Impulsivity and self-control during intertemporal decision making linked to the neural dynamics of reward value representation

A characteristic marker of impulsive decision making is the discounting of delayed rewards, demonstrated via choice preferences and choice-related brain activity. However, delay discounting may also arise from how subjective reward value is dynamically represented in the brain when anticipating an upcoming chosen reward. In the current study, brain activity was continuously monitored as human participants freely selected an immediate or delayed primary liquid reward and then waited for the specified delay before consuming it. The ventromedial prefrontal cortex (vmPFC) exhibited a characteristic pattern of activity dynamics during the delay period, as well as modulation during choice, that is consistent with the time-discounted coding of subjective value. The ventral striatum (VS) exhibited a similar activity pattern, but preferentially in impulsive individuals. A contrasting profile of delay-related and choice activation was observed in the anterior PFC (aPFC), but selectively in patient individuals. Functional connectivity analyses indicated that both vmPFC and aPFC exerted modulatory, but opposite, influences on VS activation. These results link behavioral impulsivity and self-control to dynamically evolving neural representations of future reward value, not just during choice, but also during postchoice delay periods

The role of psychometrics in individual differences research in cognition: A case study of the AX-CPT

Investigating individual differences in cognition requires addressing questions not often thought about in standard experimental designs, especially regarding the psychometric properties of the task. Using the AX-CPT cognitive control task as a case study example, we address four concerns that one may encounter when researching the topic of individual differences in cognition. First, we demonstrate the importance of variability in task scores, which in turn directly impacts reliability, particularly when comparing correlations in different populations. Second, we demonstrate the importance of variability and reliability for evaluating potential failures to replicate predicted correlations, even within the same population. Third, we demonstrate how researchers can turn to evaluating psychometric properties as a way of evaluating the feasibility of utilizing the task in new settings (e.g., online administration). Lastly, we show how the examination of psychometric properties can help researchers make informed decisions when designing a study, such as determining the appropriate number of trials for a task

Positive Affect Versus Reward: Emotional and Motivational Influences on Cognitive Control

It is becoming increasingly appreciated that affective influences can contribute strongly to goal-oriented cognition and behavior. However, much work is still needed to properly characterize these influences and the mechanisms by which they contribute to cognitive processing. An important question concerns the nature of emotional manipulations (i.e., direct induction of affectively valenced subjective experience) versus motivational manipulations (e.g., delivery of performance-contingent rewards and punishments) and their impact on cognitive control. Empirical evidence suggests that both kinds of manipulations can influence cognitive control in a systematic fashion, but investigations of both have largely been conducted independently of one another. Likewise, some theoretical accounts suggest that emotion and motivation may modulate cognitive control via common neural mechanisms, while others suggest the possibility of dissociable influences. Here, we provide an analysis and synthesis of these various accounts, suggesting potentially fruitful new research directions to test competing hypotheses

Temporal dynamics of motivation-cognitive control interactions revealed by high-resolution pupillometry

Motivational manipulations, such as the presence of performance-contingent reward incentives, can have substantial influences on cognitive control. Previous evidence suggests that reward incentives may enhance cognitive performance specifically through increased preparatory, or proactive, control processes. The present study examined reward influences on cognitive control dynamics in the AX-Continuous Performance Task (AX-CPT), using high-resolution pupillometry. In the AX-CPT, contextual cues must be actively maintained over a delay in order to appropriately respond to ambiguous target probes. A key feature of the task is that it permits dissociable characterization of preparatory, proactive control processes (i.e., utilization of context) and reactive control processes (i.e., target-evoked interference resolution). Task performance profiles suggested that reward incentives enhanced proactive control (context utilization). Critically, pupil dilation was also increased on reward incentive trials during context maintenance periods, suggesting trial-specific shifts in proactive control, particularly when context cues indicated the need to overcome the dominant target response bias. Reward incentives had both transient (i.e., trial-by-trial) and sustained (i.e., block-based) effects on pupil dilation, which may reflect distinct underlying processes. The transient pupillary effects were present even when comparing against trials matched in task performance, suggesting a unique motivational influence of reward incentives. These results suggest that pupillometry may be a useful technique for investigating reward motivational signals and their dynamic influence on cognitive control

Brief reports: anticipating the consequences of action: an fMRI study of intention-based task preparation

A key component of task preparation may be to anticipate the consequences of task-appropriate actions. This task switching study examined whether such type of "intentional" preparatory control relies on the presentation of explicit action effects. Preparatory BOLD activation in a condition with task-specific motion effect feedback was compared to identical task conditions with accuracy feedback only. Switch-related activation was found selectively in the effect feedback condition in the middle mid-frontal gyrus and in the anterior intraparietal sulcus. Consistent with research on attentional control, the posterior superior parietal lobule exhibited switch-related preparatory activation irrespective of feedback type. To conclude, preparatory control can occur via complementary attentional and intentional neural mechanisms depending on whether meaningful task-specific action effects lead to the formation of explicit effect representations

