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 Toruń
https://orcid.org/0000-0001-6223-234X

Monika Lewandowska

Nicolaus Copernicus University in Toruń
https://orcid.org/0000-0002-7354-3693

Jan Nikadon

SWPS University of Social Sciences and Humanities
https://orcid.org/0000-0002-2038-254X

Joanna Dreszer

Nicolaus Copernicus University in Toruń
https://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 memory


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.   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.   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.   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.   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.   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   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.   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.   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.   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.   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.   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.   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.   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.   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.   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.   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.   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.   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.   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.   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.   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.   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.   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.   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.   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.   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.   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.   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.   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),   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.   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.   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.   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.   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.   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.   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.   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.   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.   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.   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.   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.   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.   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.   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.   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.   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.   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.   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.   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.   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.   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.   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.   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.   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.   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.   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.   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.   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.   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.   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.   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.   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.   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.   Google Scholar


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2021-12-30

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Tołpa, K., Lewandowska, M., Nikadon, J., & Dreszer, J. (2021). Temporal resolution working memory and types of errors in the Raven’s Advanced Progressive Matrices Test – a pilot study. Przegląd Psychologiczny, 64(4), 119–133. https://doi.org/10.31648/przegldpsychologiczny.7885

Krzysztof Tołpa 
Nicolaus Copernicus University in Toruń
https://orcid.org/0000-0001-6223-234X
Monika Lewandowska 
Nicolaus Copernicus University in Toruń
https://orcid.org/0000-0002-7354-3693
Jan Nikadon 
SWPS University of Social Sciences and Humanities
https://orcid.org/0000-0002-2038-254X
Joanna Dreszer 
Nicolaus Copernicus University in Toruń
https://orcid.org/0000-0002-2809-2934