Aging effects on symbolic number comparison: No deceleration of numerical information retrieval but more conservative decision-making.

Abstract

Whereas many cognitive tasks show pronounced aging effects, even in healthy older adults, other tasks seem more resilient to aging. A small number of recent studies suggests that number comparison is possibly one of the abilities that remain unaltered across the life span. We investigated the ability to compare single-digit numbers in young (19-39 years; n = 39) and healthy older (65-79 years; n = 39) adults in considerable detail, analyzing accuracy as well as mean and variance of their response time, together with several other well-established hallmarks of numerical comparison. Using a recent comprehensive process model that parsimoniously accounts quantitatively for many aspects of number comparison (Reike & Schwarz, 2016), we address two fundamental problems in the comparison of older to young adults in numerical comparison tasks: (a) to adequately correct speed measures for different levels of accuracy (older participants were significantly more accurate than young participants), and (b) to distinguish between general sensory and motor slowing on the one hand, as opposed to a specific age-related decline in the efficiency to retrieve and compare numerical magnitude representations. Our results represent strong evidence that healthy older adults compare magnitudes as efficiently as young adults, when the measure of efficiency is uncontaminated by strategic speed-accuracy trade-offs and by sensory and motor stages that are not related to numerical comparison per se. At the same time, older adults aim at a significantly higher accuracy level (risk aversion), which necessarily prolongs processing time, and they also show the well-documented general decline in sensory and/or motor functions. (PsycINFO Database Record

Examining aging and numerosity using an integrated diffusion model.

“…The linear model fit data from the B/Y task better than the log model and the log model fit data from the L/R task about the same as the linear model, except for the 70–90-year-old adults, for whom the linear model fit a little better. For both models, older adults had wider boundaries and longer nondecision times than young adults, results that have been obtained in many other experiments (e.g., Ratcliff, Thapar, Gomez et al, 2004; Ratcliff, Thapar, & McKoon, 2001, 2003, 2004, 2010, 2011; Reike & Schwarz, 2019; Spaniol, Madden, & Voss, 2006). Wider boundaries mean that older adults required more information before making decisions than young adults.…”

Development of arithmetic across the lifespan: A Registered Report

“…Similarly, the distance effect in single-digit number comparison was shown to be larger regarding speed but smaller regarding accuracy for older as compared to younger adults (Norris et al, 2015;Reike & Schwarz, 2019). From this, it can be concluded that number magnitude processing becomes more precise with lifetime experience during aging, but the processing speed in general decreases.…”

Delta plots for conflict tasks: An activation-suppression race model

“…For example, Hübner and Töbel (2019) and Wühr and Heuer (2018) modeled the nondecisional time as a constant, providing estimates of its value ranging from 100-350 ms. Similarly, analogous nondecisional times of diffusion models are often estimated to be 300-500 milliseconds in mean length (e.g., Ratcliff, 1978;Reike & Schwarz, 2019a;Schwarz & Reike, 2020;Voss et al, 2004). Modeling these times as a constant seems somewhat problematic, however, because it seems unlikely that a mental process consuming that much time on average would have negligible variance.…”