The mapping between transformed reaction time costs and models of processing in aging and cognition.

Abstract

Older adults tend to have slower response times (RTs) than younger adults on cognitive tasks. This makes the examination of domain-specific deficits in aging difficult, as differences between conditions in raw RTs (RT costs) typically increase with slower average RTs. Here, we examine the mapping between 2 established approaches to dealing with this confound in the literature. The first is to use transformed RT costs, with the z-score and proportional transforms both being commonly used. The second is to use mathematical models of choice RT behavior, such as the drift-diffusion model (Ratcliff, 1978). We simulated data for younger and older adults from the drift-diffusion model under 4 scenarios: (a) a domain specific deficit, (b) general slowing, (c) strategic slowing, and (d) a slowing of nondecision processes. In each scenario we varied the size of the difference between younger and older adults in the model parameters, and examined corresponding effect sizes and Type I error rates in the raw and transformed RT costs. The z-score transformation provided better control of Type I error rates than the raw or proportional costs, though did not fully control for differences in the general slowing and strategic slowing scenarios. We recommend that RT analyses are ideally supplemented by analyses of error rates where possible, as these may help to identify the presence of confounds. To facilitate this, it would be beneficial to include conditions that elicit below ceiling accuracy in tasks.

Strategy and processing speed eclipse individual differences in control ability in conflict tasks

“…The magnitude of the correlations we observe in the fourth scenario may surprise some readers, though they are in line with previous simulations (Hedge, Powell, Bompas, et al, 2018;Hedge, Powell, & Sumner, 2018a). The reason is that both RT costs and error costs are correlated with drift rate and boundary separation, and we impose a correlation on both these parameters simultaneously here, so they have a strong impact on behaviour.…”

“…A re-analysis of several factor analytic studies observed that individual differences in conflict tasks can be accounted for by a general processing speed factor, without need for a separate inhibition factor (Jewsbury, Bowden, & Strauss, 2016; see also Friedman & Miyake, 2017;Rey-Mermet et al, 2019). In an evidence accumulation framework, we have shown that greater efficiency in general information processing produces smaller RT costs and errors costs, thus manifesting in the same way as greater inhibitory ability (Hedge, Powell, & Sumner, 2018a).…”

“…Despite the absence of correlations in model parameters in our empirical data, we did observe a small but significant positive correlation in RT costs, as well as a similar correlation in error costs. We cautioned against the interpretation that these are evidence of common mechanisms of conflict processing, as we have previously shown that performance costs do not isolate ability in a specific cognitive domain (Hedge, Powell, Bompas, et al, 2018;Hedge, Powell, & Sumner, 2018a; see also Draheim et al, 2016;Miller & Ulrich, 2013 properties might allow us to distinguish between a world in which common mechanisms of conflict exist and one in which they do not.…”

When awareness gets in the way: Reactivation aversion effects resolve the generality/specificity paradox in sensorimotor interference tasks.

“…In order to account for age-related overall slowing, Faust, Balota, Spieler, and Ferraro (1999; see also Hedge, Powell, & Sumner, 2018) suggest to z-score the data prior to analysis. In the present Experiment 3, this method produced the same pattern of results as the analysis of RT ratios (reported in the main text).…”

Temporal preparation in patients with Neglect syndrome

“…Anticipation errors, in which participants responded before the target appeared (0.21% of trials rejected), or missing responses, in which participants did not respond when the target appeared (6.03% rejected) were not analysed further due to insufficient observations. Mean reaction times of correct responses between 100 ms and 1000 ms (3.32% rejected) were used to compute the z-scores which are recommended to minimise the type I error in the scenario of general slowing (e.g., Hedge, Powell & Sumner, 2018).…”