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Guide to the Stanford University, Symbolic Systems Program, Honors Theses
SC4-6534  
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Accession ARCH-2011-152 Theses

Box 1

Mercer, Noah, Computers and the self: approaches to agent design. 1994

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Pauly, Marc, Gödelian formalities - details of Gödel's second incompleteness theorem. 1996

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Perfors, Amy, Slow and steady doesn't win the race: the relation of infant information processing skills to later linguistic performance. 1999

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Schapiro, Anna C., A connectionist model of a continuous developmental transition in the balance scale task. 2009

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Soergel, David, OrganISM: an abstract representation paradigm for intelligent information networks. 1998

Box 1

Wobbrock, Jacob Otto, The law and policy of autonomous software agents. 1998

Box 1

Norman, Kenneth Andrew, Is consciousness the gatekeeper of memory?. 1993

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Flannery, Matthew and David Merrill, Faces and voices: social and cognitive effects of new interface paradigms. 2000

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Skolnick, Deena, Interactions between mutual exclusivity and lexical contrast in word learning. 2003

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Lindsay, Jon R., A biological basis for being-in-the-world: autopoiesis and Heideggerian phenomenology. 1995

Box 1

Barkis, William B, A new insight into a fundamental neural mechanism of learning, memory, and neuron death: understanding the regulation of NMDA receptor expression . 2001

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Tauber, Ellen R., The conceptimator: an expert system for conceptual cost estimating of building foundations. 1992

Box 1

Chiao, Joan, "Do they all look alike?": an fMRI study on expertise for same- versus other-race faces. 2000

Box 2

Soergel, David, OrganISM, an abstract representation paradigm for intelligent information networks. 1998

Box 2

Mayer, Marissa A., A natural language interface for the advisable planner. 1997

Box 2

Meganathan, Indirajith, Art therapy: complexity, expressiveness, and artistic style. 1996

Box 2

Srinivasan, Mahesh, Nominal classification and conceptual organization: a cross-linguistic perspective from English to Mandarin Chinese. 2005

Box 2

Henry, Kevin Christopher, Combining conventional search techniques with machine learning in the game of Go. no date