90 research outputs found
Using background EEG to predict baseball batting performance
No full textIn this thesis, I sought to determine whether frequency bands in the human electroencephalogram could be used to predict baseball batting performance. Past electroencephalographic (EEG) studies have found that alpha power in the human electroencephalogram predicts subsequent performance. Specifically, Mathewson and colleagues (2012) found that background brain activity, in particular, frontal alpha, had a direct correlation with one’s ability to learn a video game. Here, we decided to see if a similar result would hold true for baseball batting performance. We used a portable electroencephalographic (EEG) data collection system to record EEG data prior to batting practice. Participants sat quietly in a room with the portable EEG unit affixed to their head. Participants then stared in silence at a fixation cross in the center of a computer screen for 30 seconds and then counted backwards from 1000 by 7’s for 30 seconds as a masking task while background EEG was recorded. Player’s were then immediately given live batting practice and with performance judged by three different coaches on four different criteria. The four criteria were: batting mechanics, power, contact, and the batter’s ability to recognize good and bad pitches. Post-hoc, a frequency decomposition was performed on each participant’s EEG data to obtain power in all frequency bands. A correlation analysis of EEG power and batting performance showed that beta power and not alpha power predicted the subsequent performance of the batter. Importantly, a high correlation and significance show that predicting a batter’s performance with a portable EEG system, specifically the MUSE Headband, is highly plausible.Graduate2018-08-0
Neurocognitive mechanisms of Type 1 and Type 2 decision making processes
No full textIn an attempt to understand how humans make decisions, a wealth of researchers have explored the commonalities of different decision making demands. Two ranges of systems have been classified. Whereas Type 1 decision making is fast, automatic, and effortless, Type 2 judgments are slow, contemplative, and effortful. Here, I sought to determine underlying mechanisms of these processes. To do this, I present an extensive review and two electroencephalogram experiments. My review addresses theoretical models defining Type 1 and Type 2 decision making, discusses the debate between dual-process and continuous frameworks, proposes a novel insight into how these processes are selected and executed, and outlines neuro-anatomical findings. In one experiment, participants retained digits (Type 1 processes) and completed mathematical computations (Type 2 processes). I found that cognitive control – as reflected by frontal theta – and attentional mechanisms – as reflected by parietal alpha – are core mechanisms in Type 1 and Type 2 decision making. In a second experiment, I sought to replicate these findings when trained students diagnosed diseases. Differences in theta and alpha activity were not seen. I posit that the discrepancy between experiments may be because cognitive control relies on uncertainty which existed in experiment one but not experiment two. Moreover, attentional mechanisms involve the retrieval of knowledge in which the demands would have differed in experiment one but not two. I conclude by describing how cognitive control and attention fit into my hypothesis of different decision making steps: process selection and execution. These findings are important as they could lead to the assessment of decision making processes in real-world contexts, for example with clinicians in the hospital. Moreover, they could be used in biofeedback training to optimize decisions.Graduat
Prediction Errors of Decision Demands Influence Cost-Benefit Computations in Reasoning
No full textFor 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.Graduat
Reliability, Attenuation, and Order Effects of EEG Components Across Multiple Assessments
No full textHow does one guarantee consistency in measurements of neural activity? Realistically, this cannot be done for any individual measure, but variation from the norm will not significantly impact a large enough sample. The task, then, is to account for non-targeted neural activity common across participants to control for influences on target measures. Often, this is easier said than done. The goal of the current research was to aid in uncovering potential sources of unexplained variability in established electroencephalography (EEG) phenomena using two common tasks. Specifically, the reward positivity and P300 event-related potentials (ERPs) were captured via the two-armed bandit task and the oddball task, and were analysed across three areas: Reliability, attenuation, and order effects. Importantly, these data were captured using a unique testing schedule involving five cognitive assessments across an approximately two-hour period. Reliability was tested for both the difference and the conditional waves for each component to see if these lined up with commonly reported values. Previous attenuation studies have established this effect across long durations, but this analysis sought to determine whether this pattern held across consecutive testing sessions. Order effects were expected to occur between the bandit and oddball tasks based on the interplay between neural regions and neurotransmitter activity. Results: Excellent reliability was found for all P300 measures and for the conditional reward positivity measures. These findings support the use of conditional waves instead of difference waves regarding the reward positivity specifically, as the difference wave appears to mask the high reliability present in each conditional wave. Attenuation results were unanticipated, showing no effect for the reward positivity and an opposite effect for the P300. The suggestion invoked is that something to do with the divergences between the current study’s task and the long-duration tasks previously used to exhibit ERP attenuation altered participants’ reactions to the oddball task. Further investigation into this unusual component behaviour is warranted. No order effects were discovered across two analyses focused on these results. Although effects were anticipated, the absence is encouraging for EEG research as this suggests that order effects need not be accounted for in tasks or experiments that elicit both the reward positivity and the P300 ERPs. Altogether, these findings reveal areas wherein reward positivity and P300 components are robust, and areas in which they require further investigation.Graduate2024-09-0
Assessing the impact of concussion history on the N200, P300 and reward positivity
No full textTraumatic brain injuries (TBI) are one of the leading causes of disability worldwide (Zitnay, 2008), yet one of the least understood neurological conditions (Duncan, 2005). Research has examined short-term deficits; however, less focus has been on the consequences of multiple concussions. Previous electroencephalography (EEG) concussion research has examined the N200 and P300 human event-related potential (ERP) components, yielding inconclusive results (Duncan, Kosmidis & Mirsky, 2005). An ERP component not as frequently examined is the reward positivity, generated by the anterior cingulate cortex (ACC), a region which experiences increased anatomical stress following injury.
