Romy Frömer

To achieve a goal, people have to keep track of how much effort they are putting in (effort monitoring) and how well they are performing (performance monitoring), which can be informed by endogenous signals, or exogenous signals providing explicit feedback about whether they have met their goal. Interventions to improve performance often focus on adjusting feedback to direct the individual on how to better invest their efforts, but is it possible that this feedback itself plays a role in shaping the experience of how effortful the task feels? Here, we examine this question directly by assessing the relationship between effort monitoring and performance monitoring. Participants (N = 68) performed a task in which their goal was to squeeze a handgrip to within a target force level (not lower or higher) for a minimum duration. On most trials, they were given no feedback as to whether they met their goal, and were largely unable to detect how they had performed. On a subset of trials, however, we provided participants with (false) feedback indicating that they had either succeeded or failed at meeting their goal (positive vs. negative feedback blocks, respectively). Sporadically, participants rated their experience of effort exertion, fatigue, and confidence in having met the target grip force on that trial. Despite being non-veridical to their actual performance, we found that the type of feedback participants received influenced their experience of effort. When receiving negative (vs. positive) feedback, participants fatigued faster and adjusted their grip strength more for higher target force levels. We also found that confidence gradually increased with increasing positive feedback and decreased with increasing negative feedback, again despite feedback being uniformly uninformative. These results suggest differential influences of feedback on experiences related to effort and further shed light on the relationship between experiences related to performance monitoring and effort monitoring.

Humans can flexibly adjust how they make decisions to arbitrary goals. However, most theories in decision‐making focus on predicting one specific choice type (i.e., choosing the best option). Here, we link decision‐making and cognitive‐control research to test a theory that accounts for flexible adjustments of choice mechanisms to different goals and demands. Our biologically inspired model specifies how different features translate into evidence for the current goal, and how evidence is mapped onto different output structures. We tested the model in an eye‐tracking study in which participants were asked to choose one out of four consumer products or to appraise the entire set, each with respect to positive or negative value. The results confirmed our preregistered hypotheses that response time (RT) should decrease with the overall value of a set of options in choose‐best but increase in choose‐worst trials. As predicted, this interaction was absent in appraisal RT, which instead exhibited an inverted‐U‐shaped pattern. Furthermore, the amount of attention devoted to an option was positively related to its value in choose‐best, negatively related in choose‐worst trials, and unrelated when participants appraised entire sets of products. Time‐resolved analyses of eye movements revealed strategic goal‐dependent search processes, as attention is increasingly focused on goal‐congruent options in choice but remains more uniformly distributed in appraisal. Our findings suggest that cognitive control shapes choice and search dynamics by flexibly adjusting them to current goals and demands.

Many decisions require maintaining beliefs across multiple levels of representation, from sampling and integrating information to forming action plans. To elucidate these processes and reveal the neural mechanisms supporting them under naturalistic free-viewing conditions, we combined EEG and eye-tracking in a multi-attribute choice task. Leveraging our recently developed Multi-Attribute Search and Choice (MASC) model, we generated fixation-level predictions for value-belief updating processes encompassing attribute, option, and preference states. Behavioral and eye-tracking data confirmed search and choice dynamics as predicted by MASC. Crucially, fixation-evoked potentials (FEPs) revealed distinct neural signatures along the processing hierarchy. While attribute-level and option-level updates were reflected in an early fronto-central positivity and a centro-parietal positivity, respectively, emerging preferences were expressed in lateralized motor-preparatory activity. Our preregistered study elucidates a hierarchy of neural belief-updating processes linking information sampling to decision formation and opens new research avenues for investigating neural attention and choice dynamics in naturalistic free-viewing paradigms.

