Alosco, M. L., Garcia, S., Spitznagel, M. B., van Dulmen, M., Cohen, R., Sweet, L. H., … Gunstad, J. (2014). Cognitive performance in older adults with stable heart failure: Longitudinal evidence for stability and improvement. Aging, Neuropsychology, and Cognition, 21(2), 239–256 https://doi.org/10.1080/13825585.2013.818616.
Article
Google Scholar
Alvarez, G. A., & Cavanagh, P. (2004). The capacity of visual short-term memory is set both by visual information load and by number of objects. Psychological Science, 15(2), 106–111 https://doi.org/10.1111/j.0963-7214.2004.01502006.x.
Article
Google Scholar
Anagnostou, E., Mankad, D., Diehl, J., Lord, C., Butler, S., McDuffie, A., … Pilato, M. (2013). Nonverbal intelligence. In Encyclopedia of autism spectrum disorders, (pp. 2037–2041). New York: Springer https://doi.org/10.1007/978-1-4419-1698-3_354.
Chapter
Google Scholar
Anunciação, L., Portugal, A. C., Rabelo, I. S., Cruz, R. M., & Landeira-Fernandez, J. (2020). Propriedades psicométricas de instrumento de Memória Visual de Curto Prazo (MEMORE). Revista Neuropsicologia Latinoamericana, 12(2), 44–58 https://doi.org/10.5579/rnl.2016.0545.
Google Scholar
Borg, I. (2018). A note on the positive manifold hypothesis. Personality and Individual Differences, 134, 13–15 https://doi.org/10.1016/j.paid.2018.05.041.
Article
Google Scholar
Buchin, Z. L., & Mulligan, N. W. (2019). Divided attention and the encoding effects of retrieval. Quarterly Journal of Experimental Psychology, 72(10), 2474–2494 https://doi.org/10.1177/1747021819847141.
Article
Google Scholar
Buschke, H., Kuslansky, G., Katz, M., Stewart, W. F., Sliwinski, M. J., Eckholdt, H. M., & Lipton, R. B. (1999). Screening for dementia with the Memory Impairment Screen. Neurology, 52(2), 231. https://doi.org/10.1212/WNL.52.2.231–238.
Article
Google Scholar
Calcagno, V., & de Mazancourt, C. (2010). glmulti: An R package for easy automated model selection with (generalized) linear models. Journal of Statistical Software. https://doi.org/10.18637/jss.v034.i12
Camina, E., & Güell, F. (2017). The neuroanatomical, neurophysiological and psychological basis of memory: Current models and their origins. Frontiers in Pharmacology, 8 https://doi.org/10.3389/fphar.2017.00438.
Castejon, J. L., Perez, A. M., & Gilar, R. (2010). Confirmatory factor analysis of Project Spectrum activities. A second-order g factor or multiple intelligences? Intelligence, 38(5), 481–496 https://doi.org/10.1016/j.intell.2010.07.002.
Article
Google Scholar
Colom, R., Abad, F. J., Quiroga, M. Á., Shih, P. C., & Flores-Mendoza, C. (2008). Working memory and intelligence are highly related constructs, but why? Intelligence, 36(6), 584–606 https://doi.org/10.1016/j.intell.2008.01.002.
Article
Google Scholar
Commodari, E. (2017). Novice readers: The role of focused, selective, distributed and alternating attention at the first year of the academic curriculum. I-Perception, 8(4), 204166951771855 https://doi.org/10.1177/2041669517718557.
Article
Google Scholar
Cowan, N. (2008). Chapter 20 What are the differences between long-term, short-term, and working memory? (pp. 323–338). https://doi.org/10.1016/S0079-6123(07)00020-9
Cucina, J., & Byle, K. (2017). The bifactor model fits better than the higher-order model in more than 90% of comparisons for mental abilities test batteries. Journal of Intelligence, 5(3), 27 https://doi.org/10.3390/jintelligence5030027.
Article
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. https://doi.org/10.1037/0096-3445.128.3.309, 128(3), 309–331.
