Overlapping neural systems represent cognitive effort and reward anticipation

Anticipating a potential benefit and how difficult it will be to obtain it are valuable skills in a constantly changing environment. In the human brain, the anticipation of reward is encoded by the Anterior Cingulate Cortex (ACC) and Striatum. Naturally, potential rewards have an incentive quality, resulting in a motivational effect improving performance. Recently it has been proposed that an upcoming task requiring effort induces a similar anticipation mechanism as reward, relying on the same cortico-limbic network. However, this overlapping anticipatory activity for reward and effort has only been investigated in a perceptual task. Whether this generalizes to high-level cognitive tasks remains to be investigated. To this end, an fMRI experiment was designed to investigate anticipation of reward and effort in cognitive tasks. A mental arithmetic task was implemented, manipulating effort (difficulty), reward, and delay in reward delivery to control for temporal confounds. The goal was to test for the motivational effect induced by the expectation of bigger reward and higher effort. The results showed that the activation elicited by an upcoming difficult task overlapped with higher reward prospect in the ACC and in the striatum, thus highlighting a pivotal role of this circuit in sustaining motivated behavior

Cognitive Effort and Aphasia

Some researchers have suggested that impairments of individuals with aphasia on cognitive-linguistic tasks reflect an impaired ability to match effort with task demands (e.g. Murray et al., 1997, Clark & Robin, 1991). However, a direct physiological measure of effort IWA invest during such tasks is lacking. Heart rate variability is a well-studied measure of the stress response and is an indicator of the effort allocated to cognitively demanding tasks (Hansen et al., 2003). This research will utilize HRV to understand the relationship among perceptions of task difficulty, behavioral performance, and effort allocated to a verbal working memory task

Cognitive Effort and Memory

We propose that the concept of cognitive effort in memory is both useful and important. Cognitive effort is defined as the engaged proportion of limited-capacity central processing. It·was hypothesized that this variable might have important memorial consequences and might also be a potential confounding factor in levels-of-processing paradigms. The first experiment tested this possibility using two types of incidental-learning tasks factorially combined with two degrees of effort. It was found that high effort led to better recall than low effort, but that level-of-processing effects were nonsignificant. A second experiment clearly demonstrated the feasibility of using performance on a secondary task as an independent criterion for measuring effort, and two further experiments ruled out alternative accounts of effort effects. A reliable levels-of-processing effect was obtained in the fourth experiment in which the incidental-learning tasks were blocked. Implications and possible future applications of the cognitive effort concept are discussed

Design activities: how to analyze cognitive effort associated to cognitive treatments?

Working memory issues are important in many real activities. Thus, measuring cognitive effort (or mental load) has been a main research topic for years in cognitive ergonomics, though no consensual method to study such aspect has been proposed. In addition, we argue that cognitive effort has to be related to an analysis of the evolution of cognitive processes, which has been called "time processing". Towards this end, we present and discuss paradigms that have been used for years to study writing activities and, in experiments reported in this paper, for studying design activities, such as computer-graphic tasks or web site desig

Need for cognitive closure modulates how perceptual decisions are affected by task difficulty and outcome relevance

The aim of this study was to assess the extent to which Need for Cognitive Closure (NCC), an individual-level epistemic motivation, can explain inter-individual variability in the cognitive effort invested on a perceptual decision making task (the random motion task). High levels of NCC are manifested in a preference for clarity, order and structure and a desire for firm and stable knowledge. The study evaluated how NCC moderates the impact of two variables known to increase the amount of cognitive effort invested on a task, namely task ambiguity (i.e., the difficulty of the perceptual discrimination) and outcome relevance (i.e., the monetary gain associated with a correct discrimination). Based on previous work and current design, we assumed that reaction times (RTs) on our motion discrimination task represent a valid index of effort investment. Task ambiguity was associated with increased cognitive effort in participants with low or medium NCC but, interestingly, it did not affect the RTs of participants with high NCC. A different pattern of association was observed for outcome relevance; high outcome relevance increased cognitive effort in participants with moderate or high NCC, but did not affect the performance of low NCC participants. In summary, the performance of individuals with low NCC was affected by task difficulty but not by outcome relevance, whereas individuals with high NCC were influenced by outcome relevance but not by task difficulty; only participants with medium NCC were affected by both task difficulty and outcome relevance. These results suggest that perceptual decision making is influenced by the interaction between context and NC

Cognitive effort and active inference

This paper aims to integrate some key constructs in the cognitive neuroscience of cognitive control and executive function by formalising the notion of cognitive (or mental) effort in terms of active inference. To do so, we call upon a task used in neuropsychology to assess impulse inhibition—a Stroop task. In this task, participants must suppress the impulse to read a colour word and instead report the colour of the text of the word. The Stroop task is characteristically effortful, and we unpack a theory of mental effort in which, to perform this task accurately, participants must overcome prior beliefs about how they would normally act. However, our interest here is not in overt action, but in covert (mental) action. Mental actions change our beliefs but have no (direct) effect on the outside world—much like deploying covert attention. This account of effort as mental action lets us generate multimodal (choice, reaction time, and electrophysiological) data of the sort we might expect from a human participant engaging in this task. We analyse how parameters determining cognitive effort influence simulated responses and demonstrate that—when provided only with performance data—these parameters can be recovered, provided they are within a certain range

