Department of Computer Science & Engineering

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IIT Jodhpur

School of Artificial Intelligence and Data Science (AIDE)

Midfrontal theta increases during scenarios when conflicts are successfully resolved. Often considered a generic signal of cognitive control, its temporal nature has hardly been investigated. Using advanced spatiotemporal techniques, we uncover that midfrontal theta occurs as a transient oscillation or “event” at single trials with their timing reflecting computationally distinct modes. Single-trial analyses of electrophysiological data from participants performing the Flanker (N = 24) and Simon task (N = 15) were used to probe the relationship between theta and metrics of stimulus-response conflict. We specifically investigated “partial errors”, in which a small burst of muscle activity in the incorrect response effector occurred, quickly followed by a correction. We found that transient theta events in single trials could be categorized into two distinct theta modes based on their relative timing to different task events. Theta events from the first mode occurred briefly after the task stimulus and might reflect conflict-related processing of the stimulus. In contrast, theta events from the second mode were more likely to occur around the time partial errors were committed, suggesting they were elicited by a potential upcoming error. Importantly, in trials in which a full error was committed, this “error-related theta” occurred too late with respect to the onset of the erroneous muscle response, supporting the role of theta also in error correction. We conclude that different modes of transient midfrontal theta can be adopted in single trials not only to process stimulus-response conflict, but also to correct erroneous responses. © 2023

World University root node

NeuroImage

Two modes of midfrontal theta suggest a role in conflict and error processing

With the advent of COVID-19, urban transportation has been severely disrupted. We develop a stylized analytical model to study the effect of COVID-19 on the optimal pricing and hygiene level decisions of e-hailing taxi services, which play a crucial role in urban transportation. We analyze whether autonomous vehicles can aid the e-hailing taxi firms to sustain operations during these troubled times. In particular, we provide conditions under which the e-hailing taxi firms should collaborate with an original equipment manufacturer to introduce autonomous vehicles for urban transportation. Owing to the high fixed cost associated with acquiring autonomous vehicles, the e-hailing taxi firms intend to collaborate with an original equipment manufacturer. If the e-hailing taxi firm obtains a significant share from each ride in its collaboration, the hygiene level with autonomous vehicles is always greater than the hygiene level without autonomous vehicles, which is vital during the pandemic. With respect to the question of whether the e-hailing taxi firm should collaborate with the original equipment manufacturer to introduce autonomous vehicles from consumers’ viewpoint, we surprisingly find that the consumers are better off when the autonomous vehicles are introduced without collaboration both in the short-run and the long-run. However, given the high fixed cost of introducing autonomous vehicles, the e-hailing taxi firm will find it profitable to collaborate with an original equipment manufacturer if and only if its share from each ride is significant enough. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

Annals of Operations Research

Towards sharing economy 2.0 with autonomous vehicles? Modeling the impact of COVID-19 on the e-hailing taxi service industry

The impact of COVID-19 on urban travel behavior has been unprecedented. It has significantly influenced the travel mode choices of different urban commuters in various countries across the globe. Given that the public transport providers need to tradeoff between minimizing the spread of COVID-19 and providing an affordable travel choice in this environment, we develop a strategic queueing model to analyze the effect of different pricing strategies on the commuter behavior. In particular, we consider a Markovian queue in front of a public transport ticket counter wherein strategic commuters arrive at the service facility and make joining or balking decisions based on their derived utilities. In contrast to conventional wisdom, we suggest that the public transport provider needs to decrease the price to filter out the wealthy commuters who possess feasible alternative travel options from using public transport and promote the commuters with no alternatives in using public transport. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

Fulltext

Public Transport

To increase or to decrease the price? Managing public transport queues during COVID-19 in the presence of strategic commuters

Cortex

Does action-stopping involve separate pause and cancel processes? A view from premotor cortex

We aimed to test the idea that rhythmic transcranial magnetic stimulation (TMS) entrains cortical oscillations. To do this, we examined oscillatory responses in the electroencephalogram (EEG) to TMS over primary motor cortex. In particular, we contrasted responses to real TMS with those to sham TMS in order to dissociate the contributions of direct (transcranial) activation and indirect activation (via auditory/sensory input) of the brain. We first showed that real single pulse TMS elicited a brief (∼200 ms) increase in sensorimotor beta power whose frequency closely matched that of each individual's post-movement beta rebound (PMBR, ∼18 Hz). Sham TMS triggered minimal oscillatory activity. Together this implies that real TMS interacts with endogenous oscillations via direct brain activation. We then showed that although trains of real rhythmic TMS delivered at each individuals PMBR frequency produced a brief increase in beta power at the same frequency, real arrhythmic TMS also elicited an equivalent increase in beta. The implication is that the oscillatory response is independent of the rhythm of stimulation. By contrast, sham stimulation elicited minimal activity in the beta band, and the responses to rhythmic and arrhythmic sham TMS were broadly similar, showing that sham rhythmic stimulation did not produce entrainment via sensory rhythms. Together, the data demonstrate that the beta oscillatory response of M1 to real TMS predominantly reflects direct activation of the underlying cortex. However, the data do not support the notion of rhythmic TMS enhancing oscillatory activity via entrainment-like mechanisms, at least within the constraints of the current experimental set-up. © 2022

