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Dissecting the contributions of rewards and effort on motivation

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Valton, V., Mkrtchian, A., Payne, M., Gray, A., Samborska, V., Van Urk, S., et al. (2018). Dissecting the contributions of rewards and effort on motivation. Poster presented at Computational and Systems Neuroscience Meeting (COSYNE 2018), Denver, CO, USA.

Cite as: https://hdl.handle.net/21.11116/0000-0003-E85D-0
Motivation is an essential component of neuro-economic decisions. It can be defined as the willingness to exert
effort to attain a particular outcome, which is dependent on the magnitude of anticipated rewards and the effort
costs required. Understanding motivation has wide ranging implications from being able to better gauge inter-
individual variation in healthy decision-making, to dissecting symptoms such as anhedonia in depression and
Here we present a computational analysis of motivation, as assessed by the Apple Gathering Task (AGT), in
two independent datasets: a healthy sample (n=103), and a clinical sample (n=163—Low-risk, Familial Risk,
Remitted, Depressed). In the AGT, participants squeezed a hand-dynamometer to win points. Effort required
(force) and potential reward (money) were manipulated on a trial-by-trial basis. Subjects could either accept the
challenge and exert effort, or refuse and skip the trial.
We analysed participants’ choices using 70 competing hierarchical models of trial-by-trial behaviour. After identi-
fying the winning model through model comparison we extracted parameters reflecting individual levels of reward
and effort sensitivity. The same model won on both dataset. We then compared the model parameters to four la-
tent variables measuring low-mood/anxiety, anhedonia, apathy and dysfunctional attitudes: these were extracted
using factor analysis on 10 clinical questionnaires, with similar factor solutions found in both datasets.
As expected participants’ willingness to accept an offer decreased significantly with increasing effort level, and
increased significantly with increasing reward level; this pattern was accurately captured by our computational
analysis. Most importantly, subjects with higher effort sensitivity parameters scored higher on anhedonia (r=0.22,
p=0.037) and dysfunctional attitudes (r=0.25, p=0.017), while those with lower reward sensitivity parameters
scored higher on low-mood/anxiety (r=0.35, p=0.0004).
This analysis suggests that we may be able to dissect the individual contributions of reward and effort on motiva-