Advertisement

Investigation of eye movement pattern parameters of individuals with different fluid intelligence

  • Bahman Abdi Sargezeh
  • Ahmad Ayatollahi
  • Mohammad Reza Daliri
Research Article

Abstract

Eye movement studies are subject of interest in human cognition. Cortical activity and cognitive load impress eye movement influentially. Here, we investigated whether fluid intelligence (FI) has any effect on eye movement pattern in a comparative visual search (CVS) task. FI of individuals was measured using the Cattell test, and participants were divided into three groups: low FI, middle FI, and high FI. Eye movements of individuals were then recorded during the CVS task. Eye movement patterns were extracted and compared statistically among the three groups. Our experiment demonstrated that eye movement patterns were significantly different among the three groups. Pearson correlation coefficients between FI and eye movement parameters were also calculated to assess which of the eye movement parameters were most affected by FI. Our findings illustrate that saccade peak velocity had the greatest positive correlation with FI score and the ratio of total fixation duration to total saccade duration had the greatest negative correlation with FI. Next, we extracted 24 features from eye movement patterns and designed: (1) a classifier to categorize individuals and (2) a regression analysis to predict the FI score of individuals. In the best case examined, the classifier categorized subjects with 68.3% accuracy, and the regression predicted FI of individuals with a 0.54 correlation between observed FI and predicted FI. In our investigation, the results have emphasized that imposed loads on low FI individuals is greater than that of high FI individuals in the cognitive load tasks.

Keywords

Eye movement Fluid intelligence Comparative visual search Individual categorization Predicting fluid intelligence 