Motor cortex oscillates at its intrinsic post-movement beta rhythm following real (but not sham) single pulse, rhythmic and arrhythmic transcranial magnetic stimulation

The capacity to stop impending or ongoing actions contributes to executive control over behavior. Action-stopping, however, is difficult to directly quantify. It is therefore assayed via computational modeling of behavior in the stop signal task to estimate the latency of stopping (stop signal reaction time, SSRT) and, more recently, the reliability of stopping in terms of the distribution of SSRTs (standard deviation, SD-SSRT) and the frequency with which one outright fails to react to a stop signal (trigger failures, TF). Critically, the validity of computational estimates remains unknown because we currently have no direct readouts of behavior against which to compare them. Here, we developed a method for providing single-trial behavioral readouts of SSRT and trigger failures. The method relies on an adaptation of the stop signal task in which participants respond by moving a computer mouse. In two online experiments, we used movement kinematics to quantify stopping performance (SSRT, SD-SSRT, and TF), and then applied the standard Race Model and recent BEESTS model in order to examine the convergent validity of the methods. Overall, we demonstrate good correspondence between kinematics- and model-based estimates of stopping performance at the group and individual level. We conclude that the new method provides valid estimates of stopping performance that, unlike model-based estimates, can be read out at the level of single trials. Our approach might therefore be useful for interrogating single-trial neurophysiological correlates of stopping and for large-scale, online studies of behavioral stopping. © 2022, The Author(s).

Behavior Research Methods

Failing to attend versus failing to stop: Single-trial decomposition of action-stopping in the stop signal task

Action-stopping in humans involves bursts of beta oscillations in prefrontal-basal ganglia regions. To determine the functional role of these beta bursts we took advantage of the Race Model framework describing action-stopping. We incorporated beta bursts in three race model variants, each implementing a different functional contribution of beta to action-stopping. In these variants, we hypothesized that a transient increase in beta could (1) modulate decision thresholds, (2) change stop accumulation rates, or (3) promote the interaction between the Stop and the Go process. We then tested the model predictions using EEG recordings in humans performing a Stop-signal task. We found that the model variant in which beta increased decision thresholds for a brief period of time best explained the empirical data. The model parameters fitted to the empirical data indicated that beta bursts involve a stronger decision threshold modulation for the Go process than for the Stop process. This suggests that prefrontal beta influences stopping by temporarily holding the response from execution. Our study further suggests that human action-stopping could be multi-staged with the beta acting as a pause, increasing the response threshold for the Stop process to modulate behavior successfully. Overall, our approach of introducing transient oscillations into the race model and testing against human electrophysiological data provides a novel account of the puzzle of prefrontal beta in executive control. © 2022

Transient beta modulates decision thresholds during human action-stopping

The intrinsic function of the human brain is dynamic, giving rise to numerous behavioral subtypes that fluctuate distinctively at multiple timescales. One of the key dynamical processes that takes place in the brain is the interaction between core-periphery brain regions, which undergoes constant fluctuations associated with developmental time frames. Core-periphery dynamical changes associated with macroscale brain network dynamics span multiple timescales and may lead to atypical behavior and clinical symptoms. For example, recent evidence suggests that brain regions with shorter intrinsic timescales are located at the periphery of brain networks (e.g., sensorimotor hand, face areas) and are implicated in perception and movement. On the contrary, brain regions with longer timescales are core hub regions. These hubs are important for regulating interactions between the brain and the body during self-related cognition and emotion. In this review, we summarize a large body of converging evidence derived from time-resolved fMRI studies in autism to characterize atypical core-periphery brain dynamics and how they relate to core and contextual sensory and cognitive profiles. © 2021 Massachusetts Institute of Technology Published under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.

Network Neuroscience

Atypical core-periphery brain dynamics in autism

Group decision-making (GDM) is a complex process. The diversity, discrimination, and inevitable uncertainty due to human intervention characterize such problems that add to this complexity. To circumvent this challenge, there is an urge for an appropriate knowledge representation and decision-making approaches. The present paper is concerned with a prescriptive approach to GDM that can aid a group of decision-makers (DMs) to arrive at a decision. To this end, the recent concept of probabilistic linguistic term set is utilized. The discrimination among the alternatives, as in the real world, are mimicked using an integrated framework that adopts CRITIC and variance methods for attribute weight calculation, Gini index for calculating the weights of DMs, Maclaurin symmetric mean for aggregating preferences, and weighted distance-based approximation for prioritization of alternatives. A real-world problem on electric bike selection illustrates the usefulness of the proposed work. Finally, comparative analysis with extant methods demonstrates the technical results, and it is inferred that the proposed work is (i) highly consistent (from Spearman correlation) and (ii) produces broad rank values (from standard deviation) that could be efficiently discriminated for rational decision-making and backup management during critical situations. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.

Soft Computing

An integrated and discriminative approach for group decision-making with probabilistic linguistic information

In the real world, an attribute value is perceived differently by different individuals. Emphasizing on this aspect, we extend the discrete choice models with perceived values that are subjective and specific to a decision-maker (DM). The proposed choice models are augmented with the parameters of an entropy function, besides the utility coefficients, to model a DM's complex choice behavior. A variety of higher order choice models are also proposed. The proposed models are further extended with a DM's reference-value for each attribute. A real and illustrative application is included. © 2013 IEEE.