Effect of Implicit Theory on Effort Allocation Strategies in Multiple Task-Choice Situations: An Investigation From a Socio-Ecological Perspective

Implicit theories refer to two assumptions that people make about the malleability of one’s ability. Previous studies have argued that incremental theorists (who believe that ability is malleable) are more adaptive than entity theorists (who believe that ability is fixed) when facing achievement setbacks. In the present research, we assumed that the adaptive implicit theory would be different when people could choose from a wider range of tasks. It was hypothesized that incremental theorists would sustain their efforts in the first task even when it was difficult, whereas entity theorists would try to find the most appropriate task. In a pair of laboratory experiments, participants had to maximize their outcomes when allowed to choose a task to engage in, from two options. When participants were allowed to practice the two tasks (Study 1), incremental theorists tended to allocate their effort solely to the first task, whereas entity theorists tended to put equal effort into both. When participants were informed that they could switch from the assigned task (Study 2), incremental theorists tended to persist in the first task regardless of its difficulty, whereas entity theorists tended to switch more quickly if the task was difficult. These results supported our hypothesis of two effort allocation strategies and implied that, in certain situations, entity theorists could be more adaptive than incremental theorists. Based on these findings, we conducted a social survey on the difficulty of switching tasks with a real-life setting as an environmental factor that determines the adaptive implicit theory (Study 3). It was revealed that the academic performance of incremental and entity theorists was moderated by the difficulty of switching tasks in their learning environment at school. Cultural differences in implicit theories may be explained by differences in the difficulty of switching tasks in education and career choices in each society.

An integrative effort : Bridging motivational intensity theory and recent neurocomputational and neuronal models of effort and control allocation

An increasing number of cognitive, neurobiological and computational models have been proposed in the last decade, seeking to explain how humans allocate physical or cognitive effort. Most models share conceptual similarities with motivational intensity theory (MIT), an influential classic psychological theory of motivation. Yet, little effort has been made to integrate such models, which remain confined within the explanatory level for which they were developed, i.e. psychological, computational, neurobiological and neuronal. In this critical review, we derive novel analyses of three recent computational and neuronal models of effort allocation—the expected value of control (EVC) theory, the reinforcement meta-learner (RML) model, and the neuronal model of attentional effort— and establish a formal relationship between these models and MIT. Our analyses reveal striking similarities between predictions made by these models, with a shared key tenet: a non-monotonic relationship between perceived task difficulty and effort mobilization, following a saw-tooth or inverted-U shape. In addition, the models converge on the proposition that the dorsal anterior cingulate cortex (dACC) may be responsible for determining the allocation of effort and cognitive control. We conclude by discussing the distinct contributions and strengths of each theory toward understanding neurocomputational processes of effort allocation. Finally, we highlight the necessity for a unified understanding of effort allocation, by drawing novel connections between different theorizing of adaptive effort allocation as described by the presented models (cognitive, neurobiological, and neuronal levels of analysis).

Getting a tool gives wings even in schizophrenia: underestimation of tool-related effort in a motor imagery task

Humans frequently use tools to reduce action-related efforts. Interestingly, several studies have demonstrated that individuals had tool-related biases in terms of perceived effort reduction during motor imagery tasks, despite the lack of evidence of real benefits. Reduced effort allocation has been repeatedly found in schizophrenia, but it remains unknown how schizophrenia patients perceive tool-related benefits regarding effort. Twenty-four schizophrenia patients and twenty-four nonclinical participants were instructed to move the same quantities of objects with their hands or with a tool in both real and imagined situations. Imagined and real movement durations were recorded. Similarly to nonclinical participants, patients overestimated tool-related benefits and underestimated tool-related effort in terms of time when they mentally simulated a task requiring the use of a tool. No association between movement durations and psychotic symptoms was found. Our results open new perspectives on the issue of effort in schizophrenia.

