Response-level processing during visual feature search: Effects of frontoparietal activation and adult age

Abstract

Previous research suggests that feature search performance is relatively resistant to age-related decline. However, little is known regarding the neural mechanisms underlying the age-related constancy of feature search. In this experiment, we used a diffusion decision model of reaction time (RT), and event-related functional magnetic resonance imaging (fMRI) to investigate age-related differences in response-level processing during visual feature search. Participants were 80 healthy, right-handed, communitydwelling individuals, 19-79 years of age. Analyses of search performance indicated that targets accompanied by responseincompatible distractors were associated with a significant increase in the nondecision-time (t0) model parameter, possibly reflecting the additional time required for response execution. Nondecision time increased significantly with increasing age, but no age-related effects were evident in drift rate, cautiousness (boundary separation, a), or in the specific effects of response compatibility. Nondecision time was also associated with a pattern of activation and deactivation in frontoparietal regions. The relation of age to nondecision time was indirect, mediated by this pattern of frontoparietal activation and deactivation. Responsecompatible and-incompatible trials were associated with specific patterns of activation in the medial and superior parietal cortex, and frontal eye field, but these activation effects did not mediate the relation between age and search performance. These findings suggest that, in the context of a highly efficient feature search task, the age-related influence of frontoparietal activation is operative at a relatively general level, which is common to the task conditions, rather than at the response level specifically.

Age-related changes in brain metabolites underlie slowing of speed-accuracy trade-off

“…Non-decision time has previously been linked to fronto-parietal fMRI activity in older adults, therefore these observed findings complement and extend these results (Madden et al, 2020). Similarly, age-related increases in variability and reductions in visual functions requiring the suppression of irrelevant perceptual information were associated with individual variability in fronto-parietal white matter microstructure (Chadick et al, 2014).…”

“…Thus, age-related deterioration in the SLF - a white matter pathway crucial for the effective communication within the fronto-parietal attention network and hence between sensory and motor networks - is likely to contribute to the overall slowing of sensory and/or motor processing components reflected in the non-decision time. In addition, it has also been documented that older adults show reduced functional connectivity in fronto-parietal networks and that this is related to increased distractibility and diminished attentional focus with age (Campbell et al, 2012, Madden et al, 2020). Thus, the observed pattern of associations between age-related variation in non-decision time and SLF microstructure may reflect age-related difficulties in perceptual suppression or inhibition (Chadick et al, 2014) and/or a greater reliance in top-down processing as a response to diminished low-level sensory input (Lai et al, 2020).…”

“…Drift rate did not significantly differ between younger and older adults; however, our results showed it to be predicted by FA and L1 in the ILF and MD in the SLF3, suggesting that the rate of evidence accumulation through the perceptual system is critically dependent on the microstructural integrity of white matter connections that propagate that information. Previous evidence suggests that drift rate was related to attentional allocation (Madden et al, 2010, 2020) and the SLF 3 is thought to be pivotal for the allocation of visuo-spatial attention (De Schotten et al, 2011). Moreover, information processing speed, to which drift rate is closely related, is associated with temporal lobe oscillations (Chauvière, 2020) and stimulus-related attention has been associated with neural activity in the inferior temporal cortex (Zhang et al, 2011).…”

Age-related differences in frontoparietal activation for target and distractor singletons during visual search

“…Target identification can be enhanced when the target has increased salience, from either bottom-up (featural) or top-down (cognitive) sources (Proulx, 2007;Theeuwes, 2010), and this improvement in target identification is associated with significant deactivation, particularly in FEF (Liu & Pleskac, 2011;Madden, Siciliano, et al, 2020), analogous to priming effects (Henson, 2003;Lustig & Buckner, 2004;Schacter et al, 2007). Whereas a salient target improves performance, salient distractor items often interfere with performance, by capturing attention that could otherwise be devoted to target identification (Theeuwes, 2014;Yantis, 1996).…”

Associative and Identity Words Promote the Speed of Visual Categorization: A Hierarchical Drift Diffusion Account

“…For example, in an fMRI study investigating age-related performance in a visual search task, changes in non-decision time were associated with targets accompanied by response-incompatible distractors in the elderly group. T er was correlated with the Frontal eye fields (FEF) and dorsal fronto-parietal regions, which suggested a major contribution from the visual encoding process (Madden et al, 2019). An Electroencephalography (EEG) study of figure-ground segregation instead found a correlation between N200 latency and the non-decision component, suggesting that N200 tracks the completion of visual encoding (Nunez et al, 2019).…”