Changing Task Demands Research Articles

Systematic changes in neural selectivity reflect the acquired salience of category-diagnostic dimensions.

Humans and other animals develop remarkable behavioral specializations for identifying, differentiating, and acting on classes of ecologically important signals. Ultimately, this expertise is flexible enough to support diverse perceptual judgments: a voice, for example, simultaneously conveys what a talker says as well as myriad cues about her identity and state. Mature perception across complex signals thus involves both discovering and learning regularities that best inform diverse perceptual judgments, and weighting this information flexibly as task demands change. Here, we test whether this flexibility may involve endogenous attentional gain to task-relevant dimensions. We use two prospective auditory category learning tasks to relate a complex, entirely novel soundscape to four classes of "alien identity" and two classes of "alien size." Identity, but not size, categorization requires discovery and learning of patterned acoustic input situated in one of two simultaneous, frequency-delimited bands. This allows us to capitalize on the coarsely segregated frequency-band-specific channels of auditory tonotopic maps using fMRI to ask whether category-relevant perceptual information is prioritized relative to simultaneous, uninformative information. Among participants expert at alien identity categorization, we observe prioritization of the diagnostic frequency band that persists even when the diagnostic information becomes irrelevant in the size categorization task. Tellingly, the neural selectivity evoked implicitly in categorization aligns closely with activation driven by explicit, sustained selective attention to other sounds presented in the same frequency band. Additionally, we observe fingerprints of individual differences in the learning trajectories taken to achieve expert-level categorization in patterns of neural activity associated with the diagnostic dimension. In all, this indicates that acquiring categories can drive the emergence of acquired attentional salience to dimensions of acoustic input.

Dynamic self-regulation and coregulation of respiratory sinus arrhythmia in mother-child and father-child interactions: Moderating effects of proximal and distal stressors.

This study examined how proximal and distal familial stressors influenced the real-time, dynamic individual and dyadic regulation of respiratory sinus arrhythmia (RSA) in mother-preschooler and father-preschooler interactions in at-risk families (N = 94, Mage = 3.03 years, 47% males, 77% White, 20% Latinx, data collected 2013-2017). Proximal stressors were operationalized as changing task demands (baseline, challenge, recovery) across a dyadic puzzle task. Distal stressors were measured as parent-reported stressful life events. Multilevel models revealed that greater proximal and distal stressors were related to weaker dynamic self-regulation of RSA in mothers, fathers, and children, and more discordant mother-child and father-child coregulation of RSA. Findings affirm that stress is transmitted across levels and persons to compromise real-time regulatory functioning in early, developmentally formative caregiver-child interactions.

Rapid Compensation for Noisy Voluntary Movements in Adults with Primary Tic Disorders.

It has been proposed that tics and premonitory urges in primary tic disorders (PTD), like Tourette syndrome, are a manifestation of sensorimotor noise. However, patients with tics show no obvious movement imprecision in everyday life. One reason could be that patients have strategies to compensate for noise that disrupts performance (ie, noise that is task-relevant). Our goal was to unmask effects of elevated sensorimotor noise on the variability of voluntary movements in patients with PTD. We tested 30 adult patients with PTD (23 male) and 30 matched controls in a reaching task designed to unmask latent noise. Subjects reached to targets whose shape allowed for variability either in movement direction or extent. This enabled us to decompose variability into task-relevant versus less task-relevant components, where the latter should be less affected by compensatory strategies than the former. In alternating blocks, the task-relevant target dimension switched, allowing us to explore the temporal dynamics with which participants adjusted movement variability to changes in task demands. Both groups accurately reached to targets, and adjusted movement precision based on target shape. However, when task-relevant dimensions of the target changed, patients initially produced movements that were more variable than controls, before regaining precision after several reaches. This effect persisted across repeated changes in the task-relevant dimension across the experiment, and therefore did not reflect an effect ofnovelty, or differences in learning. Our results suggest that patients with PTD generate noisier voluntary movements compared with controls, but rapidly compensate according to current task demands. © 2024 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Learned switch readiness via concurrent activation of task sets: Evidence from task specificity and memory load.

