An integrative effort : Bridging motivational intensity theory and recent neurocomputational and neuronal models of effort and control allocation

Abstract

An increasing number of cognitive, neurobiological and computational models have been proposed in the last decade, seeking to explain how humans allocate physical or cognitive effort. Most models share conceptual similarities with motivational intensity theory (MIT), an influential classic psychological theory of motivation. Yet, little effort has been made to integrate such models, which remain confined within the explanatory level for which they were developed, i.e. psychological, computational, neurobiological and neuronal. In this critical review, we derive novel analyses of three recent computational and neuronal models of effort allocation—the expected value of control (EVC) theory, the reinforcement meta-learner (RML) model, and the neuronal model of attentional effort— and establish a formal relationship between these models and MIT. Our analyses reveal striking similarities between predictions made by these models, with a shared key tenet: a non-monotonic relationship between perceived task difficulty and effort mobilization, following a saw-tooth or inverted-U shape. In addition, the models converge on the proposition that the dorsal anterior cingulate cortex (dACC) may be responsible for determining the allocation of effort and cognitive control. We conclude by discussing the distinct contributions and strengths of each theory toward understanding neurocomputational processes of effort allocation. Finally, we highlight the necessity for a unified understanding of effort allocation, by drawing novel connections between different theorizing of adaptive effort allocation as described by the presented models (cognitive, neurobiological, and neuronal levels of analysis).

Task performance errors and rewards affect voluntary task choices

“…Previous work suggests that such an integration might occur based on a common currency, such as negative affect (e.g., Delgado, 2007 ; Dignath et al, 2019 ). This comports with theoretical and computational work (Musslick et al, 2015 ; Shenhav et al, 2013 ; Silvestrini et al, 2022 ; Silvetti et al, 2018 ), as well as empirical work applying one task (Chong et al, 2017 ; Gilzenrat et al, 2010 ; Westbrook et al, 2013 , 2020 ) emphasizing that both rewards and task performance are consolidated into a common expected value of choosing a particular task. While thus far, computational models considered how both rewards and cognitive costs associated with each task are considered in voluntary task choices (Musslick et al, 2015 ; Shenhav et al, 2013 ; Silvestrini et al, 2022 ; Silvetti et al, 2018 ), empirical evidence is missing to show that rewards and cognitive costs associated with task performance (such as error rates and errors) are also considered when deciding which of two tasks to select (for evidence on task difficulty affecting within task choices see: Chong et al, 2017 ; Gilzenrat et al, 2010 ; Westbrook et al, 2013 , 2020 ).…”

“…In the first experiment, we sought to establish that increasing reward differences 3 favoring the alternative task would result in voluntary task switches towards the task associated with relatively higher rewards. We additionally expected higher switch rates after errors, compared to accurate responses, as errors signal an increase in cognitive control costs associated with the performed task (Musslick et al, 2015 ; Silvestrini et al, 2022 ).…”

“…Theories of control allocation suggest that task selection is guided by weighing the benefits of performing a task against its respective cognitive control costs associated with task performance (Lieder et al, 2018 ; Musslick et al, 2015 ; Shenhav et al, 2013 ; Silvestrini et al, 2022 ). According to the expected value of control (EVC) theory, several factors determine the decision to switch from one task to another task such as monetary rewards and costs associated with the amount of control associated with performing a task, error commission, reconfiguration costs associated with adjusting cognitive control (Musslick et al, 2015 ; Shenhav et al, 2013 ).…”

Choosing task characteristics oneself justifies effort: A study on cardiac response and the critical role of task difficulty.

“…We do not see it as necessary to include a link to action outcomes in the definition of the intensity of action input. Accordingly, we see effort and performance as independent constructs, which can but do not have to be linked (for more details, see Silvestrini et al, 2021; Silvestrini & Gendolla, 2019). In the tradition of MIT (Brehm & Self, 1989) and its precursors we focus on the process of resource mobilization itself and have operationalized effort accordingly as sympathetic nervous system impact (i.e., the activation system) on the body’s resource transport network—the cardiovascular system (Wright, 1996).…”

Efforts and their feelings

“…The principle applies particularly to mental efforts: humans are traditionally considered to behave as "cognitive misers" (Taylor, 1981) who tend to avoid cognitively demanding solutions to a problem and rely on simple heuristics. The research program around motivational intensity theory has shown that agents exert effort only to the extent to which expected outcomes justify it and success is sufficiently likely (Brehm & Self, 1989;Gendolla et al, 2019;Silvestrini et al, 2021). Recently developed methods have been used to show that, in deciding what to do, an action's demand for cognitive effort registers as a cost that counts against its selection (Kool et al, 2010;.…”