Estimating invested effort is a core dimension for evaluating own and others' actions, and views on the relationship between effort and rewards are deeply ingrained in various societal attitudes. Internal representations of effort, however, are inherently noisy, e.g. due to the variability of sensorimotor and visceral responses to physical exertion. The uncertainty in effort judgments is further aggravated when there is no direct access to the internal representations of exertion-such as when estimating the effort of another person. Bayesian cue integration suggests that this uncertainty can be resolved by incorporating additional cues that are predictive of effort, e.g. received rewards. We hypothesized that judgments about the effort spent on a task will be influenced by the magnitude of received rewards. Additionally, we surmised that such influence might further depend on individual beliefs regarding the relationship between hard work and prosperity, as exemplified by a conservative work ethic. To test these predictions, participants performed an effortful task interleaved with a partner and were informed about the obtained reward before rating either their own or the partner's effort. We show that higher rewards led to higher estimations of exerted effort in self-judgments, and this effect was even more pronounced for other-judgments. In both types of judgment, computational modelling revealed that reward information and sensorimotor markers of exertion were combined in a Bayes-optimal manner in order to reduce uncertainty. Remarkably, the extent to which rewards influenced effort judgments was associated with conservative world-views, indicating links between this phenomenon and general beliefs about the relationship between effort and earnings in society.
When comparing themselves with others, people often perceive their own actions and behaviour favourably. This phenomenon is often categorised as a bias of attribution, with favourable self-evaluation resulting from differing explanations of one's own behaviour and that of others. However, studies on availability biases offer an alternative explanation, ascribing egocentric biases to the inherent sensory asymmetries between performing an action and merely observing it. In this study, we used a paradigm that allowed us to directly compare these two distinct sources of bias. Participants perceived the tasks they performed to be harder than the tasks they observed, but demonstrated no bias driven by favourable self-evaluation. Furthermore, the degree of overestimation of the difficulty of performed tasks was magnified as overall task difficulty increased. These findings suggest that egocentric biases are in part derived from sensory asymmetries inherent to the first-person perspective.
Estimating the effort someone has spent on a task is a core dimension for evaluating own and other's actions. Interestingly, it has been shown that self-judgments of effort are influenced by the magnitude of obtained rewards (Pooresmaeili et al., 2015). However, it is unclear whether the influence of reward on effort estimations is limited to self-judgments (i.e. a form of self-serving bias) or whether reward information is invariably incorporated when judging effort, thus extending also to social contexts. Here we show that people also integrate reward magnitude when judging the effort exerted by another person. Participants (N=48, 24 pairs) performed an effortful task interleaved with a partner. In half of the trials, participants performed the task themselves and rated their own effort. In the other half, participants rated the other person's effort after watching them performing the task. After each trial but before the effort rating, both participants were informed of the gained reward for that trial. Our results show that higher rewards led to higher estimations of exerted effort. Importantly, this was true for self-as well as other-judgments. In line with a Bayesian cue integration framework, reward magnitude had a stronger effect when judging the effort of another person, compared to self-judgments, since people should have more reliable internal estimates of own effort. Supporting this claim, computational modelling revealed that reward information and objective performance criteria related to the exertion level were combined in a Bayes optimal manner to form effort estimates. Interestingly, the extent to which rewards influenced effort judgments was positively correlated with conservative world-views, suggesting links between this effect and broader social attitudes.
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