Temporal resolution working memory and types of errors in the Raven’s Advanced Progressive Matrices Test – a pilot study
Krzysztof Tołpa
Nicolaus Copernicus University in Toruńhttps://orcid.org/0000-0001-6223-234X
Monika Lewandowska
Nicolaus Copernicus University in Toruńhttps://orcid.org/0000-0002-7354-3693
Jan Nikadon
SWPS University of Social Sciences and Humanitieshttps://orcid.org/0000-0002-2038-254X
Joanna Dreszer
Nicolaus Copernicus University in Toruńhttps://orcid.org/0000-0002-2809-2934
Abstrakt
Aim
The aim of this study was to investigate the relationship between temporal resolution in the millisecond range, working memory and psychometric intelligence, taking into account qualitative analysis of error types in Raven’s Advanced Progressive Matrices RAPM.
Method
Thirty-six subjects (24 males and 12 females, in age 17–19 years) performed the temporal resolution task, Automated Operation Span Task Aospan and RAPM. A temporal resolution was measured by the temporal order threshold TOT which was estimated using an adaptive algorithm for 75% correctness level.
Results
There was a tendency towards less frequent Wrong Principle WP errors in the RAPM coexisting with lower TOT values: rho(34) = 0.46, p < 0.05. Moreover, a significant relationship was observed between Aospan and RAPM scores, for both percent of correctly recalled letters (rho(34) = 0.55, p < 0.01) and the percent of correctly recalled sequences (rho(34) = 0.43, p = 0.05).
Conclusions
This is the first study demonstrating the relationship between temporal resolution in the millisecond range and the types of errors in a general intelligence test. Individuals with higher TOT values showed a tendency to commit more WP errors in the RAPM indicating difficulty in finding the correct rule of reasoning. Such tendency may reflect less working memory resources allocated to solve the problem.
Słowa kluczowe:
temporal information processing, temporal resolution, general intelligence, working memoryBibliografia
Babcock, R. L. (2002). Analysis of age differences in types of errors on the Raven’s Advanced Progressive Matrices. Intelligence, 30(6), 485–503. DOI : https://doi.org/10.1016/S0160-2896(02)00124-1.
Crossref
Google Scholar
Bartholomew, A. J., Meck, W. H., Cirulli, E. T. (2015). Analysis of Genetic and Non-Genetic Factors Influencing Timing and Time Perception. PLOS ONE, 19. DOI : https://doi.org/10.1371/journal.pone.0143873.
Crossref
Google Scholar
Block, R.A. (1990). Cognitive models of psychological time. New York: Lawrence Erlbaum Associates. Google Scholar
Chelonis, J., Flake R. A., Baldwin, R. L., Blake, D. J., Merle, G. P. (2004). Developmental aspects of timing behavior in children. Neurotoxicology and Teratology, 26(3), 461–476. DOI: https://doi.org/10.1016/j.ntt.2004.01.004.
Crossref
Google Scholar
Chuderski, A. (2015). Why People Fail on the Fluid Intelligence Tests. Journal of Individual Differences, 36(3), 138–149. DOI: https://doi.org/10.1027/1614-0001/a000164.
Crossref
Google Scholar
Coyle, T. R., Pillow, D. R., Snyder, A. C., Kochunov, P. (2011). Processing Speed Mediates the Development of General Intelligence ( g ) in Adolescence. Psychological Science, 22(10), 1265–1269. DOI: https://doi.org/10.1177/0956797611418243.
Crossref
Google Scholar
Deary, I.J. (1995). Auditory inspection time and intelligence: What is the direction of causation? Developmental Psychology, 31, 237–250. DOI : https://doi.org/10.1037/0012-1649.31.2.237
Crossref
Google Scholar
Deary, I.J. (2000). Looking down on human intelligence. From psychometrics to the brain. Oxford: Oxford University Press. DOI : https://doi.org/10.1093/acprof:oso/9780198524175.001.0001.
Crossref
Google Scholar
Der, G., & Deary, I. J. (2017). The relationship between intelligence and reaction time varies with age: Results from three representative narrow-age age cohorts at 30, 50 and 69 years. Intelligence, 64, 89–97. DOI: https://doi.org/10.1016/j.intell.2017.08.001.
