Preliminary evidence for a role of the personality trait in visual perceptual learning

The interactive effect of external rewards and self-determined choice on memory

Both external motivational incentives (e.g., monetary reward) and internal motivational incentives (e.g., self-determined choice) have been found to promote memory, but much less is known about how these two types of incentives interact with each other to affect memory. The current study (N = 108) examined how performance-dependent monetary rewards affected the role of self-determined choice in memory performance, also known as the choice effect. Using a modified and better controlled version of the choice paradigm and manipulating levels of reward, we demonstrated an interactive effect between monetary reward and self-determined choice on 1-day delayed memory performance. Specifically, the choice effect on memory decreased when we introduced the performance-dependent external rewards. These results are discussed in terms of understanding how external and internal motivators interact to impact learning and memory.

Performance-dependent reward hurts performance: The non-monotonic attentional load modulation on task-irrelevant distractor processing

Selective attention is essential when we face sensory inputs with distractions. In the past decades, Lavie's load theory of selective attention delineates a complete picture of distractor suppression under different attentional control load. The present study was originally designed to explore how reward modulates the load effect of attentional selection. Unexpectedly, it revealed new findings under extended attentional load that was not involved in previous work. Participants were asked to complete a rewarded attentive visual tracking task while presented with irrelevant auditory oddball stimuli, with their behavioral performance, event-related potentials and pupillary responses recorded. We found that although the behavioral performance and pupil sizes varied unidirectionally with the attentional load, the processing of distractors as reflected by the mismatch negativity (MMN) increased first and then decreased. In contrast to the prediction of Lavie's theory that attentional control fails to effectively suppress distractor processing under high attentional control load, our finding suggests that extremely high attentional control load may instead require suppression of distractor processing at a stage as early as possible. Besides, P3a, a positive-polarity response sometimes following the MMN, was not affected by the attentional load, but both N1 (a negative-polarity component peaking ~100 ms from sound onset) and P3a were weakened at higher reward, indicating that reward leads to attenuated early processing of distractor and thus suppresses the attentional orienting towards distractors. These findings altogether complement Lavie's load theory of selective attention, presenting a more complex picture of how attentional load and reward affects selective attention.

Reward-driven attention alters perceived salience

Many studies have revealed that reward-associated features capture attention. Neurophysiological evidence further suggests that this reward-driven attention effect modulates visual processes by enhancing low-level visual salience. However, no behavioral study to date has directly examined whether reward-driven attention changes how people see. Combining the two-phase paradigm with a psychophysical method, the current study found that compared with nonsalient cues associated with lower reward, the nonsalient cues associated with higher reward captured more attention, and increased the perceived contrast of the subsequent stimuli. This is the first direct behavioral evidence of the effect of reward-driven attention on low-level visual perception.

The Bright and Dark Sides of Performance-Dependent Monetary Rewards: Evidence From Visual Perception Tasks

Studies have shown that performance-dependent monetary rewards facilitate visual perception. However, no study has examined whether such a positive effect is limited to the rewarded task or may be generalized to other tasks. In the current study, two groups of people were asked to perform two visual perception tasks, one being a reward-relevant task and the other being a reward-irrelevant task. For the reward-relevant task, the experimental group received performance-dependent monetary rewards, whereas the control group did not. For the reward-irrelevant task, both groups were not rewarded. The two tasks were randomly intermixed trial by trial (Experiment 1) or presented block by block (Experiment 2) or session by session (Experiments 3a, 3b, and 3c). Results showed that performance-dependent monetary rewards improved participants' performance on the relevant task in all experiments and impaired their performance on the irrelevant task in Experiments 2, 3a, 3b, and 3c. These results suggested that monetary rewards might incur a cost on reward-irrelevant tasks. Finally, the benefit of monetary rewards disappeared when they were no longer provided during the final session. This is the first study that reveals both the bright and dark sides of the performance-dependent monetary rewards in visual perception.

Towards a whole brain model of Perceptual Learning

A hallmark of modern Perceptual Learning (PL) is the extent to which learning is specific to the trained stimuli. Such specificity to orientation, spatial location and even eye of training has been used as psychophysical evidence of the neural basis of learning. This argument that specificity of PL implies regionalization of brain plasticity implicitly assumes that examination of a singular locus of PL is an appropriate approach to understand learning. However, recent research shows that learning effects once thought to be specific depend on subtleties of the training paradigm and that within even a simple training procedure there are multiple aspects of the task and stimuli that are learned simultaneously. Here, we suggest that learning on any task involves a broad network of brain regions undergoing changes in representations, read-out weights, decision rules, attention and feedback processes as well as oculomotor changes. However, importantly, the distribution of learning across the neural system depends upon the details of the training procedure and the characterstics of the individual being trained. We propose that to advance our understanding of PL, the field must move towards understanding how distributed brain processes jointly contribute to behavioral learning effects.