The new Tweety puzzle: arguments against monistic Bayesian approaches in epistemology and cognitive science
- 339 Downloads
In this paper we discuss the new Tweety puzzle. The original Tweety puzzle was addressed by approaches in non-monotonic logic, which aim to adequately represent the Tweety case, namely that Tweety is a penguin and, thus, an exceptional bird, which cannot fly, although in general birds can fly. The new Tweety puzzle is intended as a challenge for probabilistic theories of epistemic states. In the first part of the paper we argue against monistic Bayesians, who assume that epistemic states can at any given time be adequately described by a single subjective probability function. We show that monistic Bayesians cannot provide an adequate solution to the new Tweety puzzle, because this requires one to refer to a frequency-based probability function. We conclude that monistic Bayesianism cannot be a fully adequate theory of epistemic states. In the second part we describe an empirical study, which provides support for the thesis that monistic Bayesianism is also inadequate as a descriptive theory of cognitive states. In the final part of the paper we criticize Bayesian approaches in cognitive science, insofar as their monistic tendency cannot adequately address the new Tweety puzzle. We, further, argue against monistic Bayesianism in cognitive science by means of a case study. In this case study we show that Oaksford and Chater’s (2007, 2008) model of conditional inference—contrary to the authors’ theoretical position—has to refer also to a frequency-based probability function.
KeywordsNew Tweety puzzle Probability Frequency Probabilism Monistic Bayesianism Objective Bayesianism Bayesian rationality Oaksford and Chater Conditional inference MP-MT asymmetry Cognitive science
Unable to display preview. Download preview PDF.
- Anderson J. R. (1990) The adaptive character of thought. Lawrence Erlbaum Associates, HillsdaleGoogle Scholar
- Bacchus F. (1990) Representing and reasoning with probabilistic knowledge. The MIT Press, Cambridge, MAGoogle Scholar
- Bovens L., Hartmann S. (2003) Bayesian epistemology. Oxford University Press, OxfordGoogle Scholar
- Brewka G. (1991) Nonmonotonic reasoning. Logical foundations of commonsense. Cambridge University Press, CambridgeGoogle Scholar
- Carnap R. (1962) Logical foundations of probability (2nd ed.). University of Chicago Press, ChicagoGoogle Scholar
- Carnap R. (1971) Inductive logic and rational decisions. In: Carnap R., Jeffrey R. C. (eds) Studies in inductive logic and probability, Vol. I. University of California Press, Berkeley, pp 5–31Google Scholar
- de Finetti, B. (1973). Foresight. Its logical laws, Its subjective sources. Reprinted In H. E. Kyburg & H. E. Smokler (Eds.), Studies in Subjective Probability, (pp. 93–158). New York: Wiley.Google Scholar
- Earman J. (1992) Bayes or bust? A critical examination of Bayesian confirmation theory. MIT Press, Cambridge, MAGoogle Scholar
- Evans J. S. (1982) The psychology of deductive reasoning. Routledge & Kegan Paul, LondonGoogle Scholar
- Evans, J. S., & Twyman-Musgrove, J. (1998). Conditional reasoning with inducements and advice. Cognition, 69, B11–B16.Google Scholar
- Gillies D. (2000) Philosophical theories of probabilities. Routledge, LondonGoogle Scholar
- Hájek, A. (2012). Interpretations of probability. In E. N. Zalta (Ed.), The Stanford Encyclopedia of Philosophy. Retrieved from http://plato.stanford.edu/archives/sum2012/entries/probabilityinterpret/.
- Howson C., Urbach P. (2006) Scientific reasoning. The Bayesian approach (3rd ed). Open Court, ChicagoGoogle Scholar
- Lewis D. (1980) A subjectivist’s guide to objective chance. In: Jeffrey R. C. (ed) Studies in inductive logic and probability, Vol. 2. University of California Press, Berkeley, pp 263–293Google Scholar
- Marr D. (1982) Vision. W. H. Freeman, San FranciscoGoogle Scholar
- Pearl, J. (1988). Probabilistic reasoning in intelligent systems. Networks of plausible inference. San Francisco: Morgan Kaufmann Publishers. (Revised Second Printing)Google Scholar
- Reichenbach H. (1949) The theory of probability. University of California Press, BerkeleyGoogle Scholar
- Rogers T. T., McClelland J. L. (2004) Semantic cognition. A parallel distributed processing approach. MIT Press, Cambridge, MAGoogle Scholar
- Schurz G. (2011) Tweety, or why probabilism and even Bayesianism need objective and evidential probabilities. In: Dieks D., Gonzales W. J., Hartmann S., Stöltzner M., Weber M. (eds) Probabilities, laws, and structures. Springer, New York, pp 57–74Google Scholar
- Schurz G. (2012) Prototypes and their composition from an evolutionary point of view. In: Werning M., Hinzen W., Machery E. (eds) The Oxford handbook of compositionality (pp. 530–553). Oxford University Press, OxfordGoogle Scholar
- Talbott, W. (2008). Bayesian epistemology. In E. N. Zalta (Ed.), The Stanford Encyclopedia of Philosophy. Retrieved from http://plato.stanford.edu/entries/epistemology-bayesian/.