Instructional Science

, Volume 47, Issue 4, pp 481–498 | Cite as

Cognitive ability and cognitive style: finding a connection through resource use behavior

  • Natalie ToomeyEmail author
  • Misook Heo
Original Research


The goal of this study was to investigate cognitive style (the visualizer–verbalizer dimension) and cognitive ability (spatial and verbal abilities) in terms of corresponding resource use behavior. The study further examined the potential link between cognitive style and cognitive ability based on observable behavior. A total of 67 university students participated in the study by completing an online survey containing a series of questionnaires, tests, and tasks, which assessed their cognitive style, cognitive ability, and resource use behavior. Multinomial logistic regression analyses revealed that cognitive style in general predicts resource use behavior. The findings also showed that spatial ability, particularly lower spatial ability, predicts resource use behavior. This study thus contributes to the literature with theory-based, empirical evidence that cognitive ability is reflected in cognitive style. This study further provides information needed to better understand the interplay between individuals’ cognitive style and cognitive ability and how these may be addressed in the design and implementation of learning environments.


Cognitive ability Cognitive style Visualizer–verbalizer Spatial ability Resource use behavior 



  1. Ausburn, L. J., & Ausburn, F. B. (1978). Cognitive styles: Some information and implications for instructional design. Educational Technology Research and Development, 26, 337–354. Scholar
  2. Ayers, P., & Sweller, J. (2014). The split-attention principle in multimedia learning. In R. E. Mayer (Ed.), The Cambridge handbook of multimedia learning (2nd ed., pp. 206–226). New York: Cambridge University Press.Google Scholar
  3. Baddeley, A. D., & Hitch, G. (1974). Working memory. Psychology of Learning and Motivation, 8, 47–89. Scholar
  4. Blazhenkova, O., Becker, M., & Kozhevnikov, M. (2011). Object–spatial imagery and verbal cognitive styles in children and adolescents: Developmental trajectories in relation to ability. Learning and Individual Differences, 21(3), 281–287. Scholar
  5. Blazhenkova, O., & Kozhevnikov, M. (2009). The new object-spatial-verbal cognitive style model: Theory and measurement. Applied Cognitive Psychology, 23, 638–663. Scholar
  6. Butcher, K. R. (2014). The multimedia principle. In R. E. Mayer (Ed.), The Cambridge handbook of multimedia learning (2nd ed., pp. 174–205). New York: Cambridge University Press.Google Scholar
  7. Cheng, S., & Long, J. S. (2007). Testing for IIA in the multinomial logit model. Sociological Methods & Research, 35, 583–600. Scholar
  8. Clark, J. M., & Paivio, A. (1991). Dual coding theory and education. Educational Psychology Review, 3, 149–210. Scholar
  9. Cole, J. S., & Gonyea, R. M. (2010). Accuracy of self-reported SAT and ACT test scores: Implications for research. Research in Higher Education, 51(4), 305–319. Scholar
  10. Deary, I. J. (2001). Human intelligence differences: A recent history. Trends in Cognitive Sciences, 5, 127–130. Scholar
  11. Deary, I. J., Penke, L., & Johnson, W. (2010). The neuroscience of human intelligence differences. Nature Reviews Neuroscience, 11, 201–211. Scholar
  12. DiStefano, C., Zhu, M., & Mindrila, D. (2009). Understanding and using factor scores: Considerations for the applied researcher. Practical Assessment, Research, & Evaluation, 14(20), 1–11.Google Scholar
  13. Ekstrom, R. B., French, J. W., & Harman, H. H. (1976). Manual for kit of factor-referenced cognitive tests. Retrieved from
  14. Evans, C., Cools, E., & Charlesworth, Z. M. (2010). Learning in higher education—How cognitive and learning styles matter. Teaching in Higher Education, 15, 467–478. Scholar
  15. Field, A. (2013). Discovering Statistics using IBM SPSS statistics (4th ed.). London: Sage.Google Scholar
  16. Figl, K., & Recker, J. (2016). Exploring cognitive style and task-specific preferences for process representations. Requirements Engineering, 21(1), 63–85. Scholar
  17. French, J. W., Ekstrom, R. B., & Price, L. A. (1963). Manual for kit of reference tests for cognitive factors (revised edition). Princeton NJ: Educational Testing Service.Google Scholar
