For each of these measures, we calculated the change measure by subtracting the value in block 1 from the value in block 5. None of the relationships were found to be significant. To examine the effects of cognitive fatigue on our economic decision making metrics preferences and choice strategies , we compared the mean difference in changes in economic measures post-pre between the fatigue and control group Tables 1 and 3.
Cognitive fatigue did not alter response times in either the gains or losses domains.
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For each metric, we calculated the change in preference or strategy post-pre and compared between the fatigue and control groups with a simple independent sample t-test Table 1. Relationship between pre- and post- manipulation risk and ambiguity premiums values for fatigue blue and control orange groups in the gains and losses domains. Analyses of choice strategy metrics revealed a similar pattern as with the preference metrics. As the choice strategy metric is a composite, and a lack of difference in the composite measure does not mean there is no change in the components, we also examined whether there were any significant differences in the use of rEV and pWIN information separately.
Post-hoc, we noticed a pattern of reduced test-retest correlations within the fatigue group across the economic decision making measures. Relationship between pre- and post- manipulation independent r-squared values of A strategy, B rEV and C pWIN on trial-by-trial choice behaviour for both fatigue and control groups. It is possible that individual changes in fatigue or effort could account for individual differences in the economic metrics.
For example, while there is no mean shift in risk preference across groups, it is still possible that there could be a linear relationship between individual change in fatigue and individual change in risk preference. To specifically test for this post-hoc, across all participants, we correlated the change in fatigue and effort against our economic metrics of risk preference, ambiguity preference, choice strategy, the rEV r 2 , and the pWIN r 2 , in both the gains and losses domains.
None of these factors indicated a relationship that survived the necessary multiple comparison correction. We also post-hoc examined whether individual levels self-reported fatigue or effort RSME relate to changes in our economic measures risk preference, ambiguity preference, the rEV r 2 , and the pWIN r 2. None of the 8 correlations were significant. We examined how cognitive fatigue impacts economic decision making, focusing on uncertainty preferences risk and ambiguity and choice strategies in both the gains and losses domains. We found no mean shifts in preferences or strategies across either domain.
Rather, post-hoc analyses revealed that cognitive fatigue resulted in decreases in test-retest stability across both risk preferences and choice strategies. We found no mean shifts of uncertainty preferences risk or ambiguity due to cognitive fatigue. Rather, cognitive fatigue reduced the stability of risk preferences.
When fatigued, subjects were inconsistent in their risk preferences, demonstrating more variable risk attitudes. Multiple studies have suggested that suboptimal mental states are associated with increased intra-individual variability in performance measures such as reaction time [ 32 — 34 ]. Only one study, by Levy and colleagues , has previously reported on altered within-subject variability in risk preferences due to a state manipulation, finding that food deprivation led to decreased variability in risk preferences [ 35 ]. It is likely the case that alterations in decision making due to cognitive fatigue and food deprivation occur through different mechanisms, and it is interesting to see opposite effects on choice variability.
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These results highlight the importance of considering alterations in intra-individual variability in addition to the standard mean shifts. In contrast to increased instability in risk preferences, we found no changes in ambiguity preferences. This finding appears to concur with neurobiological evidence that these two preferences may be dissociable [ 36 ]. However, it is not clear if our results should be taken as further evidence of such dissociation, as we also found weaker test-retest reliability for ambiguity within the control group greater within-subject variability for ambiguity preferences , which would have limited our power for detecting alterations due to cognitive fatigue see Table 4.
In both the gains and losses domains, fatigue reduced the stability of choice strategy without leading to mean shifts i. It is quite interesting that this result mirrors the alterations we found for risk preferences, as risk preferences and choice strategy are not correlated across our participants, and ostensibly measure different aspects of decision making.
Of note, reduced effort in decision making is potentially an attractive explanation for how cognitive fatigue increased the noise in our choice strategy metric. However, such a motivational change would actually have presented as an increase in satisficing behaviour decreased choice strategy —exactly the shift that we initially hypothesized and did not find.
Rather, participants continue to, on average, employ the same relative levels of satisficing and maximizing strategies in their choice behaviour, suggesting that they are maintaining their behavioural motivation in our incentive compatible tasks. Cognitive fatigue results in greater variability in both risk preferences and choice strategies, which will result in decreased consistency in the actual choices made. Insofar as choice quality can be simply judged based upon whether you would repeat your choice given the same options, these results indicate that cognitive fatigue reduces choice quality.
In relation to economic theory, inconsistent preferences or strategies may lead to failures of transitivity if we prefer A to B and B to C, we should choose A over C a key rule of rational choice behaviour [ 37 ]. The implications of these results are that decisions made under cognitive fatigue are likely to result in more variable choices than those performed under a non-fatigued state, with the potential for regret when the chooser returns to a rested state.
