Temporal characteristics of the influence of punishment on perceptual decision making in the human brain

Perceptual decision making is the process by which information from sensory systems is combined and used to influence our behavior. In addition to the sensory input, this process can be affected by other factors, such as reward and punishment for correct and incorrect responses. To investigate the temporal dynamics of how monetary punishment influences perceptual decision making in humans, we collected electroencephalography (EEG) data during a perceptual categorization task whereby the punishment level for incorrect responses was parametrically manipulated across blocks of trials. Behaviorally, we observed improved accuracy for high relative to low punishment levels. Using multivariate linear discriminant analysis of the EEG, we identified multiple punishment-induced discriminating components with spatially distinct scalp topographies. Compared with components related to sensory evidence, components discriminating punishment levels appeared later in the trial, suggesting that punishment affects primarily late postsensory, decision-related processing. Crucially, the amplitude of these punishment components across participants was predictive of the size of the behavioral improvements induced by punishment. Finally, trial-by-trial changes in prestimulus oscillatory activity in the alpha and gamma bands were good predictors of the amplitude of these components. We discuss these findings in the context of increased motivation/attention, resulting from increases in punishment, which in turn yields improved decision-related processing

Visual search performance is predicted by both prestimulus and poststimulus electrical brain activity

© The Author(s) 2016. An individual's performance on cognitive and perceptual tasks varies considerably across time and circumstances. We investigated neural mechanisms underlying such performance variability using regression-based analyses to examine trial-by-trial relationships between response times (RTs) and different facets of electrical brain activity. Thirteen participants trained five days on a color-popout visual-search task, with EEG recorded on days one and five. The task was to find a color-popout target ellipse in a briefly presented array of ellipses and discriminate its orientation. Later within a session, better preparatory attention (reflected by less prestimulus Alpha-band oscillatory activity) and better poststimulus early visual responses (reflected by larger sensory N1 waves) correlated with faster RTs. However, N1 amplitudes decreased by half throughout each session, suggesting adoption of a more efficient search strategy within a session. Additionally, fast RTs were preceded by earlier and larger lateralized N2pc waves, reflecting faster and stronger attentional orienting to the targets. Finally, SPCN waves associated with target-orientation discrimination were smaller for fast RTs in the first but not the fifth session, suggesting optimization with practice. Collectively, these results delineate variations in visual search processes that change over an experimental session, while also pointing to cortical mechanisms underlying performance in visual search

Neural correlates of conscious visual processing

The objective of the current thesis is to evaluate the role of alpha band activity and neural trial-to-trial variability in conscious visual perception and their relationship to each other. We investigate these measures in electrophysiological recordings of monkeys as well as the electroencephalogram (EEG) of humans using a generalized flash suppression (GFS) paradigm.2021-06-0

Alpha-band brain oscillations shape the processing of perceptible as well as imperceptible somatosensory stimuli during selective attention

Attention filters and weights sensory information according to behavioral demands. Stimulus-related neural responses are increased for the attended stimulus. Does alpha-band activity mediate this effect and is it restricted to conscious sensory events (suprathreshold), or does it also extend to unconscious stimuli (subthreshold)? To address these questions, we recorded EEG in healthy male and female volunteers undergoing subthreshold and suprathreshold somatosensory electrical stimulation to the left or right index finger. The task was to detect stimulation at the randomly alternated cued index finger. Under attention, amplitudes of somatosensory evoked potentials increased 50--60 ms after stimulation (P1) for both suprathreshold and subthreshold events. Pre-stimulus amplitude of peri-Rolandic alpha, that is mu, showed an inverse relationship to P1 amplitude during attention, compared to when the finger was unattended. Interestingly, intermediate and high amplitudes of mu rhythm were associated with the highest P1 amplitudes during attention and smallest P1 during lack of attention, that is, these levels of alpha rhythm seemed to optimally support the behavioral goal (“detect” stimuli at the cued finger while ignoring the other finger). Our results show that attention enhances neural processing for both suprathreshold and subthreshold stimuli and they highlight a rather complex interaction between attention, Rolandic alpha activity, and their effects on stimulus processing