  18. Garson, G. D. (2014). Logistic regression: Binary and multinomial. Asheboro, NC: Statistical Associates.Google Scholar
  19. Gottfried, A. E. (1990). Academic intrinsic motivation in young elementary school children. Journal of Educational Psychology, 82(3), 525–538. Scholar
  20. Green, K. E., & Schroeder, D. H. (1990). Psychometric quality of the Verbalizer-Visualizer Questionnaire as a measure of cognitive style. Psychological Reports, 66, 939–945. Scholar
  21. Hausman, J., & McFadden, D. (1984). Specification tests for the multinomial logit model. Econometrics, 52, 1219–1240. Scholar
  22. Hegarty, M., & Kozhevnikov, M. (1999). Types of visual-spatial representations and mathematical problem solving. Journal of Educational Psychology, 91, 684–689. Scholar
  23. Hilbert, S., Bühner, M., Sarubin, N., Koschutnig, K., Weiss, E., Papousek, I., et al. (2015a). The influence of cognitive styles and strategies in the digit span backwards task: Effects on performance and neuronal activity. Personality and Individual Differences, 87, 242–247. Scholar
  24. Hilbert, S., Nakagawa, T. T., Puci, P., Zech, A., & Bühner, M. (2015b). The digital span backwards task: Verbal and visual cognitive strategies in working memory assessment. European Journal of Psychological Assessment, 31, 174–180. Scholar
  25. Höffler, T. N. (2010). Spatial ability: Its influence on learning with visualizations—A meta-analytic review. Educational Psychology Review, 22, 245–269. Scholar
  26. Höffler, T. N., Koć-Januchta, M., & Leutner, D. (2017). More evidence for three types of cognitive style: Validating object-spatial imagery and verbal questionnaire using eye tracking when learning with text and pictures. Applied Cognitive Psychology. Scholar
  27. Höffler, T. N., & Leutner, D. (2011). The role of spatial ability in learning from instructional animations: Evidence for an ability-as-compensator hypothesis. Computers in Human Behavior, 27, 209–216. Scholar
  28. Hosmer, D., Lemeshow, S., & Sturdivant, R. (2013). Applied logistic regression (3rd ed.). Hoboken, NJ: Wiley.Google Scholar
  29. Huk, T. (2006). Who benefits from learning with 3D models? The case of spatial ability. Journal of computer assisted learning, 22, 392–404. Scholar
  30. Hyde, J. S. (2005). The gender similarities hypothesis. American Psychologist, 60(6), 581–592. Scholar
  31. Jonassen, D. H., & Grabowski, B. L. (1993). Handbook of individual differences, learning, and instruction. Hillsdale, NJ: Lawrence Erlbaum.Google Scholar
  32. Kalyuga, S. (2012). Instructional benefits of spoken words: A review of cognitive load factors. Educational Research Review, 7, 145–159. Scholar
  33. King, K., Huff, K., Ewing, M., & Andrews, M. (2005). Assessing the reliability of skills measured by the SAT (Report No. 24). New York, NY: College Board.Google Scholar
  34. Kirby, J. R., Moore, P. J., & Schofield, N. J. (1988). Verbal and visual learning styles. Contemporary Educational Psychology, 13(2), 169–184. Scholar
  35. Kirschner, P. A. (2017). Stop propagating the learning styles myth. Computers & Education, 106, 166–171. Scholar
  36. Kirschner, P. A., & van Merriënboer, J. J. (2013). Do learners really know best? Urban legends in education. Educational psychologist, 48, 169–183. Scholar
  37. Klein, P. D. (2003). Rethinking the multiplicity of cognitive resources and curricular representations: alternatives to ‘learning styles’ and ‘multiple intelligences’. Journal of Curriculum Studies, 35, 45–81. Scholar
  38. Koć-Januchta, M., Höffler, T., Thoma, G.-B., Prechtl, H., & Leutner, D. (2017). Visualizers versus verbalizers: Effects of cognitive style on learning with texts and pictures—An eye-tracking study. Computers in Human Behavior, 68, 170–179. Scholar
  39. Kollöffel, B. (2012). Exploring the relation between visualizer-verbalizer cognitive styles and performance with visual or verbal learning material. Computers & Education, 58, 697–706. Scholar
  40. Kozhevnikov, M. (2007). Cognitive styles in the context of modern psychology: toward an integrated framework of cognitive style. Psychological Bulletin, 133, 464–481. Scholar
