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Behavioral modeling of human choices reveals dissociable effects of physical effort and temporal delay on reward devaluation.

Klein-Flügge MC, Kennerley SW, Saraiva AC, Penny WD, Bestmann S - PLoS Comput. Biol. (2015)

Bottom Line: There has been considerable interest from the fields of biology, economics, psychology, and ecology about how decision costs decrease the value of rewarding outcomes.Our results provide a novel characterization of human effort discounting behavior and its first dissociation from delay discounting.This enables accurate modelling of cost-benefit decisions, a prerequisite for the investigation of the neural underpinnings of effort-guided choice and for understanding the deficits in clinical disorders characterized by behavioral inactivity.

View Article: PubMed Central - PubMed

Affiliation: Sobell Department of Motor Neuroscience and Movement Disorders, UCL Institute of Neurology, University College London (UCL), London, United Kingdom; Wellcome Trust Centre for Neuroimaging, University College London (UCL), London, United Kingdom; Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom.

ABSTRACT
There has been considerable interest from the fields of biology, economics, psychology, and ecology about how decision costs decrease the value of rewarding outcomes. For example, formal descriptions of how reward value changes with increasing temporal delays allow for quantifying individual decision preferences, as in animal species populating different habitats, or normal and clinical human populations. Strikingly, it remains largely unclear how humans evaluate rewards when these are tied to energetic costs, despite the surge of interest in the neural basis of effort-guided decision-making and the prevalence of disorders showing a diminished willingness to exert effort (e.g., depression). One common assumption is that effort discounts reward in a similar way to delay. Here we challenge this assumption by formally comparing competing hypotheses about effort and delay discounting. We used a design specifically optimized to compare discounting behavior for both effort and delay over a wide range of decision costs (Experiment 1). We then additionally characterized the profile of effort discounting free of model assumptions (Experiment 2). Contrary to previous reports, in both experiments effort costs devalued reward in a manner opposite to delay, with small devaluations for lower efforts, and progressively larger devaluations for higher effort-levels (concave shape). Bayesian model comparison confirmed that delay-choices were best predicted by a hyperbolic model, with the largest reward devaluations occurring at shorter delays. In contrast, an altogether different relationship was observed for effort-choices, which were best described by a model of inverse sigmoidal shape that is initially concave. Our results provide a novel characterization of human effort discounting behavior and its first dissociation from delay discounting. This enables accurate modelling of cost-benefit decisions, a prerequisite for the investigation of the neural underpinnings of effort-guided choice and for understanding the deficits in clinical disorders characterized by behavioral inactivity.

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Experiment 1: Comparison of effort and delay discounting.A, Two models were fitted to participants’ choice data; the hyperbolic model has previously been proposed to characterize effort and delay discounting. The sigmoidal model was included because it fulfills two particular features: it can obtain initially concave shapes, in line with work showing that the sense of effort increases as a power function of the target force with decreasing sensitivity at lower effort levels; and it entails a turning point after which effort discounting becomes progressively less steep. The equations are as follows: hyperbolic: V = M/(1+kC), sigmoidal: V = M (1- (1/(1+exp(-k*(C-p)))- 1/(1+exp(k*p))) (1 + 1/exp(k*p))). B, Bayesian model comparison of the hyperbolic and the sigmoidal model showed a clear dissociation: the hyperbolic model best explained delay-based choices (left), whereas the sigmoidal model best explained effort-based choices (right). C, Individual and average fits of the sigmoidal winning model for effort and the hyperbolic winning model for delay. D, Mean squared error, indicating the goodness of fit of the two competing models for the effort and delay task. E, Individual and average model fits, as in C, but these fits were obtained by using the utility instead of reward magnitude during parameter estimation. This has a negligible effect on the shape of discounting we observe.
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pcbi.1004116.g003: Experiment 1: Comparison of effort and delay discounting.A, Two models were fitted to participants’ choice data; the hyperbolic model has previously been proposed to characterize effort and delay discounting. The sigmoidal model was included because it fulfills two particular features: it can obtain initially concave shapes, in line with work showing that the sense of effort increases as a power function of the target force with decreasing sensitivity at lower effort levels; and it entails a turning point after which effort discounting becomes progressively less steep. The equations are as follows: hyperbolic: V = M/(1+kC), sigmoidal: V = M (1- (1/(1+exp(-k*(C-p)))- 1/(1+exp(k*p))) (1 + 1/exp(k*p))). B, Bayesian model comparison of the hyperbolic and the sigmoidal model showed a clear dissociation: the hyperbolic model best explained delay-based choices (left), whereas the sigmoidal model best explained effort-based choices (right). C, Individual and average fits of the sigmoidal winning model for effort and the hyperbolic winning model for delay. D, Mean squared error, indicating the goodness of fit of the two competing models for the effort and delay task. E, Individual and average model fits, as in C, but these fits were obtained by using the utility instead of reward magnitude during parameter estimation. This has a negligible effect on the shape of discounting we observe.