Cognitive flexibility increases when switch demands increase. In task switching experiments, repeated pairing of flexibility-demanding situations with specific contexts leads subjects to become more prepared to adapt to changing task demands in those contexts. One form of such upregulated cognitive flexibility has been demonstrated with a list-wide switch probability (LWSP) effect, where switch costs are smaller in lists with frequent switches than in lists with rare switches. According to a recent proposal, the LWSP effect is supported by a concurrent activation mechanism whereby both task rules are kept available simultaneously in working memory. We conducted four experiments to test two key features in this concurrent activation account of LWSP effects. First, we asked whether the LWSP effects are limited to only the trained tasks, and second, we asked whether concurrent working memory load would reduce the LWSP effects. In Experiment 1, we replicated and extended previous findings that the LWSP manipulation modulates both performance (switch costs) and voluntary switch rates, indicating that context-driven increases in flexibility are generalizable so long as the task-sets remain the same. Results of Experiments 2 and 3 showed that novel tasks do not benefit from the concurrent activation of the two other tasks, suggesting that the LWSP effect is task specific. Experiment 4 showed that holding additional information in working memory reduces the LWSP effect. While these findings support the hypothesis of concurrent activation underlying the increased flexibility in the LWSP effect, caveats remain; additional research is needed to further test this account.

The medial prefrontal cortex during flexible decisions: Evidence for its role in distinct working memory processes.

During decisions that involve working memory, task-related information must be encoded, maintained across delays, and retrieved. Few studies have attempted to causally disambiguate how different brain structures contribute to each of these components of working memory. In the present study, we used transient optogenetic disruptions of rat medial prefrontal cortex (mPFC) during a serial spatial reversal learning (SSRL) task to test its role in these specific working memory processes. By analyzing numerous performance metrics, we found: (1) mPFC disruption impaired performance during only the choice epoch of initial discrimination learning of the SSRL task; (2) mPFC disruption impaired performance in dissociable ways across all task epochs (delay, choice, return) during flexible decision-making; (3) mPFC disruption resulted in a reduction of the typical vicarious-trial-and-error rate modulation that was related to changes in task demands. Taken together, these findings suggest that the mPFC plays an outsized role in working memory retrieval, becomes involved in encoding and maintenance when recent memories conflict with task demands, and enables animals to flexibly utilize working memory to update behavior as environments change.

Effects of protective step training on proactive and reactive motor adaptations in Parkinson's disease patients.

The aim of this study was to investigate to what extent PD affects the ability to walk, respond to balance perturbations in a single training session, and produce acute short-term effects to improve compensatory reactions and control of unperturbed walking stability. Understanding the mechanism of compensation and neuroplasticity to unexpected step perturbation training during walking and static stance can inform treatment of PD by helping to design effective training regimens that remediate fall risk. Current rehabilitation therapies are inadequate at reducing falls in people with Parkinson's disease (PD). While pharmacologic and surgical treatments have proved largely ineffective in treating postural instability and gait dysfunction in people with PD, studies have demonstrated that therapy specifically focusing on posture, gait, and balance may significantly improve these factors and reduce falls. The primary goal of this study was to assess the effectiveness of a novel and promising intervention therapy (protective step training - i.e., PST) to improve balance and reduce falls in people with PD. A secondary goal was to understand the effects of PST on proactive and reactive feedback responses during stance and gait tasks. Multiple-baseline, repeated measures analyses were performed on the multitude of proactive and reactive performance measures to assess the effects of PST on gait and postural stability parameters. In general, the results indicate that participants with PD were able to use experiences with perturbation training to integrate and adapt feedforward and feedback behaviors to reduce falls. The ability of the participants with PD to adapt to changes in task demands suggests that individuals with PD could benefit from the protective step training to facilitate balance control during rehabilitation.

