions. Model-based strategies simulate a model to select actions that maximize expected performance, while model-free policy-based strategies learn mappings between stimuli and response options specific to training examples (i.e no generalization). A natural trade-off exists between these strategies, wherein model-based out-perform model-free under conditions of stimulus variability and limited training, while abundant training on few stimuli favor model-free strategies. These ideas predict when specificity should occur – in experimental conditions in which policies outperform predictive models. We tested this prediction using a motion extrapolation task that supports both model- and policy-based strategies. We predicted participants would transition to policy-based strategies and exhibit specificity in learning only under conditions where policy-based strategies were more reliable. We quantified these conditions (e.g. stimulus set sizes and training durations) by simulating performance of a model-based Kalman filter and policy-based Q-learning algorithm. For 200 training trials, 4 paths (4P) predicts transition, while 20 paths (20P) does not. The task required selecting the reemergence location (one of 20 bins) of a dot that traveled along a circular arc of variable curvature and orientation before disappearing behind an occluder. Separate groups were trained in the 4P and 20P conditions. We monitored observers' extrapolation strategies through no-feedback pre-test, post-test, and interleaved test sessions among training sessions. While both groups performed similarly during pre-test, relying on default models, transition to a policy-based strategy occurred only in the 4P condition, resulting in improved performance on trained stimuli, but decreased performance on novel stimuli. The results comprise the first quantitative prediction and successful test of when specificity in learning will occur, and show that specificity should result whenever response policies are the most reliable way to satisfy task demands.

The effects of naps on the magnitude and specificity of perceptual learning of motion direction discrimination

The effects of naps on the magnitude and specificity of perceptual learning of motion direction discrimination

Cholinergic enhancement augments the magnitude and specificity of perceptual learning in the human visual system: a pharmacological fMRI study

Cholinergic enhancement augments the magnitude and specificity of perceptual learning in the human visual system: a pharmacological fMRI study

Location specificity in perceptual learning: A revisit

Location specificity in perceptual learning: A revisit

Location specificity of perceptual learning of depth discrimination in random-dot stereograms

Location specificity of perceptual learning of depth discrimination in random-dot stereograms

Evaluation of token specificity in perceptual learning of phase discrimination

Evaluation of token specificity in perceptual learning of phase discrimination

Specificity of perceptual learning for difficult tasks during simultaneous training

Specificity of perceptual learning for difficult tasks during simultaneous training

Hebbian reweighting on stable representations in perceptual learning

Perceptual learning is the improvement in perceptual task performance with practice or training. The observation of specificity in perceptual learning has been widely associated with plasticity in early visual cortex representations. Here, we review the evidence supporting the plastic reweighting of readout from stable sensory representations, originally proposed by Dosher & Lu (1998), as an alternative explanation of perceptual learning. A task-analysis that identifies circumstances in which specificity supports representation enhancement and those in which it implies reweighting provides a framework for evaluating the literature; reweighting is broadly consistent with the behavioral results and almost all of the physiological reports. We also consider the evidence that the primary mode of perceptual learning is through augmented Hebbian learning of the reweighted associations, which has implications for the role and importance of feedback. Feedback is not necessary for perceptual learning, but can improve it in some circumstances, and in some cases block feedback is also helpful - all effects that are generally compatible with an augmented Hebbian model (Petrov, Dosher, & Lu, 2005). The two principles of perceptual learning through reweighting evidence from stable sensory representations and of augmented Hebbian learning provide a theoretical structure for the consideration of issues such as task difficulty, task roving, and cuing in perceptual learning.

The specificity of perceptual learning in speech processing

We conducted four experiments to investigate the specificity of perceptual adjustments made to unusual speech sounds. Dutch listeners heard a female talker produce an ambiguous fricative [?] (between [f] and [s]) in [f]- or [s]-biased lexical contexts. Listeners with [f]-biased exposure (e.g., [witlo?]; from witlof, "chicory"; witlos is meaningless) subsequently categorized more sounds on an [epsilonf]-[epsilons] continuum as [f] than did listeners with [s]-biased exposure. This occurred when the continuum was based on the exposure talker's speech (Experiment 1), and when the same test fricatives appeared after vowels spoken by novel female and male talkers (Experiments 1 and 2). When the continuum was made entirely from a novel talker's speech, there was no exposure effect (Experiment 3) unless fricatives from that talker had been spliced into the exposure talker's speech during exposure (Experiment 4). We conclude that perceptual learning about idiosyncratic speech is applied at a segmental level and is, under these exposure conditions, talker specific.

