From Algorithmic to Subjective Randomness
Abstract
We explore the phenomena of subjective randomness as a case study in understanding how people discover structure embedded in noise. We present a rational account of randomness perception based on the statis- tical problem of model selection: given a stimulus, inferring whether the process that generated it was random or regular. Inspired by the mathe- matical definition of randomness given by Kolmogorov complexity, we characterize regularity in terms of a hierarchy of automata that augment a finite controller with different forms of memory. We find that the reg- ularities detected in binary sequences depend upon presentation format, and that the kinds of automata that can identify these regularities are in- formative about the cognitive processes engaged by different formats.
Cite
Text
Griffiths and Tenenbaum. "From Algorithmic to Subjective Randomness." Neural Information Processing Systems, 2003.Markdown
[Griffiths and Tenenbaum. "From Algorithmic to Subjective Randomness." Neural Information Processing Systems, 2003.](https://mlanthology.org/neurips/2003/griffiths2003neurips-algorithmic/)BibTeX
@inproceedings{griffiths2003neurips-algorithmic,
title = {{From Algorithmic to Subjective Randomness}},
author = {Griffiths, Thomas L. and Tenenbaum, Joshua B.},
booktitle = {Neural Information Processing Systems},
year = {2003},
pages = {953-960},
url = {https://mlanthology.org/neurips/2003/griffiths2003neurips-algorithmic/}
}