Can Real-Time Gaze Sharing Help Team Collaboration? A Preliminary Examination of its Effectiveness with Pairs

Complex and dynamic domains rely on operators to collaborate on multiple tasks and cope with changes in task demands. Gaze sharing is a means of communication used to exchange visual information by allowing teammates to view each other’s gaze points on their displays. Existing work on gaze sharing focuses on relatively simple task-specific domains and no work-to-date addresses how to use gaze sharing in data-rich environments. For this study, nine pairs of participants completed a UAV search and rescue command-and-control task with three visualization techniques: no gaze sharing, gaze sharing using the real-time dot, and gaze sharing using the real-time fixation trail. Our preliminary results show that performance scores using the real-time fixation trail were statistically significantly higher than when no gaze sharing was present. This suggests that the real-time fixation trail is a promising tool to better understand operators’ strategies and could form the basis of an adaptive display.

Changes in affect during the pursuit of performance goals.

The aim of this article is to examine how affect changes when people are pursuing performance goals. We do this using the circumplex model of affect, in which a person's current affective state is represented as a point on a plane defined by the latent dimensions of pleasure and activation. We test competing hypotheses regarding the direction of changes within this 2-dimensional space. The first set of hypotheses are derived from Carver and Scheier's (1998) theory of self-regulation, which predicts that changes in the prospects of goal attainment should produce changes along axes offset 45° from the pleasure and activation dimensions. The second set of hypotheses are derived from the concept of core affect (Russell, 2003), which predicts that changes in the prospects of goal attainment should produce changes in pleasure, while changes in task demands should produce changes in activation. Two studies are reported in which participants provided ratings of momentary affect during goal pursuit. We developed a latent change model to estimate the direction and magnitude of changes in affect attributable to changes in the prospects of goal attainment and task demand. The results are more consistent with the hypotheses derived from the core affect account than with the hypotheses derived from the Carver and Scheier account. Theoretical and practical implications are discussed, with a focus on prospects for the development of an integrative theory, which accounts for both the motivational and affective components of goal pursuit. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Augmented Deep Reinforcement Learning for Online Energy Minimization of Wireless Powered Mobile Edge Computing

Mobile edge computing (MEC) offers an opportunity for devices relying on wireless power transfer (WPT), to accomplish computationally demanding tasks. Such WPT-powered MEC systems have yet to be optimized for long-term efficiency, due to random and changing task demands and wireless channel states of the devices. This paper presents an augmented two-staged deep Q-network (DQN), referred to as “TS-DQN”, for online optimization of WPT-powered MEC systems, where the WPT, offloading schedule, channel allocation, and the CPU configurations of the edge server and devices are jointly optimized to minimize the long-term average energy requirement of the systems. The key idea is to design a DQN for learning the channel allocation and task admission, while the WPT, offloading time and CPU configurations are efficiently optimized to precisely evaluate the reward of the DQN and substantially reduce its action space. Another important aspect is that a new action generation method is developed to expand and diversify the actions of the DQN, further accelerating its convergence. As validated by simulations, the proposed TS-DQN is much more energy efficient and converges much faster, than its potential alternative directly using the state-of-the-art Deep Deterministic Policy Gradient algorithm to learn all decision variables.

M1-selective muscarinic allosteric modulation enhances cognitive flexibility and effective salience in nonhuman primates