Crossref
Google Scholar
Drake, C., Jones, M. R., Baruch, C. (2000).The development of rhythmic attending in auditory sequences: Attunement, referent period, focal attending. Cognition, 77, 251–288. DOI : https://doi.org/10.1016/S0010-0277(00)00106-2.
Crossref
Google Scholar
Duan, X., Dan, Z., Shi, J. (2013). The Speed of Information Processing of 9- to 13-Year-Old Intellectually Gifted Children. Psychological Reports, 112(1), 20–32. DOI: https://doi.org/10.2466/04.10.49.PR0.112.1.20-32.
Crossref
Google Scholar
Engle, R. W., Laughlin, J. E., Tuholski, S. W., Conway, A. R. A. (1999). Working Memory, Short-Term Memory, and General Fluid Intelligence: A Latent-Variable Approach. Journal of Experimental Psychology: General, 128(3), 309–331. DOI: https://doi.org/10.1037/0096-3445.128.3.309.
Crossref
Google Scholar
Engle, R. W. (2018). Working Memory and Executive Attention: A Revisit. Perspectives on Psychological Science, 13(2), 190–193. DOI : https://doi.org/10.1177/1745691617720478.
Crossref
Google Scholar
Forbes, A. R. (1964). An Item Analysis Of The Advanced Matrices. British Journal of Educational Psychology, 34(3), 223–236. DOI: https://doi.org/10.1111/j.2044-8279.1964.tb00632.x.
Crossref
Google Scholar
Fraisse, P. (1984). Perception and estimation of time. Annual Review of Psychology, 35, 1–36. DOI : https://doi.org/10.1146/annurev.ps.35.020184.000245.
Crossref
Google Scholar
Gibbon, J. (1991). Origin of scalar timing. Learning and Motivation, 22, 3–38. DOI: https://doi.org/10.1016/0023-9690(91)90015-Z.
Crossref
Google Scholar
Grudnik, J. L., & Kranzler, J. H. (2001). Meta-analysis of the relationship between intelligence and inspection time. Intelligence, 29(6), 523–535. DOI: https://doi.org/10.1016/S0160-2896(01)00078-2.
Crossref
Google Scholar
Habib, M. (2021). The Neurological Basis of Developmental Dyslexia and Related Disorders: A Reappraisal of the Temporal Hypothesis, Twenty Years on. Brain Sciences, 11(6), 708. DOI: https://doi.org/10.3390/brainsci11060708.
Crossref
Google Scholar
Helmbold, N., Troche, S., Rammsayer, T. (2006). Temporal information processing and pitch discrimination as predictors of general intelligence. Canadian Journal of Experimental Psychology/Revue Canadienne de Psychologie Expérimentale, 60(4), 294–306. DOI: https://doi.org/10.1037/cjep2006027.
Crossref
Google Scholar
Helmbold, N., Troche, S., Rammsayer, T. (2007). Processing of Temporal and Nontemporal Information as Predictors of Psychometric Intelligence: A Structural-Equation-Modeling Approach. Journal of Personality, 75(5), 985–1006. DOI: https://doi.org/10.1111/j.1467-6494.2007.00463.x.
Crossref
Google Scholar
Holm, L., Ullén, F., Madison, G. (2011). Intelligence and temporal accuracy of behaviour: Unique and shared associations with reaction time and motor timing. Experimental Brain Research, 214(2), 175–183. DOI: https://doi.org/10.1007/s00221-011-2817-6.
Crossref
Google Scholar
Horn, J. L., & Cattell, R. B. (1967). Age differences in fluid and crystallized intelligence. Acta Psychologica, 26, 107–129. DOI : https://doi.org/10.1016/0001-6918(67)90011-X.
Crossref
Google Scholar
Hove, M. J., Gravel, N., Spencer, R. M. C., Valera, E. M. (2017). Finger tapping and pre-attentive sensorimotor timing in adults with ADHD. Experimental Brain Research, 235(12), 3663–3672. DOI: https://doi.org/10.1007/s00221-017-5089-y.
Crossref
Google Scholar
Israel, N. (2006). Raven’s Advanced Progressive Matrices within a South African context. Google Scholar
Unpublished Masters Research Report, University of the Witwatersrand, Johannesburg. Google Scholar
Ivry, R. B., & Spencer, R. M. C. (2004). The neural representation of time. Current Opinion in Neurobiology, 14, 225–232. DOI: https://doi.org/10.1016/j.conb.2004.03.013.
