The feedback-related negativity (FRN) is a negative event-related potential (ERP) component associated with the presentation of task feedback. The possibility that the FRN may habituate has been briefly mentioned in previous research (Garrido Chaves et al., 2020), but not yet been actively investigated. Thus, the current study is the first one to explicity investigate the possibility of short-term (across trials) and long-term (across blocks) habituation effects on the FRN. This was done by using electroencephalography (EEG) and a time-estimation paradigm during which participants were tasked with guessing the duration of one second. Following each estimate, participants were presented with either positive or negative visual feedback (however, only trials with negative feedback were included in the subsequent statistical analysis). It was hypothesized that mean FRN amplitude would decrease, i.e. habituate, upon increased exposure to negative feedback. Contrary to the expected effect, repeated-measures analyses of variance (ANOVA) and t-tests revealed a stong, significant sensitization effect of FRN mean amplitude in the short-term comparison. However, there appeared to be multiple confounds involved, which made these results ambigous and difficult to interpret. No significant results were found for the long-term comparison, although the ERP waveforms suggested that there might be a (non-significant) habituation effect. This effect may become significant provided a greater sample size. Replication with a greater sample size is thus required before any firm conclusions can be drawn.

ABSTRACT In the context of increasing incidence of stroke (but also an increasing rate of survival), non-invasive brain stimulation techniques (NIBS) are more frequently used for patients with post-stroke aphasia (PWA) and post-stroke depression (PSD). NIBS techniques, modulating brain plasticity, might offer valid, alternative therapeutic strategies. The aim is to reach a better outcome because treatment of aphasia can also improve post-stroke depression and vice versa. Based on two literature reviews on NIBS effects on PSD and post-stroke aphasia the conclusion is that, although the field is relatively new, and many more investigations with larger samples of patients are required, transcranial magnetic stimulation (TMS) and transcranial direct current stimulation (tDCS) clinical application is well tolerated, safe, and feasible. Starting from these encouraging data, we used a combination of TMS and electroencephalography (EEG) to explore the excitability modulation before and after active (20 sessions) and sham (20 sessions) tDCS in a double-blind crossover experiment. Four chronic non fluent PWA underwent 8 weeks of verbal exercises coupled with tDCS over the perilesional areas close to the left inferior frontal gyrus. To evaluate changes induced by tDCS, TMS-EEG responses over Brodmann area 6 (BA6) were computed using five different parameters. In addition, these data were compared with those recorded from a matched control group. The results indicated a slight improvement after tDCS stimulation (as compared to sham) for patients with Broca’s aphasia, but not for those with global aphasia. Also, TMS-evoked EEG responses recorded from the ipsilesional hemisphere were abnormal in individuals with chronic post-stroke aphasia (slower and simple responses with higher amplitudes) when compared to responses from the contralesional hemisphere and from the control group. Critically, the Global Mean Field Power (GMFP), Local Mean Field Power (LMFP) and Natural Frequency values were modulated by anodal tDCS. Despite these interesting results, further data are needed in order the obtain more direct, stronger evidence linking behavioral tDCS effects and neurophysiological data.

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For each decision we make, we must first determine the degree of effort that we are going to exert, and this can range from no effort to full effort. To select a reasoning strategy (e.g., withholding or exerting effort), it has been proposed that we must first integrate internal and external factors to compute the degree of effort necessary and solve the problem at hand. In this dissertation, I sought to determine the mechanisms underlying selecting such reasoning strategies by leveraging electroencephalographic imaging techniques. My investigations began by exploring neural correlates of effortful contemplation and evolved to test assumptions of prediction errors as it became apparent that they were an influential factor. I then tied this mechanism to the strategy selection phase of reasoning and cost-benefit computations. From these findings, I proposed that prediction errors of decision demands function to lessen or remove the burden of cost-benefit computations. Specifically, repeated encounters of the same or similar decisions provide an opportunity to develop expectations of the prospective costs and benefits of those judgments and these expectations facilitate the reasoning process. I consider two possible explanations as to how prediction errors may influence reasoning: first, our expectations provide our cost-benefit computations with a starting point to be adjusted if necessary, and second, our expectations act as a gating mechanism for cost-benefit computations. Although more research is needed to test these hypotheses, I hope my work provides grounds for advancing this field of study. / Graduate

