Michael D. Nunez

University of Amsterdam | UVA · Department of Psychological Methods

Objective: High frequency oscillations (HFOs) recorded by intracranial electrodes have generated excitement for their potential to help localize epileptic tissue for surgical resection. However, the number of HFOs per minute (i.e. the HFO "rate") is not stable over the duration of intracranial recordings; for example, the rate of HFOs increases du...

Despite advances in techniques for exploring reciprocity in brain-behavior relations, few studies focus on building neurocognitive models that describe both human EEG and behavioral modalities at the single-trial level. Here, we introduce a new integrative joint modeling framework for the simultaneous description of single-trial EEG measures and co...

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 specifical...

Encoding of a sensory stimulus is believed to be the first step in perceptual decision making. Previous research has shown that electrical signals recorded from the human brain track evidence accumulation during perceptual decision making (Gold and Shadlen, 2007; O’Connell et al., 2012; Philiastides et al., 2014). In this study we directly tested t...

Scalp-recorded electroencephalography (EEG) is thought to be driven by both local and global oscillations dependent on the cognitive state and task of the individual. However, many EEG studies assume that the activity is local, especially when inverse modeling EEG activity. In this work, we show that a simple model of purely macroscopic connections...

As the field of computational cognitive neuroscience continues to expand and generate new theories, there is a growing need for more advanced methods to test the hypothesis of brain-behavior relationships. Recent progress in Bayesian cognitive modeling has enabled the combination of neural and behavioral models into a single unifying framework. How...

Diffusion Decision Models (DDMs) are a widely used class of models that assume an accumulation of evidence during a quick decision. These models are often used as measurement models to assess individual differences in cognitive processes such as evidence accumulation rate and response caution. An underlying assumption of these models is that there...

We explored the underlying latent process of spatial prioritisation in perceptual decision processes, based on the drift-diffusion model, and subsequent nested model comparison. Our hierarchical cognitive modelling analysis revealed that spatial attention changed the non-decision time parameter across experimental conditions, quantified using the d...

Visual perceptual decision-making involves multiple components including visual encoding, attention, accumulation of evidence, and motor execution. Recent research suggests that EEG signals can identify the time of encoding and the onset of evidence accumulation during perceptual decision-making. Although scientists show that spatial attention impr...

Despite advances in techniques for exploring reciprocity in brain-behavior relations, few studies focus on building neurocognitive models that describe both human EEG and behavioral modalities at the single-trial level. Here, we introduce a new integrative joint modeling framework for the simultaneous description of single-trial EEG measures and co...

Human decision making behavior is observed with choice-response time data during psychological experiments. Drift-diffusion models of this data consist of a Wiener first-passage time (WFPT) distribution and are described by cognitive parameters: drift rate, boundary separation, and starting point. These estimated parameters are of interest to neuro...

Visual perceptual decision-making involves multiple components including visual encoding, attention, accumulation of evidence, and motor execution. Recent research suggests that EEG oscillations can identify the time of encoding and the onset of evidence accumulation during perceptual decision-making. Although scientists show that spatial attention...

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 specifical...

Trained monkeys performed a two-choice perceptual decision-making task in which they reported the perceived orientation of a dynamic Glass pattern, before and after unilateral, reversible, inactivation of a brainstem area—the superior colliculus (SC)—involved in preparing eye movements. We found that unilateral SC inactivation produced significant...

Joint computational modeling of human EEG and behavior reveal cognition during decision making

Decision-making in two-alternative forced choice tasks has several underlying components including stimulus encoding, perceptual categorization, response selection, and response execution. Sequential sampling models of decision-making are based on an evidence accumulation process to a decision boundary. Animal and human studies have focused on perc...

Decision-making in two-alternative forced choice tasks has several underlying components including stimulus encoding, perceptual categorization, response selection, and response execution. Sequential sampling models of decision-making are based on an evidence accumulation process to a decision boundary. Animal and human studies have focused on perc...

Relationships between neuroimaging measures and behavior provide important clues about brain function and cognition in healthy and clinical populations. While electroencephalography (EEG) provides a portable, low cost measure of brain dynamics, it has been somewhat underrepresented in the emerging field of model-based inference. We seek to address...

