Psychophysiological indices of recognition memory

It has recently been found that during recognition memory tests participants’ pupils dilate more when they view old items compared to novel items. This thesis sought to replicate this novel ‘‘Pupil Old/New Effect’’ (PONE) and to determine its relationship to implicit and explicit mnemonic processes, the veracity of participants’ responses, and the analogous Event-Related Potential (ERP) old/new effect. Across 9 experiments, pupil-size was measured with a video-based eye-tracker during a variety of recognition tasks, and, in the case of Experiment 8, with concurrent Electroencephalography (EEG). The main findings of this thesis are that: - the PONE occurs in a standard explicit test of recognition memory but not in “implicit” tests of either perceptual fluency or artificial grammar learning; - the PONE is present even when participants are asked to give false behavioural answers in a malingering task, or are asked not to respond at all; - the PONE is present when attention is divided both at learning and during recognition; - the PONE is accompanied by a posterior ERP old/new effect; - the PONE does not occur when participants are asked to read previously encountered words without making a recognition decision; - the PONE does not occur if participants preload an “old/new” response; - the PONE is not enhanced by repetition during learning. These findings are discussed in the context of current models of recognition memory and other psychophysiological indices of mnemonic processes. It is argued that together these findings suggest that the increase in pupil-size which occurs when participants encounter previously studied items is not under conscious control and may reflect primarily recollective processes associated with recognition memory

DEVELOPMENT OF NOVEL EEG-BASED METHODOLOGY FOR INVESTIGATION OF NEURAL CORRELATES OF HUMAN DECEPTION

Ph.DDOCTOR OF PHILOSOPH

Analisis Perbedaan Pola Sinyal EEG Berdasarkan Jenis Kelamin Yang Berbeda Saat Numerical Stroop Task

Cognitive process show how brain work from stimulus reception until stimuls reaction. With electroencephalogram (EEG) device, cognate process can be observerd in brain signal or EEG signal form. In cognitive process different kind of stimulus could affect generated brain signal. Also, given interference in cognitive prcess could affect brain signal. In this research, conducted observation whether gender difference has effect in cognitive process. Numerical stroop task with three kinds of conditions (congruence, incongruence, and neutral) are used as reference in signal observation process which is generated when the cognitive process in difference genders are done. The resulting EEG signal then conducted three kinds of analysis that is ERP analysis, reaction time, and energy analysis. The result of ERP analysis show both subject class have difference in response time that indicated with P3 peak time. On average, respons time in female (kongruent = 623,34 ms; inkongruent = 645,18 ms ; neutral = 614,91 ms)subject class is faster than male (kongruent = 709,67 ms; inkongruent = 745,00 ms; neutral =715,37 ms) subject class. Energy analysis show when numerical stroop task takes place, left side of the brain (51,36%) and cetral side of the brain (50,65%) more dominant than others parts of the brain

Interpersonal deceit and lie-detection using computer-mediated communication

This thesis examines the use of computer-mediated communication for lie-detection and interpersonal deceit. The literature within the fields of lie-detection and mediated communication are reviewed and it is proposed that there is a lack of knowledge surrounding how people use CMC to deceive one another. Qualitative research was carried out in order to address this shortcoming, exploring the self-reported experiences of chat room users who have been exposed to online deceit. Reports were provided that describe the misrepresentation of age, gender, vocation, affection, and appearance. The importance of stereotypes in driving suspicions is also emphasised within the reports. It is suggested that this key characteristic has more dominance in CMC than it would do face-to-face because of the occlusion of the traditional nonstrategic clues to deceit. Evidence for an alternative set of nonstrategic leakage clues was examined further by conducting a variant of the Guilty-Knowledge test within the context of a CMC based crime. It was found that participants exhibited a response time inhibition effect when presented with 'guilty knowledge' and that this effect was detectable through a standard two-button mouse. The use of such nonstrategic cues to deceit was explored further in a study that examined how CMC might be used to add additional control to a Statement Validity Assessment truth-validation test. It was found that the content analysis technique used by SVA was unable in its present form to correctly distinguish between truthful and fabricated statements of participants interviewed using a CMC chat program. In addition, it was found that the deletion-behaviours of participants fabricating a story within CMC provided no quantitative or qualitative evidence that they were lying

