Behavioral Modeling of Human Choices Reveals Dissociable Effects of Physical Effort and Temporal Delay on Reward Devaluation
Publication Date
March 27, 2015
Journal
PLOS Computational Biology
Authors
Miriam C. Klein Flügge, Steven W. Kennerley, Ana C. Saraiva, Will D. Penny, et al
Volume
11
Issue
3
Pages
e1004116
DOI
https://dx.plos.org/10.1371/journal.pcbi.1004116
Publisher URL
http://journals.plos.org/ploscompbiol/article?id=10.1371%2Fjournal.pcbi.1004116
PubMed
http://www.ncbi.nlm.nih.gov/pubmed/25816114
PubMed Central
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4376637
Europe PMC
http://europepmc.org/abstract/MED/25816114
Web of Science
000352195700025
Scopus
84926304996
Mendeley
http://www.mendeley.com/research/behavioral-modeling-human-choices-reveals-dissociable-effects-physical-effort-temporal-delay-reward
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Mendeley | Further Information

{"title"=>"Behavioral Modeling of Human Choices Reveals Dissociable Effects of Physical Effort and Temporal Delay on Reward Devaluation", "type"=>"journal", "authors"=>[{"first_name"=>"Miriam C.", "last_name"=>"Klein-Flügge", "scopus_author_id"=>"54395569800"}, {"first_name"=>"Steven W.", "last_name"=>"Kennerley", "scopus_author_id"=>"6603245038"}, {"first_name"=>"Ana C.", "last_name"=>"Saraiva", "scopus_author_id"=>"55988778200"}, {"first_name"=>"Will D.", "last_name"=>"Penny", "scopus_author_id"=>"7006713116"}, {"first_name"=>"Sven", "last_name"=>"Bestmann", "scopus_author_id"=>"6602390376"}], "year"=>2015, "source"=>"PLoS Computational Biology", "identifiers"=>{"pmid"=>"25816114", "doi"=>"10.1371/journal.pcbi.1004116", "sgr"=>"84926304996", "isbn"=>"1553-7358 (Electronic)\\r1553-734X (Linking)", "scopus"=>"2-s2.0-84926304996", "issn"=>"15537358", "pui"=>"603513906"}, "id"=>"2b33643e-97cd-3e4a-82b4-50565bf6d246", "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.", "link"=>"http://www.mendeley.com/research/behavioral-modeling-human-choices-reveals-dissociable-effects-physical-effort-temporal-delay-reward", "reader_count"=>90, "reader_count_by_academic_status"=>{"Unspecified"=>2, "Professor > Associate Professor"=>2, "Researcher"=>9, "Student > Doctoral Student"=>5, "Student > Ph. D. Student"=>26, "Student > Postgraduate"=>11, "Other"=>5, "Student > Master"=>13, "Student > Bachelor"=>12, "Lecturer"=>2, "Professor"=>3}, "reader_count_by_user_role"=>{"Unspecified"=>2, "Professor > Associate Professor"=>2, "Researcher"=>9, "Student > Doctoral Student"=>5, "Student > Ph. D. 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Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1991793"], "description"=>"<p>Force traces from trials in which a 12s grip was exerted in Experiment 2 (A,C,E,G) and Experiment 1 (B,D,F,H). <b>(A,B)</b> Trial-by-trial (top) and trial-average (bottom) percentage of participants that had reached the required effort level (color bar: 0 = no participant, 1 = all participants). <b>(C,D)</b> Group average 12s force trace for six different effort levels, expressed as percentage of participants’ maximum voluntary contraction (MVC; Experiment 1: equally sized bins between [0,1]; Experiment 2: [0.15 0.25 0.4 0.55 0.75 1];). There was an offset at t = 0 in Experiment 1 because the 12s-effort directly followed the response which was indicated by briefly squeezing the gripper of the corresponding hand at a level of 0.35 (see <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004116#sec013\" target=\"_blank\">Materials and Methods</a>). <b>(E,F)</b> Within-trace average variability (STD = standard deviation) for the same six effort levels increases with increasing difficulty (group average ± SEM). <b>(G,H)</b> Required force levels are plotted against mean exerted force levels for every effort trial, showing a strong correlation in both experiments. The produced force consistently exceeded the required force level by a small amount because participants were instructed to maintain their force just above the target bar and because they kept a ‘safety margin’ to account for slight variations in force.</p>", "links"=>[], "tags"=>["devaluation", "effort discounts reward", "delay discounting", "decision costs decrease", "experiments effort costs", "reward value changes", "Bayesian model comparison", "assumption", "effort discounting behavior"], "article_id"=>1358336, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Miriam C. Klein-Flügge", "Steven W. Kennerley", "Ana C. Saraiva", "Will D. Penny", "Sven Bestmann"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004116.g002", "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Force_grips_Completion_of_force_grip_on_effort_trials_/1358336", "title"=>"Force-grips: Completion of force-grip on effort trials.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-03-27 03:05:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/1991804", "https://ndownloader.figshare.com/files/1991806", "https://ndownloader.figshare.com/files/1991809", "https://ndownloader.figshare.com/files/1991811", "https://ndownloader.figshare.com/files/1991812", "https://ndownloader.figshare.com/files/1991814", "https://ndownloader.figshare.com/files/1991816", "https://ndownloader.figshare.com/files/1991817", "https://ndownloader.figshare.com/files/1991819", "https://ndownloader.figshare.com/files/1991820"], "description"=>"<div><p>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.