Humans can flexibly adjust how they make decisions to arbitrary goals. However, most theories in decision-making focus on predicting one specific choice type (i.e., choosing the best option). Here, we link decision-making and cognitive-control research to test a theory that accounts for flexible adjustments of choice mechanisms to different goals and demands. Our biologically inspired model specifies how different features translate into evidence for the current goal, and how evidence is mapped onto different output structures. We tested the model in an eye-tracking study in which participants were asked to choose one out of four consumer products or to appraise the entire set, each with respect to positive or negative value. The results confirmed our preregistered hypotheses that response time (RT) should decrease with the overall value of a set of options in choose-best but increase in choose-worst trials. As predicted, this interaction was absent in appraisal RT, which instead exhibited an inverted-U-shaped pattern. Furthermore, the amount of attention devoted to an option was positively related to its value in choose-best, negatively related in choose-worst trials, and unrelated when participants appraised entire sets of products. Time-resolved analyses of eye movements revealed strategic goal-dependent search processes, as attention is increasingly focused on goal-congruent options in choice but remains more uniformly distributed in appraisal. Our findings suggest that cognitive control shapes choice and search dynamics by flexibly adjusting them to current goals and demands.

Decision scientists have grown increasingly interested in how people adaptively control their decision making, exploring how metacognitive factors influence how people accumulate evidence and commit to a choice. A recent study proposed a novel form of such adaptive control, whereby the values of one's options contribute to both the formation of a decision and the effortful invigoration of a response. In this framework, the control process was operationalized in a drift diffusion model as the lowering of the decision threshold on difficult trials. Reanalyzing the data from this experiment, we establish alternative explanations for these findings. We show that the reported evidence for controlled threshold adjustments can be explained away by task confounds, time-dependent collapses in decision thresholds, and stimulus-driven dynamics in an alternative form of evidence accumulation. Our findings challenge the specific evidence for this new theory of motivated control while at the same time revealing paths and pitfalls in computational approaches to a more general understanding when and how control guides decision-making.

Reinforcement learning models focus on reward prediction errors as the driver of behavior. However, recent evidence indicates that deviations from emotion expectations, termed affective prediction errors, also crucially shape behavior. Whether there is neural separability between emotion and reward signals remains unknown. We employ electroencephalography during social learning to investigate the neural signatures of reward and affective prediction errors. Behavioral results reveal that affective prediction errors are associated with choices when little is known about how a partner will behave. This behavioral evidence is mirrored neurally by engagement of separate event-related potentials. More specifically, the feedback-related negativity is largely and consistently indexed by reward prediction errors, while the P3b is more consistently tracked by affective prediction errors. The P3b in particular is linked to subsequent choices, highlighting the mechanistic influence of emotion during social learning. These findings present evidence for a neurobiologically viable emotion learning signal that is partially distinguishable, at both the behavior and neural levels, from reward.

When making decisions, we often have more information about some options than others. Previous work has shown that people are more likely to choose options that they look at more and those that they are more confident in. But should one always prefer options one knows more about? Intuition suggests not. Rather, how additional information impacts our preferences should depend critically on how valuable we expect the options to be. Here, we formalize this intuition in a Bayesian sequential sampling model where attention and confidence influence the precision of momentary evidence. Our model makes a key prediction: attention and confidence both increase choice probability for better-than-average options, and both decrease choice probability for worse-than-average options. We confirm this prediction in two experiments in which we independently manipulate value and attention. Our results offer a novel perspective on prior work on the role of attention and confidence in decision-making, showing that people rely on contextual knowledge and uncertainty estimates to adaptively learn about their options and make better decisions.

When making decisions, we often have more information about some options than others. Previous work has shown that people are more likely to choose options that they look at more and those that they are more confident in. But should one always prefer options one knows more about? Intuition suggests not. Rather, how additional information impacts our preferences should depend critically on how valuable we expect the options to be. Here, we formalize this intuition in a Bayesian sequential sampling model where attention and confidence influence the precision of momentary evidence. Our model makes a key prediction: attention and confidence both increase choice probability for better-than-average options, and both decrease choice probability for worse-than-average options. We confirm this prediction in two experiments, in which we independently manipulate value and attention. Our results offer a novel perspective on prior work on the role of attention and confidence in decision-making, showing that people rely on contextual knowledge and uncertainty estimates to adaptively learn about their options and make better decisions.