Electrocortical underpinnings of error monitoring in health and pathology

It becomes clear from the literature described above (Chapter 1), that the error monitoring mechanisms play a fundamental role in signalling the need for cognitive control. Many studies already provided a consistent evidence on the existence of peculiar ways in which the brain signals this need through electrophysiological changes. However, the following set of empirical studies aims to gain further insight into these complex processes by measuring brain activity changes in situations that alter the way one experience errors. The second Chapter (Chapter 2) consists of a brief commentary that was made in response to an article on the brain activity to action errors. In this commentary we propose new possibilities to explore our topic of interest, by taking advantage of EEG and modern virtual reality facilities. The thesis includes three EEG-VR studies: one on the error-mechanism in healthy participants (Chapter 3) and two studies on error monitoring system in pathological populations (Chapter 4, 5), as main parts of the core of the thesis. As a collateral project, in the Appendix, there is an EEG study on action observation in elite players (Chapter 7). In the first study (Chapter 3), we investigated a very simple but fundamental question. As we saw in the introduction, error-related signatures are evoked when an error occurs. But it is not clear how much of this is due to the occurrence of a violation of the intended goal or simply to the observation of a rare – thus less predictable – event. To this aim, we used a paradigm developed in the former years in our laboratory (Pavone et al., 2016; Spinelli et al., 2017), characterized by a setup in immersive Virtual Reality (VR) and simultaneous EEG recording. Building on the previous findings, we designed an EEG-VR study in which we manipulated the probability of observing errors in actions. In another study (Chapter 4) we investigated how erroneous actions are experienced by people with brain damage and diagnosis of Apraxia. Apraxic patients are people with hemispheric lesions and defective awareness on a variety of aspects that cover perceptuo-motor, cognitive or emotional domains. This study was developed after the results obtained by Canzano and colleagues (2014) in a behavioral study in which apraxic patients were asked to imitate the actions executed by the experimenter and judge their correctness; results revealed that bucco-facial apraxic patients manifest a specific deficit in detecting their own gestural errors when they are explicitly asked to judge them. With the present study we wanted to investigate apraxic brain’ response to action errors, while they embody an avatar in first person perspective (EEG-VR setup). The third study (Chapter 5) investigates the integrity of the error-monitoring system in Parkinson’s Disease and the impact of the dopaminergic treatment in the brain response to errors. To this aim we used the proposed VR action-observation paradigm, in which Parkinson patients observed successful and unsuccessful reach-to-grasp actions in first person perspective while EEG activity was recorded; the same patients were tested while being under dopaminergic treatment and during a dopaminergic withdrawal state. In another chapter we provide a critical overview of the findings of this work (General Discussion, Chapter 6). In the last chapter, the Appendix (Chapter 7), there is a collateral project of another research line of the Laboratory, in which I have being involved. In this study we are investigating the cortical underpinning of elite players during observation of goal-directed actions, in their domain of expertise. We recorded the EEG activity of elite wheelchair basketball players while observing free-throws performed by paraplegic athletes. We expected their brain correlates to be different from novice players and to be able to easily discriminate whether a basketball shot would be successful or unsuccessful (project still ongoing)

Self-Referential Processing: An Investigation of the Mediating Role of Alpha Power

The EEG correlates of valenced self- and other-referential processing (SRP-ORP) are relatively little understood. This study examined the immediate effects of mindfulness meditation (MM) and EEG alpha neurofeedback (NFB) on resting state EEG alpha amplitudes and alpha event related (de-)synchronization (ERD/S) during an experimental implicit and explicit SRP-ORP task. Undergraduate students (n = 93) were randomized to a single session of MM, NFB alpha synchronization training (“alpha-up”), NFB alpha desynchronization training (“alpha-down”), or sham (placebo control) NFB before completing the Visual-Verbal Self-Other Referential Processing Task (VV-SORP-T). A reduction in resting-state alpha power over posterior cortex was observed across groups relative to pre-treatment baseline, with no differential effects observed between groups. During both SRP and ORP, however, less negative affect (NA) was experienced by participants in the alpha-down group. Alpha ERD was highest during negative ORP relative to other task conditions across groups, with the alpha-down group trending toward showing increased ERD across all conditions of the VV-SORP-T relative to the alpha-up group. Study limitations and future research directions are discussed

