Dissociating Reward- and Attention-driven Biasing of Global Feature-based Selection in Human Visual Cortex

From learned value to sustained bias: how reward conditioning changes attentional priority

Introduction

Attentional bias to reward-associated stimuli can occur even when it interferes with goal-driven behavior. One theory posits that dopaminergic signaling in the striatum during reward conditioning leads to changes in visual cortical and parietal representations of the stimulus used, and this, in turn, sustains attentional bias even when reward is discontinued. However, only a few studies have examined neural activity during both rewarded and unrewarded task phases.

Methods

In the current study, participants first completed a reward-conditioning phase, during which responses to certain stimuli were associated with monetary reward. These stimuli were then included as non-predictive cues in a spatial cueing task. Participants underwent functional brain imaging during both task phases.

Results

The results show that striatal activity during the learning phase predicted increased visual cortical and parietal activity and decreased ventro-medial prefrontal cortex activity in response to conditioned stimuli during the test. Striatal activity was also associated with anterior cingulate cortex activation when the reward-conditioned stimulus directed attention away from the target.

Discussion

Our findings suggest that striatal activity during reward conditioning predicts the degree to which reward history biases attention through learning-induced changes in visual and parietal activities.

Modelling decision-making biases

Biases are a fundamental aspect of everyday life decision-making. A variety of modelling approaches have been suggested to capture decision-making biases. Statistical models are a means to describe the data, but the results are usually interpreted according to a verbal theory. This can lead to an ambiguous interpretation of the data. Mathematical cognitive models of decision-making outline the structure of the decision process with formal assumptions, providing advantages in terms of prediction, simulation, and interpretability compared to statistical models. We compare studies that used both signal detection theory and evidence accumulation models as models of decision-making biases, concluding that the latter provides a more comprehensive account of the decision-making phenomena by including response time behavior. We conclude by reviewing recent studies investigating attention and expectation biases with evidence accumulation models. Previous findings, reporting an exclusive influence of attention on the speed of evidence accumulation and prior probability on starting point, are challenged by novel results suggesting an additional effect of attention on non-decision time and prior probability on drift rate.

Dynamic Interplay between Reward and Voluntary Attention Determines Stimulus Processing in Visual Cortex

Reward enhances stimulus processing in the visual cortex, but the mechanisms through which this effect occurs remain unclear. Reward prospect can both increase the deployment of voluntary attention and increase the salience of previously neutral stimuli. In this study, we orthogonally manipulated reward and voluntary attention while human participants performed a global motion detection task. We recorded steady-state visual evoked potentials to simultaneously measure the processing of attended and unattended stimuli linked to different reward probabilities, as they compete for attentional resources. The processing of the high rewarded feature was enhanced independently of voluntary attention, but this gain diminished once rewards were no longer available. Neither the voluntary attention nor the salience account alone can fully explain these results. Instead, we propose how these two accounts can be integrated to allow for the flexible balance between reward-driven increase in salience and voluntary attention.

Differing Time Courses of Reward-Related Attentional Processing: An EEG Source-Space Analysis

Since our environment typically contains more information than can be processed at any one time due to the limited capacity of our visual system, we are bound to differentiate between relevant and irrelevant information. This process, termed attentional selection, is usually categorized into bottom-up and top-down processes. However, recent research suggests reward might also be an important factor in guiding attention. Monetary reward can bias attentional selection in favor of task-relevant targets and reduce the efficiency of visual search when a reward-associated, but task-irrelevant distractor is present. This study is the first to investigate reward-related target and distractor processing in an additional singleton task using neurophysiological measures and source space analysis. Based on previous studies, we hypothesized that source space analysis would find enhanced neural activity in regions of the value-based attention network, such as the visual cortex and the anterior cingulate. Additionally, we went further and explored the time courses of the underlying attentional mechanisms. Our neurophysiological results showed that rewarding distractors led to a stronger attentional capture. In line with this, we found that reward-associated distractors (compared with reward-associated targets) enhanced activation in frontal regions, indicating the involvement of top-down control processes. As hypothesized, source space analysis demonstrated that reward-related targets and reward-related distractors elicited activation in regions of the value-based attention network. However, these activations showed time-dependent differences, indicating that the neural mechanisms underlying reward biasing might be different for task-relevant and task-irrelevant stimuli.

Social comparison impacts stimulus evaluation in a competitive social learning task

When we perform an action, the outcome that follows it can change the value we place on that behaviour, making it more or less likely to be repeated in the future. However, the values that we learn are not objective: we interpret the outcomes that we receive for ourselves relative to those that share our environment, i.e. we engage in social comparison. The temporal dynamics of physiological responses to stimulus valuation in social learning tasks are poorly understood, particularly in human participants. Therefore, we recorded stimulus-locked event-related potentials with 64-channel EEG to examine stimulus valuation, following the design of a study previously used in macaques. Pairs of participants performed a social learning task in which they received outcomes sequentially for a presented stimulus (partner first) by pressing a button in response to a cue. There were two conditions: one in which stimulus values varied for the participant but output a constant rate of reward for the partner (self-variable blocks), and another condition in which this payout was reversed (other-variable blocks). We then measured participants' self-reported competitiveness. Approximately 200 ms post-stimulus, an ERP related to stimulus evaluation and attentional processing appeared to encode own stimulus value in self-variable blocks. In other-variable blocks the same pattern of activity was reversed, even though the value of the stimulus for the participant did not depend on the stimulus presented. Outcome-locked analyses further showed that attention dedicated to the partner's outcome was greater in more competitive participants. We conclude that subjective stimulus value can be reflected in early stimulus-locked ERP responses and that competitive participants may be more invested in their own performance relative to the other player, hence their increased interest in the outcome of their partner.