Acetylcholine (ACh) in cortical neural circuits mediates how selective attention is sustained in the presence of distractors and how flexible cognition adjusts to changing task demands. The cognitive domains of attention and cognitive flexibility might be differentially supported by the M1 muscarinic acetylcholine receptor (mAChR) subtype. Understanding how M1 mAChR mechanisms support these cognitive subdomains is of highest importance for advancing novel drug treatments for conditions with altered attention and reduced cognitive control including Alzheimer's disease or schizophrenia. Here, we tested this question by assessing how the subtype-selective M1 mAChR positive allosteric modulator (PAM) VU0453595 affects visual search and flexible reward learning in nonhuman primates. We found that allosteric potentiation of M1 mAChRs enhanced flexible learning performance by improving extradimensional set shifting, reducing latent inhibition from previously experienced distractors and reducing response perseveration in the absence of adverse side effects. These procognitive effects occurred in the absence of apparent changes of attentional performance during visual search. In contrast, nonselective ACh modulation using the acetylcholinesterase inhibitor (AChEI) donepezil improved attention during visual search at doses that did not alter cognitive flexibility and that already triggered gastrointestinal cholinergic side effects. These findings illustrate that M1 mAChRpositive allosteric modulation enhances cognitive flexibility without affecting attentional filtering of distraction, consistent with M1 activity boosting the effective salience of relevant over irrelevant objects specifically during learning. These results suggest that M1 PAMs are versatile compounds for enhancing cognitive flexibility in disorders spanning schizophrenia and Alzheimer's diseases.

Cortical Patterns Shift from Sequence Feature Separation during Planning to Integration during Motor Execution.

Performing sequences of movements from memory and adapting them to changing task demands is a hallmark of skilled human behavior, from handwriting to playing a musical instrument. Prior studies showed a fine-grained tuning of cortical primary motor, premotor, and parietal regions to motor sequences: from the low-level specification of individual movements to high-level sequence features, such as sequence order and timing. However, it is not known how tuning in these regions unfolds dynamically across planning and execution. To address this, we trained 24 healthy right-handed human participants (14 females, 10 males) to produce four five-element finger press sequences with a particular finger order and timing structure in a delayed sequence production paradigm entirely from memory. Local cortical fMRI patterns during preparation and production phases were extracted from separate No-Go and Go trials, respectively, to tease out activity related to these perimovement phases. During sequence planning, premotor and parietal areas increased tuning to movement order or timing, regardless of their combinations. In contrast, patterns reflecting the unique integration of sequence features emerged in these regions during execution only, alongside timing-specific tuning in the ventral premotor, supplementary motor, and superior parietal areas. This was in line with the participants' behavioral transfer of trained timing, but not of order to new sequence feature combinations. Our findings suggest a general informational state shift from high-level feature separation to low-level feature integration within cortical regions for movement execution. Recompiling sequence features trial-by-trial during planning may enable flexible last-minute adjustment before movement initiation.SIGNIFICANCE STATEMENT Musicians and athletes can modify the timing and order of movements in a sequence trial-by-trial, allowing for a vast repertoire of flexible behaviors. How does the brain put together these high-level sequence features into an integrated whole? We found that, trial-by-trial, the control of sequence features undergoes a state shift from separation during planning to integration during execution across a network of motor-related cortical areas. These findings have implications for understanding the hierarchical control of skilled movement sequences, as well as how information in brain areas unfolds across planning and execution.

Cortical theta-gamma coupling governs the adaptive control of motor commands.

Motor control requires the adaptive updating of internal models to successfully target desired outcomes. This adaptive control can be proactive, such that imminent actions and corresponding sensorimotor programmes are anticipated prior to movement, or reactive, such that online error correction is necessary to adjust to sudden changes. While substantial evidence implicates a distributed cortical network serving adaptive control when behavioural changes are required (e.g. response inhibition), the neural dynamics serving such control when the target motor commands are to remain intact are poorly understood. To address this, we developed a novel proactive-reactive cued finger tapping paradigm that was performed during magnetoencephalography by 25 healthy adults. Importantly, to ensure condition-wise differences in adaptive cueing were not attributable to changes in movement kinematics, motor selection and planning processes were held constant despite changes in task demands. All data were imaged in the time-frequency domain using a beamformer to evaluate the effect of proactive and reactive cues on movement-related oscillations and subsequent performance. Our results indicated spectrally specific increases in low (i.e. theta) and high (i.e. gamma) frequency oscillations during motor execution as a function of adaptive cueing. Additionally, we observed robust cross-frequency coupling of theta and gamma oscillatory power in the contralateral motor cortex and further, the strength of this theta-gamma coupling during motor execution was differentially predictive of behavioural improvements and decrements during reactive and proactive trials, respectively. These data indicate that functional oscillatory coupling may govern the adaptive control of movement in the healthy brain and importantly, may serve as effective proxies for characterizing declines in motor function in clinical populations in the future.