Article
Google Scholar
Fenn, K. M., & Hambrick, D. Z. (2015). General intelligence predicts memory change across sleep. Psychonomic Bulletin & Review, 22(3), 791–799 https://doi.org/10.3758/s13423-014-0731-1.
Article
Google Scholar
Fukuda, K., & Vogel, E. K. (2010). Visual short term memory serves as a gateway to long term memory. Journal of Vision, 10(7), 730–730 https://doi.org/10.1167/10.7.730.
Article
Google Scholar
Gazzaniga, M. S., & Halpern, D. F. (2015). Psychological science (fifth edition). W. W. Norton & Company.
Haavisto, M.-L., & Lehto, J. E. (2005). Fluid/spatial and crystallized intelligence in relation to domain-specific working memory: A latent-variable approach. Learning and Individual Differences, 15(1), 1–21 https://doi.org/10.1016/j.lindif.2004.04.002.
Article
Google Scholar
Hambrick, D. Z., Kane, M. J., & Engle, R. W. (2004). The role of working memory in higher-level cognition: Domain-specific versus domain-general perspectives. In Cognition and Intelligence, (pp. 104–121). Cambridge University Press https://doi.org/10.1017/CBO9780511607073.007.
Hasan Örkcü, H. (2013). Subset selection in multiple linear regression models: a hybrid of genetic and simulated annealing algorithms. Applied Mathematics and Computation, 219(23), 11018–11028 https://doi.org/10.1016/j.amc.2013.05.016.
Hebbali, A. (2018). olsrr: Tools for building OLS regression models. R package version 0.5.2. https://cran.r-project.org/package=olsrr
Jiang, Y., Olson, I. R., & Chun, M. M. (2000). Organization of visual short-term memory. Journal of Experimental Psychology: Learning, Memory, and Cognition, 26(3), 683–702 https://doi.org/10.1037/0278-7393.26.3.683.
PubMed
Google Scholar
Jurado, M. B., & Rosselli, M. (2007). The elusive nature of executive functions: A review of our current understanding. Neuropsychology Review, 17(3), 213–233 https://doi.org/10.1007/s11065-007-9040-z.
Article
Google Scholar
Kärner, T. (2017). A mixed-methods study of physiological reactivity to domain-specific problem solving: Methodological perspectives for process-accompanying research in VET. Empirical Research in Vocational Education and Training, 9(1), 10 https://doi.org/10.1186/s40461-017-0054-3.
Article
Google Scholar
Kuslansky, G., Buschke, H., Katz, M., Sliwinski, M., & Lipton, R. B. (2002). Screening for Alzheimer’s disease: The memory impairment screen versus the conventional three-word memory test. Journal of the American Geriatrics Society, 50(6), 1086–1091 https://doi.org/10.1046/j.1532-5415.2002.50265.x.
Article
Google Scholar
Leclercq, A.-L., & Majerus, S. (2010). Serial-order short-term memory predicts vocabulary development: Evidence from a longitudinal study. Developmental Psychology, 46(2), 417–427 https://doi.org/10.1037/a0018540.
Article
Google Scholar
Lepsien, J., Thornton, I., & Nobre, A. C. (2011). Modulation of working-memory maintenance by directed attention. Neuropsychologia, 49(6), 1569–1577 https://doi.org/10.1016/j.neuropsychologia.2011.03.011.
Article
Google Scholar
Maljkovic, V., & Martini, P. (2005). Implicit short-term memory and event frequency effects in visual search. Vision Research, 45(21), 2831–2846 https://doi.org/10.1016/j.visres.2005.05.019.
Article
Google Scholar
Miller, L. A., Spitznagel, J., Hughes, J., Rosneck, G., & Gunstad, J. (2018). Final Program Forty Sixth Annual Meeting International Neuropsychological Society. Journal of the International Neuropsychological Society, 24(s1), a-325 https://doi.org/10.1017/S1355617718000528.
