The role of cognitive effort in subjective reward devaluation and risky decision-making

Abstract

Motivation is underpinned by cost-benefit valuations where costs-such as physical effort or outcome risk-are subjectively weighed against available rewards. However, in many environments risks pertain not to the variance of outcomes, but to variance in the possible levels of effort required to obtain rewards (effort risks). Moreover, motivation is often guided by the extent to which cognitive-not physical-effort devalues rewards (effort discounting). Yet, very little is known about the mechanisms that underpin the influence of cognitive effort risks or discounting on motivation. We used two cost-benefit decision-making tasks to probe subjective sensitivity to cognitive effort (number of shifts of spatial attention) and to effort risks. Our results show that shifts of spatial attention when monitoring rapidly presented visual stimuli are perceived as effortful and devalue rewards. Additionally, most people are risk-averse, preferring safe, known amounts of effort over risky offers. However, there was no correlation between their effort and risk sensitivity. We show for the first time that people are averse to variance in the possible amount of cognitive effort to be exerted. These results suggest that cognitive effort sensitivity and risk sensitivity are underpinned by distinct psychological and neurobiological mechanisms.

Figure 1. RSVP Trial Structure.

(A) To manipulate cognitive effort we controlled the number of peripheral shifts of attention in an RSVP task. Participants were required to maintain central fixation as an array of letters changed rapidly and attend to a “target” stream presented horizontally to the left or right of a central stream, in order to detect targets (the number “7”). The initial target side was indicated at the beginning of the trial by an arrow. During each trial a cue in the centre of the screen (a number “3) indicated that the target side was switching, requiring participants to make a peripheral shift of attention. Effort was manipulated by controlling the number of presentations of shift cues from one to six. In the training session feedback was provided in the form of credits (1 credit or 0) at the end of each trial if participants successfully detected a sufficient number of targets. (B) Effort discounting task (EDT). Choices were made between a fixed “baseline” and a variable “offer”. The baseline was fixed at the lowest effort and reward (1 credit, 1 shift). The offer varied in terms of reward and effort (2, 4, 6, 8, 10 credits and 2, 3, 4, 5, 6 shifts). Choices on this task indexed the extent to which rewards were devalued by shifts of attention. (C) Risky Effort Task (RET). Choices were made between a safe option, with fixed effort and reward levels (3 shifts, 4 credits) and a risky option which varied over trials in terms of reward (2, 4, 6 credits) and risk (low or high). The risky option was associated with a 50% probability of having to perform one of two effort levels that were low (2 or 4 shifts) or high (1 or 5 shifts) in variance. The extent to which the safe option was chosen indexed cognitive effort risk-aversion or risk-sensitivity.

Figure 2. Shifts of attention are effortful and devalue rewards.

(A) Proportion of trials where the higher effort option (y-axis) was preferred, as a function of the number of shifts of attention in the offer (x-axis). As the number of shifts of attention offered increased, the less likely it was that the offer was chosen. (B) Proportion of trials the higher reward option (y-axis) was selected, as a function of the reward on offer (x-axis). As the amount of reward offered increased, the less likely it was that the offer was chosen. (C) Results of a logistic regression. Mean normalised betas for predictors of choosing the higher effort, higher reward offer. Effort was a significantly better predictor of choice than two other control predictors, the number of button presses and a task success predictor. (D) Results from the NASA-TLX. Participants completed the self-report NASA-TLX for each effort level, rating how demanding they found each number of shifts (x-axis) from −10 to + 10 (y-axis). Crucially, the higher the number of shifts of attention, the more mentally demanding (blue) and effortful (red) the ratings. Error bars depict SEM.

Figure 3. Aversion to cognitive effort risk.

(A) Proportion of trials on which the risky offer was chosen (y-axis), as a function of the reward level in the risky offer, which was either lower, equal to, or higher than the reward for the safe option (4 credits). We found that people were averse to the risky offers, choosing it on less than 50% of trials even when the reward in the risky offer was equal to the reward in the safe option. Moreover, we showed a risk level x reward interaction highlighting that people were more likely to choose the safe option when the variance in the risky option was high. (B) Logistic regression. Mean, normalised betas (y-axis) for predictors of choosing the risky effort option, showing an effect of risk level and reward, but no effect of physical effort (mean no. of button presses in training session) and failure rate (a mean of the number of false alarms and misses in the training session). N.S = not significant. Error bars depict SEM.

Figure 4. Fixation and saccades during the training session.

(A) Heatmap showing a histogram of the fixation locations during the training session, overlaid on an array from the RSVP trials. The heat map shows that participants were performing the task as instructed, fixating centrally and not on the target streams. (B) Graph of the average number of saccades (y-axis) per each effort level (x-axis), showing that as the number of shifts of attention increased, the mean number of saccades did not. (C) Graph of the same data as in (B) but plotted as a function of the effort levels in the safe option (3 shifts), the low risk (2 and 4 shifts) or high risk (1 and 5 shifts). Error bars depict SEM.