Understanding and predicting the actions decisions of individuals in team and multiagent task contexts

Introduction: Effective team behaviour requires that co-actors reciprocally coordinate their actions with respect to each other and changing task demands. Pivotal to the structural organization of such behaviour is the ability of actors to effectively decide how and when to act, with robust decision-making differentiating expert from novice performance. Indeed, task expertise reflects the trained attunement of actors to information that best specifies what action possibilities will ensure task success1. The current study explored whether state-of-the art Supervised Machine Learning (SML), Long-Short Term Memory artificial neural networks (LSTMNN), and explainable-AI could be employed to model, predict, and explicate the action decisions of expert and novice individuals during team performance. Methods: We modelled the target selection decisions of pairs of expert and novice individuals playing a simulated, fast paced herding game2. For this task, pairs of players controlled virtual herder agents to corral a herd of four virtual cows (targets), dispersed around a game field. We then used the recorded performance data to training LSTMNN, via SML, to predict the future target selection decisions of players. Following model development and validation, we then analysed the resultant LSTMNN models using the explainable-AI technique SHapley Additive exPlanation (SHAP)3 to identify the different sources of task information that defined the action decisions of expert and novice players. Results: Using 1 second task state information sequences, we were able to train LSTMNN models to successfully predict the target selection decisions of both expert and novice players at an average accuracy above 95%. Moreover, accurate target predictions could be made between 640 ms and 2.4 seconds prior to player decisions being enacted or observable within the state input sequence. Another key finding was that the LSTMNN models were expertise specific; when the expertise level of the training and test data was mismatched, prediction performance dropped to near chance levels. The SHAP analysis revealed that this specificity was because experts were more influenced by information about the state of their co-herders and target direction of motion information compared to novices. Discussion: The findings demonstrated how SML trained LSTMNN and the explainable-AI technique SHAP could provide powerful tools for understanding the decision-making process of human actors during team behaviour, including what information best supports optimal task performance. The implications for both basic scientific research and the applied development of task training and decision-making assessment tools are significant. References 1Araujo D, Davids K, Hristovski R. The ecological dynamics of decision making in sport. Psychol Sport Exerc, vol. 7, no. 6, pp. 653–676, 2006. 2Rigoli LM, et al. Employing Models of Human Social Motor Behavior for Artificial Agent Trainers. in Proceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2020), 2020, p. 9. 3Lundberg SM, et al. From local explanations to global understanding with explainable AI for trees. Nat Mach Intell, vol. 2, no. 1, pp. 56–67, 2020.

Attentional modulations of alpha power are sensitive to the task-relevance of auditory spatial information

The topographical distribution of oscillatory power in the alpha band is known to vary depending on the current focus of spatial attention. Here, we investigated to what extend univariate and multivariate measures of post-stimulus alpha power are sensitive to the required spatial specificity of a task. To this end, we varied the perceptual load and the spatial demand in an auditory search paradigm. A centrally presented sound at the beginning of each trial indicated the to-be-localized target sound. This spatially unspecific pre-cue was followed by a sound array, containing either two (low perceptual load) or four (high perceptual load) simultaneously presented lateralized sound stimuli. In separate task blocks, participants were instructed either to report whether the target was located on the left or the right side of the sound array (low spatial demand) or to indicate the exact target location (high spatial demand). Univariate alpha lateralization magnitude was neither affected by perceptual load nor by spatial demand. However, an analysis of onset latencies revealed that alpha lateralization emerged earlier in low (vs high) perceptual load trials as well as in low (vs high) spatial demand trials. Finally, we trained a classifier to decode the specific target location based on the multivariate alpha power scalp topography. A comparison of decoding accuracy in the low and high spatial demand conditions suggests that the amount of spatial information present in the scalp distribution of alpha-band power increases as the task demands a higher degree of spatial specificity. Altogether, the results offer new insights into how the dynamic adaption of alpha-band oscillations in response to changing task demands is associated with post-stimulus attentional processing.

