Does the d2 Test of Attention only assess sustained attention? Evidence of working memory processes involved

References

• Adams, E. J., Nguyen, A. T., & Cowan, N. (2018). Theories of working memory: Differences in definition, degree of modularity, role of attention, and purpose. Language, Speech, and Hearing Services in Schools, 49(3), 340–355. https://doi.org/10.1044/2018_LSHSS-17-0114
   PubMed Web of Science ®Google Scholar
• Bahmani, Z., Clark, K., Merrikhi, Y., Mueller, A., Pettine, W., Isabel Vanegas, M., Moore, T., & Noudoost, B. (2019). Prefrontal contributions to attention and working memory. Current Topics in Behavioral Neurosciences, 41, 129–153. https://doi.org/10.1007/7854_2018_74
   PubMedGoogle Scholar
• Bates, M. E., & Lemay, E. P. (2004). The d2 Test of Attention: Construct validity and extensions in scoring techniques. Journal of the International Neuropsychological Society: JINS, 10(3), 392–400. https://doi.org/10.1017/S135561770410307X
   PubMed Web of Science ®Google Scholar
• Blotenberg, I., & Schmidt-Atzert, L. (2019). On the locus of the practice effect in sustained attention tests. Journal of Intelligence, 7(2), 12. https://doi.org/10.3390/jintelligence7020012
   PubMed Web of Science ®Google Scholar
• Bouchacourt, F., & Buschman, T. J. (2019). A flexible model of working memory. Neuron, 103(1), 147–160. https:// https://doi.org/10.1016/j.neuron.2019.04.020
   PubMed Web of Science ®Google Scholar
• Brickenkamp, R. (2000). Teste d2: Atenção concentrada: manual, instruções, avaliaçaõ, interpretaçaõ. Casa do Psicólogo.
   Google Scholar
• Brickenkamp, R., & Zilmer, E. (1998). The d2 Test of Attention. Hogrefe & Huber Publishers. https://doi.org/10.1037/t03299-000
   Google Scholar
• Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Routledge.
   Google Scholar
• Cooley, E. L., & Morris, R. D. (1990). Attention in children: A neuropsychologically based model for assessment. Developmental Neuropsychology, 6(3), 239–274. https://doi.org/10.1080/87565649009540465
   Web of Science ®Google Scholar
• da Silva-Sauer, L., Valero-Aguayo, L., de la Torre-Luque, a., Ron-Angevin, R., & Varona-Moya, S. (2016). Concentration on performance with P300-based BCI systems: A matter of interface features. Applied Ergonomics, 52, 325–332. https://doi.org/10.1016/j.apergo.2015.08.002
   PubMed Web of Science ®Google Scholar
• Demeter, E., & Woldorff, M. G. (2016). Transient distraction and attentional control during a sustained selective attention task. Journal of Cognitive Neuroscience, 28(7), 935–947. https://doi.org/10.1162/jocn_a_00949
   PubMed Web of Science ®Google Scholar
• Desimone, R., & Duncan, J. (1995). Neural mechanisms of selective visual attention. Annual Review of Neuroscience, 18(1), 193–222. https://doi.org/10.1146/annurev.ne.18.030195.001205
   PubMed Web of Science ®Google Scholar
• Dockree, P. M., Kelly, S. P., Foxe, J. J., Reilly, R. B., & Robertson, I. H. (2007). Optimal sustained attention is linked to the spectral content of background EEG activity: Greater ongoing tonic alpha (∼10 Hz) power supports successful phasic goal activation. The European Journal of Neuroscience, 25(3), 900–907. https:// https://doi.org/10.1111/j.1460-9568.2007.05324.x
   PubMed Web of Science ®Google Scholar
• Dong, S., Reder, L. M., Yao, Y., Liu, Y., & Chen, F. (2015). Individual differences in working memory capacity are reflected in different ERP and EEG patterns to task difficulty. Brain Research, 1616, 146–156. https://doi.org/10.1016/j.brainres.2015.05.003
