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Analytical and Insight Problem Solving Analyzed through EEGLAB

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Human potential is characterized by our ability to think flexibly and develop novel solutions to problems. In cognitive neuroscience, problem solving is studied using various tasks. For example, IQ can be tested using the RAVEN, which measures abstract reasoning. Analytical

Human potential is characterized by our ability to think flexibly and develop novel solutions to problems. In cognitive neuroscience, problem solving is studied using various tasks. For example, IQ can be tested using the RAVEN, which measures abstract reasoning. Analytical problem solving can be tested using algebra, and insight can be tested using a nine-dot test. Our class of problem-solving tasks blends analytical and insight processes. This can be done by measuring multiply-constrained problem solving (MCPS). MCPS occurs when an individual problem has several solutions, but when grouped with simultaneous problems only one correct solution presents itself. The most common test for MCPS is known at the CRAT, or compound remote associate task. For example, when given the three target words “water, skate, and cream” there are many compound associates that can be assigned each of the target words individually (i.e. salt-water, roller-skate, whipped-cream), but only one that works with all three (ice-water, ice-skate, ice-cream).
This thesis is a tutorial for a MATLAB user-interface, known as EEGLAB. Cognitive and neural correlates of analytical and insight processes were evaluated and analyzed in the CRAT using EEG. It was hypothesized that different EEG signals will be measured for analytical versus insight problem solving, primarily observed in the gamma wave production. The data was interpreted using EEGLAB, which allows psychological processes to be quantified based on physiological response. I have written a tutorial showing how to process the EEG signal through filtering, extracting epochs, artifact detection, independent component analysis, and the production of a time – frequency plot. This project has combined my interest in psychology with my knowledge of engineering and expand my knowledge of bioinstrumentation.

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Date Created
2019-05

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The Impact of Blocked vs. Distributed Category Learning on Subsequent Generalization

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It is a well-established finding in memory research that spacing or distributing information, as opposed to blocking all the information together, results in an enhanced memory of the learned material. Recently, researchers have decided to investigate if this spacing effect

It is a well-established finding in memory research that spacing or distributing information, as opposed to blocking all the information together, results in an enhanced memory of the learned material. Recently, researchers have decided to investigate if this spacing effect is also beneficial in category learning. In a set of experiments, Carvalho & Goldstone (2013), demonstrated that a blocked presentation showed an advantage during learning, but that ultimately, the distributed presentation yielded better performance during a post-learning transfer test. However, we have identified a major methodological issue in this study that we believe contaminates the results in a way that leads to an inflation and misrepresentation of learning levels. The present study aimed to correct this issue and re-examine whether a blocked or distributed presentation enhances the learning and subsequent generalization of categories. We also introduced two shaping variables, category size and distortion level at transfer, in addition to the mode of presentation (blocked versus distributed). Results showed no significant differences of mode of presentation at either the learning or transfer phases, thus supporting our concern about the previous study. Additional findings showed benefits in learning categories with a greater category size, as well as higher classification accuracy of novel stimuli at lower-distortion levels.

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2017-12

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Optimizing Biofeedback and Learning in an EEG-Based Brain-Computer Interface

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Brain-computer interface technology establishes communication between the brain and a computer, allowing users to control devices, machines, or virtual objects using their thoughts. This study investigates optimal conditions to facilitate learning to operate this interface. It compares two biofeedback methods,

Brain-computer interface technology establishes communication between the brain and a computer, allowing users to control devices, machines, or virtual objects using their thoughts. This study investigates optimal conditions to facilitate learning to operate this interface. It compares two biofeedback methods, which dictate the relationship between brain activity and the movement of a virtual ball in a target-hitting task. Preliminary results indicate that a method in which the position of the virtual object directly relates to the amplitude of brain signals is most conducive to success. In addition, this research explores learning in the context of neural signals during training with a BCI task. Specifically, it investigates whether subjects can adapt to parameters of the interface without guidance. This experiment prompts subjects to modulate brain signals spectrally, spatially, and temporally, as well differentially to discriminate between two different targets. However, subjects are not given knowledge regarding these desired changes, nor are they given instruction on how to move the virtual ball. Preliminary analysis of signal trends suggests that some successful participants are able to adapt brain wave activity in certain pre-specified locations and frequency bands over time in order to achieve control. Future studies will further explore these phenomena, and future BCI projects will be advised by these methods, which will give insight into the creation of more intuitive and reliable BCI technology.

