Is there more room to improve? The lifespan trajectory of procedural learning and its relationship to the between- and within-group differences in average response times

Abstract

Characterizing the developmental trajectories of cognitive functions such as learning, memory and decision making across the lifespan faces fundamental challenges. Cognitive functions typically encompass several processes that can be differentially affected by age. Methodological issues also arise when comparisons are made across age groups that differ in basic performance measures, such as in average response times (RTs). Here we focus on procedural learning–a fundamental cognitive function that underlies the acquisition of cognitive, social, and motor skills–and demonstrate how disentangling subprocesses of learning and controlling for differences in average RTs can reveal different developmental trajectories across the human lifespan. Two hundred-seventy participants aged between 7 and 85 years performed a probabilistic sequence learning task that enabled us to separately measure two processes of procedural learning, namely general skill learning and statistical learning. Using raw RT measures, in between -group comparisons, we found a U-shaped trajectory with children and older adults exhibiting greater general skill learning compared to adolescents and younger adults. However, when we controlled for differences in average RTs (either by using ratio scores or focusing on a subsample of participants with similar average speed), only children (but not older adults) demonstrated superior general skill learning consistently across analyses. Testing the relationship between average RTs and general skill learning within age groups shed light on further age-related differences, suggesting that general skill learning measures are more affected by average speed in some age groups. Consistent with previous studies of learning probabilistic regularities, statistical learning showed a gradual decline across the lifespan, and learning performance seemed to be independent of average speed, regardless of the age group. Overall, our results suggest that children are superior learners in various aspects of procedural learning, including both general skill and statistical learning. Our study also highlights the importance to test, and control for, the effect of average speed on other RT measures of cognitive functions, which can fundamentally affect the interpretation of group differences in developmental, aging and clinical psychology and neuroscience studies.

The relationship between subjective sleep quality and cognitive performance in healthy young adults: Evidence from three empirical studies

“…Moreover, Gao et al 57 in a recent study showed that www.nature.com/scientificreports www.nature.com/scientificreports/ above-average cognitive abilities buffer against insufficient sleep durations. However, not all cognitive functions peak in adulthood: while previous studies have reported the best performance in working memory and executive functions in young adulthood [58][59][60][61] , some aspects of procedural learning (as measured by the ASRT task) has been shown to peak in childhood and to decline already around adolescents 44,62,63 . Consequently, a cognitive peak may explain finding no relationship between subjective sleep quality and aspects of working memory and executive functions, while this explanation for the measures of procedural learning seems unlikely.…”

Rapid phonotactic constraint learning in aging: Evidence from speech errors

“…However, older adults benefit less from intervening sleep than younger ones, which indicates that the consolidation process in particular seems to be affected by ageing (Howard & Howard, 2013;Nemeth & Janacsek, 2011). Other studies found that older adults performed as well or even better than young adults (e.g., Juhasz et al, 2019). For instance, Verneau and colleagues (2014) found that older adults performed as well as younger adults on the ASRT when no explicit information about underlying rules was given; and in contrast to the younger group, their performance did LANGUAGE LEARNING AND AGEING 4 not benefit from such information.…”

“…Such learning underlies the acquisition of, for instance, motor and language skills. It is often assumed that implicit learning skills -in contrast to other cognitive abilities such as working memory, attention, and cognitive control (e.g., Hedden & Gabrieli, 2004;Craik & Bialystok, 2006) -are relatively preserved in ageing (e.g., Verneau, van der Kamp, Savelsbergh, & de Looze, 2014; see Howard & Howard, 2013, for a review) and may serve as a shield against age-related cognitive decline (Palmer, Hutson & Mattys, 2018;Juhasz, Nemeth & Janacsek, 2019). The current study aims to investigate whether preserved learning across age also extends to more complex skills that involve learning arbitrary dependencies implicitly, such as language.…”

Implicit anticipation of probabilistic regularities: Larger CNV emerges for unpredictable events

“…This is in line with previous behavioral (cf. Juhasz et al, 2019; Szegedi-Hallgató et al, 2019; Török, Janacsek, Nagy, Orbán, & Nemeth, 2017) and ERP results (Kóbor, Horváth, Kardos, Takács, et al, 2019; Kóbor et al, 2018), proposing that the sensitivity to multiple probabilistic regularities has possibly been grounded in the implicit extraction of the triplet-level probability structure.…”

“…Independent of triplet types, general skill improvements (faster RTs) reflecting more efficient visuomotor and motor-motor coordination due to practice were also considered (cf. Hallgató, Győri-Dani, Pekár, Janacsek, & Nemeth, 2013; Juhasz, Nemeth, & Janacsek, 2019).…”