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That individual alterations are unsystematic and unpredictable consequently make it harder to insure against these uncharacteristic and capricious decisions. Interestingly, our results resemble the behavioural effects of ventromedial prefrontal cortex vmPFC damage, which leads to inconsistent or erratic preference judgements without a slowing of response times [ 41 , 42 ]. This suggests that cognitive fatigue has a transient effect that results in alterations of behaviour in ways similar to those with physical damage to their vmPFC. The vmPFC is further implicated in the production or subjective experience of cognitive fatigue, as individuals with focal lesions in the vmPFC reported significantly greater levels of fatigue as compared to individuals with lesions in other locations within the prefrontal cortex PFC [ 43 ].
Participants in the fatigue group maintained a high level of performance on the N-back task despite reporting increasing levels of fatigue and indicating that high levels of mental effort were required to perform each subsequent block. This observed pattern of unchanged task performance under cognitive fatigue is not uncommon [ 44 ]. Surprisingly, despite the reliability in observing fatigue carry-over effects, primary time-on-task decrements are far less consistently seen, with success in inducing fatigue usually determined by its effects on the secondary task [ 11 , 20 , 45 ].
It is unclear whether this discrepancy may be mediated by the demands of the task that is used to induce fatigue. Shigihara and colleagues reported that while task performance was not diminished in 30 min of a 2-back task, performance decreased over time when participants performed the relatively easier 0-back task for the same amount of time [ 22 ].
The authors suggest that mundane tasks may increase sleepiness, while more challenging tasks paradoxically increase motivation and lead to maintenance of high levels of performance. A future challenge may be to clarify which types of cognitive tasks are most affected by fatigue and to construct a unifying explanation of why certain tasks reveal a time-on-task effect while others remain impervious to fatigue.
It is unclear how cognitive fatigue relates to other state alterations, such as sleep deprivation, aging, or ego-depletion. A fruitful area for future investigations would be to investigate how various state alterations may share common mechanisms. A priori, one may expect that there would be similarities between the effects of cognitive fatigue and sleep deprivation. Such a model cannot account for the effects of our study, as the hypothesized effects would have been apparent in two ways in the comparison of the cognitive fatigue group to the control group, both of which were absent, 1 reduced r-squared values for either the rEV or pWIN or both and 2 reduced response times.
Our cognitive fatigue manipulation involved 60 to 90 minutes of a strenuous task that engaged executive control. In ego-depletion studies, following the manipulation task, participants are presented with situations or tasks in which they would normally exercise willpower to exert self-control over their behaviour. It is unclear how to conceptually relate cognitive fatigue and ego-depletion, but given the similar methodologies, it is interesting to consider their potential overlap. Baumeister hypothesized that ego-depletion should result in more impulsive consumer behaviours.
No prior studies have tested this experimentally [ 48 ]. Potentially, our current study may be considered to test and disprove this hypothesis.
Of specific interest for future studies will be examining how cognitive fatigue may dissociably alter sub-components of the cognitive processes engaged during economic decision making. As we find a general effect across economic decision making metrics, this suggests that cognitive fatigue is altering cognitive processes that are engaged across these economic metrics. Possible examples include interfering with working memory, preventing memory consolidation of prior choices, interfering with the application of preferences, or interfering directly with the comparison process.
There is potentially great utility in examining and comparing how varied state modulations commonly and dissociably alter economic decision making. For example, in these same tasks, we recently showed that aging produces specific shifts of risk preferences and strategies in the losses domain, without altering these processes in the gains domain [ 16 ].
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The duration of our cognitive fatigue manipulation 60 to 90 minutes was specifically aimed at examining a duration that is common in everyday life. However, we saw no significant differences between the 5-blocks and 7-blocks cognitive fatigue groups 60 and 90 minutes, respectively. The results of this study show that cognitive fatigue results in destabilization of risk preferences and the informational strategies participants employ. This increased variability in choice behaviour can undermine the integrity of decisions, resulting in diminished choice behaviour quality.
Performed the experiments: RLFL. Browse Subject Areas? Click through the PLOS taxonomy to find articles in your field. Abstract Objective It is common for individuals to engage in taxing cognitive activity for prolonged periods of time, resulting in cognitive fatigue that has the potential to produce significant effects in behaviour and decision making.
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Methods We employed a between-subject manipulation design, inducing fatigue through 60 to 90 minutes of taxing cognitive engagement against a control group that watched relaxing videos for a matched period of time. Results The fatigue manipulation resulted in significantly greater levels of reported subjective fatigue, with correspondingly higher levels of reported effort during the cognitively taxing activity.
Introduction Cognitive fatigue is a ubiquitous human condition, the result of sustained cognitive engagement that taxes our mental resources. Study procedure Each experimental session lasted approximately 2. Download: PPT. Initial questionnaires. Pre-manipulation phase. Manipulation phase.
Post-manipulation phase. Experimental design Subjective fatigue measures. Self-reported fatigue. Cognitive fatigue manipulation: N-back task. Economic decision making tasks. Uncertainty preference metrics Across both the gains choice task and the losses choice task, we were interested in examining the effects of cognitive fatigue on four different uncertainty preferences—risk in gains, ambiguity in gains, risk in losses, and ambiguity in losses. Table 1. Mean standard deviation differences in uncertainty premiums and information strategy metrics in the gains and losses domains between controls and fatigued subjects.