The subjective value of cognitive effort is encoded by a domain-general valuation network

Cognitive control is necessary for goal-directed behavior, yet people treat cognitive control demand as a cost, which discounts the value of rewards in a similar manner as other costs, such as delay or risk. It is unclear, however, whether the subjective value (SV) of cognitive effort is encoded in the same putatively domain-general brain valuation network implicated in other cost domains, or instead engages a distinct frontoparietal network, as implied by recent studies. Here, we provide rigorous evidence that the valuation network, with core foci in the ventromedial prefrontal cortex and ventral striatum, also encodes SV during cognitive effort-based decision-making in healthy, male and female adult humans. We doubly dissociate this network from frontoparietal regions that are instead recruited as a function of decision difficulty. We show that the domain-general valuation network jointly and independently encodes both reward benefits and cognitive effort costs. We also demonstrate that cognitive effort SV signals predict choice and are influenced by state and trait motivation, including sensitivity to reward and anticipated task performance. These findings unify cognitive effort with other cost domains, and suggest candidate neural mechanisms underlying state and trait variation in willingness to expend cognitive effort

DFORM: Diffeomorphic vector field alignment for assessing dynamics across learned models

Dynamical system models such as Recurrent Neural Networks (RNNs) have become increasingly popular as hypothesis-generating tools in scientific research. Evaluating the dynamics in such networks is key to understanding their learned generative mechanisms. However, comparison of learned dynamics across models is challenging due to their inherent nonlinearity and because a priori there is no enforced equivalence of their coordinate systems. Here, we propose the DFORM (Diffeomorphic vector field alignment for comparing dynamics across learned models) framework. DFORM learns a nonlinear coordinate transformation which provides a continuous, maximally one-to-one mapping between the trajectories of learned models, thus approximating a diffeomorphism between them. The mismatch between DFORM-transformed vector fields defines the orbital similarity between two models, thus providing a generalization of the concepts of smooth orbital and topological equivalence. As an example, we apply DFORM to models trained on a canonical neuroscience task, showing that learned dynamics may be functionally similar, despite overt differences in attractor landscapes.Comment: 12 pages, 8 figure

When planning results in loss of control: intention-based reflexivity and working-memory

In this review, the authors discuss the seemingly paradoxical loss of control associated with states of high readiness to execute a plan, termed “intention-based reflexivity.” The review suggests that the neuro-cognitive systems involved in the preparation of novel plans are different than those involved in preparation of practiced plans (i.e., those that have been executed beforehand). When the plans are practiced, intention-based reflexivity depends on the prior availability of response codes in long-term memory (LTM). When the plans are novel, reflexivity is observed when the plan is pending and the goal has not yet been achieved. Intention-based reflexivity also depends on the availability of working-memory (WM) limited resources and the motivation to prepare. Reflexivity is probably related to the fact that, unlike reactive control (once a plan is prepared), proactive control tends to be relatively rigid

An fMRI protocol for administering liquid incentives to human participants

This protocol describes the materials and approaches for administering liquid incentives to human participants during fMRI scanning. We first describe preparation of the liquid solutions (e.g., neutral solution and saltwater) and liquid delivery setups. We then detail steps to connect the setups to the computer-controlled syringe pump in the MRI control room, followed by procedures for testing the syringe pump dispensing a liquid bolus during the task. Description of custom software and required adapters for implementing the liquid setup are included. For complete details on the use and execution of this protocol, please refer to Yee et al. (2021)

How does reward expectation influence cognition in the human brain?

The prospect of reward changes how we think and behave. We investigated how this occurs in the brain using a novel continuous performance task in which fluctuating reward expectations biased cognitive processes between competing spatial and verbal tasks. Critically, effects of reward expectancy could be distinguished from induced changes in task-related networks. Behavioral data confirm specific bias toward a reward-relevant modality. Increased reward expectation improves reaction time and accuracy in the relevant dimension while reducing sensitivity to modulations of stimuli characteristics in the irrelevant dimension. Analysis of functional magnetic resonance imaging data shows that the proximity to reward over successive trials is associated with increased activity of the medial frontal cortex regardless of the modality. However, there are modality-specific changes in brain activity in the lateral frontal, parietal, and temporal cortex. Analysis of effective connectivity suggests that reward expectancy enhances coupling in both early visual pathways and within the prefrontal cortex. These distributed changes in task-related cortical networks arise from subjects’ representations of future events and likelihood of reward