Measuring cognitive effort without difficulty

An important finding in the cognitive effort literature has been that sensitivity to the costs of effort varies between individuals, suggesting that some people find effort more aversive than others. It has been suggested this may explain individual differences in other aspects of cognition; in particular that greater effort sensitivity may underlie some of the symptoms of conditions such as depression and schizophrenia. In this paper, we highlight a major problem with existing measures of cognitive effort that hampers this line of research, specifically the confounding of effort and difficulty. This means that behaviour thought to reveal effort costs could equally be explained by cognitive capacity, which influences the frequency of success and thereby the chance of obtaining reward. To address this shortcoming, we introduce a new test, the Number Switching Task (NST), specially designed such that difficulty will be unaffected by the effort manipulation and can easily be standardised across participants. In a large, online sample, we show that these criteria are met successfully and reproduce classic effort discounting results with the NST. We also demonstrate the use of Bayesian modelling with this task, producing behavioural parameters which can be associated with other measures, and report a preliminary association with the Need for Cognition scale

Neural Dynamics Tracking Subjective Cognitive Effort

What patterns of brain activity reflect engagement with highly demanding cognitive tasks? How do these patterns relate to subjective, phenomenal effort? Answering these questions is critical to understanding what causes some people to experience cognitive tasks as more effortful than others. Subjective experience, in turn, is vital, with trait tendencies to exert effort having been linked to career and academic success. High subjective effort, as in schizophrenia and depression, can thus be extremely problematic. And yet, poor operational definitions have constrained research into basic questions about what neural dynamics track subjective effort. Here, a powerful, new behavioral economic operationalization is employed, in combination with fMRI, to investigate brain dynamics corresponding to subjectively costly cognitive effort. Brain regions varying in activity by working memory load and cognitive control demands are strong candidates for tracking subjective effort (Westbrook & Braver, 2015). To identify such regions, I examined BOLD data, collected while participants performed a well established working memory task (the N-back; Kirchner, 1958) that is both subjectively effortful, and for which subjective effort varies as a monotonic function of load (Westbrook et al., 2013). I focused my search within independently-defined networks of nodes that co-vary (within-network) across a wide range of brain states. Specifically, I examined a subset of a priori task-positive networks, as identified by Power et al. (2011), which typically show increasing, and a task-negative network which typically shows decreasing activity with greater load. Importantly, variation was examined over N-back loads for which data has never been published, thus the present study reveals novel insights about activity-load functions in independently-defined functional networks from very low (N = 1) to very high loads (N = 6). As expected, all task-positive networks showed robustly greater activity during the N-back. However, patterns of variation by load differed by network. While the task positive fronto-parietal (FP), dorsal attention (DorAtt), and salience (Sal) networks showed inverted-U functions, peaking mid-range (at the 2- or 3-back) and decreasing after, the cingulo-opercular network (CO) showed robust activity that did not further vary by load. Rather than encoding load per se, the CO simply encoded that a participant was performing the N-back. The task-negative default mode network (DMN) was robustly and increasingly de activated across all load levels examined. Given that both subjective effort (Westbrook et al., 2013) and DMN deactivation are approximately monotonic functions of load, the DMN is a strong candidate for tracking variation in subjective effort with load. By contrast, inverted-U functions in the FP, Sal, and DorAtt networks do not straightforwardly map to monotonically increasing effort. Performance measures instead suggest that inverted-U functions tracked individual differences in adaptive strategy shifting. Namely, when participants were divided by 3-back performance, better performers showed a pronounced inverted-U (over N = 1—3) while worse performers did not. Interestingly, a similar pattern was found when dividing participants according subjective effort, providing tentative support to a hypothesis that subjective effort acts as a cue to shift strategies adaptively under excessive demands. In any case, surprisingly, in none of the networks did load-specific changes in brain activity predict load-specific changes in subjective effort. Critically, although load-specific patterns of brain activity did not predict subjective effort, load-independent brain activity predicted individual differences in subjective effort. Namely, higher average brain activity in any of the task-positive networks predicted greater subjective effort. At the sub-network level, this was notably true for two key regions that have been implicated as core components of a cognitive control system, and also hypothesized to track effort costs: the dorsal anterior cingulate cortex (dACC) and the dorsolateral prefrontal cortex (dlPFC) (McGuire et al., 2010). Importantly, after controlling for performance, the dACC remained a reliable predictor of subjective effort, while the dlPFC did not, supporting that the dACC tracks cognitive effort apart from task difficulty (while the dlPFC may not). This is consistent with strong prior theory implicating the dACC in regulating the intensity of cognitive control in response to flagging performance and in proportion to the expected value of doing so (Shenhav et al., 2013). The present results begin to answer basic questions about how the brain tracks subjective effort. They also lay the foundation for future work addressing why subjective effort can be so much greater for some individuals, like those with schizophrenia or depression, and also future work developing interventions for promoting desirable effort expenditure

A Computational Analysis of Cognitive Effort

Cognitive effort is a concept of unquestionable utility in understanding human behaviour. However, cognitive effort has been defined in several ways in literature and in everyday life, suffering from a partial understanding. It is common to say “Pay more attention in studying that subject” or “How much effort did you spend in resolving that task?”, but what does it really mean? This contribution tries to clarify the concept of cognitive effort, by introducing its main influencing factors and by presenting a formalism which provides us with a tool for precise discussion. The formalism is implementable as a computational concept and can therefore be embedded in an artificial agent and tested experimentally. Its applicability in the domain of AI is raised and the formalism provides a step towards a proper understanding and definition of human cognitive effort