In this study, 51 students from the University of Victoria took a ‘Concussion Survey’ to determine participant history and groups; no history of concussion, a single injury or multiple injuries (2+). Participants performed an oddball and decision-making task while EEG data was collected.
No significant differences were found between groups for the N200, P300 or reward positivity peak latencies or amplitudes. Both concussion groups yielded attenuated peak amplitudes, but no differences existed between the group with a single concussion versus multiple. Unexpectedly, N200 and reward positivity peak latencies were greater in the group with single injuries, compared to those with a history of multiple concussions.
This study adds to a continuous line of inconclusive research on the N200 and P300, suggesting minimal cognitive deficits result from concussive injuries. Furthermore, no noticeable differences were observed between groups with a single versus multiple injuries. While the ACC is located in a region of increased stress following TBI, functional deficits impacting the reward positivity may not be as significant as previously hypothesized. Results may be impacted by confounding variables, including not reliably being able to account for time since injury, injury severity and differences in gender dispersion of participants. With concussions on the rise, continued research, particularly longitudinally and within-subjects is critical for the advancement of both TBI prevention and management.Graduat
One step at a time: analysis of neural responses during multi-state tasks
No full textSubstantial research has been done on the electroencephalogram (EEG) neural signals generated by feedback within a simple choice task, and there is much evidence for the existence of a reward prediction error signal generated in the anterior cingulate cortex of the brain when the outcome of this type of choice does not match expectations. However, less research has been done to date on the neural responses to intermediate outcomes in a multi-step choice task. Here, I investigated the neural signals generated by a complex, non-deterministic task that involved multiple choices before final win/loss feedback in order to see if the observed signals correspond to predictions made by reinforcement learning theory. In Experiment One, I conducted an EEG experiment to record neural signals while participants performed a computerized task designed to elicit the reward positivity, an event-related brain potential (ERP) component thought to be a biological reward prediction error signal. EEG results revealed a difference in amplitude of the reward positivity ERP component between experimental conditions comparing unexpected to expected feedback, as well as an interaction between valence and expectancy of the feedback. Additionally, results of an ERP analysis of the amplitude of the P300 component also showed an interaction between valence and expectancy. In Experiment Two, I used machine learning to classify epoched EEG data from Experiment One into experimental conditions to determine if individual states within the task could be differentiated based solely on the EEG data. My results showed that individual states could be differentiated with above-chance accuracy. I conclude by discussing how these results fit with the predictions made by reinforcement learning theory about the type of task investigated herein, and implications of those findings on our understanding of learning and decision-making in humans.Graduat
Oddball Data
No full textData from a standard visual oddball task.
Markers: S201 = Fixation, S202 = Oddball, S203 = Control
Hierarchical error processing during motor control
No full textThe successful execution of goal-directed movement requires the evaluation of many levels of errors. On one hand, the motor system needs to be able to evaluate ‘high-level’ errors indicating the success or failure of a given movement. On the other hand, as a movement is executed the motor system also has to be able to correct for ‘low-level’ errors - an error in the initial motor command or change in the motor command necessary to compensate for an unexpected change in the movement environment. The goal of the present research was to provide electroencephalographic evidence that error processing during motor control is evaluated hierarchically. The present research demonstrated that high-level motor errors indicating the failure of a system goal elicited the error-related negativity, a component of the event-related brain potential (ERP) evoked by incorrect responses and error feedback. The present research also demonstrated that low-level motor errors are associated with parietally distributed ERP component related to the focusing of visuo-spatial attention and context-updating. Finally, the present research includes a viable neural model for hierarchical error processing during motor control
Reward Processing Data
No full textA standard two door gambling task to elicit the reward positivity (FRN).
Markers
S110 = Win
S111 = Loss
S108 = Invalid response
S109 = Too fas
Reward Processing Data
No full textA standard two door gambling task to elicit the reward positivity (FRN).
Markers
S110 = Win
S111 = Loss
S108 = Invalid response
S109 = Too fas
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