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The task of trying to quantify a person’s intelligence has been a goal of psychologists for over a century. The area of estimating IQ using stylometry has been a developing area of research and the effectiveness of using machine learning in stylometry analysis for the estimation of IQ has been demonstrated in literature whose conclusions suggest that using a large dataset could improve the quality of estimation. The unavailability of large datasets in this area of research has led to very few publications in IQ estimation from written text. In this paper, we review studies that have been done in IQ estimation and also that have been done in author profiling using stylometry and we conclude that based on the success of IQ estimation and author profiling with stylometry, a study on IQ estimation from written text using stylometry will yield good results if the right dataset is used.


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Estimating Intelligence Quotient Using Stylometry and Machine Learning Techniques: A Review

Show Author's information Glory O. AdebayoRoman V. Yampolskiy( )
Department of Computer Science and Engineering, University of Louisville, Louisville, KY 40208, USA

Abstract

The task of trying to quantify a person’s intelligence has been a goal of psychologists for over a century. The area of estimating IQ using stylometry has been a developing area of research and the effectiveness of using machine learning in stylometry analysis for the estimation of IQ has been demonstrated in literature whose conclusions suggest that using a large dataset could improve the quality of estimation. The unavailability of large datasets in this area of research has led to very few publications in IQ estimation from written text. In this paper, we review studies that have been done in IQ estimation and also that have been done in author profiling using stylometry and we conclude that based on the success of IQ estimation and author profiling with stylometry, a study on IQ estimation from written text using stylometry will yield good results if the right dataset is used.

Keywords: machine learning, intelligence, stylometry, IQ estimation, authorship attribution, IQ, author profiling

References(51)