   PubMed Web of Science ®Google Scholar
• Esterman, M., & Rothlein, D. (2019). Models of sustained attention. Current Opinion in Psychology, 29, 174–180. https://doi.org/10.1016/j.copsyc.2019.03.005
   PubMed Web of Science ®Google Scholar
• Fortenbaugh, F. C., DeGutis, J., & Esterman, M. (2017). Recent theoretical, neural, and clinical advances in sustained attention research. Annals of the New York Academy of Sciences, 1396(1), 70–91. https://doi.org/10.1111/nyas.13318
   PubMed Web of Science ®Google Scholar
• Flehmig, H. C., Steinborn, M., Langner, R., Scholz, A., & Westhoff, K. (2007). Assessing intraindividual variability in sustained attention: Reliability, relation to speed and accuracy, and practice effects. Psychology Science, 49(2), 132–149. https://psycnet.apa.org/record/2007-12761-004
   Google Scholar
• Gajewski, P. D., Hanisch, E., Falkenstein, M., Thönes, S., & Wascher, E. (2018). What does the n-back task measure as we get older? Relations between working-memory measures and other cognitive functions across the lifespan. Frontiers in Psychology, 9(1), 2208–2217. https://doi.org/10.3389/fpsyg.2018.02208
   PubMedGoogle Scholar
• Gropel, P., Baumeister, R. F., & Beckmann, J. (2014). Action versus state orientation and self-control performance after depletion. Personality & Social Psychology Bulletin, 40(4), 476–487. https://doi.org/10.1177/0146167213516636
   PubMed Web of Science ®Google Scholar
• Kelly, S. P., Lalor, E. C., Reilly, R. B., & Foxe, J. J. (2006). Increases in alpha oscillatory power reflect an active retinotopic mechanism for distracter suppression during sustained visuospatial attention. Journal of Neurophysiology, 95(6), 3844–3851. https://doi.org/10.1152/jn.01234.2005
   PubMed Web of Science ®Google Scholar
• Kessels, R. P. C. (2019). Improving precision in neuropsychological assessment: Bridging the gap between classic paper-and-pencil tests and paradigms from cognitive neuroscience. The Clinical Neuropsychologist, 33(2), 357–368. https://doi.org/10.1080/13854046.2018.1518489
   PubMed Web of Science ®Google Scholar
• Kim, J. H., Kim, D. W., & Im, C. H. (2017). Brain areas responsible for vigilance: An EEG source imaging study. Brain Topography, 30(3), 343–351. https://doi.org/10.1007/s10548-016-0540-0
   PubMed Web of Science ®Google Scholar
• Klimesch, W. (1999). EEG alpha and theta oscillations reflect cognitive and memory performance: A review and analysis. Brain Research. Brain Research Reviews, 29(2-3), 169–195. https://doi.org/10.1016/S0165-0173(98)00056-3
   PubMed Web of Science ®Google Scholar
• Kline, R. B. (2011). Principles and practice of structural equation modeling (5th ed.). The Guilford Press.
   Google Scholar
• Langner, R., Steinborn, M. B., Chatterjee, A., Sturm, W., & Willmes, K. (2010). Mental fatigue and temporal preparation in simple reaction-time performance. Acta psychologica, 133(1), 64–72. https://doi.org/10.1016/j.actpsy.2009.10.001
   PubMed Web of Science ®Google Scholar
• Lezak, M. D., Howieson, D. B., Bigler, E. D., & Tranel, D. (2012). Neuropsychological Assessment. Oxford University Press.
   Google Scholar
• Lindsay, G. W. (2020). Attention in psychology, neuroscience, and machine learning. Frontiers in Computational Neuroscience, 14, 29. https://doi.org/10.3389/fncom.2020.00029
   PubMed Web of Science ®Google Scholar
• Luck, S. J., & Vecera, S. P. (2002). Attention. In H. Pashler & S. Yantis (Eds.), Steven’s handbook of experimental psychology: Sensation and perception (pp. 235–286). John Wiley & Sons Inc.
