Cognitive aging research using the internet : four case studies

Data collection via the Internet has recently become popular not least because of the time, energy and resources that can be saved by testing substantial numbers of participants over a short period. However, to date, there have been few Internet studies of cognitive development or aging. We briefly discuss some advantages and disadvantages of this new methodology before illustrating its potential by presenting four case studies of (1) task switching across the life span, (2) age and gender effects on intraindividual variability, (3) impulsivity and delay discounting across adulthood, and (4) gender and sexual orientation differences in rates of cognitive aging. We conclude that, where comparisons can be drawn, the data generally replicate laboratory findings. Moreover, they extend them in several interesting ways such as providing a much finer-grained analysis of changes with age than has previously been possible. With the enormous recent increase in access to the Internet has come a rapidly expanding literature reporting psychological experiments conducted online (see Birnbaum, 2004; Skitka & Sargis, 2006, for reviews). There are many obvious advantages of such methodology. For example, it can save researchers both time and money – once an experiment is set up, it can be run concurrently on large numbers of unpaid volunteers. These generally represent a wider demographic than the usual undergraduate population employed in laboratory-based research and hence the results may be more generalizable. Experimenter bias can be avoided because Internet experiments run automatically. Most crucially for our purposes, reaction times (RTs) can now be measured to a reasonable level of accuracy over the Internet (Reimers & Stewart, in press; see also Ulrich & Giray, 1989, for evidence that a lack of millisecond accuracy makes little difference to results anyway). Of course, there are also some obvious disadvantages associated with Internet experimentation. These include the possibility of a biased sample as although most people now have access to computers, not everyone will have the appropriate software installed for downloading and running experiments. People may not be honest, for example, in answering questions about demographics (age, education, gender, etc). They may not treat the experiment seriously (although it is not always certain that the traditional undergraduate population does so). More importantly, people may not always understand the instructions and, unlike in laboratory research, there is no opportunity to check and provide further instructions if necessary. Internet studies provide no control over the conditions under which the experiment is conducted – uncontrolled factors include monitor size, hand positions, distractions, noise, time of day, and so on. Also, there is no control over the participants‟ state – for example, they may be tired, intoxicated, or not wearing their glasses. However, in view of the large numbers of people who can be tested in Internet studies, researchers can ensure the integrity of their data by taking a conservative approach to the datasets they allow to enter the analysis (see later). In general, this methodology tends to produce effects that account for only a small proportion of the variance but are highly significant. The effects may be more generalizable to real world situations if they emerge from experiments conducted on diverse samples under poorly controlled conditions. It is therefore argued that the considerable advantages more than outweigh the disadvantages, particularly as evidence suggests that web-based studies can reliably replicate laboratory findings (see Buchanan & Smith, 1999; Gosling, Vazire, Srivastava, & John, 2004; Krantz & Dalal, 2000; McGraw, Tew, & Williams, 2000). Access to the Internet is now widespread in schools; older adults are also increasingly being encouraged to use the Internet although home access decreases with age (Cutler, Hendricks, & Guyer, 2003; Selwyn, Gorard, Furlong, & Madden, 2003). In 2004, 22% of American over-65s had Internet access, compared to 15% in 2000, and the number of „silver surfers‟ is likely to increase dramatically over the next few years (Fox, 2004). Thus, there is currently enormous potential for conducting life span research online. One recent impressive example is a study by Robins, Trzesniewski, Gosling and Potter (2002) in which 326,641 individuals aged between 9 and 90 years were asked to rate their level of self-esteem on a 5-point scale. The results resolved discrepancies in the literature by revealing a cubic relationship such that self-esteem was “high in childhood, dropped during adolescence, rose gradually throughout adulthood, and declined sharply in old age” (p. 423). However, the authors noted that their sample was obviously restricted to those with access to the Internet and suggested that their results require replication with a representative sample. In this paper, we present four further illustrations of Internet studies across a wide age range that both replicate and extend findings from more traditional laboratory experiments. Case Study I: Task Switching Executive control processes are high-level functions that organize, sequence and regulate behavior. As such, they are vital for most everyday activity, particularly that which requires planning, holding open multiple goals, or cognitive flexibility. In laboratory tests, it is apparent that executive control improves through childhood and adolescence but declines in old age (e.g., Fristoe, Salthouse, & Woodard, 1997; Zelazo & Müller, 2002). The result is an inverted-U-shaped function across the life span, although executive control is not unique in this – for example, processing speed also shows a similar shaped function across the life span (e.g., Cerella & Hale, 1994). Researchers have attempted to explain the inverted-U-shaped function of executive control across the life span in terms of frontal lobe functioning (see Reimers & Maylor, 2005, for a summary). Thus, evidence from functional imaging and neuropsychological studies suggests that tasks requiring some form of executive control depend on the use of at least a part of the frontal lobes. In children, frontal lobe development continues through adolescence over the same developmental time period as performance on executive control tasks improve. Also frontal lobe deterioration is well documented in old age. Task switching is regarded as an example of an executive control process (Miyake et al., 2000). In a typical procedure, participants would have to switch between two orthogonal classifications of stimuli. For example, the stimuli could be the numbers 1-8 and the two classifications would be on the basis of oddness (odd/even) and size (small/large). In a switch block, participants would have to alternate between two odd/even classifications followed by two small/large classifications. In single task blocks, they would either always make odd/even classifications or small/large classifications. Two different measures of executive control can be defined in task switching experiments: (1) General switch costs, also known as mixing or set selection costs, reflect the difficulty in maintaining and selecting among two or more potential response sets and are measured by the difference in performance between switch blocks and single task blocks. (2) Specific switch costs represent the difficulty in switching from one response set to another and are measured by the difference in performance between switch trials and nonswitch trials within switch blocks. Evidence on these two aspects of task switching with respect to development and aging is somewhat mixed in the literature (though it has a long history, going back at least to Brinley, 1965). However, the overall picture is that for general switch costs, even after taking into account age differences in processing speed, these are greater for older adults than for young adults (Wecker, Kramer, Hallam, & Delis, 2005). They are also greater in young children than in adults (Kray, Eber, & Lindenberger, 2004). For specific switch costs, again after taking overall speed into account, these are largely unaffected by both development and old age (Kray et al., 2004), though there are exceptions. At present, however, there is very limited evidence on the way in which task switching changes across the life span – most studies focus either on development or aging, or they test just three groups (children, young adults and older adults). But the physiological evidence suggests that the frontal lobes actually decline quite early, from at least middle age and possibly from young adulthood. We would therefore predict that task switching should decline similarly, that is, prior to old age. One task switching study that used a wide and continuous range of ages is by Cepeda, Kramer and Gonzalez de Sather (2001) who tested 152 people between the ages of 7 and 82 years. The data were analysed in nine age bands, with 12-21 people in each band. Although there was evidence of general inverted-U-shaped performance, the relatively small numbers of people in each age band made it difficult to trace age effects outside of the extremes of childhood and old age. However, the authors concluded that “switch costs decreased from childhood to young adulthood, remained fairly constant across the adult years, and then began to increase after the age of 60” (p. 726). The main aim of our study was therefore to collect sufficient data to examine task switching ability particularly through young adulthood and middle age. In order to obtain sufficient numbers to allow a fine-grained analysis across the life span, we conducted the experiment on the Internet using one of the author‟s (SR‟s) involvement with a British Broadcasting Corporation (BBC) television series (The Human Mind) to recruit participants. At the end of each episode, four million viewers were encouraged to visit the