Mental fatigue correlates with depression of task-related network and augmented DMN activity but spares the reward circuit.

Long-lasting and demanding cognitive activity typically leads to mental fatigue (MF). Indirect evidence suggests that MF may be caused by altered motivational processes. Here, we hypothesized that if MF consists in an alteration of motivational states, brain functional changes induced by MF could specifically affect the brain motivation circuit. In order to test this hypothesis, we devised a functional neuroimaging protocol to detect altered brain activity in reward-related brain regions in relation to cognitively induced mental fatigue. Twenty-five healthy participants underwent a FATIGUE and a CONTROL session on different days. In the FATIGUE session, MF was induced by performing a demanding cognitive task (adapted Stroop task) during 90 min, whereas in the CONTROL session, participants were asked to read magazines for the same period of time. We measured the neural consequences of the MF induction during a working memory task (Missing Number task) while modulating extrinsic motivation with block-wise variations in monetary reward. We also tracked participants' momentary fatigue, anxiety state and intrinsic motivation prior to and following the MF inducement and measurement. Accuracy on the Missing Number Task was lower in the FATIGUE than in the CONTROL condition. Furthermore, subjective MF, but not its behavioral manifestations, was associated with hypoactivity of the task-evoked neural responses. Importantly, activity in regions modulated by reward showed no differences between FATIGUE and CONTROL sessions. In parallel, subjective MF correlated with increased on-task activity and resting-state functional connectivity in the default mode network. These results indicate that subjective mental fatigue is not associated with altered activity in the brain motivation circuit but rather with hypoactivity in task-specific brain regions as well as relative increases of activity and connectivity in the default mode network during and after the task.

cTBS disruption of the supplementary motor area perturbs cortical sequence representation but not behavioural performance.

Neuroimaging studies have repeatedly emphasized the role of the supplementary motor area (SMA) in motor sequence learning, but interferential approaches have led to inconsistent findings. Here, we aimed to test the role of the SMA in motor skill learning by combining interferential and neuroimaging techniques. Sixteen subjects were trained on simple finger movement sequences for 4 days. Afterwards, they underwent two neuroimaging sessions, in which they executed both trained and novel sequences. Prior to entering the scanner, the subjects received inhibitory transcranial magnetic stimulation (TMS) over the SMA or a control site. Using multivariate fMRI analysis, we confirmed that motor training enhances the neural representation of motor sequences in the SMA, in accordance with previous findings. However, although SMA inhibition altered sequence representation (i.e. between-sequence decoding accuracy) in this area, behavioural performance remained unimpaired. Our findings question the causal link between the neuroimaging correlate of elementary motor sequence representation in the SMA and sequence generation, calling for a more thorough investigation of the role of this region in performance of learned motor sequences.

Chunking improves symbolic sequence processing and relies on working memory gating mechanisms.

Chunking, namely the grouping of sequence elements in clusters, is ubiquitous during sequence processing, but its impact on performance remains debated. Here, we found that participants who adopted a consistent chunking strategy during symbolic sequence learning showed a greater improvement of their performance and a larger decrease in cognitive workload over time. Stronger reliance on chunking was also associated with higher scores in a WM updating task, suggesting the contribution of WM gating mechanisms to sequence chunking. Altogether, these results indicate that chunking is a cost-saving strategy that enhances effectiveness of symbolic sequence learning.

Disruption of Broca's Area Alters Higher-order Chunking Processing during Perceptual Sequence Learning.

Because Broca's area is known to be involved in many cognitive functions, including language, music, and action processing, several attempts have been made to propose a unifying theory of its role that emphasizes a possible contribution to syntactic processing. Recently, we have postulated that Broca's area might be involved in higher-order chunk processing during implicit learning of a motor sequence. Chunking is an information-processing mechanism that consists of grouping consecutive items in a sequence and is likely to be involved in all of the aforementioned cognitive processes. Demonstrating a contribution of Broca's area to chunking during the learning of a nonmotor sequence that does not involve language could shed new light on its function. To address this issue, we used offline MRI-guided TMS in healthy volunteers to disrupt the activity of either the posterior part of Broca's area (left Brodmann's area [BA] 44) or a control site just before participants learned a perceptual sequence structured in distinct hierarchical levels. We found that disruption of the left BA 44 increased the processing time of stimuli representing the boundaries of higher-order chunks and modified the chunking strategy. The current results highlight the possible role of the left BA 44 in building up effector-independent representations of higher-order events in structured sequences. This might clarify the contribution of Broca's area in processing hierarchical structures, a key mechanism in many cognitive functions, such as language and composite actions.

Active sensing with artificial neural networks.

The fitness of behaving agents depends on their knowledge of the environment, which demands efficient exploration strategies. Active sensing formalizes exploration as reduction of uncertainty about the current state of the environment. Despite strong theoretical justifications, active sensing has had limited applicability due to difficulty in estimating information gain. Here we address this issue by proposing a linear approximation to information gain and by implementing efficient gradient-based action selection within an artificial neural network setting. We compare information gain estimation with state of the art, and validate our model on an active sensing task based on MNIST dataset. We also propose an approximation that exploits the amortized inference network, and performs equally well in certain contexts.