Exposing Internal Attentional Brain States using Single-Trial EEG Analysis with Combined Imaging Modalities

The goal of this dissertation is to explore the neural correlates of endogenous task-related attentional modulations. Natural fluctuations in task engagement are challenging to study, primarily because they are by nature not event related and thus cannot be controlled experimentally. Here we exploit well-accepted links between attention and various measures of neural activity while subjects perform simple target detection tasks that leave their minds free to wander. We use multimodal neuroimaging, specifically simultaneous electroencephalograpy and functional magnetic resonance imaging (EEG-fMRI) and EEG-pupillometry, with data-driven machine learning methods and study activity across the whole brain. We investigate BOLD fMRI correlates of EEG variability spanning each trial, enabling us to unravel a cascade of attention-related activations and determine their temporal ordering. We study activity during auditory and visual paradigms independently, and we also combine data to investigate supra modal attention systems. Without aiming to study known attention-related functional brain networks, we found correlates of attentional modulations in areas representative of the default mode network (DMN), ventral attention network (VAN), locus coeruleus norepinephrine (LC-NE) system, and regions implicated in generation of the extensively-studied P300 EEG response to target stimuli. Our results reveal complex interactions between known attentional systems, and do so non-invasively to study normal fluctuations of task engagement in the human brain

Pre‐stimulus alpha‐band power and phase fluctuations originate from different neural sources and exert distinct impact on stimulus‐evoked responses

Ongoing oscillatory neural activity before stimulus onset influences subsequent visual perception. Specifically, both the power and the phase of oscillations in the alpha-frequency band (9–13 Hz) have been reported to predict the detection of visual stimuli. Up to now, the functional mechanisms underlying pre-stimulus power and phase effects on upcoming visual percepts are debated. Here, we used magnetoencephalography recordings together with a near-threshold visual detection task to investigate the neural generators of pre-stimulus power and phase and their impact on subsequent visual-evoked responses. Pre-stimulus alpha-band power and phase opposition effects were found consistent with previous reports. Source localization suggested clearly distinct neural generators for these pre-stimulus effects: Power effects were mainly found in occipital-temporal regions, whereas phase effects also involved prefrontal areas. In order to be functionally relevant, the pre-stimulus correlates should influence post-stimulus processing. Using a trial-sorting approach, we observed that only pre-stimulus power modulated the Hits versus Misses difference in the evoked response, a well-established post-stimulus neural correlate of near-threshold perception, such that trials with stronger pre-stimulus power effect showed greater post-stimulus difference. By contrast, no influence of pre-stimulus phase effects were found. In sum, our study shows distinct generators for two pre-stimulus neural patterns predicting visual perception, and that only alpha power impacts the post-stimulus correlate of conscious access. This underlines the functional relevance of prestimulus alpha power on perceptual awareness, while questioning the role of alpha phase

