a complexity drop. We do not wonder what the alternatives are when we hear that two sixty-year old women chose to walk with their most beautiful dress into the sea early in the same morning (should they have worn pants, or chosen late afternoon?).

## Conclusion

The research initiated in the recent years on the cognitive role of descriptive complexity has already produced valuable results. The model presented in this paper is meant as a contribution to this enterprise. It should be extended in two directions. First, it is important to get a deeper understanding of the processes through which human subjects assess descriptive complexity and to see how they are compatible with the definitions adopted in the present model. Second, it will be interesting to confront the various predictions of the model with actual reactions of subjects to whom various versions of coincidences are presented, as we did for probability estimates (Dessalles 2006). Parameters can be manipulated according to the model to affect surprise. Such studies may prove delicate, however, as surprise cannot be easily elicited successively in the same subjects.

One important conclusion strongly suggested by the model is that the human mind is able to perform non-trivial formal computations. The sensitivity to complexity drop seems to be a general law, which applies across modalities and at all levels of abstraction. This means that our mind is able, in some way, to assess the complexity of its own information processing. Such a possibility opens many new perspectives.

## References

Bar-Hillel, M. (1980). The base-rate fallacy in probability judgments. *Acta Psychologica*, 44 (3), 211-233.

Chaitin, G. J. (2004). On the intelligibility of the universe and the notions of simplicity, complexity and irreducibility. In Hogrebe & Bromand (Eds.), *Grenzen* und Grenzüberschreitungen, XIX, 517-534. Berlin: Akademie Verlag.

Chater, N. (1999). The search for simplicity: A 
fundamental cognitive principle?. *The Quaterly Journal* of Experimental Psychology, 52 (A), 273-302.

Chater, N. & Vitányi, P. (2003). Simplicity: a unifying principle in cognitive science?. *Trends in cognitive* sciences, 7 (1), 19-22.

Coates, J. (2003). *Men talk.* Blackwell. Cornuéjols, A. (1996). Analogie, principe d'économie et complexité algorithmique. In *Actes des 11èmes Journées* Françaises de l'Apprentissage. Sète, France.

Dessalles, J-L. (2006). A structural model of intuitive probability. In D. Fum, F. Del Missier & A. Stocco 
(Eds.), Seventh International Conference on Cognitive Modeling, 86-91. Trieste, IT: Edizioni Goliardiche.

Diaconis, P. & Mosteller, F. (1989). Methods for studying coincidences. Journal of the American Statistical Association, 84 (408), 853-861.

Falk, R. (1989). Judgment of coincidences: Mine versus yours. American Journal of Psychology, 102 (4), 477-493.

Feldman, J. (2004). How surprising is a simple pattern? 

Quantifying 'Eureka!'. *Cognition, 93*, 199-224.

Griffiths, T. L. & Tenenbaum, J. B. (2001). Randomness and coincidences: Reconciling intuition and probability theory. In J. D. Moore & K. Stenning (Eds.), 23rd annual conference of the Cognitive Science Society, 370-375. Edinburgh, UK.

Griffiths, T. L. & Tenenbaum, J. B. (2003). Probability, algorithmic complexity, and subjective randomness. In R. 

Alterman & D. Kirsh (Eds.), *25th annual conference of* the Cognitive Science Society. L.E.A.

Griffiths, T. L. & Tenenbaum, J. B. (2007). From mere coincidences to meaningful discoveries. *Cognition, 103* (2), 180-226.

Kahneman, D. & Tversky, A. (1972). Subjective probability: A judgement of representativeness. Cognitive Psychology, 3, 430-454.

Kern, K. & Brown, K. (2001). Using the list of creepy coincidences as an educational opportunity. The history teacher, 34 (4), 531-536.

Li, M. & Vitányi, P. (1993). An introduction to Kolmogorov complexity and its applications. New York: Springer Verlag, ed. 1997.

Shannon, C. E. (1948). A mathematical theory of communication. *The Bell System Technical Journal, 27*, 379-423,623-656.

Simon, H. A. (1972). Complexity and the representation of patterned sequences of symbols. *Psychological Review*, 79 (5), 369-382.

Solomonoff, R. J. (1978). Complexity-based induction systems: Comparisons and convergence theorems. IEEE transactions on Information Theory, 24 (4), 422-432.

Solomonoff, R. J. (1997). The discovery of algorithmic probability. Journal of Computer and System *Sciences, 55* (1), 73-88.

Tversky, A. & Kahneman, D. (1973). Availability: A 
heuristic for judging frequency and probability. Cognitive Psychology, 5 (2), 207-232.

Tversky, A. & Koehler, D. J. (1994). Support theory: A 
nonextensional representation of subjective probability. 

Psychological Review, 101 (4), 547-567.

Warren, C. N. (1934). *Modern news reporting.* New York: 
Harper & Brothers, ed. 1959.

Weaver, W. (1948). Probability, rarity, interest, and surprise. *The Scientific Monthly*, 67, 390-392.