[1]
P. Hallinan, Book review: Psychological testing (5th edn), Aust. Educ. Dev. Psychol., vol. 2, no. 2, p. 18, 1985,
[2]
[3]
K. Cherry, Alfred Binet and the Simon-Binet intelligence scale, https://www.verywellmind.com/alfred-binet-biography-2795503, 2020.
[4]
E. Byington and W. Felps, Why do IQ scores predict job performance? An alternative, sociological explanation, Res. Organ. Behav., vol. 30, pp.175-202, 2010.
[5]
Historical importance of ASVAB testing, https://asvabmilitarytest.com/history-of-asvab-test, 2022.
[6]
F. J. Tweedie, S. Singh, and D. I. Holmes, Neural network applications in stylometry: The federalist papers, Comput. Hum., vol. 30, no. 1, pp. 1-10, 1996.
[7]
N. Ali, M. Hindi, and R. V. Yampolskiy, Evaluation of authorship attribution software on a chat bot corpus, in Proc. 2011 23rd Int. Symp. Information, Commun. Autom. Technol., Sarajevo, Bosnia and Herzegovina, 2011, pp. 1-6.
[8]
N. Ali, D. Schaeffer, and R. V. Yampolskiy, Linguistic profiling and behavioral drift in chat bots, CEUR Workshop Proceedings, vol. 841, pp. 27-30, 2012.
[9]
R. V. Yampolskiy, N. Ali, D. D’Souza, and A. A. Mohamed, Behavioral biometrics, Int. J. Nat. Comput. Res., vol. 4, no. 3, pp. 85-118, 2014.
[10]
R. J. Sternberg, Intelligence, Dialogues Clin. Neurosci., vol. 14, no. 1, pp. 19-27, 2012.
[11]
V. Čavojová and E. B. Mikušková, Does intelligence predict academic achievement? Two case studies, Procedia - Soc. Behav. Sci., vol. 174, pp. 3462-3469, 2015.
[12]
L. Wang, C. -Y. Wee, H. -I. Suk, X. Tang, and D. Shen, MRI-based intelligence quotient (IQ) estimation with sparse learning, PLoS One, vol. 10, no. 3, p. e0117295, 2015.
[13]
M. Badar, M. Haris, and A. Fatima, Application of deep learning for retinal image analysis: A review, Comput. Sci. Rev., vol. 35, p. 100203, 2020.
[14]
M. Brammer, The role of neuroimaging in diagnosis and personalized medicine-current position and likely future directions, Dialogues Clin. Neurosci., vol. 11, no. 4, pp. 389-396, 2009.
[15]
C. J. Price, S. Ramsden, T. M. H. Hope, K. J. Friston, and M. L. Seghier, Predicting IQ change from brain structure: A cross-validation study, Dev. Cogn. Neurosci., vol. 5, pp. 172-184, 2013.
[16]
T. Ohtani, P. G. Nestor, S. Bouix, Y. Saito, T. Hosokawa, and M. Kubicki, Medial frontal white and gray matter contributions to general intelligence, PLoS One, vol. 9, no. 12, p. e112691, 2014.
[17]
F. J. Navas-Sánchez, Y. Alemán-Gómez, J. Sánchez-Gonzalez, J. A. Guzmán-De-Villoria, C. Franco, O. Robles, C. Arango, and M. Desco, White matter microstructure correlates of mathematical giftedness and intelligence quotient., Hum. Brain Mapp., vol. 35, no. 6, pp. 2619-2631, 2014.
[18]
K. L. Narr, R. P. Woods, P. M. Thompson, P. Szeszko, D. Robinson, T. Dimtcheva, M. Gurbani, A. W. Toga, and R. M. Bilder, Relationships between IQ and regional cortical gray matter thickness in healthy adults, Cereb. Cortex, vol. 17, no. 9, pp. 2163-2171, 2007.
[19]
G. S. P. Pamplona, G. S. Santos Neto, S. R. E. Rosset, B. P. Rogers, and C. E. G. Salmon, Analyzing the association between functional connectivity of the brain and intellectual performance, Front. Hum. Neurosci., vol. 9, p. 61, 2015.
[20]
H. E. H. Pol, H. G. Schnack, D. Posthuma, R. C. W. Mandl, W. F. Baaré, C. V. Oel, N. E. V. Haren, D. L. Collins, A. C. Evans, K. Amunts, et al., Genetic contributions to human brain morphology and intelligence, J. Neurosci., vol. 26, no. 40, pp. 10235-10242, 2006.
[21]
A. Arya and M. Manuel, Intelligence quotient classification from human MRI brain images using convolutional neural network, in Proc. 2020 12th Int. Conf. Comput. Intell. Commun. Networks (CICN), Bhimtal, India, 2020, pp. 75-80.
[22]
R. Jiang, S. Qi, Y. Du, W. Yan, V. D. Calhoun, T. Jiang, and J. Sui, Predicting individualized intelligence quotient scores using brainnetome-atlas based functional connectivity, in Proc. 2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP), Tokyo, Japan, 2017, pp. 1-6.
[23]
S. Firooz and S. K. Setarehdan, IQ estimation by means of EEG-fNIRS recordings during a logical-mathematical intelligence test, Comput. Biol. Med., vol. 110, pp. 218-226, 2019.
[24]
K. Simonyan and A. Zisserman, Very deep convolutional networks for large-scale image recognition, arXiv preprint arXiv: 1409.1556, 2015.
[25]
Brainnetome Atlas, https://atlas.brainnetome.org/, 2021.
[26]
V. J. Schmithorst and S. K. Holland, Sex differences in the development of neuroanatomical functional connectivity underlying intelligence found using Bayesian connectivity analysis, NeuroImage, vol. 35, no. 1, pp. 406-419, 2007.
[27]
N. Jaušovec, Differences in cognitive processes between gifted, intelligent, creative, and average individuals while solving complex problems: An EEG study, Intelligence, vol. 28, no. 3, pp. 213-237, 2000.