Crossref
Google Scholar
Jabłońska, K., Piotrowska, M., Bednarek, H., Szymaszek, A., Marchewka, A., Wypych, M., Szeląg, E. (2020). Maintenance vs. Manipulation in Auditory Verbal Working Memory in the Elderly: New Insights Based on Temporal Dynamics of Information Processing in the Millisecond Time Range. Frontiers in Aging Neuroscience, 12, 194. DOI: https://doi.org/10.3389/fnagi.2020.00194.
Crossref
Google Scholar
Jarosz, A. F., & Wiley, J. (2012). Why does working memory capacity predict RAPM performance? A possible role of distraction. Intelligence, 40(5), 427–438. DOI: https://doi.org/10.1016/j.intell.2012.06.001.
Crossref
Google Scholar
Jensen, A. R. (2005). Psychometric G and Mental Chronometry. Cortex, 41(2), 230–231. DOI: https://doi.org/10.1016/S0010-9452(08)70902-X.
Crossref
Google Scholar
Jensen, A. R. (1982). Reaction Time and Psychometric g. W H. J. Eysenck (Red.), A Model for Intelligence (s. 93–132). Springer Berlin Heidelberg. DOI: https://doi.org/10.1007/978-3-642-68664-1_4.
Crossref
Google Scholar
Jensen, A. R. (1993). Why Is Reaction Time Correlated with Psychometric g? Current Directions in Psychological Science, 2(2), 53–56. DOI: https://doi.org/10.1111/1467-8721.ep10770697.
Crossref
Google Scholar
Karampela, O., Madison, G., Holm, L. (2020). Motor timing training improves sustained attention performance but not fluid intelligence: Near but not far transfer. Experimental Brain Research, 238(4), 1051–1060. DOI: https://doi.org/10.1007/s00221-020-05780-4.
Crossref
Google Scholar
Kołodziejczyk, I., & Szeląg, E. (2008). Auditory perception of temporal order in Centenarians in comparison with young and elderly subjects. Acta Neurobiologiae Experimentalis, 68(3),
Crossref
Google Scholar
–381. Google Scholar
Kranzler, J. H., & Jensen, A. R. (1989). Inspection time and intelligence: A meta-analysis. Intelligence, 13(4), 329–347. DOI: https://doi.org/10.1016/S0160-2896(89)80006-6.
Crossref
Google Scholar
Miller, L. T., & Vernon, P. A. (1996). Intelligence, reaction time, and working memory in 4- to 6-year-old children. Intelligence, 22(2), 155–190. DOI: https://doi.org/10.1016/S0160-2896(96)90014-8.
Crossref
Google Scholar
Madison, G., Forsman, L., Blom, Ö., Karabanov, A., Ullén, F. (2009). Correlations between intelligence and components of serial timing variability. Intelligence, 37, 68–75. DOI: https://doi.org/10.1016/j.intell.2008.07.006.
Crossref
Google Scholar
Mueller, S. T., & Piper, B. J. (2014). The Psychology Experiment Building Language (PEBL) and PEBL Test Battery. Journal of Neuroscience Methods, 222, 250–259. DOI: https://doi.org/10.1016/j.jneumeth.2013.10.024.
Crossref
Google Scholar
Nettelbeck, T., & Lally, M. (1976). Inspection time and measured intelligence. British Journal Google Scholar
of Psychology, 67, 17–22. DOI: https://doi.org/10.1111/j.2044-8295.1976.tb01493.x.
Crossref
Google Scholar
O’Connor, T. A., & Burns, N. R. (2003). Inspection time and general speed of processing. Personality and Individual Differences, 35(3), 713–724. DOI: https://doi.org/10.1016/S0191-8869(02)00264-7.
Crossref
Google Scholar
Oroń, A., Szymaszek, A., Szeląg, E. (2015). Temporal information processing as a basis for auditory comprehension: clinical evidence from aphasic patients. International Journal of Language & Communication Disorders, 50(5), 604–615. DOI: https://doi.org/10.1111/1460-6984.12160.