   Google Scholar
• Mackie, M. A., Van Dam, N. T., & Fan, J. (2013). Cognitive control and attentional functions. Brain and Cognition, 82(3), 301–312. https://doi.org/10.1016/j.bandc.2013.05.004
   PubMed Web of Science ®Google Scholar
• Miller, J. B., & Barr, W. B. (2017). The technology crisis in neuropsychology. Archives of Clinical Neuropsychology: The Official Journal of the National Academy of Neuropsychologists, 32(5), 541–554. https://doi.org/10.1093/arclin/acx050
   PubMed Web of Science ®Google Scholar
• Moore, T., & Zirnsak, M. (2017). Neural mechanisms of selective visual attention. Annual Review of Psychology, 68(1), 47–72. https://doi.org/10.1146/annurev-psych-122414-033400
   PubMed Web of Science ®Google Scholar
• Moorselaar, D., Foster, J. J., Sutterer, D. W., Theeuwes, J., Olivers, C. N. L., & Awh, E. (2018). Spatially selective alpha oscillations reveal moment-by-moment trade-offs between working memory and attention. Journal of Cognitive Neuroscience, 30(2), 256–266. https://doi.org/10.1162/jocn_a_01198
   PubMed Web of Science ®Google Scholar
• Neubauer, A. C., & Knorr, E. (1998). Three paper-and-pencil tests for speed of information processing: Psychometric properties and correlations with intelligence. Intelligence, 26(2), 123–151. https://doi.org/10.1016/S0160-2896(99)80058-0
   Web of Science ®Google Scholar
• Nobre, A. C., & Stokes, M. G. (2011). Attention and short-term memory: Crossroads. Neuropsychologia, 49(6), 1391–1392. https://doi.org/10.1016/j.neuropsychologia.2011.04.014
   PubMed Web of Science ®Google Scholar
• Owen, A. M., Mcmillan, K. M., Laird, A. R., & Bullmore, E. (2005). N-back working memory paradigm: A meta-analyzus of normative functional neuroimaging studies. Human Brain Mapping, 25(1), 46–59. https://doi.org/10.1002/hbm.20131
   PubMed Web of Science ®Google Scholar
• Polich, J. (2007). Updating P300: An integrative theory of P3a and P3b. Clinical Neurophysiology : Official Journal of the International Federation of Clinical Neurophysiology, 118(10), 2128–2148. https://doi.org/10.1016/j.clinph.2007.04.019
   PubMed Web of Science ®Google Scholar
• Rac-Lubashevsky, R., & Kessler, Y. (2016). Dissociating working memory updating and automatic updating: the reference-back paradigm. Journal of Experimental Psychology. Learning, Memory, and Cognition, 42(6), 951–969. https://doi.org/10.1037/xlm0000219
   PubMed Web of Science ®Google Scholar
• Ricker, T. J., Nieuwenstein, M. R., Bayliss, D. M., & Barrouillet, P. (2018). Working memory consolidation: Insights from studies on attention and working memory. Annals of the New York Academy of Sciences, 1424(1), 8–18. https://doi.org/10.1111/nyas.13633
   PubMed Web of Science ®Google Scholar
• Rosvold, H. E., Mirsky, A. F., Sarason, I., Bransome, E. D., & Beck, L. H. (1956). A continuous performance test of brain damage. Journal of Consulting Psychology, 20(5), 343–350. https://doi.org/10.1037/h0043220
   PubMedGoogle Scholar
• Sauseng, P., Hoppe, J., Klimesch, W., Gerloff, C., & Hummel, F. (2007). Dissociation of sustained attention from central executive functions: Local activity and interregional connectivity in the theta range. The European Journal of Neuroscience, 25(2), 587–593. https://doi.org/10.1111/j.1460-9568.2006.05286.x
   PubMed Web of Science ®Google Scholar
• Scharinger, C., Soutschek, A., Schubert, T., & Gerjets, P. (2017). Comparison of the working memory load in N-Back and working memory span tasks by means of EEG frequency band power and P300 amplitude. Frontiers in Human Neurosciensce, 11, Article 6. https://doi.org/10.3389/fnhum.2017.00006
   Web of Science ®Google Scholar
• Schneider, W., Eschman, A., & Zuccolotto, A. (2002). E-prime user’s guide. Psychology Software Tools Inc.