Humans tend to preferentially process information relevant to themselves. For instance, in experiments where participants learn to manipulate stimuli referenced to themselves or someone else, participants exhibit larger reward processing signals for themselves. Additionally, attention and perception are biased not only towards one’s self but those related to them. However, the aspect of processing information related to known-others has not been addressed in reward learning. Here, I sought to address this issue. Specifically, I recorded electroencephalographic (EEG) data from 15 undergraduate student participants who played a simple two-choice “bandit” gambling game where a photo presented before each gamble indicated whether it benefited either the participant, an individual they knew, or a stranger. EEG data from 64 electrodes on a standard 10-20 layout were analyzed for event-related potentials (ERPs) elicited by target photos and gambling outcomes. Post experiment, I examined the relationship between relatedness and the amplitude reward learning ERPs, namely the reward positivity and the P300, with one-way repeated measures analyses of variance. My results demonstrate that the amplitudes of reward learning ERPs are sensitive to the target of a gamble. A secondary goal of this research was to determine if these differences could be explained by attentional and perceptual responses to cues of who a given gamble was for. Indeed, stepwise linear regression analyses identified the P2, N2, and P3 indexed relevance to self as predictors of resultant reward signals. My findings provide further evidence that a reward learning system within the medial-frontal cortex is sensitive to others with varying self-relevance, which may be a function of biases in attention and perception. / Graduate

A recent line of inquiry has examined a specific question about how an observer's own experiences with actions may change how his or her brain processes those actions when they are subsequently observed. In short, how does prior experience with action affect the subsequent perception of others' actions? The current study investigated this question using electroencephalography (EEG) to test the hypothesis that receiving experience with an action would subsequently lead to different activation of sensorimotor cortex depending on the predicted consequences of observed actions. While EEG was recorded, three groups of participants watched video clips showing an actor lifting objects, and then each group received information about the sensorimotor properties (i.e., weight) of the objects. One group received extended sensorimotor experience with the objects (EE group), a second group received brief sensorimotor experience with the objects (BE group), and the third group read written information describing the objects' weights (semantic information, SI group). Following the experience, participants again viewed the video clips. Time-frequency analyses showed that for participants in the EE and BE groups, EEG during the observation of action was sensitive to the predicted sensorimotor consequences of the observed action. This was not found for the SI group. As well, all three groups showed increased alpha and beta suppression following experience. Overall, these results lead to two main conclusions: 1) experience with action facilitates subsequent neural mirroring processes, and 2) sensorimotor experience leads to differential activation of the sensorimotor cortex depending on the predicted consequences of observed action. / Psychology

<p> EEG is a noninvasive technique useful for the human brain mapping and for the estimation of neural electrical activities in human brain. A goal of processing EEG signals of a subject is the localization of neural current sources in human brain known as dipoles. Although this location estimation problem can be modeled as a particular kind of parameter estimation problem as in array signal processing, the nonlinear structure of an EEG electrode array, which is much more complicated than a traditional sensor array, makes the problem more difficult. </p> <p> In this thesis, we formulate the inverse problem of the forward model on computing the scalp EEG at a finite set of sensors from multiple dipole sources. It is observed that the geometric structure of the EEG array plays a crucial role in ensuring a unique solution for this problem. We first present a necessary and sufficient condition in the model of a single rotating dipole, that guarantees its location to be uniquely determined, when the second-order statistic of the EEG observation is available. In addition, for a single rotating dipole, a closed-form solution to uniquely determine its position is obtained by exploiting the geometrical structure of the EEG array. </p> <p> In the case of multiple dipoles, we suggest the use of the Maximum Likelihood (ML) estimator, which is often considered optimum in parameter estimation. We propose an efficient localization algorithm based on QR decomposition. Depending on whether or not the probability density functions of the dipole amplitude and the noise are available, we utilize the non-coherent ML or the LS as the criterion to develop a unified successive localization algorithm, so that solving the original multi-dipole optimization problem can be approximated by successively solving a series of single-dipole optimization problems. Numerical simulations show that our methods have much smaller estimation errors than the existing RAP-MUSIC method under non-ideal situations such as low SNR with small number of EEG sensors. </p> / Thesis / Master of Applied Science (MASc)