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Record W3014244050 · doi:10.22215/etd/2014-10476

Self-Consciousness and The Self-As-Subject

2014· dissertation· en· W3014244050 on OpenAlexaff
Ted Lougheed

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldNeuroscience
TopicMemory and Neural Mechanisms
Canadian institutionsSocial Sciences and Humanities Research CouncilCarleton University
FundersUniversity of Oxford
KeywordsConsciousnessPsychologySubject (documents)Self-consciousnessSelfCognitionEpisodic memoryObject (grammar)Cognitive psychologyCognitive scienceSocial psychologyComputer science

Abstract

fetched live from OpenAlex

The overarching purpose of this project is to expand upon our understanding of the nature of self-consciousness.Specifically, I investigate our sense of self as a subject of experience, as one and the same experiencer extended over time.Conceptual inquiry and cognitive psychology studies are the primary means of investigation.I seek to fulfill two primary goals.First, I make the case for the distinction between consciousness of the self-as-subject (SAS) and self-as-object (SAO).I lay forth the conceptual foundations for this distinction and discuss the properties of SAS consciousness.I also review empirical literature that reveals, intentionally or not, the importance of the distinction.Second, after reviewing the present state of existing studies pertaining to SAS consciousness, I present a complete study and a preliminary study that I undertook to identify and investigate the mechanism responsible for the phenomenon.I examine the relationship between SAS consciousness and other cognitive faculties, specifically episodic memory and attention.I claim that episodic memory is dependent upon SAS consciousness, and that attention must be paid to self for episodic memory to form.In the first study, I examine the effects of distraction on SAS consciousness in adult participants.In the second, preliminary study, I turn my attention to children, investigating how SAS consciousness develops in 3-5 year-olds, with respect to other aspects of self-consciousness.iii Acknowledgements I would like to thank my supervisor, Andrew Brook, for his guidance and encouragement that began before I even joined the program.I am honored to have had the opportunity to work with him.I would also like to thank Deepthi Kamawar for all her help in learning the ways of empirical research and especially research with young children, with all the trials and tribulations that entails.I would also like to thank the women

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.010
Scholarly communication0.0040.005
Open science0.0000.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.022
GPT teacher head0.283
Teacher spread0.261 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2014
Admission routes1
Has abstractyes

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