Mentions: Our main aim was to directly compare choice behavior when rewards were tied to two different types of decision costs, physical effort versus temporal delay. To this end, we initially compared two behavioral models of subjective discounted value, and contrasted their performance for choices on the effort and delay task. The first was the hyperbolic model widely accepted as the best characterization of delay discounting behavior [8,10–12], but which has also been suggested for effort discounting [29] (Fig. 3A). The second was a sigmoidal model with an initially concave shape and a flexible turning point (see Materials and Methods). Such a model can accommodate discounting behaviour in which effort increases at low effort levels have a smaller effect on value than increases at higher effort levels. Such a behaviour would be consistent with studies showing that the perceived sense of effort increases as a power function of the exerted force level, with a reduced sensitivity to lower compared to higher efforts [41,42]. To strengthen our claim that effort discounting is concave and dissociable from delay discounting, we also performed a comparison of a larger set of models. This comparison included three one-parameter models previously suggested for effort discounting (hyperbolic [29]; linear [37]; quadratic [40]), and a two-parameter power function, with the latter two sharing the initially concave nature with the inverse sigmoidal model (see S2A–B Fig.).


Behavioral modeling of human choices reveals dissociable effects of physical effort and temporal delay on reward devaluation.

Klein-Flügge MC, Kennerley SW, Saraiva AC, Penny WD, Bestmann S - PLoS Comput. Biol. (2015)

Experiment 1: Comparison of effort and delay discounting.A, Two models were fitted to participants’ choice data; the hyperbolic model has previously been proposed to characterize effort and delay discounting. The sigmoidal model was included because it fulfills two particular features: it can obtain initially concave shapes, in line with work showing that the sense of effort increases as a power function of the target force with decreasing sensitivity at lower effort levels; and it entails a turning point after which effort discounting becomes progressively less steep. The equations are as follows: hyperbolic: V = M/(1+kC), sigmoidal: V = M (1- (1/(1+exp(-k*(C-p)))- 1/(1+exp(k*p))) (1 + 1/exp(k*p))). B, Bayesian model comparison of the hyperbolic and the sigmoidal model showed a clear dissociation: the hyperbolic model best explained delay-based choices (left), whereas the sigmoidal model best explained effort-based choices (right). C, Individual and average fits of the sigmoidal winning model for effort and the hyperbolic winning model for delay. D, Mean squared error, indicating the goodness of fit of the two competing models for the effort and delay task. E, Individual and average model fits, as in C, but these fits were obtained by using the utility instead of reward magnitude during parameter estimation. This has a negligible effect on the shape of discounting we observe.
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getmorefigures.php?uid=PMC4376637&req=5