</p></div>", "links"=>[], "tags"=>["devaluation", "effort discounts reward", "delay discounting", "decision costs decrease", "experiments effort costs", "reward value changes", "Bayesian model comparison", "assumption", "effort discounting behavior"], "article_id"=>1358342, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Miriam C. Klein-Flügge", "Steven W. Kennerley", "Ana C. Saraiva", "Will D. Penny", "Sven Bestmann"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1004116.s001", "https://dx.doi.org/10.1371/journal.pcbi.1004116.s002", "https://dx.doi.org/10.1371/journal.pcbi.1004116.s003", "https://dx.doi.org/10.1371/journal.pcbi.1004116.s004", "https://dx.doi.org/10.1371/journal.pcbi.1004116.s005", "https://dx.doi.org/10.1371/journal.pcbi.1004116.s006", "https://dx.doi.org/10.1371/journal.pcbi.1004116.s007", "https://dx.doi.org/10.1371/journal.pcbi.1004116.s008", "https://dx.doi.org/10.1371/journal.pcbi.1004116.s009", "https://dx.doi.org/10.1371/journal.pcbi.1004116.s010"], "stats"=>{"downloads"=>19, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Behavioral_Modeling_of_Human_Choices_Reveals_Dissociable_Effects_of_Physical_Effort_and_Temporal_Delay_on_Reward_Devaluation_/1358342", "title"=>"Behavioral Modeling of Human Choices Reveals Dissociable Effects of Physical Effort and Temporal Delay on Reward Devaluation", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2015-03-27 03:05:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/1991796"], "description"=>"<p>Choices explained by the model (Experiment 1).</p>", "links"=>[], "tags"=>["devaluation", "effort discounts reward", "delay discounting", "decision costs decrease", "experiments effort costs", "reward value changes", "Bayesian model comparison", "assumption", "effort discounting behavior"], "article_id"=>1358339, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Miriam C. Klein-Flügge", "Steven W. Kennerley", "Ana C. Saraiva", "Will D. Penny", "Sven Bestmann"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004116.t001", "stats"=>{"downloads"=>0, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Choices_explained_by_the_model_Experiment_1_/1358339", "title"=>"Choices explained by the model (Experiment 1).", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-03-27 03:05:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/1991795"], "description"=>"<p><b>A</b>, Offered stimuli and choices from Experiment 2 are shown for the participants with the steepest, median and shallowest discounting, respectively, separately for the block with a default reward magnitude of 40p (left) and £2 (right). Stimuli were dynamically adjusted using a hidden staircase procedure to identify individual participant’s indifference points. For each effort level, the offered reward magnitude is displayed as the default option divided by the alternative option, thus yielding values <1 when the alternative option was associated with more reward (for alternative representations of indifference points, see <b>panel C</b>). Choices of the higher-reward/higher-cost option are indicated in green, choices of the effortless default option in red, and inferred multiplicative indifference points in black. <b>B,</b> Procedure for determining indifference points: A sigmoid function was fitted separately to the choices generated at each effort level (y axis: 0 = default option chosen, 1 = alternative option chosen), given the reward magnitude of the alternative option (x axis). The point of subjective indifference between the default and the alternative option was defined as the reward magnitude at which where the sigmoid crossed y = 0.5. Exemplary choices and fits are shown for one subject and two effort levels in the £2 (200p) block. <b>C</b>, Individual (coloured) and average (black) group indifference points show that effort discounting follows a concave, rather than convex, shape and scales with offer magnitude (40p vs. £2). Shown are the raw untransformed indifference magnitudes (top), a multiplicative representation of indifference points, i.e. default magnitude / indifferent magnitude (middle), and an additive representation of indifference points, i.e. (default magnitude—(indifferent magnitude—default magnitude)) (bottom). A multiplicative representation yields a percentage decrease, whereas an additive representation reflects the absolute decrease in value, which can easily lead to negative values. These data do not depend on any model fits and in all cases clearly reflect the concave nature of effort discounting, the main message of this study.</p>", "links"=>[], "tags"=>["devaluation", "effort discounts reward", "delay discounting", "decision costs decrease", "experiments effort costs", "reward value changes", "Bayesian model comparison", "assumption", "effort discounting behavior"], "article_id"=>1358338, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Miriam C. Klein-Flügge", "Steven W. Kennerley", "Ana C. Saraiva", "Will D. Penny", "Sven Bestmann"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004116.g004", "stats"=>{"downloads"=>1, "page_views"=>26, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Experiment_2_Model_free_analysis_of_effort_discounting_/1358338", "title"=>"Experiment 2: Model-free analysis of effort discounting.