Mind the instructions: Reward cues are liked first, wanted later.

Current theories propose that mental effort is invested only when the anticipated benefits, such as rewards, outweigh the associated costs, like task difficulty. Yet, it remains unclear whether this motivational and mitigating aspect of reward processing is reflected in the evaluation of reward/difficulty cues as such, and to what extent it depends on task experience. In a pre-registered experiment (N = 84), we used the affect misattribution procedure (AMP) to gauge affective evaluations of nonword cues predicting reward and task difficulty levels. Contrary to previous studies, the AMP was administered at the outset, after cue instructions, and after the cues were used in a random dot motion (RDM) task. Compared to baseline, cues predicting a larger reward were evaluated more positively after RDM task experience, and most importantly, already after cue instructions, with no difference between the two phases. This evaluative effect manifested in increased performance after larger reward cues in the RDM task. Our results suggest that AMP effects may generally capture performance expectations which are independent of task experience. Importantly, these instructed expectations of reward and difficulty play a crucial role in the evaluation and subsequent investment of mental effort.

Attentional set and explicit expectations of perceptual load determine flanker interference.

Task-irrelevant stimuli often capture our attention despite our best efforts to ignore them. It has been noted that tasks involving perceptually complex displays can lead to reduced interference from distractors. The mechanism behind this effect is debated, with some accounts emphasizing the "perceptual load" of the stimuli themselves and others emphasizing the role of proactive control. Here, in three experiments, we investigated the roles of perceptual load, proactive control, and reward motivation in determining distractor interference. Participants performed a visual search task of high, low, or intermediate load, with flanking task-irrelevant distractors. Each trial was preceded by a cue indicating the level of perceptual load (Experiments 1-3) as well as the potential reward that could be earned (Experiments 2 and 3). In all three experiments, the attentional set induced by the preceding trial and cued proactive expectation of perceptual load interacted to determine flanker interference, which was significant for all trial types except trials cued as high load which were also preceded by high load. These effects were not modulated by reward motivation, although in the final experiment reward did significantly improve performance overall. Thus, successful distractor exclusion does not depend upon motivation or load per se but does require an expectation of high load. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Selective reinforcement of conflict processing in the Stroop task.

Motivation signals have been shown to influence the engagement of cognitive control processes. However, most studies focus on the invigorating effect of reward prospect, rather than the reinforcing effect of reward feedback. The present study aimed to test whether people strategically adapt conflict processing when confronted with condition-specific congruency-reward contingencies in a manual Stroop task. Results show that the size of the Stroop effect can be affected by selectively rewarding responses following incongruent versus congruent trials. However, our findings also suggest important boundary conditions. Our first two experiments only show a modulation of the Stroop effect in the first half of the experimental blocks, possibly due to our adaptive threshold procedure demotivating adaptive behavior over time. The third experiment showed an overall modulation of the Stroop effect, but did not find evidence for a similar modulation on test items, leaving open whether this effect generalizes to the congruency conditions, or is stimulus-specific. More generally, our results are consistent with computational models of cognitive control and support contemporary learning perspectives on cognitive control. The findings also offer new guidelines and directions for future investigations on the selective reinforcement of cognitive control processes.