Abstract
Information technology (IT) and serious games allow older population to remain independent for longer. Hence, when designing technology for this population, developmental changes, such as attention and/or perception, should be considered. For instance, a crucial developmental change has been related to cognitive speed in terms of reaction time (RT). However, this variable presents a skewed distribution that difficult data analysis. An alternative strategy is to characterize the data to an ex-Gaussian function. Furthermore, this procedure provides different parameters that have been related to underlying cognitive processes in the literature. Another issue to be considered is the optimal data recording, storing and processing. For that purpose mobile devices (smart phones and tablets) are a good option for targeting serious games where valuable information can be stored (time spent in the application, reaction time, frequency of use, and a long etcetera). The data stored inside the smartphones and tablets can be sent to a central computer (cloud storage) in order to store the data collected to not only fill the distribution of reaction times to mathematical functions, but also to estimate parameters which may reflect cognitive processes underlying language, aging, and decisional process.
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Central Intelligence Agency (2001) The CIAWorld Factbook. http://www.cia.gov/cia/publications/factbook/
Osorio AR (2007) Os idosos na sociedade atual. In: Osório AR, Pinto FC (eds) As pessoas idosas: contexto social e intervenção educativa. Instituto PIAGET, Lisboa
Abt C (1987) Serious games. University Press of America, Washington, DC, USA
Van Muijden J, Band GP, Hommel B (2012) Online games training aging brains: limited transfer to cognitive control functions. Front Hum Neurosci 6:221
Ball K, Edwards JD, Ross LA (2007) The impact of speed of processing training on cognitive and everyday functions. J Gerontol B Psychol Sci Soc Sci. doi:10.1093/geronb/62.special_issue_1.19
Noack H, Lövdén M, Schmiedek F, Lindenberger U (2009) Cognitive plasticity in adulthood and old age: gauging the generality of cognitive intervention effects. Restor Neurol Neurosci. doi:10.3233/RNN-2009-0496
Moret-Tatay C (2013) Analysis of developmental changes in lexical decision tasks: differences between well elderly and university students. Doctoral Dissertation, Universidad Politécnica de Valencia
Moret-Tatay C, Moreno-Cid A, Argimon IIL et al (2014) The effects of age and emotional valence on recognition memory: an ex-Gaussian components analysis. Scand J Psychol. doi:10.1111/sjop.12136
Navarro-Pardo E, Navarro-Prados AB, Gamermann D et al (2013) Differences between young and old university students on a lexical decision task: evidence through an ex-Gaussian approach. J Gen Psychol. doi:10.1080/00221309.2013.817964
Lacouture Y, Cousineau D (2008) How to use MATLAB to fit the ex-Gaussian and other probability functions to a distribution of response times. Tutor Quant Methods Psychol 4:35–45
Luce RD (1986) Response times: their role in inferring elementary mental organization. Oxford University Press, New York
Matzke D, Wagenmakers EJ (2009) Psychological interpretation of the ex-Gaussian and shifted Wald parameters: a diffusion model analysis. Psychon Bull Rev. doi:10.3758/PBR.16.5.798
Leth-Steensen C, Elbaz ZK, Douglas VI (2000) Mean response times, variability, and skew in the responding of ADHD children: a response time distributional approach. Acta Psychol. doi:10.1016/S0001-6918(00)00019-6
Spieler DH, Balota DA, Faust ME (1996) Stroop performance in healthy younger and older adults and in individuals with dementia of the Alzheimer's type. J Exp Psychol Hum Percept Perform 22(2):461
West R, Murphy KJ, Armilio ML et al (2002) Lapses of intention and performance variability reveal age-related increases in fluctuations of executive control. Brain Cogn. doi:10.1006/brcg.2001.1507
Ijsselsteijn W, Nap HH, de Kort Y et al (2007) Digital game design for elderly users. In: Proceedings of the 2007 conference on future play. ACM, pp 17–22
Rogers WA, Fisk AD (2000) Human factors, applied cognition, and aging. In: Craik FIM, Salthhouse TA (eds) The handbook of aging and cognition, 2nd edn. Lawrence Erlbaum Associates, NJ, pp 559–592
Fozard JL, Thomas JC, Waugh NC (1976) Effects of age and frequency of stimulus repetitions on two-choice reaction time. J Gerontol 31(5):556–563
Forster KI, Forster JC (2003) DMDX: a windows display program with millisecond accuracy. Behav Res Methods Instrum Comput. doi: 10.3758/BF03195503
Dufau S, Duñabeitia JA, Moret-Tatay C et al (2011) Smart phone, smart science: how the use of smartphones can revolutionize research in cognitive science. PLoS One. doi:10.1371/journal.pone.0024974
Cappeliez P, O’Rourke N, Chadbury H (2005) Functions in reminiscence and mental health in later life. Aging Ment Health 9:295–301
Damian MF (2010) Does variability in human performance outweigh imprecision in response devices such as computer keyboards? Behav Res Methods. doi:10.3758/BRM.42.1.205
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Lemus-Zúñiga, LG., Navarro-Pardo, E., Moret-Tatay, C., Pocinho, R. (2015). Serious Games for Elderly Continuous Monitoring. In: Fernández-Llatas, C., García-Gómez, J. (eds) Data Mining in Clinical Medicine. Methods in Molecular Biology, vol 1246. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-1985-7_16
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DOI: https://doi.org/10.1007/978-1-4939-1985-7_16
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