Rozdzielczość czasowa, pamięć robocza a rodzaje błędów w Teście Matryc Ravena – badanie pilotażowe

Krzysztof Tołpa

Uniwersytet Mikołaja Kopernika w Toruniu
https://orcid.org/0000-0001-6223-234X

Monika Lewandowska

Uniwersytet Mikołaja Kopernika w Toruniu
https://orcid.org/0000-0002-7354-3693

Jan Nikadon

SWPS Uniwersytet Humanistycznospołeczny
https://orcid.org/0000-0002-2038-254X

Joanna Dreszer

Uniwersytet Mikołaja Kopernika w Toruniu
https://orcid.org/0000-0002-2809-2934


Abstract

Cel
Celem badania pilotażowego było sprawdzenie zależności pomiędzy rozdzielczością czasową w zakresie milisekundowym, pamięcią roboczą oraz inteligencją psychometryczną z uwzględnieniem analizy jakościowej błędów w Teście Matryc Ravena w wersji dla Zaawansowanych TMZ.

Metoda
Trzydzieści sześć osób (24 mężczyzn i 12 kobiet, w wieku 17–19 lat) wykonało zadanie polegające na prezentowaniu par bodźców w szybkim następstwie czasowym, a następnie rozwiązywało zadanie mierzące pamięć roboczą Automated Operation Span Task Aospan oraz TMZ. Rozdzielczość czasową mierzono za pomocą progu postrzegania kolejności bodźców PPK, wyznaczanego za pomocą algorytmu adaptacyjnego dla poprawności 75%.

Wyniki
Wykazano tendencję do rzadszego popełniania błędów typu Błędna Zasada w TMZ przez osoby uzyskujące niskie wartości PPK: rho(34) = 0,46, p < 0,05. Ponadto zaobserwowano związek między wynikami Aospan i TMZ, dla procentu poprawnie odpamiętanych liter (rho(34) = 0,55, p < 0,01), zaś dla procentu poprawnie odpamiętanych sekwencji (rho(34) = 0,43, p = 0,05).

Konkluzje
Prezentowane badanie jest pierwszym, w którym wykazano związek czasowego opracowywania informacji na poziomie milisekund z typami błędów popełnianymi w teście inteligencji ogólnej. Osoby, które uzyskały wyższe progi postrzegania kolejności bodźców częściej stosowały przy wyborze odpowiedzi jakościowo odmienne od poprawnych reguły rozumowania, co może odzwierciedlać mniejsze zasoby pamięci roboczej potrzebne do odkrycia właściwej reguły.


Keywords:

czasowe przetwarzanie informacji, rozdzielczość czasowa, inteligencja ogólna, pamięć robocza


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. Rozprawa doktorska. 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. Nieopublikowana rozprawa doktorska (promotor: prof. dr hab. E. Szeląg). Instytut Biologii Doświadczalnej PAN, 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., i in. (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


Published
2021-12-30

Cited by

Tołpa, K., Lewandowska, M. ., Nikadon, J., & Dreszer, J. (2021). Rozdzielczość czasowa, pamięć robocza a rodzaje błędów w Teście Matryc Ravena – badanie pilotażowe. The Review of Psychology, 64(4), 25–40. https://doi.org/10.31648/pp.7355

Krzysztof Tołpa 
Uniwersytet Mikołaja Kopernika w Toruniu
https://orcid.org/0000-0001-6223-234X
Monika Lewandowska 
Uniwersytet Mikołaja Kopernika w Toruniu
https://orcid.org/0000-0002-7354-3693
Jan Nikadon 
SWPS Uniwersytet Humanistycznospołeczny
https://orcid.org/0000-0002-2038-254X
Joanna Dreszer 
Uniwersytet Mikołaja Kopernika w Toruniu
https://orcid.org/0000-0002-2809-2934