Cognitive Neuroscience Commons^™

A Predictive Coding Account Of Spatial Working Memory Following Prophylactic Levetiracetam Administration Prior To Traumatic Brain Injury, Omeima Mutwali

Dissertations, Theses, and Capstone Projects

Traumatic brain injury (TBI) symptom prevention and remediation is an important area of research that would benefit vulnerable groups, including active-duty and veteran soldiers. These patients can sustain penetrative forces in fields of combat or in training, which result in focal lesions that trigger inflammatory and degenerative processes in the brain. Both primary and secondary injuries are associated with changes to cognition, behavior and affective state. This disease poses increased risk of epileptogenesis, as well. Given these outcomes, prior research has evaluated levetiracetam (LEV) as a prophylactic treatment for seizures, cognitive deficits and negative emotionality. LEV acts as a presynaptic …

Somatosensory Cortex Responses To Illusory Touch In A Visual-Tactile Integration Task, Aditi Surendra Pawar

Dissertations, Theses, and Capstone Projects

Perceptual illusions arising from multisensory integration reveal how the brain constructs subjective experience from sensory input, and provide a powerful means of allowing the investigation of neural processes that track perceptual interpretation rather than physical stimulation alone. While visual illusions induced by auditory or tactile stimuli have been fairly well studied, the neural mechanisms underlying visually-induced illusory touch remain unclear. This study investigated whether illusory tactile perception engages the somatosensory cortex in a manner similar to veridical touch. Twenty healthy adults participated in an event-related functional magnetic resonance imaging (fMRI) study while performing a tactile numerosity judgment task. Each trial …

Towards Computational Methods In Medical Data Analysis: From Speech And Text To Imaging, Kristin Qi

Graduate Doctoral Dissertations

Early detection of cognitive decline and efficient medical image analysis remain critical challenges in healthcare. Traditional clinical assessments are infrequent and resource-intensive, while everyday speech data and unlabeled medical images remain largely unexploited. This dissertation develops computational methods integrating machine learning and artificial intelligence across speech, text, and imaging modalities to address challenges in medical data processing. For cognitive monitoring, this work first introduces methods using voice assistant systems to collect longitudinal speech data in home environments, demonstrating that incorporating historical session patterns significantly enhances detection of mild cognitive impairment. Building on this foundation, a framework combining large language model-driven …

Capturing The Representational Dynamics Of Face Perception In Deep Recurrent Neural Networks, Hossein Adeli, Chase W. King, Nikolaus Kriegeskorte

MODVIS Workshop

We investigate the representational dynamics of recurrent convolutional neural networks (RCNNs) in order to understand the time-course of visual recognition. We explore a family of models with bottom-up and lateral connections that were optimized for face-identification and object-recognition tasks. Using representational similarity analysis (RSA), we observed that only models that were trained for face identification showed a late-emerging prominent distinction of identities as seen in the monkey face patch AM. Early model responses strongly separated the objects from the faces. These findings suggest that the dynamics of face recognition that emerges in a hierarchical recurrent neural network prioritizes category-level recognition …

Attention Improves Classification Performance Of Locations In Ventral And Dorsal Visual Pathways, Jinho Lee, Sung-Mu Lee, Ishita Agarwal, Anne Sereno

MODVIS Workshop

Visual processing is divided into a ventral and dorsal pathway, thought to be important for object recognition and spatial cognition, respectively. Yet both streams encode shape and spatial information, and both are modulated by attention and task demands. This study compared how attention influences fMRI bold responses in a ventral region (fusiform gyrus, FG) and dorsal region (superior parietal gyrus, SPG). Specifically, using multivariate pattern analysis within each region, we examined whether attention modulated spatial location classification performance. During fMRI scans, participants performed one of 3 possible tasks: a 1-back task under attend-to-shape (detect a shape repetition); a 1-back task …

Modeling Effects Of Edge Classification And Perceptual Organization On The Appearance Of Disk/Annulus Stimuli, Michael E. Rudd

