Unraveling the Relation between EEG Correlates of Attentional Orienting and Sound Localization Performance: A Diffusion Model Approach

A tutorial on fitting joint models of M/EEG and behavior to understand cognition

We present motivation and practical steps necessary to find parameter estimates of joint models of behavior and neural electrophysiological data. This tutorial is written for researchers wishing to build joint models of human behavior and scalp and intracranial electroencephalographic (EEG) or magnetoencephalographic (MEG) data, and more specifically those researchers who seek to understand human cognition. Although these techniques could easily be applied to animal models, the focus of this tutorial is on human participants. Joint modeling of M/EEG and behavior requires some knowledge of existing computational and cognitive theories, M/EEG artifact correction, M/EEG analysis techniques, cognitive modeling, and programming for statistical modeling implementation. This paper seeks to give an introduction to these techniques as they apply to estimating parameters from neurocognitive models of M/EEG and human behavior, and to evaluate model results and compare models. Due to our research and knowledge on the subject matter, our examples in this paper will focus on testing specific hypotheses in human decision-making theory. However, most of the motivation and discussion of this paper applies across many modeling procedures and applications. We provide Python (and linked R) code examples in the tutorial and appendix. Readers are encouraged to try the exercises at the end of the document.

Modelling decision-making biases

Biases are a fundamental aspect of everyday life decision-making. A variety of modelling approaches have been suggested to capture decision-making biases. Statistical models are a means to describe the data, but the results are usually interpreted according to a verbal theory. This can lead to an ambiguous interpretation of the data. Mathematical cognitive models of decision-making outline the structure of the decision process with formal assumptions, providing advantages in terms of prediction, simulation, and interpretability compared to statistical models. We compare studies that used both signal detection theory and evidence accumulation models as models of decision-making biases, concluding that the latter provides a more comprehensive account of the decision-making phenomena by including response time behavior. We conclude by reviewing recent studies investigating attention and expectation biases with evidence accumulation models. Previous findings, reporting an exclusive influence of attention on the speed of evidence accumulation and prior probability on starting point, are challenged by novel results suggesting an additional effect of attention on non-decision time and prior probability on drift rate.

How the time-of-day affects the EEG signatures of vigilance fluctuation

Previous research suggested the homeostatic effect on the top-down control system as a major factor for daytime vigilance decrement, yet how it alters the cognitive processes of vigilance remains unclear. Using EEG, the current study measured the vigilance of 28 participants under three states: the morning, the midafternoon after napping and no-nap. The drift-diffusion model was applied to decompose vigilant reaction time into decision and non-decision components. From morning to midafternoon, vigilance declined during sustained wakefulness, but remained stable after midday napping. Increased sleep pressure negatively affected decision time and drift rate, but did not significantly alter the non-decision process. Frontocentral N2 amplitude decreased from morning to no-nap afternoon, associated with slowing decision time. In contrast, parietal P3 had no diurnal alterations during sustained wakefulness, but enhanced after napping. Pre-stimulus parietooccipital alpha power enhanced under high sleep pressure relative to low, accompanied by more lapses in no-nap vs. post-napping conditions. The homeostasis effect is a major contributor to daily vigilance fluctuation, specifically targeting top-down control processes during the pre-stimulus and decision-making stages. Under the influence of sleep homeostasis, the speed of decision-making declines with degradation in target monitoring from morning to afternoon, leading to post-noon vigilance decrement.

Examining reaction time variability on the stop-signal task in the ABCD study

OBJECTIVE

Reaction time variability (RTV) has been estimated using Gaussian, ex-Gaussian, and diffusion model (DM) indices. Rarely have studies examined interrelationships among these performance indices in childhood, and the use of reaction time (RT) computational models has been slow to take hold in the developmental psychopathology literature. Here, we extend prior work in adults by examining the interrelationships among different model parameters in the ABCD sample and demonstrate how computational models of RT can clarify mechanisms of time-on-task effects and sex differences in RTs.

