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

Cognitive effort in the Beauty Contest Game

This paper analyzes cognitive effort in 6 different one-shot p-beauty games. We use both Raven and Cognitive Reflection tests to identify subjects' abilities. We find that the Raven test does not provide any insight on beauty contest game playing but CRT does: subjects with higher scores on this test are more prone to play dominant strategies.Beauty Contest Game, Raven, Cognitive Reflection Test

Eye-tracking as a measure of cognitive effort for post-editing of machine translation

The three measurements for post-editing effort as proposed by Krings (2001) have been adopted by many researchers in subsequent studies and publications. These measurements comprise temporal effort (the speed or productivity rate of post-editing, often measured in words per second or per minute at the segment level), technical effort (the number of actual edits performed by the post-editor, sometimes approximated using the Translation Edit Rate metric (Snover et al. 2006), again usually at the segment level), and cognitive effort. Cognitive effort has been measured using Think-Aloud Protocols, pause measurement, and, increasingly, eye-tracking. This chapter provides a review of studies of post-editing effort using eye-tracking, noting the influence of publications by Danks et al. (1997), and O’Brien (2006, 2008), before describing a single study in detail. The detailed study examines whether predicted effort indicators affect post-editing effort and results were previously published as Moorkens et al. (2015). Most of the eye-tracking data analysed were unused in the previou

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

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

Conscious cognitive effort in cognitive control

[eng] Cognitive effort is thought to be familiar in everyday life, ubiquitous across multiple variations of task and circumstance, and integral to cost/benefit computations that are themselves central to the proper functioning of cognitive control. In particular, cognitive effort is thought to be closely related to the assessment of cognitive control's costs. I argue here that the construct of cognitive effort, as it is deployed in cognitive psychology and neuroscience, is problematically unclear. The result is that talk of cognitive effort may paper over significant disagreement regarding the nature of cognitive effort, and its key functions for cognitive control. I highlight key points of disagreement, and several open questions regarding what causes cognitive effort, what cognitive effort represents, cognitive effort's relationship to action, and cognitive effort's relationship to consciousness. I also suggest that pluralism about cognitive effort—that cognitive effort may manifest as a range of intentional or nonintentional actions the function of which is to promote greater success at paradigmatic cognitive control tasks—may be a fruitful and irenic way to conceive of cognitive effort. Finally, I suggest that recent trends in work on cognitive control suggests that we might fruitfully conceive of cognitive effort as one key node in a complex network of mental value, and that studying this complex network may illuminate the nature of cognitive control, and the role of consciousness in cognitive control's proper functioning

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

Cognitive effort modulates connectivity between dorsal anterior cingulate cortex and task-relevant cortical areas

Investment of cognitive effort is required in everyday life and has received ample attention in recent neurocognitive frameworks. The neural mechanism of effort investment is thought to be structured hierarchically, with dorsal anterior cingulate cortex (dACC) at the highest level, recruiting task-specific upstream areas. In the current fMRI study, we tested whether dACC is generally active when effort demand is high across tasks with different stimuli, and whether connectivity between dACC and task-specific areas is increased depending on the task requirements and effort level at hand. For that purpose, a perceptual detection task was administered that required male and female human participants to detect either a face or a house in a noisy image. Effort demand was manipulated by adding little (low effort) or much (high effort) noise to the images. Results showed a network of dACC, anterior insula (AI), and intraparietal sulcus (IPS) to be more active when effort demand was high, independent of the performed task (face or house detection). Importantly, effort demand modulated functional connectivity between dACC and face-responsive or house-responsive perceptual areas, depending on the task at hand. This shows that dACC, AI, and IPS constitute a general effort-responsive network and suggests that the neural implementation of cognitive effort involves dACC-initiated sensitization of task-relevant areas

Electrophysiological indices of anterior cingulate cortex function reveal changing levels of cognitive effort and reward valuation that sustain task performance

Successful execution of goal-directed behaviors often requires the deployment of cognitive control, which is thought to require cognitive effort. Recent theories have proposed that anterior cingulate cortex (ACC) regulates control levels by weighing the reward-related benefits of control against its effort-related costs. However, given that the sensations of cognitive effort and reward valuation are available only to introspection, this hypothesis is difficult to investigate empirically. We have proposed that two electrophysiological indices of ACC function, frontal midline theta and the reward positivity (RewP), provide objective measures of these functions. To explore this issue, we recorded the electroencephalogram (EEG) from participants engaged in an extended, cognitively-demanding task. Participants performed a time estimation task for 2 h in which they received reward and error feedback according to their task performance. We observed that the amplitude of the RewP, a feedback-locked component of the event related brain potential associated with reward processing, decreased with time-on-task. Conversely, frontal midline theta power, which consists of 4-8 Hz EEG oscillations associated with cognitive effort, increased with time-on-task. We also explored how these phenomena changed over time by conducting within-participant multi-level modeling analyses. Our results suggest that extended execution of a cognitively-demanding task is characterized by an early phase in which high control levels foster rapid improvements in task performance, and a later phase in which high control levels were necessary to maintain stable task performance, perhaps counteracting waning reward valuation