pcbi.1004116.g003: Experiment 1: Comparison of effort and delay discounting.A, Two models were fitted to participants’ choice data; the hyperbolic model has previously been proposed to characterize effort and delay discounting. The sigmoidal model was included because it fulfills two particular features: it can obtain initially concave shapes, in line with work showing that the sense of effort increases as a power function of the target force with decreasing sensitivity at lower effort levels; and it entails a turning point after which effort discounting becomes progressively less steep. The equations are as follows: hyperbolic: V = M/(1+kC), sigmoidal: V = M (1- (1/(1+exp(-k*(C-p)))- 1/(1+exp(k*p))) (1 + 1/exp(k*p))). B, Bayesian model comparison of the hyperbolic and the sigmoidal model showed a clear dissociation: the hyperbolic model best explained delay-based choices (left), whereas the sigmoidal model best explained effort-based choices (right). C, Individual and average fits of the sigmoidal winning model for effort and the hyperbolic winning model for delay. D, Mean squared error, indicating the goodness of fit of the two competing models for the effort and delay task. E, Individual and average model fits, as in C, but these fits were obtained by using the utility instead of reward magnitude during parameter estimation. This has a negligible effect on the shape of discounting we observe.
Mentions: Our main aim was to directly compare choice behavior when rewards were tied to two different types of decision costs, physical effort versus temporal delay. To this end, we initially compared two behavioral models of subjective discounted value, and contrasted their performance for choices on the effort and delay task. The first was the hyperbolic model widely accepted as the best characterization of delay discounting behavior [8,10–12], but which has also been suggested for effort discounting [29] (Fig. 3A). The second was a sigmoidal model with an initially concave shape and a flexible turning point (see Materials and Methods). Such a model can accommodate discounting behaviour in which effort increases at low effort levels have a smaller effect on value than increases at higher effort levels. Such a behaviour would be consistent with studies showing that the perceived sense of effort increases as a power function of the exerted force level, with a reduced sensitivity to lower compared to higher efforts [41,42]. To strengthen our claim that effort discounting is concave and dissociable from delay discounting, we also performed a comparison of a larger set of models. This comparison included three one-parameter models previously suggested for effort discounting (hyperbolic [29]; linear [37]; quadratic [40]), and a two-parameter power function, with the latter two sharing the initially concave nature with the inverse sigmoidal model (see S2A–B Fig.).

Bottom Line: There has been considerable interest from the fields of biology, economics, psychology, and ecology about how decision costs decrease the value of rewarding outcomes.Our results provide a novel characterization of human effort discounting behavior and its first dissociation from delay discounting.This enables accurate modelling of cost-benefit decisions, a prerequisite for the investigation of the neural underpinnings of effort-guided choice and for understanding the deficits in clinical disorders characterized by behavioral inactivity.

View Article: PubMed Central - PubMed

Affiliation: Sobell Department of Motor Neuroscience and Movement Disorders, UCL Institute of Neurology, University College London (UCL), London, United Kingdom; Wellcome Trust Centre for Neuroimaging, University College London (UCL), London, United Kingdom; Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom.

ABSTRACT
There has been considerable interest from the fields of biology, economics, psychology, and ecology about how decision costs decrease the value of rewarding outcomes. For example, formal descriptions of how reward value changes with increasing temporal delays allow for quantifying individual decision preferences, as in animal species populating different habitats, or normal and clinical human populations. Strikingly, it remains largely unclear how humans evaluate rewards when these are tied to energetic costs, despite the surge of interest in the neural basis of effort-guided decision-making and the prevalence of disorders showing a diminished willingness to exert effort (e.g., depression). One common assumption is that effort discounts reward in a similar way to delay. Here we challenge this assumption by formally comparing competing hypotheses about effort and delay discounting. We used a design specifically optimized to compare discounting behavior for both effort and delay over a wide range of decision costs (Experiment 1). We then additionally characterized the profile of effort discounting free of model assumptions (Experiment 2). Contrary to previous reports, in both experiments effort costs devalued reward in a manner opposite to delay, with small devaluations for lower efforts, and progressively larger devaluations for higher effort-levels (concave shape). Bayesian model comparison confirmed that delay-choices were best predicted by a hyperbolic model, with the largest reward devaluations occurring at shorter delays. In contrast, an altogether different relationship was observed for effort-choices, which were best described by a model of inverse sigmoidal shape that is initially concave. Our results provide a novel characterization of human effort discounting behavior and its first dissociation from delay discounting. This enables accurate modelling of cost-benefit decisions, a prerequisite for the investigation of the neural underpinnings of effort-guided choice and for understanding the deficits in clinical disorders characterized by behavioral inactivity.

No MeSH data available.


Related in: MedlinePlus