Cognitive Dissertation

Cognitive Dissertation-72
My dissertation investigates the cognitive mechanisms that support this balance between stability and flexibility.Chapter 1 introduces the overarching theoretical framework of communication as a meta-learning problem.Result includes all theses and dissertations — from all sources — held in the Stanford Libraries and Digital Repository. is that people are individually in control of and responsible for their lives--the individual is considered as separate and apart from others (Markus & Kitayama, 1991; 1994; Markus, 2017).

My dissertation investigates the cognitive mechanisms that support this balance between stability and flexibility.Chapter 1 introduces the overarching theoretical framework of communication as a meta-learning problem.

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Computational models of semantic meaning must explain both the speaker's initial conventional expectations about how words will be understood by novel partners and the dynamics of how these expectations may shift over the course of a particular conversation.

Chapter 2 proposes a computational model that formalizes the problem of coordinating on meaning as hierarchical probabilistic inference, which I argue satisfies both of these conditions.

These changes are known to be in part the result of small tweaks to the representation of visual stimuli in sensory cortex, but are also the result of context-dependent selection occurring after sensory processing has gone to completion.

How attention implements this balance of sensory change and selection is a central problem for the neuroscience of vision. Human languages are a powerful solution to this challenging coordination problem.

In this dissertation, I examine how foregrounding either individual autonomy or accountability to others as the salient guiding narrative can influence the meaning and likelihood of certain behaviors.

Cognitive Dissertation How To Write A Good College Research Paper

In Part 1, I first demonstrate that people have differing ideologies about the importance of autonomy and accountability in community and social commitments.Our work highlights the importance of the required and varying statistical properties of a self-regulation marker depending on the research question.With respect to within-subjects analyses we present the largest comparison of behavioral self-regulation measures in their suitability for individual difference analyses using retest-reliability.Community-level expectations provide a stable prior, and dynamics within an interaction are driven by partner-specific learning.Chapter 3 exploits recent connections between this hierarchical Bayesian framework and continual learning in deep neural networks to propose and evaluate a computationally efficient algorithm implementing this same model at scale in an adaptive neural image-captioning agent.In 4 studies I manipulate which social expectation was foregrounded by using subtle cues in language (Study 3) and then explicitly by creating different attendance policies for a Community Club (Study 4) and an academic workshop (Studies 5a and 5b).In the autonomy foregrounding ("opt-in") condition, I drew on the results from Part 1 to create a community that prioritizes individual autonomy.They provide stable, shared expectations about how the words we say correspond to the beliefs and intentions in our heads.However, to handle an ever-changing environment where we constantly face new things to talk about and new partners to talk with, linguistic knowledge must be flexible: we give old words new meaning on the fly.I show that though people valued autonomy, an emphasis on autonomy in community (Study 1) and social commitments (Study 2) predicted greater loneliness and had no relationship with life satisfaction.An emphasis on accountability in social commitments, on the other hand, predicted less loneliness and greater life satisfaction (Study 2).

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