Do humans prefer cognitive effort over doing nothing?

Humans and other animals find mental (and physical) effort aversive and have the fundamental drive to avoid it. However, exerting no effort, doing nothing, is also aversive: it leads to boredom. Here, we ask whether people choose to exert effort when the alternative is to do nothing at all. Across nine studies, participants completed variants of the demand selection task, in which they repeatedly selected between a cognitively effortful task (e.g., simple addition, Stroop task) and a task that required no effort (e.g., doing nothing, watching the computer complete the Stroop). We then tabulated people’s choices. Across all studies and a mini meta-analysis, we found no evidence of effort avoidance and sometimes even a preference for effort when the alternative was doing nothing. Our findings reveal the limits of effort avoidance, suggesting that people do not seek to completely minimize effort expenditure.

More Effort, Less Fatigue: The Role of Interest in Increasing Effort and Reducing Mental Fatigue

People generally prefer easier over more difficult mental tasks. Using two different adaptations of a demand selection task, we show that interest can influence this effect, such that participants choose options with a higher cognitive workload. Interest was also associated with lower feelings of fatigue. In two studies, participants (N = 63 and N = 158) repeatedly made a choice between completing a difficult or easy math problem. Results show that liking math predicts choosing more difficult (vs. easy) math problems (even after controlling for perceived math skill). Two additional studies used the Academic Diligence Task (Galla et al., 2014), where high school students (N = 447 and N = 884) could toggle between a math task and playing a video game/watching videos. In these studies, we again find that math interest relates to greater proportion of time spent on the math problems. Three of these four studies also examined perceived fatigue, finding that interest relates to lower fatigue. An internal meta-analysis of the four studies finds a small but robust effect of interest on both the willingness to exert greater effort and the experience of less fatigue (despite engaging in more effort).

Acute Psychosocial Stress Increases Cognitive-Effort Avoidance

Adverse effects following acute stress are traditionally thought to reflect functional impairments of central executive-dependent cognitive-control processes. However, recent evidence demonstrates that cognitive-control application is perceived as effortful and aversive, indicating that stress-related decrements in cognitive performance could denote decreased motivation to expend effort instead. To investigate this hypothesis, we tested 40 young, healthy individuals (20 female, 20 male) under both stress and control conditions in a 2-day study that had a within-subjects design. Cognitive-effort avoidance was assessed using the demand-selection task, in which participants chose between performing low-demand and high-demand variants of a task-switching paradigm. We found that acute stress indeed increased participants’ preference for less demanding behavior, whereas task-switching performance remained intact. Additional Bayesian and multiverse analyses confirmed the robustness of this effect. Our findings provide novel insights into how stressful experiences shape behavior by modulating our motivation to employ cognitive control.

Information about task progress modulates cognitive demand avoidance

People tend to avoid engaging in cognitively demanding tasks unless it is ‘worth our while’—that is, if the benefits outweigh the costs of effortful action. Yet, we seemingly partake in a variety of effortful mental activities (e.g. playing chess, completing Sudoku puzzles) because they impart a sense of progress. Here, we examine the possibility that information about progress—specifically, the number of trials completed of a demanding cognitive control task, relative to the total number of trials to be completed—reduces individuals’ aversion to cognitively effort activity, across four experiments. In Experiment 1, we provide an initial demonstration that presenting progress information reduces individuals’ avoidance of cognitively demanding activity avoidance using a variant of the well-characterized Demand Selection Task (DST). The subsequent experiments buttress this finding using a more sophisticated within-subjects versions of the DST, independently manipulating progress information and demand level to further demonstrate that, 1) people prefer receiving information about temporal progress in a task, and 2) all else being equal, individuals will choose to exert greater levels of cognitive effort when it confers information about their progress in a task. Together, these results suggest that progress information can motivate cognitive effort expenditure and, in some cases, override individuals’ default bias towards demand avoidance.

Deep learning guided image-based droplet sorting for on-demand selection and analysis of single cells and 3D cell cultures

To uncover the heterogeneity of cellular populations and multicellular constructs we show on-demand isolation of single mammalian cells and 3D cell cultures by coupling bright-field microdroplet imaging with real-time classification and sorting using convolutional neural networks.

Cognitive Effort-Avoidance in Patients with Schizophrenia Can Reflect Amotivation: An event-related potential Study

Background: Amotivation is regarded as a core negative symptom in patients with schizophrenia. There are currently no objective methods for assessing and measuring amotivation in the scientific literature, only a trend towards assessing motivation using effort-orientated, decision-making tasks. However, it remains inconclusive as to whether cognitive effort-avoidance in patients with schizophrenia can reflect their amotivation. Therefore, this study aimed to find out whether cognitive effort-avoidance in patients with schizophrenia can reflect their amotivation. Methods: In total, 28 patients with schizophrenia and 27 healthy controls were selected as participants. The demand selection task (DST) was adapted according to the feedback-based Guilty Knowledge Test (GKT) delayed response paradigm, which was combined with the mean amplitude of contingent negative variation (CNV), considered as the criterion of motivation. Results: Our results showed that: (1) patients with schizophrenia showed a lower CNV amplitude for the target stimuli compared to the probe stimuli, whereas the control group showed the opposite trend (P<0.05); (2) among patients with schizophrenia, the high cognitive effort-avoidance group showed a smaller CNV amplitude for the target stimuli compared to the probe stimuli, whereas the low cognitive effort avoidance group showed a higher CNV amplitude for the target stimuli compared to the probe stimuli; the opposite trend was observed in the control group (P<0.05). Conclusion: These findings support the claim that CNV amplitude can be used as a criterion for detecting amotivation in patients with schizophrenia. Within the context of the DST, the high and low cognitive effort-avoidance of patients with schizophrenia can reflect their state of amotivation; patients with high cognitive effort-avoidance showed severe amotivation.

A Capacitated Location-Inventory Model with Demand Selection

In this paper, we consider an integrated supply chain network design problem, which incorporates inventory and pricing decisions into the capacitated facility location model. We assume that each warehouse has a capacity limitation that limits the average demand flowing through the warehouse and that the supplier can choose whether to satisfy each potential retailer’s demand. We formulate the problem as a nonlinear integer programming model and solve the model via a Lagrangian relaxation based approach. We develop an efficient algorithm to solve the subproblem that arises from the Lagrangian relaxation procedure. Finally, we conduct extensive computational experiments to test the performance of the algorithms proposed in this paper and provide the managerial insights based on the computational results.