Predictive decoding of neural data

In the last five decades the number of techniques available for non-invasive functional imaging has increased dramatically. Researchers today can choose from a variety of imaging modalities that include EEG, MEG, PET, SPECT, MRI, and fMRI. This doctoral dissertation offers a methodology for the reliable analysis of neural data at different levels of investigation. By using statistical learning algorithms the proposed approach allows single-trial analysis of various neural data by decoding them into variables of interest. Unbiased testing of the decoder on new samples of the data provides a generalization assessment of decoding performance reliability. Through consecutive analysis of the constructed decoder\u27s sensitivity it is possible to identify neural signal components relevant to the task of interest. The proposed methodology accounts for covariance and causality structures present in the signal. This feature makes it more powerful than conventional univariate methods which currently dominate the neuroscience field. Chapter 2 describes the generic approach toward the analysis of neural data using statistical learning algorithms. Chapter 3 presents an analysis of results from four neural data modalities: extracellular recordings, EEG, MEG, and fMRI. These examples demonstrate the ability of the approach to reveal neural data components which cannot be uncovered with conventional methods. A further extension of the methodology, Chapter 4 is used to analyze data from multiple neural data modalities: EEG and fMRI. The reliable mapping of data from one modality into the other provides a better understanding of the underlying neural processes. By allowing the spatial-temporal exploration of neural signals under loose modeling assumptions, it removes potential bias in the analysis of neural data due to otherwise possible forward model misspecification. The proposed methodology has been formalized into a free and open source Python framework for statistical learning based data analysis. This framework, PyMVPA, is described in Chapter 5

Meta Heuristics based Machine Learning and Neural Mass Modelling Allied to Brain Machine Interface

New understanding of the brain function and increasing availability of low-cost-non-invasive electroencephalograms (EEGs) recording devices have made brain-computer-interface (BCI) as an alternative option to augmentation of human capabilities by providing a new non-muscular channel for sending commands, which could be used to activate electronic or mechanical devices based on modulation of thoughts. In this project, our emphasis will be on how to develop such a BCI using fuzzy rule-based systems (FRBSs), metaheuristics and Neural Mass Models (NMMs). In particular, we treat the BCI system as an integrated problem consisting of mathematical modelling, machine learning and classification. Four main steps are involved in designing a BCI system: 1) data acquisition, 2) feature extraction, 3) classification and 4) transferring the classification outcome into control commands for extended peripheral capability. Our focus has been placed on the first three steps. This research project aims to investigate and develop a novel BCI framework encompassing classification based on machine learning, optimisation and neural mass modelling. The primary aim in this project is to bridge the gap of these three different areas in a bid to design a more reliable and accurate communication path between the brain and external world. To achieve this goal, the following objectives have been investigated: 1) Steady-State Visual Evoked Potential (SSVEP) EEG data are collected from human subjects and pre-processed; 2) Feature extraction procedure is implemented to detect and quantify the characteristics of brain activities which indicates the intention of the subject.; 3) a classification mechanism called an Immune Inspired Multi-Objective Fuzzy Modelling Classification algorithm (IMOFM-C), is adapted as a binary classification approach for classifying binary EEG data. Then, the DDAG-Distance aggregation approach is proposed to aggregate the outcomes of IMOFM-C based binary classifiers for multi-class classification; 4) building on IMOFM-C, a preference-based ensemble classification framework known as IMOFM-CP is proposed to enhance the convergence performance and diversity of each individual component classifier, leading to an improved overall classification accuracy of multi-class EEG data; and 5) finally a robust parameterising approach which combines a single-objective GA and a clustering algorithm with a set of newly devised objective and penalty functions is proposed to obtain robust sets of synaptic connectivity parameters of a thalamic neural mass model (NMM). The parametrisation approach aims to cope with nonlinearity nature normally involved in describing multifarious features of brain signals

