Summary The study of human learning is complicated by the myriad of processing elements involved in conducting any behavioural task. In the case of visual perceptual learning there has been significant controversy regarding the task-processes that guide the formation of this learning, however, there is a developing consensus that top-down, task-related factors, are required for such learning to take place. Here we challenge this idea by use of a novel procedure in which human participants, who were deprived of food and water, passively viewed visual stimuli while receiving occasional drops of water as rewards. Visual orientation stimuli, which were temporally paired with the liquid-rewards, were viewed monocularly and rendered imperceptible by continuously flashing contour rich patterns to the other eye. Results show that visual learning can be formed in human adults through stimulus-reward pairing in the absence of a task and without awareness of the stimulus presentation or reward contingencies.

Visual perceptual learning (VPL) is defined as visual performance improvement after visual experiences. VPL is often highly specific for a visual feature presented during training. Such specificity is observed in behavioral tuning function changes with the highest improvement centered on the trained feature and was originally thought to be evidence for changes in the early visual system associated with VPL. However, results of neurophysiological studies have been highly controversial concerning whether the plasticity underlying VPL occurs within the visual cortex. The controversy may be partially due to the lack of observation of neural tuning function changes in multiple visual areas in association with VPL. Here using human subjects we systematically compared behavioral tuning function changes after global motion detection training with decoded tuning function changes for 8 visual areas using pattern classification analysis on functional magnetic resonance imaging (fMRI) signals. We found that the behavioral tuning function changes were extremely highly correlated to decoded tuning function changes only in V3A, which is known to be highly responsive to global motion with human subjects. We conclude that VPL of a global motion detection task involves plasticity in a specific visual cortical area.

Visual perceptual learning (VPL) can occur as a result of a repetitive stimulus-reward pairing in the absence of any task. This suggests that rules that guide Conditioning, such as stimulus-reward contingency (e.g. that stimulus predicts the likelihood of reward), may also guide the formation of VPL. To address this question, we trained subjects with an operant conditioning task in which there were contingencies between the response to one of three orientations and the presence of reward. Results showed that VPL only occurred for positive contingencies, but not for neutral or negative contingencies. These results suggest that the formation of VPL is influenced by similar rules that guide the process of Conditioning.

Our state of arousal fluctuates from moment to moment-fluctuations that can have profound impacts on behavior. Arousal has been proposed to play a powerful, widespread role in the brain, influencing processes as far ranging as perception, memory, learning, and decision making. Although arousal clearly plays a critical role in modulating behavior, the mechanisms underlying this modulation remain poorly understood. To address this knowledge gap, we examined the modulatory role of arousal on one of the cornerstones of visual perception: contrast perception. Using a reward-driven paradigm to manipulate arousal state, we discovered that elevated arousal state substantially enhances visual sensitivity, incurring a multiplicative modulation of contrast response. Contrast defines vision, determining whether objects appear visible or invisible to us, and these results indicate that one of the consequences of decreased arousal state is an impaired ability to visually process our environment.

Although it is well known that reward enhances learning and memory, how extensively such enhancement occurs remains unclear. To address this question, we examined how reward influences retrieval-induced forgetting (RIF) in which the retrieval of a nonpracticed item under the same category as a practiced item is worse than the retrieval of a nonpracticed item outside the category. Subjects were asked to try to encode category-exemplar pairs (e.g., FISH-salmon). Then, they were presented with a category name and a two-letter word stem (e.g., FISH-sa) and were asked to complete an encoded word (retrieval practice). For a correct response, apple juice was given as a reward in the reward condition and a beeping sound was presented in the no-reward condition. Finally, subjects were asked to report whether each exemplar had been presented in the first phase. RIF was replicated in the no-reward condition. However, in the reward condition, RIF was eliminated. These results suggest that reward enhances processing of retrieval of unpracticed members by mechanisms such as spreading activation within the same category, irrespective of whether items were practiced or not.reward | retrieval-induced forgetting | learning | memory A lthough it has been found that reward significantly enhances learning and memory (1-8), how extensively reward influences learning and memory processing has yet to be explored. It has been recently found that a subthreshold stimulus which was merely exposed in a visual field was learned if paired with reward, but was not if paired with no reward (5). These results suggest that in contrast to the role of attention which is to enhance only processing relevant to a given task, reward enhances learning of a presented item, irrespective of whether the item is task-relevant or not. An important question is how extensively reward influences learning and memory. To address this question, we examined how reward influences retrieval-induced forgetting (RIF) in which the retrieval of a nonpracticed item under the same category as a practiced item is worse than the retrieval of a nonpracticed item beyond the category (9). If reward completely unselectively enhances memory of any item, then it should enhance the retrieval of unpracticed items both within and beyond the category to which the practiced items belong. If reward selectively enhances only practiced items, it should not influence the retrieval of a nonpracticed item irrespective of whether it is within or beyond the category. In both cases, RIF should be observed because reward should not differentially influence the retrieval of nonpracticed items within and beyond the category. In contrast, if reward enhances items only within the category, irrespective of whether it was practiced or not, the degree of RIF should be significantly reduced or abolished.In the present study, when no reward was given on each trial of the practice session, RIF was observed. However, when reward was given, there was no significant performance difference between recalls of n...

In this review, we explore how reward signals shape perceptual learning in animals and humans. Perceptual learning is the well-established phenomenon by which extensive practice elicits selective improvement in one’s perceptual discrimination of basic visual features, such as oriented lines or moving stimuli. While perceptual learning has long been thought to rely on ‘top-down’ processes, such as attention and decision-making, a wave of recent findings suggests that these higher-level processes are, in fact, not necessary.  Rather, these recent findings indicate that reward signals alone, in the absence of the contribution of higher-level cognitive processes, are sufficient to drive the benefits of perceptual learning. Here, we will review the literature tying reward signals to perceptual learning. Based on these findings, we propose dual underlying mechanisms that give rise to perceptual learning: one mechanism that operates ‘automatically’ and is tied directly to reward signals, and another mechanism that involves more ‘top-down’, goal-directed computations.

1 2 Visual perceptual learning (VPL) is defined as a long-term performance enhancement as 3 a result of visual experiences. A number of studies have demonstrated that reward can evoke 4 VPL. However, the mechanisms of how reward evoke VPL remain unknown. One possible 5 hypothesis is that VPL is obtained through reward related reinforcement processing. If this 6 hypothesis is true, learning can only occur when reward follows the stimulus presentation. 7