References

  1. Alotaibi A, Underwood G, Smith AD (2017) Cultural differences in attention: eye movement evidence from a comparative visual search task. Conscious Cogn 55:254–265CrossRefGoogle Scholar
  2. Bahill AT, Clark MR, Stark L (1975) The main sequence, a tool for studying human eye movements. Math Biosci 24:191–204CrossRefGoogle Scholar
  3. Baratloo A, Hosseini M, Negida A, Ashal GE, Ashal GE (2015) Part 1: simple definition and calculation of accuracy, sensitivity and specificity. Emergency 3:2–3Google Scholar
  4. Burch M (2017) Visual analysis of eye movement data with fixation distance plots. In: International conference on intelligent decision technologies, pp 227–236Google Scholar
  5. Cattell RB (1963) Theory of fluid and crystallized intelligence: a critical experiment. J Educ Psychol 54(1):1–22CrossRefGoogle Scholar
  6. Cazzato V, Basso D, Cutini S, Bisiacchi P (2010) Gender differences in visuospatial planning: An eye movements study. Behav Brain Res 206(2):177–183CrossRefGoogle Scholar
  7. Chuk T, Crookes K, Hayward WG, Chan AB, Hsiao JH (2017) Hidden Markov model analysis reveals the advantage of analytic eye movement patterns in face recognition across cultures. Cognition 169:102–117CrossRefGoogle Scholar
  8. Coco MI, Keller F (2014) Classification of visual and linguistic tasks using eye-movement features. J Vis 14(3):11–11CrossRefGoogle Scholar
  9. Colby CL, Goldberg ME et al (1992) The updating of the representation of visual space in parietal cortex by intended eye movements. Science 255(5040):90–92CrossRefGoogle Scholar
  10. Conway ARA, Cowan N, Bunting MF, Therriault DJ, Minkoff SR (2002) A latent variable analysis of working memory capacity, short-term memory capacity, processing speed, and general fluid intelligence. Intelligence 30(2):163–183CrossRefGoogle Scholar
  11. Cowen L, Ball LJ, Delin J (2002) An eye movement analysis of web page usability. In: People and computers XVI-memorable yet invisible, Springer, pp 317–335Google Scholar
  12. Di Stasi LL, Marchitto M, Antolí A, Baccino T, Cañas JJ (2010) Approximation of on-line mental workload index in ATC simulated multitasks. J Air Transp Manag 16(6):330–333CrossRefGoogle Scholar
  13. Dix A, van der Meer E (2015) Arithmetic and algebraic problem solving and resource allocation: the distinct impact of fluid and numerical intelligence. Psychophysiology 52(4):544–554CrossRefGoogle Scholar
  14. Duchowski AT (2007) Eye tracking methodology. Theory Pract 328:15–27Google Scholar
  15. Fry AF, Hale S (2000) Relationships among processing speed, working memory, and fluid intelligence in children. Biol Psychol 54(1–3):1–34CrossRefGoogle Scholar
  16. Gaarder KR (1975) Eye movements, vision, and behavior. Hemisphere Publishing Corporation, Washington DCGoogle Scholar
  17. Galpin AJ, Underwood G (2005) Eye movements during search and detection in comparative visual search. Percept Psychophys 67(8):1313–1331CrossRefGoogle Scholar
  18. Garza R, Heredia RR, Cieslicka AB (2016) Male and female perception of physical attractiveness: an eye movement study. Evol Psychol 14(1):1–16CrossRefGoogle Scholar
  19. Goldberg JH, Kotval XP (1999) Computer interface evaluation using eye movements: methods and constructs. Int J Ind Ergon 24(6):631–645CrossRefGoogle Scholar
  20. Gray JR, Chabris CF, Braver TS (2003) Neural mechanisms of general fluid intelligence. Nat Neurosci 6(3):316–322CrossRefGoogle Scholar
  21. Hayes TR, Henderson JM (2017) Scan patterns during real-world scene viewing predict individual differences in cognitive capacity. J Vis 17(5):23CrossRefGoogle Scholar
  22. Hayes TR, Petrov AA (2015) Pupil Diameter tracks the exploration–exploitation trade-off during analogical reasoning and explains individual differences in fluid intelligence. J Cogn Neurosci 28:308–318CrossRefGoogle Scholar
  23. Hayes TR, Petrov A, Sederberg PB (2011) A novel method for analyzing sequential eye movements reveals strategic influence on Raven’ s advanced progressive matrices. J Vis 11:1–11Google Scholar
  24. Henn V, Baloh RW, Hepp K (1984) The sleep-wake transition in the oculomotor system. Exp Brain Res 54(1):166–176CrossRefGoogle Scholar
  25. Hirvonen K, Puttonen S, Gould K, Korpela J, Koefoed VF, Müller K (2010) Improving the saccade peak velocity measurement for detecting fatigue. J Neurosci Methods 187(2):199–206CrossRefGoogle Scholar
  26. Hutton SB (2008) Cognitive control of saccadic eye movements. Brain Cogn 68(3):327–340CrossRefGoogle Scholar
  27. Irwin DE, Brockmole JR (2000) Mental rotation is suppressed during saccadic eye movements. Psychon Bull Rev 7(4):654–661CrossRefGoogle Scholar
  28. Irwin DE, Brockmole JR (2004) Suppressing where but not what: the effect of saccades on dorsal-and ventral-stream visual processing. Psychol Sci 15(7):467–473CrossRefGoogle Scholar