MODVIS Workshop

In classical theories of sensory psychophysics, appearance matches conducted with simple stimuli consisting of disks surrounded by annuli were posited to probe low-level contrast mechanisms in the visual pathways that computed the approximate luminance ratio between the disk and annulus for the purpose of achieving lightness constancy (Wallach, 1948). Subsequent models have explained such matches on the basis of neural edge integration (Shapley & Reid, 1985; Rudd & Zemach, 2004). Here, I demonstrate that appearances matches made in the disk/annulus paradigm are influenced by at least two types of top-down effects involving edge classification and perceptual organization. I present a …

Constructing A Parameterized Stimuli Space Suitable To Induce Controlled Changes In Human And Machine Perceptual Systems, Andrew Frankel

Dissertations, Theses, and Capstone Projects

Traditional computer vision applications focus on objective tasks, such as object detection, classification, or segmentation. Much of human experience is inherently subjective, such as our personal response to artwork. Individualized experiences are challenging to replicate in a controlled laboratory environment, as those experiences depend on the unique history and internal models of the observer. This work presents a technique to construct a 2-dimensional parameterized stimuli space suitable to induce controlled changes in the perceptual systems of both human and machine observers. Our contributions are threefold:

1. Using a balanced dataset of 8,800 fine art images spanning 11 art movements and 88 …

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Vertex-Based Analysis Of Cerebral Blood Flow And Fractional Amplitude Of Low-Frequency Fluctuations (Cbf-Falff) Coupling In Moderate-To-Severe Traumatic Brain Injury During The First Year Post-Injury, Vaidehi Hemkumar Patel

Dissertations, Theses, and Capstone Projects

Traumatic brain injury (TBI) is known to involve damage in the neurovascular unit. TBI may disrupt the brain’s neurovascular coupling–the relationship between cerebral blood flow and neural activity–that can lead to cognitive and functional impairments. This study attempted to non-invasively investigate neurovascular coupling in patients with moderate to severe TBI (msTBI) during the first year post-injury using a combined metric of cerebral blood flow (CBF) and fractional amplitude of low-frequency fluctuations (fALFF), a proxy for spontaneous neural activity. Using arterial spin labeling (ASL) and functional MRI sequences, CBF and fALFF were measured in 29 msTBI patients 3-, 6- and 12-months …

Behavioral And Neural Evidence For Perceptual Inference Of Position, Sharif Saleki

Dartmouth College Ph.D Dissertations

Extensive neuroscientific research has advanced our knowledge of the mechanisms involved in the detection and recognition of visual stimuli. However, to guide flexible behavior in a changing world, the brain must also construct stable and coherent perceptual representations from retinal images that are often ambiguous. To resolve ambiguities and uncertainties, the visual system combines its input with other sources of information such as context and prior knowledge to infer the most likely interpretation of a scene. Thus, perception is inherently an inferential process that goes beyond simple detection or recognition of stimuli. Although inference has been studied in many areas …

Robust And Generalizable Representations In The Hippocampus, Hung-Tu Chen

Dartmouth College Ph.D Dissertations

An intelligent system must balance generalizing across similar experiences with maintaining the distinctiveness of each experience. This thesis explores how the hippocampus manages this balance through its neural representations to support adaptive behavior. In Chapter 1, I provide an overview of key hippocampal phenomena that contribute to this process, including remapping, splitter signal, and replay. In Chapter 2, I challenge the concept of random remapping by showing that it is possible to predict, better than chance, how a given experience will be encoded in the hippocampus across different subjects. This suggests that encoding of related experiences, which was previously thought …

Genomic Data Science Approaches For Understanding Human Diseases, Snehal Shah

All Dissertations

The intricate interplay of genetic predisposition, environmental influences, and lifestyle acts as the multifactorial landscape of diseases. Understanding this complexity presents a significant challenge. Molecular insights into disease mechanisms, particularly the interactions of DNA, RNA, and proteins with environmental and lifestyle factors, have revolutionized disease diagnosis, prognosis, and treatment. High-throughput technologies, such as next-generation sequencing, generate large amounts of molecular data, holding a wealth of knowledge. These datasets unveil the roles of genes and their interactions with various factors through analysis, shedding light on previously unknown molecular mechanisms underlying disease pathogenesis. Furthermore, they facilitate the discovery of biomarkers crucial for …

