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Articles 1 - 5 of 5
Full-Text Articles in Computational Neuroscience
Destined Failure, Chengjun Pan
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.
Individual Differences In Structure Learning, Philip Newlin
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 …
A Defense Of Pure Connectionism, Alex B. Kiefer
A Defense Of Pure Connectionism, Alex B. Kiefer
Dissertations, Theses, and Capstone Projects
Connectionism is an approach to neural-networks-based cognitive modeling that encompasses the recent deep learning movement in artificial intelligence. It came of age in the 1980s, with its roots in cybernetics and earlier attempts to model the brain as a system of simple parallel processors. Connectionist models center on statistical inference within neural networks with empirically learnable parameters, which can be represented as graphical models. More recent approaches focus on learning and inference within hierarchical generative models. Contra influential and ongoing critiques, I argue in this dissertation that the connectionist approach to cognitive science possesses in principle (and, as is becoming …
Where Do I Know That? A Distributed Multimodal Model Of Semantic Knowledge, Kevin M. Stubbs
Where Do I Know That? A Distributed Multimodal Model Of Semantic Knowledge, Kevin M. Stubbs
Undergraduate Honors Theses
As computers have grown more and more powerful, computational modeling has become an increasingly valuable tool for evaluating real world findings. Likewise, brain imaging has become increasingly powerful as is evidenced by recent fMRI findings which support the exciting possibility that semantic memory is segregated by modality in the brain (Goldberg et al., 2006b). The present study utilizes connectionist modeling to put the distributed multi-modal framework of semantic memory to the test, and represents the next step forward in the line of sensory-functional models. This model, based around the McRae et al. (2005) feature production norms, includes individual implementations of …
Reflexivity In Financial Markets: A Neuroeconomic Examination Of Uncertainty And Cognition In Financial Markets, Steven Pikelny
Reflexivity In Financial Markets: A Neuroeconomic Examination Of Uncertainty And Cognition In Financial Markets, Steven Pikelny
Senior Projects Spring 2011
Financial markets exist to disperse the risks of an unknown future in an economy. But for this process to work in an optimal fashion, investors – and subsequently markets – must have a way to interpret uncertainty. The investor rationality and market efficiency literature utilizes a methodology inadequate to address this fact, so I supplement it with the perspectives of epistemology, economic sociology, neuroscience, cognitive science, and philosophy of mind. This approach suggests that what is commonly viewed as market “inefficiency” is not necessarily caused by investor irrationality, but rather by the inherent nature of the epistemological problem faced by …