Abstract
In a well known passage in Naming and Necessity, Kripke expressed a pessimistic attitude toward a philosophical theory of reference.
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Notes
- 1.
See Recanati (2013) and authors cited therein.
- 2.
See Medin and Schaffer (1991) and Nosofsky (2011). Exemplar theory has also close connection to the k-NN (k-nearest neighbor) method in machine learning. In both traditions, an exemplar is represented as a point in a multidimensional space, whose each dimension corresponds to a feature, or its mathematical transformation, of objects. The distance defined between two points in the space represents the similarity between exemplars. I should note that both exemplar theory and the k-NN method are usually applied to classification tasks, i.e., tasks of classifying things into classes, rather than to identification tasks. However, once one accepts the idea that a subject retains a number of exemplars for a single object, it is straightforward to extend the methods to the case of identification. In fact, Nosofsy (1988) suggests that we do retain multiple exemplars for a single object. See also Murphy (2002), pp. 58–60, for a discussion of Nosofsky’s result.
- 3.
One important omission is the role of contexts in identification tasks. Undoubtedly we appeal to various contextual factors in tackling an identification task. You can recognize your colleague more easily and decisively in your office than on a beach in Okinawa, and the place, in this case, plays a crucial role. I this paper, however, I ignore any such effects of contexts just for the sake of simplicity.
- 4.
The reader should not be misled by my use of a proper name in describing a concept. At this stage, I am assuming that proper names play no roles in identification-tasks or concept revision.
- 5.
Note that I am using the term “concept” in a broader sense here than in other parts of the text. I hope what I mean here is clear enough.
- 6.
After Russell, the idea that names themselves play certain roles in explaining the “cognitive significance” of sentences involving names has been taken up by a number of distinguished authors. See, for example, Burge (1973), Kaplan (1989), Kaplan (1990), and Perry (2012). My proposal is just an addition to this series of attempts. In this article, I cannot discuss the individuation of names or words themselves—the main topic of Kaplan (1990). I hope I can deal with this important issue in a future work.
- 7.
See, for example, Feller (1970), p. 432 for a general formula calculating these probabilities. In this case, the matrix of transition probabilities P is \(\begin{pmatrix} 0.99 &{} 0.01 \\ 0.01 &{} 0.09 \end{pmatrix},\) and \(P^{49} = \begin{pmatrix} 0.685 &{} 0.314 \\ 0.314 &{} 0.685 \end{pmatrix},\) and \(P^{99} = \begin{pmatrix} 0.567 &{} 0.432 \\ 0.432 &{} 0.567 \end{pmatrix}\).
- 8.
This is basically a mere transcription of what I read out at the meeting of LENLS 13 at Keio University in November 2016. In March 2017, I had an opportunity to present the material at Reed College in Portland, Oregon. I would like to thank the participants of both these occasions—to name a few, George Bealer, Robin Cooper, Troy Cross, Paul Hovda, and William Tasheck—for helpful comments and questions. Originally my plan was to revise and augment the arguments given above partly in response to the comments and questions, but soon it became clear that such a revision would require far more space and time than is allowed for in this post proceedings. I hope in a longer version of the views presented here I can do justice to them.
I wish to thank Naoya Fujikawa, Koji Minehima, Masahide Yotsu, and other members of the study group of the philosophy of language at Tokyo Metropolitan University for helping me to shape my ideas in earlier stages. I also wish to thank Jih-wen Lin for his technical assistance on questions about game theory. Special thanks should go to David Kaplan and Paul Hovda. They read earlier drafts of this paper and gave me invaluable comments and questions, as well as suggestions about how best to conventionalize my English.
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Matsusaka, Y. (2017). Reference and Pattern Recognition. In: Kurahashi, S., Ohta, Y., Arai, S., Satoh, K., Bekki, D. (eds) New Frontiers in Artificial Intelligence. JSAI-isAI 2016. Lecture Notes in Computer Science(), vol 10247. Springer, Cham. https://doi.org/10.1007/978-3-319-61572-1_5
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