Oscillatory Network Activity in Brain Functions and Dysfunctions

Recent experimental studies point to the notion that the brain is a complex dynamical system whose behaviors relating to brain functions and dysfunctions can be described by the physics of network phenomena. The brain consists of anatomical axonal connections among neurons and neuronal populations in various spatial scales. Neuronal interactions and synchrony of neuronal oscillations are central to normal brain functions. Breakdowns in interactions and modifications in synchronization behaviors are usual hallmarks of brain dysfunctions. Here, in this dissertation for PhD degree in physics, we report discoveries of brain oscillatory network activity from two separate studies. These studies investigated the large-scale brain activity during tactile perceptual decision-making and epileptic seizures. In the perceptual decision-making study, using scalp electroencephalography (EEG) recordings of brain potentials, we investigated how oscillatory activity functionally organizes different neocortical regions as a network during a tactile discrimination task. While undergoing EEG recordings, blindfolded healthy participants felt a linear three-dot array presented electromechanically, under computer control, and reported whether the central dot was offset to the left or right. Based on the current dipole modeling in the brain, we found that the source-level peak activity appeared in the left primary somatosensory cortex (SI), right lateral occipital complex (LOC), right posterior intraparietal sulcus (pIPS) and finally left dorsolateral prefrontal cortex (dlPFC) at 45, 130, 160 and 175 ms respectively. Spectral interdependency analysis showed that fine tactile discrimination is mediated by distinct but overlapping ~15 Hz beta and ~80 Hz gamma band large-scale oscillatory networks. The beta-network that included all four nodes was dominantly feedforward, similar to the propagation of peak cortical activity, implying its role in accumulating and maintaining relevant sensory information and mapping to action. The gamma-network activity, occurring in a recurrent loop linked SI, pIPS and dlPFC, likely carrying out attentional selection of task-relevant sensory signals. Behavioral measure of task performance was correlated with the network activity in both bands. In the study of epileptic seizures, we investigated high-frequency (\u3e 50 Hz) oscillatory network activity from intracranial EEG (IEEG) recordings of patients who were the candidates for epilepsy surgery. The traditional approach of identifying brain regions for epilepsy surgery usually referred as seizure onset zones (SOZs) has not always produced clarity on SOZs. Here, we investigated directed network activity in the frequency domain and found that the high frequency (\u3e80 Hz) network activities occur before the onset of any visible ictal activity, andcausal relationships involve the recording electrodes where clinically identifiable seizures later develop. These findings suggest that high-frequency network activities and their causal relationships can assist in precise delineation of SOZs for surgical resection

Oscillatory mechanisms of conscious perception and attention

Although the prominent role of neural oscillations in perception and cognition has been continuously investigated, some critical questions remain unanswered. My PhD thesis was aimed at addressing some of them. First, can we dissociate oscillatory underpinnings of perceptual accuracy and subjective awareness? Current work would strongly suggest that this dissociation can be drawn. While the fluctuations in alpha-amplitude decide perceptual bias and metacognitive abilities, the speed of alpha activity (i.e., alpha-frequency) dictates sensory sampling, shaping perceptual accuracy. Second, how are these oscillatory mechanisms integrated during attention? The obtained results indicate that a top-down visuospatial mechanism modulates neural assemblies in visual areas via oscillatory re-alignment and coherence in the alpha/beta range within the fronto-parietal brain network. These perceptual predictions are reflected in the retinotopically distributed posterior alpha-amplitude, while perceptual accuracy is explained by the higher alpha-frequency at the to-be-attended location. Finally, sensory input, elaborated via fast gamma oscillations, is linked to specific phases of this slower activity via oscillatory nesting, enabling integration of the feedback-modulated oscillatory activity with sensory information. Third, how can we relate this oscillatory activity to other neural markers of behaviour (i.e., event-related potentials)? The obtained results favour the oscillatory model of ERP genesis, where alpha-frequency shapes the latency of early evoked-potentials, namely P1, with both neural indices being related to perceptual accuracy. On the other hand, alpha-amplitude dictates the amplitude of later P3 evoked-response, whereas both indices shape subjective awareness. Crucially, by combining different methodological approaches, including neurostimulation (TMS) and neuroimaging (EEG), current work identified these oscillatory-behavior links as causal and not just as co-occurring events. Current work aimed at ameliorating the use of the TMS-EEG approach by explaining inter-individual differences in the stimulation outcomes, which could be proven crucial in the way we design entrainment experiments and interpret the results in both research and clinical settings

Confidence Inference in Defensive Cyber Operator Decision Making

Cyber defense analysts face the challenge of validating machine generated alerts regarding network-based security threats. Operations tempo and systematic manpower issues have increased the importance of these individual analyst decisions, since they typically are not reviewed or changed. Analysts may not always be confident in their decisions. If confidence can be accurately assessed, then analyst decisions made under low confidence can be independently reviewed and analysts can be offered decision assistance or additional training. This work investigates the utility of using neurophysiological and behavioral correlates of decision confidence to train machine learning models to infer confidence in analyst decisions. Electroencephalography (EEG) and behavioral data was collected from eight participants in a two-task human-subject experiment and used to fit several popular classifiers. Results suggest that for simple decisions, it is possible to classify analyst decision confidence using EEG signals. However, more work is required to evaluate the utility of EEG signals for classification of decision confidence in complex decisions