Biology subjects

Sayali, C.

Publications and source records attributed to Sayali, C..

5 recordsLinked to original sources

bioRxiv2025

Anxiety state-related task disengagement varies with trait anxiety

Cognitively demanding tasks are often perceived as costly due to the cognitive control resources they require, leading to effort avoidance, particularly in psychiatric populations with motivational impairments. Research on anxiety and cognitive effort are mixed: some studies suggest anxiety increases the perceived effort cost and avoidance, while others indicate that cognitive effort engagement can serve as an adaptive coping strategy. To reconcile these perspectives, we examined the interaction between state and trait anxiety on cognitive effort evaluation and engagement in two experiments. We hypothesized that state anxiety enhances task engagement as difficulty increases, and that this effect is diminished in individuals with high trait anxiety. Experiment 1 assessed self-reported anxiety in an online sample, while Experiment 2 manipulated state anxiety through autobiographical recall. Both experiments employed flow induction and effort discounting paradigms. Across both studies, the effect of state anxiety on task engagement depended on trait anxiety, but the direction of the state anxiety effect was opposite to the effect we predicted. In Experiment 1, participants with low trait anxiety reported reduced task engagement, as indexed by lower flow scores, when state anxiety was higher, but only in easy tasks. This effect was attenuated in participants with higher trait anxiety. The same pattern was observed in Experiment 2, but this time the interaction between trait and state anxiety was present regardless of task difficulty. These findings suggest that trait anxiety may reflect reduced impact of state anxiety on task disengagement. Public significance statementThis study demonstrated that effects of state anxiety on task disengagement depend on individual differences in trait anxiety. People with higher trait anxiety reported reduced effects of state anxiety on task disengagement.

neuroscience↗

bioRxiv2024

Test-retest reliability of TMS-evoked potentials over fMRI-based definitions of non-motor cortical targets

ObjectiveThis study assessed the test-retest reliability of TMS-evoked potentials (TEPs) across two cortical regions--dorsolateral prefrontal cortex (DLPFC), and angular gyrus-- in comparison to motor cortex (M1), using individualized and literature-based targeting approaches. The study compared the reliability of single-pulse TMS, short-interval intracortical inhibition (SICI), and long-interval intracortical inhibition (LICI) protocols to evaluate TEP consistency in these regions. MethodsSeventeen healthy participants underwent two TMS-EEG sessions spaced by at least one week, with targets for DLPFC and angular gyrus identified using resting-state functional connectivity (RS) and Neurosynth-based functional overlays. Motor cortex was targeted using resting motor threshold (RMT). Early TEPs were quantified as peak-to-peak amplitude, in dBV. Test-retest reliability of early TEPs was calculated using the concordance correlation coefficient (CCC) for each region and protocol. ResultsM1 demonstrated the highest TEP reliability (CCCmean = 0.59), while DLPFC (CCCmean = 0.40) and angular gyrus (CCCmean = 0.45) showed lower reliability, particularly for anterior DLPFC targets. Neurosynth-based DLPFC targets exhibited slightly higher CCC values (mean CCC = 0.57) compared to RS-based targets (mean CCC = 0.30), but the difference was not statistically significant. No significant differences in reliability were found across single pulse and paired pulse protocols. Lateral targets, DLPFC and angular gyrus, showed lower reliability in comparison to motor cortex which might have been caused by muscle artifacts. ConclusionWhile individualized functional targeting methods provide advantages in engaging specific brain networks, their reliability for TEP measurements remains lower than the RMT-based approach for motor cortex. Future studies should integrate neuroimaging-based targeting with real-time TEP monitoring to enhance reliability in non-motor regions. This approach could enhance the precision of TMS-EEG protocols, especially for clinical applications targeting cortical regions like the DLPFC and angular gyrus.

neuroscience↗

bioRxiv2021

Pupillary dynamics of optimal effort

While a substantial body of work has shown that cognitive effort is aversive and costly, a separate line of research on intrinsic motivation suggests that people spontaneously seek challenging tasks. According to one prominent account of intrinsic motivation, the Learning Progress Motivation theory, the preference for difficult tasks reflects the dynamic range that these tasks yield for changes in task performance (Oudeyer, Kaplan & Hafner, 2007). Here we test this hypothesis, by asking whether greater engagement with intermediately difficult tasks, indexed by subjective ratings and objective pupil measurements, is a function of trial-wise changes in performance. In a novel paradigm, we determined each individuals capacity for task performance and used difficulty levels that are too low, intermediately challenging or high for that individual. We demonstrated that challenging tasks resulted in greater liking and engagement scores compared with easy tasks. Pupil size tracked objective task difficulty, where challenging tasks were associated with greater pupil responses than easy tasks. Most importantly, pupil responses were predicted by trial-to-trial changes in average accuracy as well as learning progress (derivative of average accuracy), while greater pupil responses also predicted greater subjective engagement scores. Together, these results substantiate the Learning Progress Motivation hypothesis stating that the link between task engagement and cognitive effort is mediated the dynamic range for changes in task performance.

neuroscience↗

bioRxiv2020

Neural systems underlying the learning of cognitive effort costs

People balance the benefits of cognitive work against the costs of cognitive effort. Models that incorporate prospective estimates of the costs of cognitive effort into decision making require a mechanism by which these costs are learned. However, it remains open what brain systems are important for this learning, particularly when learning is not tied explicitly to a decision about what task to perform. In this fMRI experiment, we parametrically manipulated the level of effort a task requires by increasing task switching frequency across six task contexts. In a scanned learning phase, participants implicitly learned about the task switching frequency in each context. In a subsequent test phase, participants made selections between pairs of these task contexts. We modeled learning within a reinforcement learning framework, and found that effort expectations that derived from task-switching probability and response time (RT) during learning were the best predictors of later choice behavior. Prediction errors (PE) from these two models were associated with FPN during distinct learning epochs. Specifically, PE derived from expected RT was most correlated with the fronto-parietal network early in learning, whereas PE derived from expected task switching frequency was correlated with the fronto-parietal network late in learning. These results suggest that multiple task-related factors are tracked by the brain while performing a task that can drive subsequent estimates of effort costs.

neuroscience↗

bioRxiv2017

Neural systems of cognitive demand avoidance

Cognitive effort is typically aversive, evident in peoples tendency to avoid cognitively demanding tasks. The cost of control hypothesis suggests that engagement of cognitive control systems of the brain makes a task costly and the currency of that cost is a reduction in anticipated rewards. However, prior studies have relied on binary hard versus easy task subtractions to manipulate cognitive effort and so have not tested this hypothesis in \"dose-response\" fashion. In a sample of 50 participants, we parametrically manipulated the level of effort during fMRI scanning by systematically increasing cognitive control demands during a demand-selection paradigm over six levels. As expected, frontoparietal control network (FPN) activity increased, and reward network activity decreased, as control demands increased across tasks. However, avoidance behavior was not attributable to the change in FPN activity, lending only partial support to the cost of control hypothesis. By contrast, we unexpectedly observed that the deactivation of a task-negative brain network corresponding to the Default Mode Network (DMN) across levels of the cognitive control manipulation predicted the change in avoidance. In summary, we find partial support for the cost of control hypothesis, while highlighting the role of task-negative brain networks in modulating effort avoidance behavior.

neuroscience↗