Crossref
Google Scholar
Pahud, O. (2017). The influence of attention on the relationship between temporal resolution power and general intelligence. Doctoral dissertation. University of Bern, Faculty of Human Sciences. Google Scholar
Pahud, O., Rammsayer, T. H., Troche, S. J. (2018). Elucidating the Functional Relationship Between Speed of Information Processing and Speed-, Capacity-, and Memory-Related Aspects of Psychometric Intelligence. Advances in Cognitive Psychology, 14(1), 3–13. DOI: https://doi.org/10.5709/acp-0233-4.
Crossref
Google Scholar
Petrill, S. A., & Deary, I. (2001). Inspection time and intelligence: Celebrating 25 years Google Scholar
of research. Intelligence, 29(6), 441–442. DOI: https://doi.org/10.1016/S0160-2896(01)00079-4.
Crossref
Google Scholar
Pöppel, E. (1997). A hierarchical model of temporal perception. Trends in Cognitive Sciences, 1, 56–61. DOI : https://doi.org/10.1016/S1364-6613(97)01008-5.
Crossref
Google Scholar
Pöppel, E. (2004). Lost in time: a historical frame, elementary processing units and the 3-second window. Acta Neurobiologiae Experimentalis, 64, 295–302.
Crossref
Google Scholar
Pöppel, E. (1994). Temporal mechanisms in perception. International Review of Neurobiology, 37, 185–202. DOI: https://doi.org/10.1016/s0074-7742(08)60246-9.
Crossref
Google Scholar
Rammsayer, T. H., & Brandler, S. (2002). On the relationship between general fluid intelligence and psychophysical indicators of temporal resolution in the brain. Journal of Research in Personality, 36, 507-530. DOI : https://doi.org/10.1016/S0092-6566(02)00006-5.
Crossref
Google Scholar
Rammsayer, T. H., & Brandler, S. (2007). Performance on temporal information processing as an index of general intelligence. Intelligence, 35(2), 123–139. DOI: https://doi.org/10.1016/j.intell.2006.04.007.
Crossref
Google Scholar
Raven, J. C. (1971). Advanced Progressive Matrices, Sets I and II. Plan and use of the scale with report of experimental work. London: H. K. Lewis and Co. Ltd. Google Scholar
Salthouse, T.A. (2001). Structural models of the relations between age and measures Google Scholar
of cognitive functioning. Intelligence, 29, 93–115. DOI: https://doi.org/10.1016/S0160-2896(00)00040-4.
Crossref
Google Scholar
Salthouse, T. A. (2011). Neuroanatomical substrates of age-related cognitive decline. Psychological Bulletin, 137(5), 753–784. DOI: https://doi.org/10.1037/a0023262.
Crossref
Google Scholar
Schütt, H. H., Harmeling, S., Macke, J. H., Wichmann, F. A. (2016). Painfree and accurate Bayesian estimation of psychometric functions for (potentially) overdispersed data. Vision Research, 122, 105–123. DOI: https://doi.org/10.1016/j.visres.2016.02.002.
Crossref
Google Scholar
Shen, Y., Dai, W., Richards, V. M. (2015). A MATLAB toolbox for the efficient estimation of the psychometric function using the updated maximum-likelihood adaptive procedure. Behavior Research Methods, 47(1), 13–26. DOI: https://doi.org/10.3758/s13428-014-0450-6.
Crossref
Google Scholar
Skolimowska, J. (2011). Charakterystyka wybranych funkcji poznawczych w zdrowym starzeniu się, łagodnych zaburzeniach poznawczych i chorobie Alzheimera. Unpublished doctoral dissertation. Nencki Institute of Experimental Biology PAS, Warszawa. Google Scholar
Spearman, C. (1904). 'General intelligence', objectively determined and measured. The American Journal of Psychology, 15(2), 201–293. DOI: https://doi.org/10.2307/1412107.
Crossref
Google Scholar
Spencer, R. M. C., & Ivry, R. B. (2005). Comparison of patients with Parkinson's disease or cerebellar lesions in the production of periodic movements involving event-based or emergent timing. Brain and Cognition, 58(1), 84–93. DOI: https://doi.org/10.1016/j.bandc.2004.09.010.
Crossref
Google Scholar
Surwillo, W.W. (1964). Age and the perception of short intervals of time. Journal Google Scholar
of Gerontology, 19, 322–324. DOI: https://doi.org/10.1093/geronj/19.3.322.