  41. Kozhevnikov, M., Kosslyn, S., & Shephard, J. (2005). Spatial versus object visualizers: A new characterization of visual cognitive style. Memory & Cognition, 33, 710–726. Scholar
  42. Leutner, D., & Plass, J. L. (1998). Measuring learning styles with questionnaires versus direct observation of preferential choice behavior in authentic learning situations: The visualizer/verbalizer behavior observation scale (VV-BOS). Computers in Human Behavior, 14, 543–557. Scholar
  43. Lohman, D. F. (1988). Spatial abilities as traits, processes, and knowledge. In R. J. Sternberg (Ed.), Advances in the psychology of human intelligence (pp. 181–232). Hillsdale, NJ: Lawrence Erlbaum Associates Inc.Google Scholar
  44. Low, R., & Sweller, J. (2014). The modality principle in multimedia learning. In R. E. Mayer (Ed.), Cambridge handbook of multimedia learning (pp. 227–246). New York: Cambridge University Press.Google Scholar
  45. Massa, L. J., & Mayer, R. E. (2006). Testing the ATI hypothesis: Should multimedia instruction accommodate verbalizer-visualizer cognitive style? Learning and Individual Differences, 16, 321–335. Scholar
  46. Mayer, R. E. (2014). Introduction to multimedia learning. In R. E. Mayer (Ed.), The Cambridge handbook of multimedia learning (2nd ed., pp. 1–26). New York: Cambridge University Press.Google Scholar
  47. Mayer, R. E., & Massa, L. J. (2003). Three facets of visual and verbal learners: Cognitive ability, cognitive style, and learning preference. Journal of Educational Psychology, 95, 833–846. Scholar
  48. Moreno, R., Mayer, R. E., Spires, H. A., & Lester, J. C. (2001). The case for social agency in computer-based teaching: Do students learn more deeply when they interact with animated pedagogical agents? Cognition and Instruction, 19, 177–213. Scholar
  49. Moskvina, V., & Kozhevnikov, M. (2011). Determining cognitive styles: Historical perspectives and directions for further research. In S. Rayner & E. Cools (Eds.), Style differences in cognition, learning, and management: Theory, research and practice (pp. 19–31). New York: Routledge.Google Scholar
  50. Paas, F., & Sweller, J. (2014). Implications of cognitive load theory for multimedia learning. In R. E. Mayer (Ed.), The Cambridge handbook of multimedia learning (2nd ed., pp. 27–42). New York: Cambridge University Press.Google Scholar
  51. Paivio, A. (1979). Imagery and verbal processes. Hillsdale, NJ: Lawrence Erlbaum Associates Inc.Google Scholar
  52. Paivio, A. (1991). Dual coding theory: Retrospect and current status. Canadian Journal of Psychology, 45, 255–287. Scholar
  53. Pashler, H., McDaniel, M., Rohrer, D., & Bjork, R. (2009). Learning styles: Concepts and evidence. Psychological Science in the Public Interest, 9, 105–119. Scholar
  54. Riding, R. (2001). The nature and effects of cognitive style. In R. J. Sternberg & L. Zhang (Eds.), Perspectives on thinking, learning, and cognitive styles (pp. 47–72). Mahwah, NJ: Lawrence Erlbaum Associates Inc.Google Scholar
  55. Roach, V. A., Fraser, G. M., Kryklywy, J. H., Mitchell, D. G., & Wilson, T. D. (2017). Different perspectives: Spatial ability influences where individuals look on a timed spatial test. Anatomical Sciences Education, 10, 224–234. Scholar
  56. Rohde, T. E., & Thompson, L. A. (2007). Predicting academic achievement with cognitive ability. Intelligence, 35(1), 83–92. Scholar
  57. Sweller, J. (1994). Cognitive load theory, learning difficulty, and instructional design. Learning and instruction, 4(4), 295–312. Scholar
  58. van Merriënboer, J. J., & Sweller, J. (2005). Cognitive load theory and complex learning: Recent developments and future directions. Educational Psychology Review, 17, 147–177. Scholar
  59. Velez, M. C., Silver, D., & Tremaine, M. (2005). Understanding visualization through spatial ability differences. In Visualization, 2005. VIS 05. IEEE (pp. 511–518).
  60. Wang, L., & Carr, M. (2014). Working memory and strategy use contribute to gender differences in spatial ability. Educational Psychologist, 49, 261–282. Scholar
  61. Zhou, M. (2014). Gender difference in web search perceptions and behavior: Does it vary by task performance? Computers & Education, 78, 174–184. Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.School of EducationDuquesne UniversityPittsburghUSA

Personalised recommendations