Aversive motivation and cognitive control

Highlights

• Motivational context clarifies aversive incentive effects on cognitive control.

• Mixed motivation (bundled incentives) is key to measure aversive motivational value.

• Dopamine and serotonin modulate motivational context in aversive neural circuit.

• Lateral habenula and dorsal ACC encode aversive values to guide cognitive control.

• Negative reinforcement and punishment linked to distinct computational mechanisms.

Abstract

Aversive motivation plays a prominent role in driving individuals to exert cognitive control. However, the complexity of behavioral responses attributed to aversive incentives creates significant challenges for developing a clear understanding of the neural mechanisms of this motivation-control interaction. We review the animal learning, systems neuroscience, and computational literatures to highlight the importance of experimental paradigms that incorporate both motivational context manipulations and mixed motivational components (e.g., bundling of appetitive and aversive incentives). Specifically, we postulate that to understand aversive incentive effects on cognitive control allocation, a critical contextual factor is whether such incentives are associated with negative reinforcement or punishment. We further illustrate how the inclusion of mixed motivational components in experimental paradigms enables increased precision in the measurement of aversive influences on cognitive control. A sharpened experimental and theoretical focus regarding the manipulation and assessment of distinct motivational dimensions promises to advance understanding of the neural, monoaminergic, and computational mechanisms that underlie the interaction of motivation and cognitive control.

Introduction

In daily life, individuals demonstrate an impressive ability to weigh the relevant incentives when deciding the amount and type of effort to invest when completing cognitively demanding tasks (Shenhav et al., 2017). These incentives can include both the potential positive outcomes obtained from task completion (e.g., bonus earned, social praise), as well as potential negative outcomes that can be avoided if the task is not completed (e.g., job termination, social admonishment). The ability to successfully adjust cognitive control based on diverse motivational incentives is highly significant for determining one's future academic, career, and social goals (Bonner and Sprinkle, 2002; Duckworth et al., 2007; Mischel et al., 1989), as well as providing a necessary intermediary step for informing how motivational and cognitive deficits may arise in clinical disorders (Barch et al., 2015; Jean-Richard-Dit-Bressel et al., 2018).

Importantly, individuals often face a mixture – or “bundle” – of positive and negative incentives that may jointly occur as in relation to their behavior (e.g., the motivation to earn a good salary and to avoid being fired may jointly drive a worker to allocate more effort to optimize their performance relative to each incentive alone). A crucial factor often neglected in cognitive neuroscience studies of motivation and cognitive control is that the impact of a negative incentive on behavior may depend strongly on the context of how it is bundled (e.g., good salary plus the fear of job termination may motivate an individual to increase their effort, whereas a good salary accompanied by frequent and harsh criticism from a supervisor may cause that same person to decrease their effort). In this review, we provide a detailed examination of how contextual factors moderate bundled incentive effects to better elucidate the mechanisms that underlie interactions of motivation and cognitive control.

Recent empirical research has shed some light on the neural mechanisms of motivation and cognitive control interactions (Botvinick and Braver, 2015; Braver et al., 2014; Yee and Braver, 2018). In particular, dopamine has been widely postulated as a key neurotransmitter (Cools, 2008, 2019; Westbrook and Braver, 2016), and a broad network of brain regions have been shown to underlie these interactions (Parro et al., 2018). Extant studies in this domain have almost exclusively focused on the impact of expected rewards (e.g., monetary bonuses, social praise) on higher-order cognition and cognitive control (Aarts et al., 2011; Bahlmann et al., 2015; Braem et al., 2014; Chiew and Braver, 2016; Duverne and Koechlin, 2017; Etzel et al., 2015; Fröber and Dreisbach, 2016; Frömer et al., 2021; Kouneiher et al., 2009; Locke and Braver, 2008; Small et al., 2005). In contrast, much less is known about the mechanisms through which negative outcomes (e.g., monetary losses, shocks) interact with cognitive control (Braem et al., 2013; Fröbose and Cools, 2018). Although this dissociation by motivational valence (e.g., rewarding vs. aversive) in decision-making is not new (Pessiglione and Delgado, 2015; Plassmann et al., 2010), it remains a significant challenge to determine whether rewarding and aversive motivational values are processed in common or separate neural circuits (Hu, 2016; Morrison and Salzman, 2009).

A recent theoretical framework that shows great promise for integrating the role of aversive motivation in cognitive control is the Expected Value of Control (EVC) model (Shenhav et al., 2013, 2017). The EVC model utilizes a computationally explicit formulation of cognitive control in terms of reinforcement learning and decision-making processes in order to characterize how diverse motivational incentives (e.g., rewards, penalties) impact cognitive control allocation. Critically, EVC reframes adjustments in cognitive control as a fundamentally motivated process, determined by weighing effort costs against potential benefits of control to yield the integrated, net expected value. Although the EVC model has been successfully applied to characterize how rewarding incentives offset the cost of exerting cognitive control, the current cost-benefit analysis needs to be expanded to account for the diversity of strategies for control allocation that arise from aversive motivational incentives.

These important gaps in the literature highlight a ripe opportunity and unique challenge for expanding the investigation of motivation and cognitive control interactions. But why have researchers not yet made significant inroads into characterizing these mechanisms underlying aversive motivation effects on cognitive control? We argue that obstacles to progress can be attributed to two main factors. First, much of the contemporary neuroscience literature has often neglected to consider the motivational context through which aversive incentives influence different strategies for allocating cognitive control, that is, whether the motivational context is operationalized as the degree to which motivation to attain or avoid an outcome will increase (e.g., reinforcement) or decrease (e.g., punishment) behavioral responding. For example, whereas rewarding incentives typically increase behavioral responding to approach the expected reward, aversive incentives can lead an organism to either invigorate or attenuate behavioral responses to avoid the aversive outcome, depending on the motivational context (e.g., See Levy and Schiller, 2020; Mobbs et al., 2020). Second, current experimental paradigms rarely include bundled incentives (i.e., mixed motivation, when both appetitive and aversive outcomes are associated with a behavior), despite the intuition that people likely integrate diverse motivational incentives when deciding how much cognitive control to allocate in mentally demanding tasks. A particular challenge is the lack of well-controlled experimental assays that can explicitly quantify the diverse effects of aversive incentives on cognitive control.

In this review, our primary objective is to identify and highlight critical motivational dimensions (e.g., motivational context and mixed motivation), which for the most part have been neglected in prior treatments. In our opinion, these dimensions have strong potential to advance understanding regarding the neural, monoaminergic, and computational mechanisms of aversive motivational and cognitive control. In particular, we demonstrate how incorporating these motivational dimensions, which have played a prominent role in animal learning experimental paradigms, into experimental studies with humans, can improve the granularity and precision through which we can measure aversive incentive effects on cognitive control allocation. Specifically, we hypothesize that stronger consideration of the motivational context of aversive incentives can clarify the putative dissociable neural pathways and computational mechanisms through which aversive motivation may guide cognitive control allocation. Similarly, the inclusion of mixed motivational components in experimental paradigms will facilitate increased precision in measuring the aversive influences on cognitive control. In sum, we anticipate this review will invigorate greater appreciation for foundational learning and motivation theories that have guided the cornerstone discoveries over the past century, as well as catalyze innovative, groundbreaking research into the computations, brain networks, and neurotransmitter systems associated with aversive motivation and cognitive control.