Weighing up the Benefits of Work: Behavioral and Neural Analyses of Effort-Related Decision Making

By M. Walton, SW Kennerley, D. Bannerman, Pem Phillips, Mfs Rushworth. Neural Networks, 2006

453 citations recorded by the source corpus.

How we decide whether a course of action is worth undertaking is largely unknown. Recently, neuroscientists have been turning to ecological approaches to address this issue, examining how animals evaluate the costs and benefits of different options. We present here evidence from rodents and monkeys that demonstrate the degree to which they take into account work and energetic requirements when deciding what responses to make. These calculations appear to be critically mediated by the anterior cingulate cortex (ACC) and mesolimbic dopamine (DA) pathways, with damage to either causing a bias towards options that are easily obtain but yield relatively smaller reward rather than alternatives that required more work but result in greater reward. The evaluation of such decisions appears to be carried out in systems independent of those involved in delay-discounting. We suggest that top-down signals from ACC to nucleus accumbens (NAc) and/or midbrain DA cells may be vital for overcoming effort-related response costs.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/4d67e62b70b958538bdbac11fe247ee035d0b8da

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In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

Citations in the corpus

Mental fatigue impairs physical performance in humans. (2009, background): “Last, decision-making studies in rats with experimental ACC lesions demonstrate that they engage significantly less than normal rats in tasks requiring considerable physical effort to obtain a larger reward (57, 69, 70).”
The Normalization Model of Attention (2009, methodology): “The model is also related to a number of more detailed models that have been proposed as biophysically plausible implementations of biased competition or the feature-similarity gain principle (Ardid et al., 2007; Deco and Rolls, 2005; Hamker and Zirnsak, 2006; Spratling and Johnson, 2004; Tiesinga e”
Goal-directed decision making as probabilistic inference: a computational framework and potential neural correlates. (2012, background): “The common finding is that, unless the reward on the barrier side is larger by a sufficient degree, animals will forgo it, avoiding the effort required (Salamone, Correa, Farrar, & Mingote, 2007; Walton, Kennerley, Bannerman, Phillips , & Rushworth, 2006).”
A Feedback Model of Attention Explains the Diverse Effects of Attention on Neural Firing Rates and Receptive Field Structure (2016, background): “While Reynolds and Heeger do not mention RF alterations, other authors have shown that the same general principle can explain RF shifts [19,21].”
Choice, uncertainty and value in prefrontal and cingulate cortex (2008, background): “Whereas many single-neuron studies have concentrated on the encoding of rewards in a controlled learning environment, in real-life situations, decisions and actions involve cost”
Tonic dopamine: opportunity costs and the control of response vigor (2007, unknown): “Indeed, Denk et al. (2005; see also Walton et al. 2006) show that dopamine depletion significantly lengthened the latencies to reach the reward when the barrier arm was chosen.”
Conflict monitoring and decision making: Reconciling two perspectives on anterior cingulate function (2007, background): “…the relationship between our present proposals and the specific findings underlying the effort-based decision-making account, which center on a bias against effort following ACC lesions in animals (Walton et al., 2003; Walton et al., 2002; Walton et al., 2006; although see Kennerley et al., 2006).”
Separate Valuation Subsystems for Delay and Effort Decision Costs (2010, background): “However, lesion studies in rodents suggest at least partial dissociations between the neural structures used to assess delay- and effort-based decision making (Rudebeck et al., 2006; Walton et al., 2006; Floresco et al., 2008).”
Neurons in the Frontal Lobe Encode the Value of Multiple Decision Variables (2009, result): “Our results are consistent with the growing evidence emphasizing the importance of the ACC in diverse aspects of decision making (Behrens, Woolrich, Walton, & Rushworth, 2007; Sallet et al., 2007; Seo & Lee, 2007; Amiez et al., 2006; Kennerley et al., 2006; Walton et al., 2006).”
Effort-related functions of nucleus accumbens dopamine and associated forebrain circuits (2007, background): “Studies of the brain systems regulating effortrelated processes have become more common in recent years (e.g., Salamone et al. 2003; Rushworth et al. 2004; Walton et al. 2006; Phillips et al. 2007), not only because of the basic scientific importance of understanding activational aspects of motivati”
Reconsidering Anhedonia in Depression: Lessons from Translational Neuroscience (2010, background): “As with the effects of NAcc DA depletion, ablation of rat ACC—but not neighboring infralimbic cortex—results in a behavioral shift towards LC/LR options during effort-based decision-making (Schweimer and Hauber, 2005; Schweimer et al., 2005; Walton et al., 2003; Walton et al., 2002; Walton et al., 2”
A mechanistic cortical microcircuit of attention for amplification, normalization and suppression. (2015, background): “Neurons in layer 4 converge onto neurons in layer 2/3 which spatially pool over them in a broad area (a similar idea as in Hamker & Zirnsak, 2006; Compte & Wang, 2006).”
Real consequences matter: Why hypothetical biases in the valuation of time persist even in controlled lab experiments (2019, background): “Several studies shows that humans are usually inclined to keep the cognitive effort exerted on tasks at a minimum, since cognitive effort has an opportunity cost (Walton et al., 2006; Kool et al., 2010; Kurzban et al., 2013; Westbrook and Braver, 2015; Shenhav et al., 2017).”