The impact of natural aging on computational and neural indices of perceptual decision making: A review

Abstract

It is well established that natural aging negatively impacts on a wide variety of cognitive functions and research has sought to identify core neural mechanisms that may account for these disparate changes. A central feature of any cognitive task is the requirement to translate sensory information into an appropriate action - a process commonly known as perceptual decision making. While computational, psychophysical, and neurophysiological research has made substantial progress in establishing the key computations and neural mechanisms underpinning decision making, it is only relatively recently that this knowledge has begun to be applied to research on aging. The purpose of this review is to provide an overview of this work which is beginning to offer new insights into the core psychological processes that mediate age-related cognitive decline in adults aged 65 years and over. Mathematical modelling studies have consistently reported that older adults display longer non-decisional processing times and implement more conservative decision policies than their younger counterparts. However, there are limits on what we can learn from behavioural modeling alone and neurophysiological analyses can play an essential role in empirically validating model predictions and in pinpointing the precise neural mechanisms that are impacted by aging. Although few studies to date have explicitly examined correspondences between computational models and neural data with respect to cognitive aging, neurophysiological studies have already highlighted age-related changes at multiple levels of the sensorimotor hierarchy that are likely to be consequential for decision making behaviour. Here, we provide an overview of this literature and suggest some future directions for the field.

Disentangling the Effects of Processing Speed on the Association between Age Differences and Fluid Intelligence

“…Finally, the non-decision time t 0 is a composite of the time needed for all non-decisional processes such as sensory processing, encoding, and response execution. Because the diffusion model provides a process-based account of decision making that allows the measurement and mathematical separation of different processes involved in decision making, it has become increasingly popular in individual differences and aging research (e.g., Dirk et al 2017;Dully et al 2018;Frischkorn and Schubert 2018;Huff and Aschenbrenner 2018;Nunez et al 2015;Ratcliff et al 2003;Schubert et al 2015Schubert et al , 2016Schubert et al , 2019Schmiedek et al 2007;Schmitz and Wilhelm 2016;Schmitz et al 2018;Spaniol et al 2008;Yap et al 2012;Ratcliff et al 2004;Ratcliff et al 2010;Ratcliff et al 2011). The drift rate parameter in particular has been consistently associated with intelligence (Ratcliff et al 2010(Ratcliff et al , 2011Schmiedek et al 2007;Schmitz and Wilhelm 2016;Schmitz et al 2018;Schubert et al 2015;Schubert et al 2019), suggesting that smarter individuals benefit from a higher rate of evidence accumulation.…”

Increasing objective cardiometabolic burden associated with attenuations in the P3b event-related potential component in older adults

“…For example, P3b peak amplitudes gradually decline from adolescence to older age (Rossini, Rossi, Babiloni, & Polich, 2007). Reductions in participants' P3b amplitudes have been interpreted as requiring less evidence to reach a decision, yet computational modelling studies indicate that older adults are actually more conservative in their decision-making, requiring more evidence for a decision than younger adults (reviewed in Dully, McGovern, & O'Connell, 2018). This discrepancy between ERPs and patterns of behavioural results may be partly explained by structural and functional changes associated with cardiometabolic burden, as reported in our study.…”

Learning R Programming Language With Psi Chi

“…In previous research, differences in the P300 component have been cited between both age and sensation seeking group. In general, the P300 component has been found to increase in latency with increasing age (Dully et al, 2018;Falkenstein, Hoormann, & Hohnsbein, 2002;Polich, 1997) and decrease in amplitude (Dully et al, 2018;Schmiedt Ferh & BasalEroglu, 2011). Sensation seeking has also been implicated in differences in the P300.…”

“…Based on previous literature, older adults were expected to have lower sensationseeking scores than younger adults (Roalf et al, 2011;Roth et al, 2005). Additionally, younger adults have previously demonstrated a larger P300 amplitude and shorter latency than older adults, which may indicate that older adults struggle more with inhibition (Dully et al, 2018;Polich, 2007;SchmiedtFehr & Basar Eroglu, 2011).…”

“…Research has shown that both the amplitude and latency of the P300 differ in individuals of different ages. The P300 shows a larger amplitude and shorter latency in young adults, whereas older adults show a smaller amplitude and increased latency of this component (Dully, McGovern, & O'Connell, 2018;Polich, 2007;SchmiedtFehr & BasarEroglu, 2011). Age differences in an ERP component used to indicate inhibition may hint at overall differences in inhibition abilities in individuals as they age.…”