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  1. Karl F. MacDorman, Sandosh K. Vasudevan & Chin-Chang Ho (2009). Does Japan Really Have Robot Mania? Comparing Attitudes by Implicit and Explicit Measures. AI and Society 23 (4):485-510.
    Japan has more robots than any other country with robots contributing to many areas of society, including manufacturing, healthcare, and entertainment. However, few studies have examined Japanese attitudes toward robots, and none has used implicit measures. This study compares attitudes among the faculty of a US and a Japanese university. Although the Japanese faculty reported many more experiences with robots, implicit measures indicated both faculties had more pleasant associations with humans. In addition, although the US faculty reported people were more (...)
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  2. Karl F. MacDorman (2007). Life After the Symbol System Metaphor. Interaction Studies 8 (1):143-158.
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  3. Karl F. MacDorman & Peter H. Kahn Jr (2007). Introduction to the Special Issue on Psychological Benchmarks of Human–Robot Interaction. Interaction Studies 8 (3):359-362.
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  4. Karl F. MacDorman & Hiroshi Ishiguro (2006). Opening Pandora's Uncanny Box: Reply to Commentaries on “The Uncanny Advantage of Using Androids in Social and Cognitive Science Research”. Interaction Studies 7 (3):361-368.
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  5. Karl F. MacDorman & Hiroshi Ishiguro (2006). Toward Social Mechanisms of Android Science: A CogSci 2005 Workshop: 25 and 26 July 2005, Stresa, Italy. Interaction Studies 7 (2):289-296.
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  6. Karl F. MacDorman & Hiroshi Ishiguro (2006). Toward Social Mechanisms of Android Science. Interaction Studies 7 (2):289-296.
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  7. Karl F. MacDorman & Hiroshi Ishiguro (2006). The Uncanny Advantage of Using Androids in Cognitive and Social Science Research. Interaction Studies 7 (3):297-337.
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  8. Karl F. MacDorman (2004). Extending the Medium Hypothesis: The Dennett-Mangan Controversy and Beyond. Journal of Mind and Behavior 25 (3):237-257.
     
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  9. Karl F. MacDorman (1998). Feature Learning, Multiresolution Analysis, and Symbol Grounding. Behavioral and Brain Sciences 21 (1):32-33.
    Cognitive theories based on a fixed feature set suffer from frame and symbol grounding problems. Flexible features and other empirically acquired constraints (e.g., analog-to-analog mappings) provide a framework for letting extrinsic relations influence symbol manipulation. By offering a biologically plausible basis for feature learning, nonorthogonal multiresolution analysis and dimensionality reduction, informed by functional constraints, may contribute to a solution to the symbol grounding problem.
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  10. Karl F. MacDorman (1997). How to Ground Symbols Adaptively. In S. O'Nuillain, Paul McKevitt & E. MacAogain (eds.), Two Sciences of Mind. John Benjamins. 9--135.
  11. G. Sommerhoff & Karl F. MacDorman (1994). An Account of Consciousness in Physical and Functional Terms: A Target for Research in the Neurosciences. Integrative Physiological and Behavioral Science 29:151-81.