A popular model of decision-making suggests that in primates, including humans, decisions evolve within forebrain structures responsible for preparing voluntary actions; a concert referred to as embodied cognition. Embodied cognition posits that in decision tasks, neuronal activity generally associated with preparing an action, actually reflects th...

Objective High frequency oscillations (HFOs) recorded by intracranial electrodes have generated excitement for their potential to help localize epileptic tissue for surgical resection. However, the number of HFOs per minute (i.e. the HFO “rate”) is not stable over the duration of intracranial recordings; for example, the rate of HFOs increases duri...

A biophysical framework needed to interpret electrophysiological data recorded at multiple spatial scales of brain tissue is developed. Micro current sources at membrane surfaces produce local field potentials, electrocorticography, and electroencephalography (EEG). We categorize multi-scale sources as genuine, equivalent, or representative. Genuin...

Previous research has shown that individuals with greater cognitive abilities display a greater speed of higher-order cognitive processing. These results suggest that speeded neural information processing may facilitate evidence accumulation during decision making and memory updating and thus yield advantages in general cognitive abilities. We used...

Previous research has shown that individuals with greater cognitive abilities display a greater speed of higher-order cognitive processing. These results suggest that speeded neural information processing may facilitate evidence accumulation during decision making and memory updating and thus yield advantages in general cognitive abilities. We used...

High rates of HFOs may be used to localize epileptic tissue for surgical resection. However previous research has shown that the rates of HFOs are not stable over the duration of intracranial recordings. The rate of HFOs increases during periods of slow-wave sleep, and the rate may trend up or down within each sleep stage (von Ellenrieder et al., 2...

A theory of decision making predicts distinct time periods that contribute to a response time (RT): visual encoding time (VET; figure-ground segregation), visual evidence accumulation (VEA), motor evidence accumulation (MEA), and motor execution time (MET). It is our goal to accurately measure these time periods within participants using EEG and hu...

Objectives: Provide a biophysical framework to interpret of electrophysiological data recorded from multiple spatial scales of brain tissue. Methods: Apply the physics of conductive media to brain tissue. Micro current sources at membrane surfaces produce local field potentials, electrocorticography, and electroencephalography. Sources may be categ...

Encoding of a sensory stimulus is believed to be the first step in perceptual decision making. Previous research has shown that electrical signals recorded from the human brain track evidence accumulation during perceptual decision making (Gold and Shadlen, 2007; O’Connell et al., 2012; Philiastides et al., 2014). In this study we directly tested t...

Previous research has shown that individuals with greater cognitive abilities display a greater speed of higher-order cognitive processing. These results suggest that speeded neural information-processing may facilitate evidence accumulation during decision making and memory updating and thus yield advantages in general cognitive abilities. We used...

High frequency oscillations (HFOs) > 80 Hz are a promising biomarker of epileptic tissue. Recent evidence has shown that spontaneous HFOs can be recorded from the scalp, but detection of these electrographic events remains a challenge. Here, we modified a simple automatic detector, used originally for intracranial EEG (iEEG) recordings, to detect r...

Relationships between neuroimaging measures and behavior provide important clues about brain function and cognition in healthy and clinical populations. While electroencephalography (EEG) provides a portable, low cost measure of brain dynamics, it has been somewhat underrepresented in the emerging field of model-based inference. We seek to address...

Exploring methods to verify human cognitive processing times with EEG, human behavior and hierarchical Bayesian methods

The cognitive process and time course of quick human decision making was evaluated using reaction time, choice distributions, and human electrophysiology as recorded by EEG. These data were used to evaluate drift-diffusion models, a class of decision-making models that assume a stochastic accumulation of evidence on each trial, within hierarchical...

Perceptual decision making can be accounted for by drift-diffusion models, a class of decision-making models that assume a stochastic accumulation of evidence on each trial. Fitting response time and accuracy to a drift-diffusion model produces evidence accumulation rate and non-decision time parameter estimates that reflect cognitive processes. Ou...

Sequential sampling decision-making models have been successful in accounting for reaction time (RT) and accuracy data in two-alternative forced choice tasks. These models have been used to describe the behavior of populations of participants, and explanatory structures have been proposed to account for between individual variability in model param...