An opportunity cost model of subjective effort and task performance - PubMed (original) (raw)

An opportunity cost model of subjective effort and task performance

Robert Kurzban et al. Behav Brain Sci. 2013 Dec.

Abstract

Why does performing certain tasks cause the aversive experience of mental effort and concomitant deterioration in task performance? One explanation posits a physical resource that is depleted over time. We propose an alternative explanation that centers on mental representations of the costs and benefits associated with task performance. Specifically, certain computational mechanisms, especially those associated with executive function, can be deployed for only a limited number of simultaneous tasks at any given moment. Consequently, the deployment of these computational mechanisms carries an opportunity cost--that is, the next-best use to which these systems might be put. We argue that the phenomenology of effort can be understood as the felt output of these cost/benefit computations. In turn, the subjective experience of effort motivates reduced deployment of these computational mechanisms in the service of the present task. These opportunity cost representations, then, together with other cost/benefit calculations, determine effort expended and, everything else equal, result in performance reductions. In making our case for this position, we review alternative explanations for both the phenomenology of effort associated with these tasks and for performance reductions over time. Likewise, we review the broad range of relevant empirical results from across sub-disciplines, especially psychology and neuroscience. We hope that our proposal will help to build links among the diverse fields that have been addressing similar questions from different perspectives, and we emphasize ways in which alternative models might be empirically distinguished.

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Figures

Figure 1

Figure 1

A schematic diagram of the proposed opportunity cost model. The expected costs and benefits of target and nontarget tasks are estimated (top). These computations give rise to phenomenology (e.g., qualia such as frustration, boredom, flow), which, in turn, motivates the allocation of computational processes to tasks that are expected to optimize costs and benefits. This allocation determines performance, on both the target and nontarget tasks. The experienced costs and benefits then recursively feed into another iteration of the same sequence, with continued adjustment of allocation decisions, but without depletion of any physical resource.

Figure 2

Figure 2

Hypothetical utilities of different actions a research participant might engage in, illustrating how opportunity costs depend on the set of actions available.

Figure 3

Figure 3

How hypothetical utilities of different actions might change for a research participant with the experimenter present/absent, illustrating opportunity costs and the optimal action changing in different contexts.

Figure 4

Figure 4

Hypothetical utilities of dedicating computational processes to one task or dividing them between two tasks, illustrating how opportunity costs apply not just to the selection of tasks but also the allocation of processes among tasks.

Figure 5

Figure 5

For the simple model outlined in the text, whether one should focus attention on only the highest valued action or divide attention between the two best actions, as a function of the relative utility (RU) of the next-best action and the fraction of the value (β) one gains from a task when dividing processing capacity. These two factors determine the opportunity cost, and it is better to divide attention when the opportunity cost is high. The locations x and y in (B) provide an example of how to think about the dynamics of effort and performance. A person will feel an increased sense of effort, and be motivated to reallocate attention/mental processes in a way that reduces performance on a task, when the perceived costs and benefits of the task move from position x to position y.

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References

    1. Ackerman JM, Goldstein NJ, Shapiro JR, Bargh JA. You wear me out: The vicarious depletion of self-control. Psychological Science. 2009;20(3):326–32. [aRK] -PMC -PubMed
    1. Ackerman PL. 100 years without resting. In: Ackerman PL, editor. Cognitive fatigue: Multidisciplinary perspectives on current research and future applications. American Psychological Association; 2011. pp. 11–43. [aRK]
    1. Akerlof GA, Yellen JL. The fair wage–effort hypothesis and unemployment. The Quarterly Journal of Economics. 1990;105(2):255–83. [aRK]
    1. Alexander GE, Crutcher MD. Functional architecture of basal ganglia circuits: Neural substrates of parallel processing. Trends in Neurosciences. 1990;13(7):266–71. [aRK] -PubMed
    1. Arai T. Mental fatigue. Teachers College, Columbia University; 1912. [aRK]

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