Google Scholar
Moore Sohlberg, M., McLaughlin, K. A., Pavese, A., Heidrich, A., & Posner, M. I. (2000). Evaluation of attention process training and brain injury education in persons with acquired brain injury. Journal of Clinical and Experimental Neuropsychology, 22(5), 656–676. https://doi.org/10.1076/1380-3395(200010)22:5;1-9;FT656
Naveh-Benjamin, M., Guez, J., Hara, Y., Brubaker, M. S., & Lowenschuss-Erlich, I. (2014). The effects of divided attention on encoding processes under incidental and intentional learning instructions: Underlying mechanisms? Quarterly Journal of Experimental Psychology, 67(9), 1682–1696 https://doi.org/10.1080/17470218.2013.867517.
Article
Google Scholar
Oren, N., Shapira-Lichter, I., Lerner, Y., Tarrasch, R., Hendler, T., Giladi, N., & Ash, E. L. (2016). How attention modulates encoding of dynamic stimuli. Frontiers in Human Neuroscience, 10 https://doi.org/10.3389/fnhum.2016.00507.
Posner, M. I., & Boies, S. J. (1971). Components of attention. Psychological Review, 78(5), 391–408 https://doi.org/10.1037/h0031333.
Article
Google Scholar
Rabelo, I. S., Cruz, R., & Castro, N. R. (2020). Bateria Rotas de Atenção: Rota da Atenção Concentrada (ROTA C), Rota da Atenção Dividida (ROTA D) e Rota da Atenção Alternada (Rota A). São Paulo: Editora NilaPress.
Google Scholar
Ramsden, S., Richardson, F. M., Josse, G., Thomas, M. S. C., Ellis, C., Shakeshaft, C., … Price, C. J. (2011). Verbal and non-verbal intelligence changes in the teenage brain. Nature, 479(7371), 113–116 https://doi.org/10.1038/nature10514.
Article
Google Scholar
Richard, G., Petersen, A., Ulrichsen, K. M., Kolskår, K. K., Alnæs, D., Sanders, A.-M., … Westlye, L. T. (2020). TVA-based modeling of short-term memory capacity, speed of processing and perceptual threshold in chronic stroke patients undergoing cognitive training: Case-control differences, reliability, and associations with cognitive performance. PeerJ, 8, e9948 https://doi.org/10.7717/peerj.9948.
Article
Google Scholar
Sijtsma, K. (2012). Psychological measurement between physics and statistics. Theory & Psychology, 22(6), 786–809 https://doi.org/10.1177/0959354312454353.
Article
Google Scholar
Silva, M. A. da. (2014). Estudos sobre a dimensionalidade do R-1: Teste não verbal de inteligência. Boletim de Psicologia.
Google Scholar
Sohlberg, M. M., & Mateer, C. A. (1987). Effectiveness of an attention-training program. Journal of Clinical and Experimental Neuropsychology, 9(2), 117–130 https://doi.org/10.1080/01688638708405352.
Article
Google Scholar
Stevens, C., & Bavelier, D. (2012). The role of selective attention on academic foundations: A cognitive neuroscience perspective. Developmental Cognitive Neuroscience, 2, S30–S48 https://doi.org/10.1016/j.dcn.2011.11.001.
Article
Google Scholar
Unsworth, N., & Engle, R. (2005). Working memory capacity and fluid abilities: Examining the correlation between Operation Span and Raven. Intelligence, 33(1), 67–81 https://doi.org/10.1016/j.intell.2004.08.003.
Article
Google Scholar
Veer, I. M., Luyten, H., Mulder, H., van Tuijl, C., & Sleegers, P. J. C. (2017). Selective attention relates to the development of executive functions in 2,5- to 3-year-olds: A longitudinal study. Early Childhood Research Quarterly, 41, 84–94 https://doi.org/10.1016/j.ecresq.2017.06.005.
Article
Google Scholar
Wickham, H. (2016). tidyverse: Easily install and load “Tidyverse” packages. In R package version 1.0.0. https://cran.r-project.org/package=tidyverse
Zanto, T. P., & Gazzaley, A. (2009). Neural suppression of irrelevant information underlies optimal working memory performance. Journal of Neuroscience, 29(10), 3059–3066 https://doi.org/10.1523/JNEUROSCI.4621-08.2009.
Article
Google Scholar