An information-theoretic perspective on the costs of cognition.

In statistics and machine learning, model accuracy is traded off with complexity, which can be viewed as the amount of information extracted from the data. Here, we discuss how cognitive costs can be expressed in terms of similar information costs, i.e. as a function of the amount of information required to update a person's prior knowledge (or internal model) to effectively solve a task. We then examine the theoretical consequences that ensue from this assumption. This framework naturally explains why some tasks - for example, unfamiliar or dual tasks - are costly and permits to quantify these costs using information-theoretic measures. Finally, we discuss brain implementation of this principle and show that subjective cognitive costs can originate either from local or global capacity limitations on information processing or from increased rate of metabolic alterations. These views shed light on the potential adaptive value of cost-avoidance mechanisms.

Cognitive task avoidance correlates with fatigue-induced performance decrement but not with subjective fatigue.

Mentally demanding tasks feel effortful and are usually avoided. Furthermore, prolonged cognitive engagement leads to mental fatigue, consisting of subjective feeling of exhaustion and decline in performance. Despite the intuitive characterization of fatigue as an increase in subjective effort perception, the effect of fatigue on effort cost has never been tested experimentally. To this end, sixty participants in 2 separate experiments underwent a forced-choice working memory task following either a fatigue-inducing (i.e. cognitive task involving working memory, conflict and switch costs) or a control manipulation. We measured fatigue in terms of subjective feeling and performance decrement and assessed effort in terms of subjective perception and task avoidance. Subjects exhibited only weak avoidance of the working memory task, with stronger influence of reward than task difficulty on their decisions. In addition, we found that task avoidance did not systematically change following the fatigue manipulation but that variations in task avoidance correlated with fatigue-induced performance decline. The other measures of fatigue and effort were unrelated to each other. Our findings suggest that subjective fatigue may develop independently of task avoidance and suggest an "anticipatory regulation" model in which fatigue urges subjects to stop in anticipation of possible, future adverse consequences.

Strong Conscious Cues Suppress Preferential Gaze Allocation to Unconscious Cues.

Visual attention allows relevant information to be selected for further processing. Both conscious and unconscious visual stimuli can bias attentional allocation, but how these two types of visual information interact to guide attention remains unclear. In this study, we explored attentional allocation during a motion discrimination task with varied motion strength and unconscious associations between stimuli and cues. Participants were instructed to report the motion direction of two colored patches of dots. Unbeknown to participants, dot colors were sometimes informative of the correct response. We found that subjects learnt the associations between colors and motion direction but failed to report this association using the questionnaire filled at the end of the experiment, confirming that learning remained unconscious. The eye movement analyses revealed that allocation of attention to unconscious sources of information occurred mostly when motion coherence was low, indicating that unconscious cues influence attentional allocation only in the absence of strong conscious cues. All in all, our results reveal that conscious and unconscious sources of information interact with each other to influence attentional allocation and suggest a selection process that weights cues in proportion to their reliability.

Locus Coeruleus atrophy doesn't relate to fatigue in Parkinson's disease.

Fatigue is a frequent complaint among healthy population and one of the earliest and most debilitating symptoms in Parkinson's disease (PD). Earlier studies have examined the role of dopamine and serotonin in pathogenesis of fatigue, but the plausible role of noradrenalin (NA) remains underexplored. We investigated the relationship between fatigue in Parkinsonian patients and the extent of degeneration of Locus Coeruleus (LC), the main source of NA in the brain. We quantified LC and Substantia Nigra (SN) atrophy using neuromelanin-sensitive imaging, analyzed with a novel, fully automated algorithm. We also assessed patients' fatigue, depression, sleep disturbance and vigilance. We found that LC degeneration correlated with the levels of depression and vigilance but not with fatigue, while fatigue correlated weakly with atrophy of SN. These results indicate that LC degeneration in Parkinson's disease is unlikely to cause fatigue, but may be involved in mood and vigilance alterations.

Non-parametric Algorithm to Isolate Chunks in Response Sequences.

Chunking consists in grouping items of a sequence into small clusters, named chunks, with the assumed goal of lessening working memory load. Despite extensive research, the current methods used to detect chunks, and to identify different chunking strategies, remain discordant and difficult to implement. Here, we propose a simple and reliable method to identify chunks in a sequence and to determine their stability across blocks. This algorithm is based on a ranking method and its major novelty is that it provides concomitantly both the features of individual chunk in a given sequence, and an overall index that quantifies the chunking pattern consistency across sequences. The analysis of simulated data confirmed the validity of our method in different conditions of noise, chunk lengths and chunk numbers; moreover, we found that this algorithm was particularly efficient in the noise range observed in real data, provided that at least 4 sequence repetitions were included in each experimental block. Furthermore, we applied this algorithm to actual reaction time series gathered from 3 published experiments and were able to confirm the findings obtained in the original reports. In conclusion, this novel algorithm is easy to implement, is robust to outliers and provides concurrent and reliable estimation of chunk position and chunking dynamics, making it useful to study both sequence-specific and general chunking effects. The algorithm is available at: https://github.com/artipago/Non-parametric-algorithm-to-isolate-chunks-in-response-sequences.