Response effort discounts the subjective value of rewards

Validation of a novel effort-discounting assessment and evaluation of the effort-delay confound on effort discounting

A vast literature highlights the prevalence of impulsive decision making in maladaptive outcomes. Most research has focused on one form-delay discounting. Less research has focused on effort discounting, possibly because of a lack of a standardized task for assessment. In published effort-discounting tasks, effort is conceptualized in many ways, making it difficult to compare findings across studies. Additionally, most effort-discounting tasks do not control for the time inherent in completing the effortful task, which makes it difficult to disentangle effort discounting from delay discounting. The current study evaluated the validity of a novel hypothetical effort-discounting task. The novel task was used to evaluate the influence of the effort-delay confound on rates of effort discounting in humans. Participants were randomly assigned to complete a confounded or a controlled version of the novel effort-discounting task. The effort-discounting data were well described by hyperbolic and exponential functions. When effort and delay were confounded, effort-discounting rates were significantly higher than when effort alone influenced discounting. The results suggest that data that are produced by effort-discounting tasks that do not control the effort-delay confound should be interpreted cautiously because they are also influenced by delay discounting. Task limitations and future directions are discussed.

Carrots for the donkey: Influence of evaluative conditioning and training on self-paced exercise intensity and delay discounting of exercise in healthy adults

To choose exercise over alternative behaviours, subjective reward evaluation of the potential choices is a principal step in decision making. However, the selection of exercise intensity might integrate acute visceral responses (i.e. pleasant or unpleasant feelings) and motives related to goals (i.e. enjoyment, competition, health). To understand the factors determining the selection of exercise in its intensity and evaluation as a modality, we conducted a study combining exercise training and evaluative conditioning. Evaluative conditioning was performed by using a novel technique using a primary reinforcer (sweetness) as the unconditioned stimulus and physical strain i.e. heart rate elevation as the conditioned stimulus during interval training, using a randomized control design (N = 58). Pre, post-three weeks interval training w/o conditioning, and after 4 weeks follow-up, participants were tested on self-paced speed selection on treadmill measuring heart rate, subjective pleasantness, and effort levels, as well as delay-discounting of exercise and food rewards. Results revealed that the selection of exercise intensity was significantly increased by adaptation to training and evaluative conditioning, revealing the importance of visceral factors as well as learned expected rewards. Delay discounting rates of self-paced exercise were transiently reduced by training but not affected by evaluative conditioning. In conclusion, exercise decisions are suggested to separate the decision-making process into a modality-specific cognitive evaluation of exercise, and an exercise intensity selection based on acute visceral experience integrating effort, pleasantness, and learned rewards.

Gain-loss asymmetry in delay and effort discounting of different amounts

Loss aversion entails that people attribute greater weight to losses than to equivalent gains. In terms of discounting, it is reflected in a higher discounting rate for gains than for losses. Research on delay discounting indicates that such gain-loss asymmetry may depend on the amount of the outcome being considered. Consequently, here we address the question of how gains and losses are discounted in delay or effort conditions (physical or cognitive) across four outcome amounts. Our results replicate previous findings for intertemporal choices by showing that losses are discounted more slowly than gains, but only for smaller amounts-with no evidence of an asymmetrical evaluation for larger amounts. In physical effort discounting, we found an inverse asymmetry for the smallest amount tested (gains are discounted less steeply than losses). However, this effect was absent for larger amounts. We found no evidence to support a gain-loss asymmetry in the evaluation of gains or losses in cognitive effort. Overall, our findings indicate that loss aversion may not be as pervasive as previously expected, at least when decisions are effort-based.