  29. Irwin DE, Carlson-Radvansky LA (1996) Cognitive suppression during saccadic eye movements. Psychol Sci 7(2):83–88CrossRefGoogle Scholar
  30. Itoh H (2002) Correlation of primate caudate neural activity and saccade parameters in reward-oriented behavior. J Neurophysiol 89(4):1774–1783CrossRefGoogle Scholar
  31. Jellinger KA (2009) The Neurology of Eye Movements 4th edn. Eur J Neurol 16(7):e132–e132CrossRefGoogle Scholar
  32. Just MA, Carpenter PA (1976) Eye fixations and cognitive processes. Cogn Psychol 8(4):441–480CrossRefGoogle Scholar
  33. Just MA, Carpenter PA (1980) A theory of reading: from eye fixations ot comprehension. Psychol Rev 87(4):329–354CrossRefGoogle Scholar
  34. Kirkorian HL, Anderson DR, Keen R (2012) Age differences in online processing of video: an eye movement study. Child Dev 83(2):497–507PubMedPubMedCentralGoogle Scholar
  35. Leigh RJ, Zee DS (2015) The neurology of eye movements, vol 90. Oxford University Press, CaryGoogle Scholar
  36. Meghanathan RN, van Leeuwen C, Nikolaev AR (2015) Fixation duration surpasses pupil size as a measure of memory load in free viewing. Front Hum Neurosci 8:1–9CrossRefGoogle Scholar
  37. Momtaz HZ, Daliri MR (2016) Differences of eye movement pattern in natural and man-made scenes and image categorization with the help of these patterns. J Integr Neurosci 15(01):37–54CrossRefGoogle Scholar
  38. Munoz DP, Broughton JR, Goldring JE, Armstrong IT (1998) Age-related performance of human subjects on saccadic eye movement tasks. Exp Brain Res 121(4):391–400CrossRefGoogle Scholar
  39. Nesbit LL (1981) Relationship between eye movement, learning, and picture complexity. Educ Technol Res Dev 29(2):109–116Google Scholar
  40. Nettelbeck T, Edwards C, Vreugdenhil A (1986) Inspection time and IQ: evidence for a mental speed-ability association. Pers Individ Dif 7(5):633–641CrossRefGoogle Scholar
  41. Pierrot-Deseilligny C, Müri RM, Ploner CJ, Gaymard B, Rivaud-Péchoux S (2003) Cortical control of ocular saccades in humans: a model for motricity. Prog Brain Res 142:3–17CrossRefGoogle Scholar
  42. Pierrot-Deseilligny C, Milea D, Müri RM (2004) Eye movement control by the cerebral cortex. Curr Opin Neurol 17(1):17–25CrossRefGoogle Scholar
  43. Pomplun M, Reingold EM, Shen J (2001) Investigating the visual span in comparative search: The effects of task difficulty and divided attention. Cognition 81(2):57–67CrossRefGoogle Scholar
  44. Rayner K, Li X, Williams CC, Cave KR, Well AD (2007) Eye movements during information processing tasks: individual differences and cultural effects. Vis Res 47(21):2714–2726CrossRefGoogle Scholar
  45. Rowland LM et al (2005) Oculomotor responses during partial and total sleep deprivation. Aviat Space Environ Med 76(7):C104–C113PubMedGoogle Scholar
  46. Sareen P, Ehinger KA, Wolfe JM (2016) CB Database: a change blindness database for objects in natural indoor scenes. Behav Res Methods 48(4):1343–1348CrossRefGoogle Scholar
  47. Soetedjo R, Kaneko CRS, Fuchs AF (2002) Evidence that the superior colliculus participates in the feedback control of saccadic eye movements. J Neurophysiol 87(2):679–695CrossRefGoogle Scholar
  48. Tuladhar A, Mitrousis N, Führmann T, Shoichet MS (2014) Central nervous system, no. 1. Elsevier Inc., AmsterdamGoogle Scholar
  49. Underwood G, Templeman E, Lamming L, Foulsham T (2008) Is attention necessary for object identification? Evidence from eye movements during the inspection of real-world scenes. Conscious Cogn 17(1):159–170CrossRefGoogle Scholar
  50. Van Orden KF, Limbert W, Makeig S, Jung T-P (2001) Eye activity correlates of workload during a visuospatial memory task. Hum Factors J Hum Factors Ergon Soc 43(1):111–121CrossRefGoogle Scholar
  51. Van Biesen D, McCulloch K, Janssens L, Vanlandewijck YC (2017) The relation between intelligence and reaction time in tasks with increasing cognitive load among athletes with intellectual impairment. Intelligence 64:45–51CrossRefGoogle Scholar
  52. Van Der Meer E et al (2010) Resource allocation and fluid intelligence: Insights from pupillometry. Psychophysiology 47(1):158–169CrossRefGoogle Scholar
  53. Yarbus AL (1967) Eye movements during perception of complex objects. Eye movements and vision. Springer, Berlin, pp 171–211Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Bahman Abdi Sargezeh
    • 1
    • 2
  • Ahmad Ayatollahi
    • 2
  • Mohammad Reza Daliri
    • 1
  1. 1.Neuroscience and Neuroengineering Research Lab., Biomedical Engineering Department, School of Electrical EngineeringIran University of Science and Technology (IUST)NarmakIran
  2. 2.Electronics Engineering Department, School of Electrical EngineeringIran University of Science and Technology (IUST)NarmakIran

Personalised recommendations