Analysis of English Free Association Network Reveals Mechanisms of Efficient Solution of Remote Association Tests

Overview

Journal PLoS One

Specialties General Medicine
Science

Date 2021 Apr 6

PMID 33822802

Citations 2

Authors

Olga Valba

Alexander Gorsky

Sergei Nechaev

Mikhail Tamm

Affiliations

Soon will be listed here.

Abstract

We study correlations between the structure and properties of a free association network of the English language, and solutions of psycholinguistic Remote Association Tests (RATs). We show that average hardness of individual RATs is largely determined by relative positions of test words (stimuli and response) on the free association network. We argue that the solution of RATs can be interpreted as a first passage search problem on a network whose vertices are words and links are associations between words. We propose different heuristic search algorithms and demonstrate that in "easily-solving" RATs (those that are solved in 15 seconds by more than 64% subjects) the solution is governed by "strong" network links (i.e. strong associations) directly connecting stimuli and response, and thus the efficient strategy consist in activating such strong links. In turn, the most efficient mechanism of solving medium and hard RATs consists of preferentially following sequence of "moderately weak" associations.

Citing Articles

Modeling the Remote Associates Test as Retrievals from Semantic Memory.

Schatz J, Jones S, Laird J Cogn Sci. 2022; 46(6):e13145.

PMID: 35665954 PMC: 9286825. DOI: 10.1111/cogs.13145.

K-clique percolation in free association networks and the possible mechanism behind the [Formula: see text] law.

Valba O, Gorsky A Sci Rep. 2022; 12(1):5540.

PMID: 35365717 PMC: 8975849. DOI: 10.1038/s41598-022-09499-w.

References

Bowden E, Jung-Beeman M . Normative data for 144 compound remote associate problems. Behav Res Methods Instrum Comput. 2004; 35(4):634-9. DOI: 10.3758/bf03195543. View

Kenett Y, Anaki D, Faust M . Investigating the structure of semantic networks in low and high creative persons. Front Hum Neurosci. 2014; 8:407. PMC: 4051268. DOI: 10.3389/fnhum.2014.00407. View

Krioukov D, Papadopoulos F, Kitsak M, Vahdat A, Boguna M . Hyperbolic geometry of complex networks. Phys Rev E Stat Nonlin Soft Matter Phys. 2011; 82(3 Pt 2):036106. DOI: 10.1103/PhysRevE.82.036106. View

Papadopoulos F, Kitsak M, Serrano M, Boguna M, Krioukov D . Popularity versus similarity in growing networks. Nature. 2012; 489(7417):537-40. DOI: 10.1038/nature11459. View

Stella M, Beckage N, Brede M, De Domenico M . Multiplex model of mental lexicon reveals explosive learning in humans. Sci Rep. 2018; 8(1):2259. PMC: 5797130. DOI: 10.1038/s41598-018-20730-5. View

Mednick S . The associative basis of the creative process. Psychol Rev. 1962; 69:220-32. DOI: 10.1037/h0048850. View

Kounios J, Beeman M . The cognitive neuroscience of insight. Annu Rev Psychol. 2014; 65:71-93. DOI: 10.1146/annurev-psych-010213-115154. View

Alstott J, Bullmore E, Plenz D . Powerlaw: a Python package for analysis of heavy-tailed distributions. PLoS One. 2014; 9(1):e85777. PMC: 3906378. DOI: 10.1371/journal.pone.0085777. View

Vitevitch M . What can graph theory tell us about word learning and lexical retrieval?. J Speech Lang Hear Res. 2008; 51(2):408-22. PMC: 2535910. DOI: 10.1044/1092-4388(2008/030). View

10.

Steyvers M, Tenenbaum J . The large-scale structure of semantic networks: statistical analyses and a model of semantic growth. Cogn Sci. 2011; 29(1):41-78. DOI: 10.1207/s15516709cog2901_3. View

11.

Evans M, Majumdar S . Diffusion with stochastic resetting. Phys Rev Lett. 2011; 106(16):160601. DOI: 10.1103/PhysRevLett.106.160601. View

12.

Kenett Y, Levy O, Kenett D, Stanley H, Faust M, Havlin S . Flexibility of thought in high creative individuals represented by percolation analysis. Proc Natl Acad Sci U S A. 2018; 115(5):867-872. PMC: 5798367. DOI: 10.1073/pnas.1717362115. View

13.

Benedek M, Neubauer A . Revisiting Mednick's Model on Creativity-Related Differences in Associative Hierarchies. Evidence for a Common Path to Uncommon Thought. J Creat Behav. 2014; 47(4):273-289. PMC: 3924568. DOI: 10.1002/jocb.35. View

14.

De Deyne S, Navarro D, Perfors A, Brysbaert M, Storms G . The "Small World of Words" English word association norms for over 12,000 cue words. Behav Res Methods. 2018; 51(3):987-1006. DOI: 10.3758/s13428-018-1115-7. View

15.

Benedek M, Panzierer L, Jauk E, Neubauer A . Creativity on tap? Effects of alcohol intoxication on creative cognition. Conscious Cogn. 2017; 56:128-134. PMC: 5700800. DOI: 10.1016/j.concog.2017.06.020. View

16.

Kajic I, Gosmann J, Stewart T, Wennekers T, Eliasmith C . A Spiking Neuron Model of Word Associations for the Remote Associates Test. Front Psychol. 2017; 8:99. PMC: 5288385. DOI: 10.3389/fpsyg.2017.00099. View

17.

Dorogovtsev S, Mendes J . Scaling properties of scale-free evolving networks: continuous approach. Phys Rev E Stat Nonlin Soft Matter Phys. 2001; 63(5 Pt 2):056125. DOI: 10.1103/PhysRevE.63.056125. View

18.

Jung-Beeman M, Bowden E, Haberman J, Frymiare J, Arambel-Liu S, Greenblatt R . Neural activity when people solve verbal problems with insight. PLoS Biol. 2004; 2(4):E97. PMC: 387268. DOI: 10.1371/journal.pbio.0020097. View

19.

Smith K, Huber D, Vul E . Multiply-constrained semantic search in the Remote Associates Test. Cognition. 2013; 128(1):64-75. DOI: 10.1016/j.cognition.2013.03.001. View

20.

Nelson D, McEvoy C, Schreiber T . The University of South Florida free association, rhyme, and word fragment norms. Behav Res Methods Instrum Comput. 2005; 36(3):402-7. DOI: 10.3758/bf03195588. View