   Google Scholar
• Soto, D., Hodsoll, J., Rotshtein, P., & Humphreys, G. W. (2008). Automatic guidance of attention from working memory. Trends in Cognitive Sciences, 12(9), 342–348. https://doi.org/10.1016/j.tics.2008.05.007
   PubMed Web of Science ®Google Scholar
• Steinborn, M. B., & Huestegge, L. (2020). Socially alerted cognition evoked by a confederate’s mere presence: analysis of reaction-time distributions and delta plots. Psychological Research, 84(5), 1424–1439. https://doi.org/10.1007/s00426-019-01143-z
   PubMed Web of Science ®Google Scholar
• Steinborn, M. B., Langner, R., Flehmig, H. C., & Huestegge, L. (2018). Methodology of performance scoring in the d2 sustained-attention test: Cumulative-reliability functions and practical guidelines. Psychological Assessment, 30(3), 339–357. https://doi.org/10.1037/pas0000482
   PubMed Web of Science ®Google Scholar
• Strauss, E., Sherman, E. M., & Spreen, O. (2006). A compendium of neuropsychological tests: Administration, norms, and commentary (3rd ed.). Oxford University Press.
   Google Scholar
• Talland, G. A., & Schwab, R. S. (1964). Performance with multiple sets in Parkinson’s disease. Neuropsychologia, 2(1), 45–53. https://doi.org/10.1016/0028-3932(64)90030-2
   Web of Science ®Google Scholar
• Ullman, H., Almeida, R., & Klingberg, T. (2014). Structural maturation and brain activity predict future working memory capacity during childhood development. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience, 34(5), 1592–1598. https://doi.org/10.1523/JNEUROSCI.0842-13.2014
   PubMed Web of Science ®Google Scholar
• Unsworth, N., & Robison, M. K. (2020). Working memory capacity and sustained attention: A cognitive-energetic perspective. Journal of Experimental Psychology. Learning, Memory, and Cognition, 46(1), 77–103. https://doi.org/10.1037/xlm0000712
   PubMed Web of Science ®Google Scholar
• Van Breukelen, G. J., Roskam, E. E., Eling, P. A., Jansen, R. W., Souren, D. A., & Ickenroth, J. G. (1995). A model and diagnostic measures for response time series on tests of concentration: Historical background, conceptual framework, and some applications. Brain and Cognition, 27(2), 147–179. https://doi.org/10.1006/brcg.1995.1015
   PubMed Web of Science ®Google Scholar
• van Rentergem, J. A. A., de Vent, N. R., Schmand, B. A., Murre, J. M. J., Staaks, J. P. C., & Huizenga, H. M, ANDI Consortium, & Huizenga, H. M. (2020). The factor structure of cognitive functioning in cognitively healthy participants: A meta-analysis and meta-analysis of individual participant data. Neuropsychology Review, 30(1), 51–96. https://doi.org/10.1007/s11065-019-09423-6
   PubMed Web of Science ®Google Scholar
• Watter, S., Geffen, G. M., & Geffen, L. B. (2001). The n-back as a dual-task: P300 morphology under divided attention. Psychophysiology, 38(6), 998–1003. https://doi.org/10.1111/1469-8986.3860998
   PubMed Web of Science ®Google Scholar
• Weakley, A., Williams, J. A., Schmitter-Edgecombe, M., & Cook, D. J. (2015). Neuropsychological test selection for cognitive impairment classification: A machine learning approach. Journal of Clinical and Experimental Neuropsychology, 37(9), 899–916. https://doi.org/10.1080/13803395.2015.1067290
   PubMed Web of Science ®Google Scholar
• Wühr, P. (2019). Test-specific learning contributes to learning effects in tests of sustained attention. Swiss Journal of Psychology, 78(1–2), 29–35. https://doi.org/10.1024/1421-0185/a000221
   Web of Science ®Google Scholar