Local Geometry Of Elementary Visual Computations, Peter Neri

MODVIS Workshop

Visual operators (e.g. edge detectors) are classically modelled using small circuits involving canonical computations, such as template-matching and gain control. Circuit models explain many aspects of the empirical descriptors that are used to characterize local visual operators, from sensitivity to classification images. Notwithstanding their utility, these models fail to provide a unified framework encompassing the variety of effects observed experimentally, such as the impact of contrast, SNR, and attention on the above descriptors. My goal is to start with a simple, plausible geometrical representation of the perceptual operation carried out by the observer, and to show that this representation is …

Dissecting Bayes: Using Influence Measures To Test Normative Use Of Probability Density Information Derived From A Sample, Laurence T. Maloney, Keiji Ota

MODVIS Workshop

Bayesian decision theory (BDT) is used to model human performance in tasks where the decision maker must compensate for uncertainty in order to gain rewards and avoid losses. BDT prescribes how the decision maker can combine available data, prior knowledge, and value to reach a decision maximizing expected winnings. Do human decision makers actually use BDT in making decisions? Researchers typically compare overall human performance (total winnings or overall percent correct) to the predictions of BDT but we cannot conclude that BDT is an adequate model for human performance based on just overall performance. We break BDT down into elementary …

Biophysical Model Of Retraction Motor Neurons And Their Modification By Operant Conditioning, Maria Rasheed

Dissertations and Theses (Open Access)

Operant conditioning (OC) is a form of associative learning in which an animal modifies its behavior based on the consequences that follow that behavior. Despite its ubiquity, the underlying mechanisms of OC are poorly understood. Insights into the mechanisms of OC can be obtained by studying Aplysia feeding behavior as it can be modified by OC. This behavior is mediated by a central pattern generator (CPG) network in the buccal ganglia that contains a relatively small number of neurons. This CPG generates rhythmic motor patterns (BMPs) that move food into the gut by closing a tongue-like structure (i.e., radula) during …

Utilizing Ai Integrated Neuroimaging Technology To Expand Upon Machine Learning In Positron Emission Tomography Technology With The Aim Of Detecting Amyloid Beta Biomarkers Early In The Onset Of Alzheimer's., Ethan S. Terman

Undergraduate Research Posters

Early intervention in Alzheimer's is vital for treatment. The earlier a professional can detect symptoms and make a diagnosis the earlier a prognosis can be implemented. With the prevalence of data in our day-to-day world combined with Artificial intelligence (AI), utilizing both for machine learning can pave the way for more accurate and efficient detection of Alzheimer's and other neurodegenerative diseases. AI combined with Machine learning (ML) increases diagnostic efficiency and reduces human errors, making it a valuable resource for physicians and clinicians alike. With the increasing amount of data processing and image interpretation required, the ability to use AI …

Voluntary Action And Conscious Intention, Jake Gavenas

Computational and Data Sciences (PhD) Dissertations

Traditional experimental methodology in neuroscience involves comparing responses to different stimuli to make inferences about the human brain. However, human volition entails making decisions and acting in a manner that is underdetermined by the external environment. Investigations of the neuronal basis of volition have led to new controversies regarding the existence of free will, and offer potential directions for medical treatments of disorders such as addiction, akinetic mutism, and locked-in syndrome. However, because volition experiments leave aspects of responses “up to” participants, neuroscientists must utilize computational techniques such as modeling, simulation, and advanced analyses to progress our understanding of human …

Unraveling The Neural Basis Of Emotions: Advancing Understanding With Ecologically Valid Paradigms And High-Resolution Intracranial Eeg, Tiankang Xie

Dartmouth College Ph.D Dissertations

Background

Emotion arises from integrating information about the external world with memories of past experiences, current homeostatic states, and future goals. They play a vital role in regulating our thoughts, feelings and behaviors, significantly impacting our mental health. Thus, it is important to understand the neurobiological mechanisms that give rise to emotions. While there has been considerable work investigating the neural basis of emotions, progress has been hampered by several methodological limitations. For example, prior work has relied on relatively simple and isolated stimuli, which often fail to effectively capture the dynamic and multifaceted nature of emotional experiences in real-life …