Specificity of perceptual learning in a frequency discrimination task.

On a variety of visual tasks, improvement in perceptual discrimination with practice (perceptual learning) has been found to be specific to features of the training stimulus, including retinal location. This specificity has been interpreted as evidence that the learning reflects changes in neuronal tuning at relatively early processing stages. The aim of the present study was to examine the frequency specificity of human auditory perceptual learning in a frequency discrimination task. Difference limens for frequency (DLFs) were determined at 5 and 8 kHz, using a three-alternative forced choice method, for two groups of eight subjects before and after extensive training at one or the other frequency. Both groups showed substantial improvement at the training frequency, and much of this improvement generalized to the nontrained frequency. However, a small but statistically significant component of the improvement was specific to the training frequency. Whether this specificity reflects changes in neural frequency tuning or attentional changes remains unclear.

Mechanisms of generalization in perceptual learning

Learning in many visual perceptual tasks has been shown to be specific to practiced stimuli, while new stimuli have to be learned from scratch. Here we demonstrate generalization using a novel paradigm in motion discrimination where learning has been previously shown to be specific. We trained subjects to discriminate directions of moving dots, and verified the previous results that learning does not transfer from a trained direction to a new one. However, by tracking the subjects’ performance across time in the new direction, we found that their speed of learning doubled. Therefore, we found generalization in a task previously considered too difficult to generalize. We also replicated, in a second experiment, transfer following training with ‘easy’ stimuli, when the difference between motion directions is enlarged. In a third experiment we found a new mode of generalization: after mastering the task with an easy stimulus, subjects who have practiced briefly to discriminate the easy stimulus in a new direction generalize to a difficult stimulus in that direction. This generalization depends on both the mastering and the brief practice. The specificity of perceptual learning and the dichotomy between learning of ‘easy’ versus ‘difficult’ tasks have been assumed to involve different learning processes at different cortical areas. Here we show how to interpret these results in terms of signal detection theory. With the assumption of limited computational capacity, we obtain the observed phenomena — direct transfer and acceleration of learning — for increasing levels of task difficulty. Human perceptual learning and generalization, therefore, concur with a generic discrimination system.

Simultaneous learning of motion discrimination in two directions

We take issue with theories about the direction specificity in perceptual learning of motion discrimination. Trials of motion discrimination in two opposite directions were interleaved in uneven proportions (2:1). Human subjects improved faster in the direction with less frequent trials, indicating that learning transferred from the more frequent to the less frequent direction.

Task difficulty and the specificity of perceptual learning.

Practising simple visual tasks leads to a dramatic improvement in performing them. This learning is specific to the stimuli used for training. We show here that the degree of specificity depends on the difficulty of the training conditions. We find that the pattern of specificities maps onto the pattern of receptive field selectivities along the visual pathway. With easy conditions, learning generalizes across orientation and retinal position, matching the spatial generalization of higher visual areas. As task difficulty increases, learning becomes more specific with respect to both orientation and position, matching the fine spatial retinotopy exhibited by lower areas. Consequently, we enjoy the benefits of learning generalization when possible, and of fine grain but specific training when necessary. The dynamics of learning show a corresponding feature. Improvement begins with easy cases (when the subject is allowed long processing times) and only subsequently proceeds to harder cases. This learning cascade implies that easy conditions guide the learning of hard ones. Taken together, the specificity and dynamics suggest that learning proceeds as a countercurrent along the cortical hierarchy. Improvement begins at higher generalizing levels, which, in turn, direct harder-condition learning to the subdomain of their lower-level inputs. As predicted by this reverse hierarchy model, learning can be effective using only difficult trials, but on condition that learning onset has previously been enabled. A single prolonged presentation suffices to initiate learning. We call this single-encounter enabling effect 'eureka'.