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2014-05

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Multitasking, an EEG Experiment

Description

In the study of the human brain’s ability to multitask, there are two perspectives: concurrent multitasking (performing multiple tasks simultaneously) and sequential multitasking (switching between tasks). The goal of this study is to investigate the human brain’s ability to “multitask”

In the study of the human brain’s ability to multitask, there are two perspectives: concurrent multitasking (performing multiple tasks simultaneously) and sequential multitasking (switching between tasks). The goal of this study is to investigate the human brain’s ability to “multitask” with multiple demanding stimuli of approximately equal concentration, from an electrophysiological perspective different than that of stimuli which don’t require full attention or exhibit impulsive multitasking responses. This study investigates the P3 component which has been experimentally proven to be associated with mental workload through information processing and cognitive function in visual and auditory tasks, where in the multitasking domain the greater attention elicited, the larger P3 waves are produced. This experiment compares the amplitude of the P3 component of individual stimulus presentation to that of multitasking trials, taking note of the brain workload. This study questions if the average wave amplitude in a multitasking ERP experiment will be the same as the grand average when performing the two tasks individually with respect to the P3 component. The hypothesis is that the P3 amplitude will be smaller in the multitasking trial than in the individual stimulus presentation, indicating that the brain is not actually concentrating on both tasks at once (sequential multitasking instead of concurrent) and that the brain is not focusing on each stimulus to the same degree when it was presented individually. Twenty undergraduate students at Barrett, the Honors College at Arizona State University (10 males and 10 females, with a mean age of 18.75 years, SD= 1.517) right handed, with normal or corrected visual acuity, English as first language, and no evidence of neurological compromise participated in the study. The experiment results revealed that one- hundred percent of participants undergo sequential multitasking in the presence of two demanding stimuli in the electrophysiological data, behavioral data, and subjective data. In this particular study, these findings indicate that the presence of additional demanding stimuli causes the workload of the brain to decrease as attention deviates in a bottleneck process to the multiple requisitions for focus, indicated by a reduced P3 voltage amplitude with the multitasking stimuli when compared to the independent. This study illustrates the feasible replication of P3 cognitive workload results for demanding stimuli, not only impulsive-response experiments, to suggest the brain’s tendency to undergo sequential multitasking when faced with multiple demanding stimuli. In brief, this study demonstrates that when higher cognitive processing is required to interpret and respond to the stimuli, the human brain results to sequential multitasking (task- switching, not concurrent multitasking) in the face of more challenging problems with each stimulus requiring a higher level of focus, workload, and attention.

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Date Created
2019-05

Reevaluating the Relationship between Contingency and Congruency via the Flanker Task

Description

The purpose of this project was to extend Whitehead 2016 to determine what neural substrates supported conflict-mediated learning. Unfortunately, as a result of the COVID-19 pandemic we were unable to address this. To repurpose the collected data, an

The purpose of this project was to extend Whitehead 2016 to determine what neural substrates supported conflict-mediated learning. Unfortunately, as a result of the COVID-19 pandemic we were unable to address this. To repurpose the collected data, an analysis of which features of the Flanker task subjects were learning was conducted. Specifically, we wanted to know if subjects were learning by using the flanking stimuli to predict the central target or vice versa. Over 14 blocks comprised of 120 trials, we found that subjects made more stroop errors than flanker and target errors, indicating subjects were responding to stimuli in context of the flanker rather than the stroop effect.

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Date Created
2020-12

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Grounding concepts: physical interaction can provide minor benefit to category learning

Description

Categories are often defined by rules regarding their features. These rules may be intensely complex yet, despite the complexity of these rules, we are often able to learn them with sufficient practice. A possible explanation for how we arrive at

Categories are often defined by rules regarding their features. These rules may be intensely complex yet, despite the complexity of these rules, we are often able to learn them with sufficient practice. A possible explanation for how we arrive at consistent category judgments despite these difficulties would be that we may define these complex categories such as chairs, tables, or stairs by understanding the simpler rules defined by potential interactions with these objects. This concept, called grounding, allows for the learning and transfer of complex categorization rules if said rules are capable of being expressed in a more simple fashion by virtue of meaningful physical interactions. The present experiment tested this hypothesis by having participants engage in either a Rule Based (RB) or Information Integration (II) categorization task with instructions to engage with the stimuli in either a non-interactive or interactive fashion. If participants were capable of grounding the categories, which were defined in the II task with a complex visual rule, to a simpler interactive rule, then participants with interactive instructions should outperform participants with non-interactive instructions. Results indicated that physical interaction with stimuli had a marginally beneficial effect on category learning, but this effect seemed most prevalent in participants were engaged in an II task.

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Date Created
2014