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-03-27 03:05:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/1991797"], "description"=>"<p>No evidence for a relationship between effort and delay discounting.</p>", "links"=>[], "tags"=>["devaluation", "effort discounts reward", "delay discounting", "decision costs decrease", "experiments effort costs", "reward value changes", "Bayesian model comparison", "assumption", "effort discounting behavior"], "article_id"=>1358340, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Miriam C. Klein-Flügge", "Steven W. Kennerley", "Ana C. Saraiva", "Will D. Penny", "Sven Bestmann"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004116.t002", "stats"=>{"downloads"=>2, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_No_evidence_for_a_relationship_between_effort_and_delay_discounting_/1358340", "title"=>"No evidence for a relationship between effort and delay discounting.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-03-27 03:05:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/1991794"], "description"=>"<p><b>A</b>, 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>B</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). <b>C</b>, Individual and average fits of the sigmoidal winning model for effort and the hyperbolic winning model for delay. <b>D</b>, Mean squared error, indicating the goodness of fit of the two competing models for the effort and delay task. <b>E</b>, Individual and average model fits, as in <b>C</b>, 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.</p>", "links"=>[], "tags"=>["devaluation", "effort discounts reward", "delay discounting", "decision costs decrease", "experiments effort costs", "reward value changes", "Bayesian model comparison", "assumption", "effort discounting behavior"], "article_id"=>1358337, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Miriam C. Klein-Flügge", "Steven W. Kennerley", "Ana C. Saraiva", "Will D. Penny", "Sven Bestmann"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004116.g003", "stats"=>{"downloads"=>0, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Experiment_1_Comparison_of_effort_and_delay_discounting_/1358337", "title"=>"Experiment 1: Comparison of effort and delay discounting.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-03-27 03:05:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/1991792"], "description"=>"<p><b>A,</b> In <b>Experiment 1</b>, participants chose between a higher-reward/higher-cost (HRHC) option and a lower-reward/lower-cost option (LRLC; reward magnitude = number; cost level = height of red bar). Magnitudes and costs of both options varied from trial to trial. To directly dissociate effort and delay discounting, decision costs entailed a fixed-duration 12s power grip adjusted to individuals’ maximum grip force on one day (effort task), and a delay to reward (0–75 weeks) on the other day (delay task). Efforts were exerted on an unpredictable 30% of trials (∼15% per hand). <b>B</b>, In <b>Experiment 2</b>, participants made choices between a ‘default’ option of 40p (block1) or £2 (block2) available for no effort (left) and an alternative option of varying effort and magnitude (right). Participants’ points of subjective indifference were obtained by independently adjusting the magnitude of the alternative option for six levels of effort ([0.15 0.25 0.4 0.55 0.75 1]) across trials, using interleaved staircases. Again, decision costs entailed a 12s grip at the indicated force level. Efforts were exerted using the right hand on an unpredictable 15% of trials, thus avoiding fatigue, and feedback was given upon completion of the required force level for 12 seconds. <b>C,</b> The height of the red bar signaled the cost, with the top of the grey box, i.e. the maximum cost, corresponding to 75 weeks (delay task, Exp 1) or a participant’s maximum 12s-grip force. All data figures display costs according to this scale. The reward size was displayed as a number above the cost display. The information in this box was explained to the participant, but was not visible on the screen.</p>", "links"=>[], "tags"=>["devaluation", "effort discounts reward", "delay discounting", "decision costs decrease", "experiments effort costs", "reward value changes", "Bayesian model comparison", "assumption", "effort discounting behavior"], "article_id"=>1358335, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Miriam C. Klein-Flügge", "Steven W. Kennerley", "Ana C. Saraiva", "Will D. Penny", "Sven Bestmann"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004116.g001", "stats"=>{"downloads"=>2, "page_views"=>64, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Task_Effort_and_delay_discounting_task_/1358335", "title"=>"Task: Effort and delay discounting task.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-03-27 03:05:06"}

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{"start_date"=>"2015-01-01T00:00:00Z", "end_date"=>"2015-12-31T00:00:00Z", "subject_areas"=>[]}
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