Crossref
Google Scholar
Surwillo, W.W. (1973). Choice reaction time and speed of information processing in old age. Perceptual and Motor Skills, 36, 321–322. DOI: https://doi.org/10.2466/pms.1973.36.1.321.
Crossref
Google Scholar
Szeląg, E., Jabłońska, K., Piotrowska, M., Szymaszek, A., Bednarek, H. (2018). Spatial and Spectral Auditory Temporal-Order Judgment (TOJ) Tasks in Elderly People Are Performed Using Different Perceptual Strategies. Frontiers in Psychology, 9, 2557. DOI: https://doi.org/10.3389/fpsyg.2018.02557.
Crossref
Google Scholar
Szeląg, E., Szymaszek, A., Aksamit-Ramotowska, A., Fink, M., Ulbrich, P., Wittmann, M., et al. (2011). Temporal processing as a base for language universals: Cross-linguistic comparisons on sequencing abilities with some implications for language therapy. Restorative Neurology and Neuroscience, (1), 35–45. DOI: https://doi.org/10.3233/RNN-2011-0574.
Crossref
Google Scholar
Szeląg, E., Lewandowska, M., Wolak, T., Seniow, J., Poniatowska, R., Pöppel, E., Szymaszek, A. (2014). Training in rapid auditory processing ameliorates auditory comprehension in aphasic patients: A randomized controlled pilot study. Journal of the Neurological Sciences, 338(1–2), 77–86. DOI: https://doi.org/10.1016/j.jns.2013.12.020.
Crossref
Google Scholar
Szymaszek, A., Sereda, M., Pöppel, E., Szeląg, E. (2009). Individual differences in the perception of temporal order: The effect of age and cognition. Cognitive Neuropsychology, 26(2), 135–147. DOI: https://doi.org/10.1080/02643290802504742.
Crossref
Google Scholar
Tallal, P. (1980). Auditory temporal perception, phonics, and reading disabilities in children. Brain and Language, 9(2), 182–198. DOI: https://doi.org/10.1016/0093-934X(80)90139-X.
Crossref
Google Scholar
Troche, S. J., & Rammsayer, T. H. (2009). The influence of temporal resolution power and working memory capacity on psychometric intelligence. Intelligence, 37(5), 479–486. DOI: https://doi.org/10.1016/j.intell.2009.06.001.
Crossref
Google Scholar
Ulbrich, P., Churan, J., Fink, M., Wittmann, M. (2009). Perception of Temporal Order: The Effects of Age, Sex, and Cognitive Factors. Aging, Neuropsychology, and Cognition, 16(2), 183–202. DOI: https://doi.org/10.1080/13825580802411758.
Crossref
Google Scholar
Ullén, F., Forsman, L., Blom, Ö., Karabanov, A., Madison, G. (2008). Intelligence Google Scholar
and variability in a simple timing task share neural substrates in the prefrontal white matter. Journal of Neuroscience, 28(16), 4238-4243. DOI : https://doi.org/10.1523/JNEUROSCI.0825-08.2008.
Crossref
Google Scholar
Unsworth, N., Heitz, R. P., Schrock, J. C., Engle, R. W. (2005). An automated version of the operation span task. Behavior Research Methods, 37(3), 498–505. DOI: https://doi.org/10.3758/BF03192720.
Crossref
Google Scholar
Wittmann, M., von Steinbüchel, N., Szeląg, E. (2001). Hemispheric specialisation for self-paced motor sequences. Cognitive Brain Research, 10, 341–344. DOI : https://doi.org/10.1016/s0926-6410(00)00052-5.
Crossref
Google Scholar
Vanneste, S., Pouthas, V., Wearden, J. H. (2001). Temporal control of rhythmic performance: Google Scholar
A comparison between young and old adults. Experimental Aging Research, 27, 83–102. DOI : https://doi.org/10.1080/036107301750046151.
Crossref
Google Scholar
Zajac, I. T., & Burns, N. R. (2011). Do Auditory Temporal Discrimination Tasks Measure Temporal Resolution of the CNS? Psychology, 02(07), 743–753. DOI: https://doi.org/10.4236/psych.2011.27114.
Crossref
Google Scholar
Licencja
Utwór dostępny jest na licencji Creative Commons Uznanie autorstwa – Użycie niekomercyjne – Bez utworów zależnych 4.0 Międzynarodowe.