Destined Failure, Chengjun Pan

Masters Theses

I attempt to examine the complex structure of human communication, explaining why it is bound to fail. By reproducing experienceable phenomena, I demonstrate how they can expose communication structure and reveal the limitations of our perception and symbolization.I divide the process of communication into six stages: input, detection, symbolization, dictionary, interpretation, and output. In this thesis, I examine the flaws and challenges that arise in the first five stages. I argue that reception acts as a filter and that understanding relies on a symbolic system that is full of redundancies. Therefore, every interpretation is destined to be a deviation.

Active Encoding Of Space Through Time, Michele Rucci, Jonathan D. Victor

MODVIS Workshop

No abstract provided.

A Dynamical Model Of Binding In Visual Cortex During Incremental Grouping And Search, Daniel Schmid, Daniel A. Braun, Heiko Neumann

MODVIS Workshop

Binding of visual information is crucial for several perceptual tasks. To incrementally group an object, elements in a space-feature neighborhood need to be bound together starting from an attended location (Roelfsema, TICS, 2005). To perform visual search, candidate locations and cued features must be evaluated conjunctively to retrieve a target (Treisman&Gormican, Psychol Rev, 1988). Despite different requirements on binding, both tasks are solved by the same neural substrate. In a model of perceptual decision-making, we give a mechanistic explanation for how this can be achieved. The architecture consists of a visual cortex module and a higher-order thalamic module. While the …

Analysis Of Electrophysiological Markers And Correlated Components Of Neural Responses To Discourse Coherence, Kurt M. Masiello

Dissertations, Theses, and Capstone Projects

Constructing meaning from spoken language is invaluable for learning, social interaction, and communication. In clinical populations with developmental disorders of speech comprehension, the severity of disruption can persist and vary from limiting occupational opportunities to lower performance outcomes. Previous research has reported an event-related potential (ERP) neural positivity over right hemisphere lateral anterior sites in response to semantic and discourse processing. Although useful as a marker for clinical populations of autism spectrum disorder (ASD) and developmental language disorder (DLD), little is understood about the dynamics and neural sources of this biological marker. In addition to traditional methods of ERP analysis, …

Accelerated Forgetting In People With Epilepsy: Pathologic Memory Loss, Its Neural Basis, And Potential Therapies, Sarah Ashley Steimel Phd

Dartmouth College Ph.D Dissertations

While forgetting is vital to human functioning, delineating between normative and disordered forgetting can become incredibly complex. This thesis characterizes a pathologic form of forgetting in epilepsy, identifies a neural basis, and investigates the potential of stimulation as a therapeutic tool. Chapter 2 presents a behavioral characterization of the time course of Accelerated Long-Term Forgetting (ALF) in people with epilepsy (PWE). This chapter shows evidence of ALF on a shorter time scale than previous studies, with a differential impact on recall and recognition. Chapter 3 builds upon the work in Chapter 2 by extending ALF time points and investigating the …

Probing The Boundaries Of Human Agency, Sook Mun Wong

Computational and Data Sciences (PhD) Dissertations

In this dissertation, I aim to advance our understanding of human agency and how it is computed. First, I probe the limits of what behavior we can and cannot produce agentically. Secondly, I explore microdosing with psychedelic drugs as a method of perturbing and studying changes to the sense of agency. Thirdly, I use transcranial magnetic stimulation to induce body movements and investigate how the sense of agency is affected. Finally, I summarise my findings and synthesize insights to further our understanding human agency.

