| Title |
Algorithmic complexity for short binary strings applied to psychology: a primer
|
|---|---|
| Published in |
Behavior Research Methods, December 2013
|
| DOI | 10.3758/s13428-013-0416-0 |
| Pubmed ID | |
| Authors |
Nicolas Gauvrit, Hector Zenil, Jean-Paul Delahaye, Fernando Soler-Toscano |
| Abstract |
As human randomness production has come to be more closely studied and used to assess executive functions (especially inhibition), many normative measures for assessing the degree to which a sequence is randomlike have been suggested. However, each of these measures focuses on one feature of randomness, leading researchers to have to use multiple measures. Although algorithmic complexity has been suggested as a means for overcoming this inconvenience, it has never been used, because standard Kolmogorov complexity is inapplicable to short strings (e.g., of length l ≤ 50), due to both computational and theoretical limitations. Here, we describe a novel technique (the coding theorem method) based on the calculation of a universal distribution, which yields an objective and universal measure of algorithmic complexity for short strings that approximates Kolmogorov-Chaitin complexity. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 43 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | 2% |
| Spain | 1 | 2% |
| United States | 1 | 2% |
| Germany | 1 | 2% |
| Unknown | 39 | 91% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 7 | 16% |
| Student > Master | 5 | 12% |
| Student > Bachelor | 5 | 12% |
| Professor > Associate Professor | 4 | 9% |
| Student > Doctoral Student | 3 | 7% |
| Other | 11 | 26% |
| Unknown | 8 | 19% |
| Readers by discipline | Count | As % |
|---|---|---|
| Psychology | 11 | 26% |
| Social Sciences | 4 | 9% |
| Medicine and Dentistry | 3 | 7% |
| Linguistics | 3 | 7% |
| Computer Science | 3 | 7% |
| Other | 9 | 21% |
| Unknown | 10 | 23% |
Attention Score in Context
This research output has an Altmetric Attention Score of 9. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 15 September 2016.
All research outputs
#4,259,984
of 25,373,627 outputs
Outputs from Behavior Research Methods
#528
of 2,525 outputs
Outputs of similar age
#46,532
of 319,937 outputs
Outputs of similar age from Behavior Research Methods
#4
of 25 outputs
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