A procedural analogue of prey detection and applied signal detection

Researchers have employed a variety of laboratory analogues of cryptic prey detection and applied signal detection to study factors influencing learning and performance in these ethological and applied scenarios. However, these procedural analogues do not appear to map closely onto their "real-world" counterparts, particularly with respect to the role of the "yes" (i.e., "attack") response and the payoff for this response (or its absence) on signal-present and signal-absent trials. Using domestic hens, we developed a procedural analogue in which a "yes" response requires some time to emit; such responses were reinforced only in the presence of a signal. In Experiment 1, we evaluated the influence of the "yes" response requirement by manipulating the number of responses required to qualify as a "yes" response. As the "yes" response requirement was increased, bias toward responding "no" increased, revealing that this is a critical factor controlling accuracy in this procedure. In Experiment 2, we evaluated the influence of signal probability and reinforcement rate on signal detection accuracy and found that neither of these factors significantly influenced accuracy or bias. These findings suggest that this procedural analogue may represent a valuable alternative for studying behaviour in relevant signal detection scenarios.

Cooperation moderates the impact of effort on reward valuation

Effort is valuable, but researchers have different opinions on whether effort can reduce or increase the valuation of rewards. The effect of cooperation on reward valuation also remains unclear. In this study, we conducted two experiments to examine the effect of effort on reward valuation (Experiment 1) and whether this effect can be influenced by cooperation (Experiment 2), using electroencephalogram (EEG) technology. We found that when participants worked alone, they generated a larger feedback-related negativity (FRN) amplitude for losing rewards than for gaining rewards, with more effort resulting in larger FRN amplitudes for losing rewards. However, when participants worked together with a partner, there was no significant difference between the amplitude for gaining rewards and that for losing rewards during low-effort tasks. Nevertheless, for high-effort tasks, the FRN amplitude for losing rewards was significantly larger than that for gaining rewards. Moreover, in both experiments, we found larger N1 amplitudes for gaining rewards than for losing rewards. Our ERP results suggest that in the early stage of processing, people pay extra attention to rewards, after that the effort level influences their reward valuation. In addition, cooperation regulates the reduced valuation of losing rewards only when people invest low effort.

Trait anxiety on effort allocation to monetary incentives: a behavioral and high-density EEG study

Trait anxiety is an important phenotype in the prediction of stress-induced neuropsychiatric disorders. While the role of trait anxiety in mental effort and cognitive impairment is well documented, much less is known about its influence on motivated behaviors and physical effort. Here, we investigated trait anxiety-related differences in behavioral and neural responses in an effort-related monetary incentive delay task. Participants prompted with different incentive levels could exert handgrip responses to earn monetary rewards while a 256-channel electroencephalography (EEG) was recorded. Participants' performance was linearly dependent on incentive level, with higher stakes prompting better accuracy and higher grip force. Importantly, we found a striking association between trait anxiety and incentive-related grip force; effort exertion was related to incentive level only in high-anxious individuals. In analyses of neural efficiency associated with effort preparation involving Contingent-negative variation (CNV), we found that the CNV amplitude was sensitive to monetary incentive levels. Source imaging analyses of CNV indicated increased activity in the anterior cingulate cortex (ACC) for the highest incentive level. Importantly, we found a significant interaction between trait anxiety and incentive level on CNV modulation at the interval ranging from -2610 to -2510 ms, with greater CNV responses to the lower monetary incentive sizes in high anxiety. Subsequent mediation analyses supported a mediation of the ACC activation on the association between trait anxiety and incentive-selective grip force. Our study reveals a role for ACC in trait anxiety-related differences on incentive processing, when rewards are dependent on effortful performance.

