5,804 research outputs found
Natural scene statistics mediate the perception of image complexity
Humans are sensitive to complexity and regularity in patterns. The subjective
perception of pattern complexity is correlated to algorithmic
(Kolmogorov-Chaitin) complexity as defined in computer science, but also to the
frequency of naturally occurring patterns. However, the possible mediational
role of natural frequencies in the perception of algorithmic complexity remains
unclear. Here we reanalyze Hsu et al. (2010) through a mediational analysis,
and complement their results in a new experiment. We conclude that human
perception of complexity seems partly shaped by natural scenes statistics,
thereby establishing a link between the perception of complexity and the effect
of natural scene statistics
Algorithmic complexity for psychology: A user-friendly implementation of the coding theorem method
Kolmogorov-Chaitin complexity has long been believed to be impossible to
approximate when it comes to short sequences (e.g. of length 5-50). However,
with the newly developed \emph{coding theorem method} the complexity of strings
of length 2-11 can now be numerically estimated. We present the theoretical
basis of algorithmic complexity for short strings (ACSS) and describe an
R-package providing functions based on ACSS that will cover psychologists'
needs and improve upon previous methods in three ways: (1) ACSS is now
available not only for binary strings, but for strings based on up to 9
different symbols, (2) ACSS no longer requires time-consuming computing, and
(3) a new approach based on ACSS gives access to an estimation of the
complexity of strings of any length. Finally, three illustrative examples show
how these tools can be applied to psychology.Comment: to appear in "Behavioral Research Methods", 14 pages in journal
format, R package at http://cran.r-project.org/web/packages/acss/index.htm
Algorithmic Complexity for Short Binary Strings Applied to Psychology: A Primer
Since human randomness production has been studied and widely used to assess
executive functions (especially inhibition), many measures have been suggested
to assess the degree to which a sequence is random-like. However, each of them
focuses on one feature of randomness, leading authors to have to use multiple
measures. Here we describe and advocate for the use of the accepted universal
measure for randomness based on algorithmic complexity, by means of a novel
previously presented technique using the the definition of algorithmic
probability. A re-analysis of the classical Radio Zenith data in the light of
the proposed measure and methodology is provided as a study case of an
application.Comment: To appear in Behavior Research Method
An Algorithmic Approach to Information and Meaning
I will survey some matters of relevance to a philosophical discussion of
information, taking into account developments in algorithmic information theory
(AIT). I will propose that meaning is deep in the sense of Bennett's logical
depth, and that algorithmic probability may provide the stability needed for a
robust algorithmic definition of meaning, one that takes into consideration the
interpretation and the recipient's own knowledge encoded in the story attached
to a message.Comment: preprint reviewed version closer to the version accepted by the
journa
A Computational Theory of Subjective Probability
In this article we demonstrate how algorithmic probability theory is applied
to situations that involve uncertainty. When people are unsure of their model
of reality, then the outcome they observe will cause them to update their
beliefs. We argue that classical probability cannot be applied in such cases,
and that subjective probability must instead be used. In Experiment 1 we show
that, when judging the probability of lottery number sequences, people apply
subjective rather than classical probability. In Experiment 2 we examine the
conjunction fallacy and demonstrate that the materials used by Tversky and
Kahneman (1983) involve model uncertainty. We then provide a formal
mathematical proof that, for every uncertain model, there exists a conjunction
of outcomes which is more subjectively probable than either of its constituents
in isolation.Comment: Maguire, P., Moser, P. Maguire, R. & Keane, M.T. (2013) "A
computational theory of subjective probability." In M. Knauff, M. Pauen, N.
Sebanz, & I. Wachsmuth (Eds.), Proceedings of the 35th Annual Conference of
the Cognitive Science Society (pp. 960-965). Austin, TX: Cognitive Science
Societ
Effective Generation of Subjectively Random Binary Sequences
We present an algorithm for effectively generating binary sequences which
would be rated by people as highly likely to have been generated by a random
process, such as flipping a fair coin.Comment: Introduction and Section 6 revise
Ultimate Intelligence Part I: Physical Completeness and Objectivity of Induction
We propose that Solomonoff induction is complete in the physical sense via
several strong physical arguments. We also argue that Solomonoff induction is
fully applicable to quantum mechanics. We show how to choose an objective
reference machine for universal induction by defining a physical message
complexity and physical message probability, and argue that this choice
dissolves some well-known objections to universal induction. We also introduce
many more variants of physical message complexity based on energy and action,
and discuss the ramifications of our proposals.Comment: Under review at AGI-2015 conference. An early draft was submitted to
ALT-2014. This paper is now being split into two papers, one philosophical,
and one more technical. We intend that all installments of the paper series
will be on the arxi
Algorithmic Randomness as Foundation of Inductive Reasoning and Artificial Intelligence
This article is a brief personal account of the past, present, and future of
algorithmic randomness, emphasizing its role in inductive inference and
artificial intelligence. It is written for a general audience interested in
science and philosophy. Intuitively, randomness is a lack of order or
predictability. If randomness is the opposite of determinism, then algorithmic
randomness is the opposite of computability. Besides many other things, these
concepts have been used to quantify Ockham's razor, solve the induction
problem, and define intelligence.Comment: 9 LaTeX page
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