Causal Inference In Psychology And Neuroscience: From Association To Causation, Dehua Liang

Computational and Data Sciences (PhD) Dissertations

In psychology and neuroscience, inferring causality in non-experimental studies is almost taboo, because data in these studies, e.g., survey data and resting-state neuroimaging data, are often contaminated by unmeasured confounders. Psychologists and neuroscientists are often cautious about their results, and reluctant to make false claims about causality in non-experimental studies. Therefore, they adopt less stringent statistical analysis techniques that can only infer associational relations. However, the ambiguity about causality in traditional statistical analysis creates much confusion in interpreting analytical results - some studies make implicit causal claims about their results using words such as “impacts”, “lead to” and “affects”. This …

The Distinction Of Logical Decision According To The Model Of The Analysis Of Brain Signals (Eeg), Akeel Abdulkareem Al-Sakaa, Zaid H. Nasralla, Mohsin Hasan Hussein, Saif A. Abd, Hazim Alsaqaa, Kesra Nermend, Anna Borawska

Karbala International Journal of Modern Science

Recently, brain signal patterns have been recruited by researchers in different life activities. Researchers have studied each life activity and how brain signal patterns appear. These patterns could then be generalised and used in different disciplines. In this paper, we study the brain state during decision making in a lottery experiment. An EEG device is used to capture brain signals during an experiment to extract the optimal state for logical decision making. After collecting data, extracting useful information and then processing it, the proposed method is able to identify rational decisions from irrational ones with a success rate of 67%.

Analysis Of The Distributed Representation Of Operant Memory In Aplysia, Renan Murillo Costa

Dissertations and Theses (Open Access)

Operant conditioning, a ubiquitous form of learning in which animals learn from the consequences of behavior, engages a high-dimensional neuronal population space spanning multiple brain regions. A complete characterization of an operant memory remains elusive. Some sites of plasticity participating in the engram underlying an example of operant memory in Aplysia have been previously uncovered. Three studies are described here that sought to draw closer to a thorough characterization of this memory. The first study used a computational model to examine the ways in which sites of plasticity (individually and in combination) contribute to memory expression. Each site of plasticity …

Closed-Loop Brain-Computer Interfaces For Memory Restoration Using Deep Brain Stimulation, David Xiaoliang Wang

Electrical Engineering Theses and Dissertations

The past two decades have witnessed the rapid growth of therapeutic brain-computer interfaces (BCI) targeting a diversity of brain dysfunctions. Among many neurosurgical procedures, deep brain stimulation (DBS) with neuromodulation technique has emerged as a fruitful treatment for neurodegenerative disorders such as epilepsy, Parkinson's disease, post-traumatic amnesia, and Alzheimer's disease, as well as neuropsychiatric disorders such as depression, obsessive-compulsive disorder, and schizophrenia. In parallel to the open-loop neuromodulation strategies for neuromotor disorders, recent investigations have demonstrated the superior performance of closed-loop neuromodulation systems for memory-relevant disorders due to the more sophisticated underlying brain circuitry during cognitive processes. Our efforts are …

Validity Of Neural Distance Measures In Representational Similarity Analysis, Fabian A. Soto, Emily R. Martin, Hyeonjeong Lee, Nafiz Ahmed, Juan Estepa, Kianoosh Hosseini, Olivia A. Stibolt, Valentina Roldan, Alycia Winters, Mohammadreza Bayat

MODVIS Workshop

No abstract provided.

Visual Expertise In An Anatomically-Inspired Model Of The Visual System, Garrison W. Cottrell, Martha Gahl, Shubham Kulkarni

MODVIS Workshop

We report on preliminary results of an anatomically-inspired deep learning model of the visual system and its role in explaining the face inversion effect. Contrary to the generally accepted wisdom, our hypothesis is that the face inversion effect can be accounted for by the representation in V1 combined with the reliance on the configuration of features due to face expertise. We take two features of the primate visual system into account: 1) The foveated retina; and 2) The log-polar mapping from retina to V1. We simulate acquisition of faces, etc., by gradually increasing the number of identities the network learns. …

Individual Differences In Structure Learning, Philip Newlin

Theses and Dissertations

Humans have a tendency to impute structure spontaneously even in simple learning tasks, however the way they approach structure learning can vary drastically. The present study sought to determine why individuals learn structure differently. One hypothesized explanation for differences in structure learning is individual differences in cognitive control. Cognitive control allows individuals to maintain representations of a task and may interact with reinforcement learning systems. It was expected that individual differences in propensity to apply cognitive control, which shares component processes with hierarchical reinforcement learning, may explain how individuals learn structure differently in a simple structure learning task. Results showed …