Augmentation of Brain Function: Facts, Fiction and Controversy. Volume III: From Clinical Applications to Ethical Issues and Futuristic Ideas

The final volume in this tripartite series on Brain Augmentation is entitled “From Clinical Applications to Ethical Issues and Futuristic Ideas”. Many of the articles within this volume deal with translational efforts taking the results of experiments on laboratory animals and applying them to humans. In many cases, these interventions are intended to help people with disabilities in such a way so as to either restore or extend brain function. Traditionally, therapies in brain augmentation have included electrical and pharmacological techniques. In contrast, some of the techniques discussed in this volume add specificity by targeting select neural populations. This approach opens the door to where and how to promote the best interventions. Along the way, results have empowered the medical profession by expanding their understanding of brain function. Articles in this volume relate novel clinical solutions for a host of neurological and psychiatric conditions such as stroke, Parkinson’s disease, Huntington’s disease, epilepsy, dementia, Alzheimer’s disease, autism spectrum disorders (ASD), traumatic brain injury, and disorders of consciousness. In disease, symptoms and signs denote a departure from normal function. Brain augmentation has now been used to target both the core symptoms that provide specificity in the diagnosis of a disease, as well as other constitutional symptoms that may greatly handicap the individual. The volume provides a report on the use of repetitive transcranial magnetic stimulation (rTMS) in ASD with reported improvements of core deficits (i.e., executive functions). TMS in this regard departs from the present-day trend towards symptomatic treatment that leaves unaltered the root cause of the condition. In diseases, such as schizophrenia, brain augmentation approaches hold promise to avoid lengthy pharmacological interventions that are usually riddled with side effects or those with limiting returns as in the case of Parkinson’s disease. Brain stimulation can also be used to treat auditory verbal hallucination, visuospatial (hemispatial) neglect, and pain in patients suffering from multiple sclerosis. The brain acts as a telecommunication transceiver wherein different bandwidth of frequencies (brainwave oscillations) transmit information. Their baseline levels correlate with certain behavioral states. The proper integration of brain oscillations provides for the phenomenon of binding and central coherence. Brain augmentation may foster the normalization of brain oscillations in nervous system disorders. These techniques hold the promise of being applied remotely (under the supervision of medical personnel), thus overcoming the obstacle of travel in order to obtain healthcare. At present, traditional thinking would argue the possibility of synergism among different modalities of brain augmentation as a way of increasing their overall effectiveness and improving therapeutic selectivity. Thinking outside of the box would also provide for the implementation of brain-to-brain interfaces where techniques, proper to artificial intelligence, could allow us to surpass the limits of natural selection or enable communications between several individual brains sharing memories, or even a global brain capable of self-organization. Not all brains are created equal. Brain stimulation studies suggest large individual variability in response that may affect overall recovery/treatment, or modify desired effects of a given intervention. The subject’s age, gender, hormonal levels may affect an individual’s cortical excitability. In addition, this volume discusses the role of social interactions in the operations of augmenting technologies. Finally, augmenting methods could be applied to modulate consciousness, even though its neural mechanisms are poorly understood. Finally, this volume should be taken as a debate on social, moral and ethical issues on neurotechnologies. Brain enhancement may transform the individual into someone or something else. These techniques bypass the usual routes of accommodation to environmental exigencies that exalted our personal fortitude: learning, exercising, and diet. This will allow humans to preselect desired characteristics and realize consequent rewards without having to overcome adversity through more laborious means. The concern is that humans may be playing God, and the possibility of an expanding gap in social equity where brain enhancements may be selectively available to the wealthier individuals. These issues are discussed by a number of articles in this volume. Also discussed are the relationship between the diminishment and enhancement following the application of brain-augmenting technologies, the problem of “mind control” with BMI technologies, free will the duty to use cognitive enhancers in high-responsibility professions, determining the population of people in need of brain enhancement, informed public policy, cognitive biases, and the hype caused by the development of brain- augmenting approaches