[28]
R. J. Haier, B. Siegel, C. Tang, L. Abel, and M. S. Buchsbaum, Intelligence and changes in regional cerebral glucose metabolic rate following learning, Intelligence, vol. 16, nos. 3&4, pp. 415-426, 1992.
[29]
U. Basten, C. Stelzel, and C. J. Fiebach, Intelligence is differentially related to neural effort in the task-positive and the task-negative brain network, Intelligence, vol. 41, no. 5, pp. 517-528, 2013.
[30]
U. Basten, K. Hilger, and C. J. Fiebach, Where smart brains are different: A quantitative meta-analysis of functional and structural brain imaging studies on intelligence, Intelligence, vol. 51, pp. 10-27, 2015.
[31]
E. Stamatatos, N. Fakotakis, and G. Kokkinakis, Automatic text categorization in terms of genre and author, Comput. Linguist., vol. 26, no. 4, pp. 471-495, 2000.
[32]
A. Abbasi and H. Chen, Applying authorship analysis to extremist-group web forum messages, IEEE Intell. Syst., vol. 20, no. 5, pp. 67-75, 2005.
[33]
E. Stamatatos, A survey of modern authorship attribution methods, J. Am. Soc. Inf. Sci. Technol., vol. 60, no. 3, pp. 538-556, 2009.
[34]
A. Hendrix and R. Yampolskiy, Automated IQ estimation from writing samples, https://aws.amazon.com/publicdatasets/common-crawl/, 2017.
[35]
P. S. Abramov and R. V. Yampolskiy, Automatic IQ estimation using stylometric methods, in Handbook of Research on Learning in the Age of Transhumanism, S. Sisman-Ugur and G. Kurubacak, eds. Hershey, PA, USA: IGI Global, 2019, pp. 32-45.
DOI
[36]
S. Argamon, M. Koppel, J. W. Pennebaker, and J. Schler, Automatically profiling the author of an anonymous text, Commun. ACM, vol. 52, no. 2, pp. 119-123, 2009.
[37]
M. Koppel, J. Schler, and K. Zigdon, Determining an author’s native language by mining a text for errors, in Proc. 11th ACM SIGKDD Int. Conf. Knowl. Discov. Data Min., Chicago, IL, USA, 2005, pp. 624-628.
[38]
K. Alrifai, G. Rebdawi, and N. Ghneim, Arabic tweeps gender and dialect prediction: Notebook for PAN at CLEF 2017, CEUR Workshop Proc., vol. 1866, pp. 1-9, 2017.
[39]
D. Kodiyan, F. Hardegger, S. Neuhaus, and M. Cieliebak, Author profiling with bidirectional RNNs using attention with GRUs: Notebook for PAN at CLEF 2017, CEUR Workshop Proc., vol. 1866, pp. 1-10, 2017.
[40]
PAN at CLEF, https://pan.webis.de/, 2022.
[41]
F. Rangel, P. Rosso, M. Potthast, and B. Stein, Overview of the 5th author profiling task at PAN 2017: Gender and language variety identification in Twitter, CEUR Workshop Proc., vol. 1866, pp. 1-26, 2017.
[42]
F. Rangel, P. Rosso, B. Verhoeven, W. Daelemans, M. Potthast, and B. Stein, Overview of the 4th author profiling task at PAN 2016: Cross-genre evaluations, CEUR Workshop Proc., vol. 1609, pp. 750-784, 2016.
[43]
M. Koppel, S. Argamon, and A. R. Shimoni, Automatically categorizing written texts by author gender, Literary and Linguistic Computing, vol. 17, no. 4, pp. 401-412, 2002.
[44]
J. A. Khan, Author profile prediction using trend and word frequency based analysis in text: Notebook for PAN at CLEF 2017, CEUR Workshop Proc., vol. 1866, pp. 1-7, 2017.
[45]
K. Nishiyama, G. O. Adebayo, and R. Yampolskiy, Authorship identification of translational algorithms, in Proc. 2021 IEEE 15th International Conference on Semantic Computing (ICSC), Laguna Hills, CA, USA, 2021, pp. 90-91
[46]
, C. He, and K. Rasheed, Using machine learning techniques for stylometry, in Proc. Int. Conf. Artif. Intell. IC-AI’04, Las Vegas, NV, USA, 2004, pp. 897-903.
[47]
Y. Adame-Arcia, D. Castro-Castro, R. O. Bueno, and R. Mu-ñoz, Author profiling, instance-based similarity classification: Notebook for PAN at CLEF 2017, CEUR Workshop Proc., vol. 1866, pp. 1-7, 2017.
[48]
G. Kheng, L. Laporte, and M. Granitzer, INSA LYON and UNI PASSAU’s participation at PAN@CLEF’17: Author profiling task: Notebook for PAN at CLEF 2017, CEUR Workshop Proc., vol. 1866, pp. 1-11, 2017.
[49]
M. Franco-Salvador, N. Plotnikova, N. Pawar, and Y. Benajiba, Subword-based deep averaging networks for author profiling in social media: Notebook for PAN at CLEF 2017, CEUR Workshop Proc., vol. 1866, pp. 1-10, 2017.
[50]
P. Bojanowski, E. Grave, A. Joulin, and T. Mikolov, Enriching word vectors with subword information, Trans. Assoc. Comput. Linguist., vol. 5, pp. 135-146, 2017.
[51]
N. Schaetti, UniNE at CLEF 2017: TF-IDF and deep-learning for author profiling: Notebook for PAN at CLEF 2017, CEUR Workshop Proc., vol. 1866, pp. 1-11, 2017.
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Received: 27 June 2021
Revised: 03 December 2021
Accepted: 21 January 2022
Published: 09 June 2022
Issue date: September 2022

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