Distinct but correlated latent factors support the regulation of learned conflict-control and task-switching

Cognitive control is guided by learning, as people adjust control to meet changing task demands. The two best-studied instances of “control-learning” are the enhancement of attentional task focus in response to increased frequencies of incongruent distracter stimuli, reflected in the list-wide proportion congruent (LWPC) effect, and the enhancement of switch-readiness in response to increased frequencies of task switches, reflected in the list-wide proportion switch (LWPS) effect. However, the latent architecture underpinning these adaptations in cognitive stability and flexibility – specifically, whether there is a single, domain-general, or multiple, domain-specific learners – is currently not known. To reveal the underlying structure of control-learning, we had a large sample of participants (N = 950) perform LWPC and LWPS paradigms, and afterwards assessed their explicit awareness of the task manipulations, as well as general cognitive ability and motivation. Structural equation modeling was used to evaluate several preregistered models representing different plausible hypotheses concerning the latent structure of control-learning. Task performance replicated standard LWPC and LWPS effects. Crucially, the model that best fit the data had correlated domain- and context-specific latent factors. Thus, people’s ability to adapt their on-task focus and between-task switch-readiness to changing levels of demand was mediated by distinct (though correlated) underlying factors. Model fit remained good when accounting for speed-accuracy trade-offs, variance in individual cognitive ability and self-reported motivation, as well as self-reported explicit awareness of manipulations and the order in which different levels of demand were experienced. Implications of these results for the cognitive architecture of dynamic cognitive control are discussed.

Chronic Exercise as a Modulator of Cognitive Control: Investigating the Electrophysiological Indices of Performance Monitoring.

Exercise may influence components of executive functioning, specifically cognitive control and action monitoring. We aimed to determine whether high level exercise improves the efficacy of cognitive control in response to differing levels of conflict. Fitter individuals were expected to demonstrate enhanced action monitoring and optimal levels of cognitive control in response to changing task demands. Participants were divided into the highly active (HA) or low-active group based on self-reported activity using the International Physical Activity Questionnaire. A modified flanker task was then performed, in which the level of conflict was modulated by distance of distractors from the target (close, far) and congruency of arrows (incongruent, congruent). Electroencephalography (EEG) was collected during 800 trials; trials were 80% congruent, 20% incongruent, 50% close, and 50% far. The error-related negativity (ERN) and error positivity (Pe) were extracted from the difference wave of correct and incorrect response locked epochs, the N2 from the difference wave of congruent and incongruent stimulus locked epochs and the P3 from stimulus locked epochs. The HA group showed a larger Pe amplitude compared to the low-active group. Close trials elicited a larger N2 amplitude than far trials in the HA group, but not the low-active group, the HA group also made fewer errors on far trials than on close trials. Finally, the P3 was smaller in the lowest conflict condition in the HA, but not the low-active group. These findings suggest that habitual, high levels of exercise may influence the endogenous processing involved in pre-response conflict detection and the post-error response.

Context-Specificity of Locomotor Learning Is Developed during Childhood.