Anticipating cognitive effort: roles of perceived error-likelihood and time demands

Why are some actions evaluated as effortful? In the present set of experiments we address this question by examining individuals' perception of effort when faced with a trade-off between two putative cognitive costs: how much time a task takes vs. how error-prone it is. Specifically, we were interested in whether individuals anticipate engaging in a small amount of hard work (i.e., low time requirement, but high error-likelihood) vs. a large amount of easy work (i.e., high time requirement, but low error-likelihood) as being more effortful. In between-subject designs, Experiments 1 through 3 demonstrated that individuals anticipate options that are high in perceived error-likelihood (yet less time consuming) as more effortful than options that are perceived to be more time consuming (yet low in error-likelihood). Further, when asked to evaluate which of the two tasks was (a) more effortful, (b) more error-prone, and (c) more time consuming, effort-based and error-based choices closely tracked one another, but this was not the case for time-based choices. Utilizing a within-subject design, Experiment 4 demonstrated overall similar pattern of judgments as Experiments 1 through 3. However, both judgments of error-likelihood and time demand similarly predicted effort judgments. Results are discussed within the context of extant accounts of cognitive control, with considerations of how error-likelihood and time demands may independently and conjunctively factor into judgments of cognitive effort.

Physical and cognitive effort discounting across different reward magnitudes: Tests of discounting models

The effort required to obtain a rewarding outcome is an important factor in decision-making. Describing the reward devaluation by increasing effort intensity is substantial to understanding human preferences, because every action and choice that we make is in itself effortful. To investigate how reward valuation is affected by physical and cognitive effort, we compared mathematical discounting functions derived from research on discounting. Seven discounting models were tested across three different reward magnitudes. To test the models, data were collected from a total of 114 participants recruited from the general population. For one-parameter models (hyperbolic, exponential, and parabolic), the data were explained best by the exponential model as given by a percentage of explained variance. However, after introducing an additional parameter, data obtained in the cognitive and physical effort conditions were best described by the power function model. Further analysis, using the second order Akaike and Bayesian Information Criteria, which account for model complexity, allowed us to identify the best model among all tested. We found that the power function best described the data, which corresponds to conventional analyses based on the R2 measure. This supports the conclusion that the function best describing reward devaluation by physical and cognitive effort is a concave one and is different from those that describe delay or probability discounting. In addition, consistent magnitude effects were observed that correspond to those in delay discounting research.

Predicting Motivation: Computational Models of PFC Can Explain Neural Coding of Motivation and Effort-based Decision-making in Health and Disease

Human behavior is strongly driven by the pursuit of rewards. In daily life, however, benefits mostly come at a cost, often requiring that effort be exerted to obtain potential benefits. Medial PFC (MPFC) and dorsolateral PFC (DLPFC) are frequently implicated in the expectation of effortful control, showing increased activity as a function of predicted task difficulty. Such activity partially overlaps with expectation of reward and has been observed both during decision-making and during task preparation. Recently, novel computational frameworks have been developed to explain activity in these regions during cognitive control, based on the principle of prediction and prediction error (predicted response-outcome [PRO] model [Alexander, W. H., & Brown, J. W. Medial prefrontal cortex as an action-outcome predictor. Nature Neuroscience, 14, 1338-1344, 2011], hierarchical error representation [HER] model [Alexander, W. H., & Brown, J. W. Hierarchical error representation: A computational model of anterior cingulate and dorsolateral prefrontal cortex. Neural Computation, 27, 2354-2410, 2015]). Despite the broad explanatory power of these models, it is not clear whether they can also accommodate effects related to the expectation of effort observed in MPFC and DLPFC. Here, we propose a translation of these computational frameworks to the domain of effort-based behavior. First, we discuss how the PRO model, based on prediction error, can explain effort-related activity in MPFC, by reframing effort-based behavior in a predictive context. We propose that MPFC activity reflects monitoring of motivationally relevant variables (such as effort and reward), by coding expectations and discrepancies from such expectations. Moreover, we derive behavioral and neural model-based predictions for healthy controls and clinical populations with impairments of motivation. Second, we illustrate the possible translation to effort-based behavior of the HER model, an extended version of PRO model based on hierarchical error prediction, developed to explain MPFC-DLPFC interactions. We derive behavioral predictions that describe how effort and reward information is coded in PFC and how changing the configuration of such environmental information might affect decision-making and task performance involving motivation.