METHOD

This study utilized trial-level data from the stop signal task from 8916 children (9-10 years old) to examine Gaussian, ex-Gaussian, and DM indicators of RTV. In addition to describing RTV patterns, we examined interrelations among these indicators, temporal patterns, and sex differences.

RESULTS

There was no one-to-one correspondence between DM and ex-Gaussian parameters. Nonetheless, drift rate was most strongly associated with standard deviation of RT and tau, while nondecisional processes were most strongly associated with RT, mu, and sigma. Performance worsened across time with changes driven primarily by decreasing drift rate. Boys were faster and less variable than girls, likely attributable to girls' wide boundary separation.

CONCLUSIONS

Intercorrelations among model parameters are similar in children as has been observed in adults. Computational approaches play a crucial role in understanding performance changes over time and can also clarify mechanisms of group differences. For example, standard RT models may incorrectly suggest slowed processing speed in girls that is actually attributable to other factors.

Selective spatial attention in lateralized multi-talker speech perception: EEG correlates and the role of age

Speech comprehension under dynamic cocktail party conditions requires auditory search for relevant speech content and focusing spatial attention on the target talker. Here, we investigated the development of these cognitive processes in a population of 329 participants aged 20-70 years. We used a multi-talker speech detection and perception task in which pairs of words (each consisting of a cue and a target word) were simultaneously presented from lateralized positions. Participants attended to predefined cue words and responded to the corresponding target. Task difficulty was varied by presenting cue and target stimuli at different intensity levels. Decline in performance was observed only in the oldest group (age range 53-70 years) and only in the most difficult condition. The EEG analysis of neurocognitive correlates of lateralized auditory attention and stimulus evaluation (N2ac, LPCpc, alpha power lateralization) revealed age-associated changes in focussing on and processing of task-relevant information, while no such deficits were found on early auditory search and target segregation. Irrespective of age, more challenging listening conditions were associated with an increased allocation of attentional resources.

Sound localization and auditory selective attention in school-aged children with ADHD

This study aimed to identify the neurophysiologic bases of auditory attention deficits in children with attention-deficit/hyperactivity disorder (ADHD), focusing on the electroencephalography component of auditory spatial selective attention [the N2 anterior contralateral component (N2ac)]. EEG data were collected from 7- to 11-year-old children with ADHD (n = 54) and age-, sex-, and IQ-matched typically developing (TD) children (n = 61), while they performed an auditory spatial selective task. For behavior, the children with ADHD showed a shorter reaction time (RT) but a higher RT coefficient of variability (RT_CV) than TD children. For ERPs, the TD group showed a significant "adult-like" N2ac component; however, the N2ac component was absent in children with ADHD. More importantly, the smaller N2ac component could predict longer RT in both groups, as well as higher severity of inattentive symptoms in children with ADHD. Our results indicated that 7- to 11-year-old TD children have developed an "adult-like" ability to balance auditory target selection and distractor suppression; the absence of N2ac in children with ADHD provided novel evidence supporting their dysfunctional auditory spatial selective attention.

Pay attention and you might miss it: Greater learning during attentional lapses

Attentional lapses have been found to impair everything from basic perception to learning and memory. Yet, despite the well-documented costs of lapses on cognition, recent work suggests that lapses might unexpectedly confer some benefits. One potential benefit is that lapses broaden our learning to integrate seemingly irrelevant content that could later prove useful-a benefit that prior research focusing only on goal-relevant memory would miss. Here, we measure how fluctuations in sustained attention influence the learning of seemingly goal-irrelevant content that competes for attention with target content. Participants completed a correlated flanker task in which they categorized central targets (letters or numbers) while ignoring peripheral flanking symbols that shared hidden probabilistic relationships with the targets. We found that across participants, higher rates of attentional lapses correlated with greater learning of the target-flanker relationships. Moreover, within participants, learning was more evident during attentional lapses. These findings address long-standing theoretical debates and reveal a benefit of attentional lapses: they expand the scope of learning and decisions beyond the strictly relevant.