Humans can perform complex movements with speed and agility in the face of constantly changing task demands. To accomplish this, motor plans are adapted to account for errors in our movements because of changes in our body (e.g., growth or injury) or in the environment (e.g., walking on sand vs ice). It has been suggested that adaptation that occurs in response to changes in the state of our body will generalize across different movement contexts and environments, whereas adaptation that occurs with alterations in the external environment will be context-specific. Here, we asked whether the ability to form generalizable versus context-specific motor memories develops during childhood. We performed a cross-sectional study of context-specific locomotor adaptation in 35 children (3–18 years old) and 7 adults (19–31 years old). Subjects first adapted their gait and learned a new walking pattern on a split-belt treadmill, which has two belts that move each leg at a different speed. Then, subjects walked overground to assess the generalization of the adapted walking pattern across different environments. Our results show that the generalization of treadmill after-effects to overground walking decreases as subjects’ age increases, indicating that age and experience are critical factors regulating the specificity of motor learning. Our results suggest that although basic locomotor patterns are established by two years of age, brain networks required for context-specific locomotor learning are still being developed throughout youth.

Understanding self-report and neurocognitive assessments of cognitive flexibility in people with and without lifetime anorexia nervosa

ABSTRACT Objective: Anorexia nervosa (AN) is a serious eating disorder associated with several cognitive difficulties including poor cognitive flexibility (i.e. difficulties in effectively adapting to changes in the environment and/or changing task demands). AN research has primarily assessed cognitive flexibility using neurocognitive tests, and little is known about the differences or similarities between self-report and neurocognitive assessments of cognitive flexibility. This study investigated the relationship between self-report and neurocognitive assessments of cognitive flexibility in people with no history of an eating disorder (n = 207) and people with a self-reported lifetime diagnosis of AN (n = 19). Methods: Participants completed self-report and neurocognitive assessments of cognitive flexibility through an online study. Results: No significant correlations were found between self-report and neurocognitive assessments of cognitive flexibility for either group of the sample, suggesting that these assessments may evaluate different aspects of cognitive flexibility. Further, negative mood and self-reported eating disorder symptoms were found to significantly relate to self-reported cognitive flexibility, but were not associated with performance on neurocognitive tests of cognitive flexibility. Conclusions: To provide a comprehensive understanding of perceived and objective cognitive flexibility in AN, future research and clinical assessments should include both self-report and neurocognitive assessments.

Multiple levels of mental attentional demand modulate peak saccade velocity and blink rate

Every day we mentally process new information that needs to be attended, encoded and retrieved. Processing demands depend on the amount of information and the mental attentional capacity of the individual. Research shows that eye movement indices such as peak saccade velocity and blink rate are related to processes of attentional control, however it is still unclear how eye movements are affected by graded changes in task demand. We examine for the first time relations of eye movements to mental attentional tasks with six levels of task demand and two interference conditions. We report data on 57 adults who completed two versions of the color matching task and provided subjective self rating for each mental attentional demand level. Results show that peak saccade velocity and blink rate decrease as a function of mental attentional demand and correlate negatively with self rating of mental effort. Theoretically, new findings related to mental attentional demand and eye movements inform models of visual processing and cognition. Practically, results point to directions for further research to better understand complex relations among eye movements and mental attentional demand in pediatric populations and individuals with cognitive deficits.

On the organization of task-order and task-specific information in dual-task situations.

Dual-tasks (DT) require the employment of task-order representations that schedule the processing of 2 tasks. Evidence for this assumption stems from the observation that in DTs with variable order, performance is improved in trials with repeated processing order relative to the preceding trial in comparison to trials with reversed processing order. So far, it is an open question whether these order representations only contain order information or whether they also integrate component task information. To tackle this question, we applied a DT with variable task-order consisting of an auditory and a visual task. In Experiment 1, in addition to task-order, the visual task varied randomly from trial to trial while the auditory task kept constant. In Experiment 2, the auditory task varied. In Experiment 3, both component tasks varied. In all experiments, performance benefits occurred in trials with a repeated relative to trials with a reversed processing order, irrespective of a repeated or a changed component task. This indicates that order representations in DTs only contain order information. The findings are in line with the view that multitasking situations are represented as an agglomeration of distinct components that can be individually adjusted to changing task demands. (PsycInfo Database Record (c) 2022 APA, all rights reserved).