Game-theoretic Analysis of Effort Allocation of Contributors to Public Projects

Public projects can succeed or fail for many reasons such as the feasibility of the original goal and coordination among contributors. One major reason for failure is that insufficient work leaves the project partially completed. For certain types of projects anything short of full completion is a failure (e.g., feature request on software projects in GitHub). Therefore, project success relies heavily on individuals allocating sufficient effort. When there are multiple public projects, each contributor needs to make decisions to best allocate his/her limited effort (e.g., time) to projects while considering the effort allocation decisions of other strategic contributors and his/her parameterized utilities based on values and costs for the projects. In this paper, we introduce a game-theoretic effort allocation model of contributors to public projects for modeling effort allocation of strategic contributors. We study the related Nash equilibrium (NE) computational problems and provide NP-hardness results for the existence of NE and polynomial-time algorithms for finding NE in restricted settings. Finally, we investigate the inefficiency of NE measured by the price of anarchy and price of stability.

Residual Effects of Cannabis Use on Effort-Based Decision-Making

Objective:Acute Δ9-tetrahydrocannabinol (THC) administration in humans (Lawn etal., 2016) and rats (Silveira, Adams, Morena, Hill, & Winstanley, 2016) has been associated with decreased effort allocation that may explain amotivation during acute cannabis intoxication. To date, however, whether residual effects of cannabis use on effort-based decision-making are present and observable in humans have not yet been determined. The goal of this study was to test whether prolonged cannabis use has residual effects on effort-based decision-making in 24-hr abstinent cannabis using adults.Method:We evaluated performance on the Effort Expenditure for Reward Task (EEfRT) in 41 adult cannabis users (mean age = 24.63 years, 21 males) and 45 nonusers (mean age = 23.90 years, 19 males). A mixed 2x3x3 ANOVA with age as a covariate was performed to examine the effect of group, probability of winning, and reward amount on EEfRT performance. EEfRT performance was operationalized as % of trials for which the hard (vs. easy) condition was chosen. Pearson’s correlations were conducted to test the relationship between EEfRT performance and measures of cannabis use, anhedonia and motivation.Results:We found that cannabis users selected hard trials significantly more than nonusers regardless of win probability or reward level. Frequency of cannabis use was positively correlated with amount of % hard trials chosen. There were no significant correlations between % hard trials chosen, self-reported anhedonia, or motivation.Conclusions:These results suggest that unlike acute effects, residual effects of cannabis following 24 hrs of abstinence are associated with greater effort allocation during effort-based decision-making.

The Effects of Mental Fatigue on Effort Allocation: Modeling and Estimation

Mental fatigue is usually accompanied by drops in task performance and reduced willingness for further exertion. A value-based theoretical account may help to explain such negative effects. In this view, mental fatigue influences perceived costs and rewards of exerting effort. However, no formal mathematical framework has yet been proposed to model and quantitatively estimate the effects of mental fatigue on subjective evaluations of effort expenditure, subject to possibly imperfect self-perceptions of internal fatigue states. We proposed a mathematical framework to model human cognitive effort allocations, assuming mental fatigue states are partially observable with semi-Markov dynamics. We modeled effort allocation decisions as consistent with the goal of maximizing cumulative subjective values over a given time horizon. We analyzed the proposed model structure and developed an estimation method to identify subjective values and hidden fatigue dynamics, which can be based on self-reports, psychophysiological indices, and behavioral effects associated with fatigue. The modeling and estimation method was tested using a simulated n-back task under a free choice paradigm, with model parameters fine-tuned from past studies. The proposed approach was able to recapitulate task performance and engagement patterns observed under mental fatigue. This work advances a reward/cost trade-off account for explaining the principles of effort exertion and suggests new avenues for both theoretically and empirically relevant understandings of how cognitive operations are affected by mental fatigue.

Task-independent metrics of computational hardness predict performance of human problem-solving

The survival of human organisms depends on our ability to solve complex tasks, which is bounded by our limited cognitive capacities. However, little is known about the factors that drive complexity of the tasks humans face and their effect on human decision-making. Here, using insights from computational complexity theory, we quantify computational hardness using a set of task-independent metrics related to the computational requirements of individual instances of a task. We then examine the relation between those metrics and human behavior and find that these metrics predict both performance and effort allocation in three canonical cognitive tasks in a similar way. Our findings demonstrate that the ability to solve complex tasks can be predicted from generic metrics of their inherent computational hardness.