Investigation of a Model-Based Working Memory Training With and Without Distractor Inhibition and Its Comparative Efficacy: A Randomized Controlled Trial on Healthy Old Adults

Background: Various working memory (WM) trainings have been tested, but differences in experimental designs, the lack of theoretical background, and the need of identifying task-related processes such as filtering efficiency limit conclusions about their comparative efficacy.Objectives: In this study, we compared the efficacy of a model-based WM training with (MB+) and without (MB) distractor inhibition on improving WM capacity to a dual n-back and active control condition.Methods: This randomized clinical trial included 123 healthy elderly adults (78 women, 45 men; aged 64.1 ± 8.3 years). All groups underwent 12 40-min training sessions over 3 weeks and four cognitive testing sessions. The first two sessions served as double baseline to account for practice effects. Primary outcome was WM capacity post-training measured by complex span tasks. Near and far transfer was assessed by simple span, n-back, visuospatial and verbal learning, processing speed, and reasoning tasks.Results: Due to preliminary termination (COVID-19), 93 subjects completed the post-training and 60 subjects the follow-up session. On a whole group level, practice effects occurred from prebaseline to baseline in WM capacity (b = 4.85, t(103) = 4.01, p < 0.001, r = 0.37). Linear mixed-effects models revealed a difference in WM capacity post-training between MB+ and MB (b = −9.62, t(82) = −2.52, p = 0.014, r = 0.27) and a trend difference between MB+ and dual n-back (b = −7.59, t(82) = −1.87, p = 0.065, r = 0.20) and control training (b = −7.08, t(82) = −1.86, p = 0.067, r = 0.20). Univariate analyses showed an increase between pre- and post-training for WM capacity within MB+ (t(22) = −3.34, p < 0.05) only. There was no difference between groups pre- and post-training regarding near and far transfer. Univariate analyses showed improved visuospatial learning within MB+ (t(21) = −3.8, p < 0.05), improved processing speed (t(23) = 2.19, p< 0.05) and n-back performance (t(23) = 2.12, p < 0.05) in MB, and improved n-back performance (t(25) = 3.83, p < 0.001) in the dual n-back training.Interpretation: A model-based WM training including filtering efficacy may be a promising approach to increase WM capacity and needs further investigation in randomized controlled studies.

Target Amplification and Distractor Inhibition: Theta Oscillatory Dynamics of Selective Attention in a Flanker Task

Selective attention is a key mechanism to monitor conflict-related processing and behaviour, by amplifying task-relevant processing and inhibiting task-irrelevant information. Conflict monitoring and resolution is typically associated with brain oscillatory power increase in the theta frequency range (3-8 Hz), as indexed by increased midfrontal theta power. We expand previous findings of theta power increase related to conflict processing and distractor inhibition by considering attentional target amplification to be represented in theta frequency as well. The present study (N = 41) examined EEG oscillatory activities associated with stimulus and response conflict in a lateralized flanker task. Depending on the perceptual (in)congruency and response (in)compatibility of distractor-target associations, resulting stimulus and response conflicts were examined in behavioural and electrophysiological data analyses. Both response and stimulus conflict emerged in RT analysis. Regarding EEG data, response-locked cluster analysis showed an increase of midfrontal theta power related to response conflict. In addition, stimulus-locked cluster analysis revealed early clusters with increased parietal theta power for nonconflicting compared to conflicting trials, followed by increased midfrontal theta power for both stimulus and response conflict. Our results suggest that conflict resolution in the flanker task relies on a combination of target amplification, depicted by parietal theta power increase, and distractor inhibition, indexed by midfrontal theta power increase, for both stimulus and response conflicts. Attentional amplification of sensory target features is discussed with regard to a domain-general conflict monitoring account.

Target amplification and distractor inhibition - Theta oscillatory dynamics of selective attention in a flanker task

Selective attention is a key mechanism to monitor conflict-related processing and behaviour, by amplifying task-relevant processing and inhibiting task-irrelevant information. Conflict monitoring and resolution is typically associated with brain oscillatory power increase in the theta frequency range (3-8 Hz), as indexed by increased midfrontal theta power. We expand previous findings of theta power increase related to conflict processing and distractor inhibition by considering attentional target amplification to be represented in theta frequency as well. The present study (N = 41) examined EEG oscillatory activities associated with stimulus and response conflict in a lateralized flanker task. Depending on the perceptual (in)congruency and response (in)compatibility of distractor-target associations, resulting stimulus and response conflicts were examined in behavioural and electrophysiological data analyses. Both response and stimulus conflict emerged in RT analysis. Regarding EEG data, response-locked cluster analysis showed an increase of midfrontal theta power related to response conflict. In addition, stimulus-locked cluster analysis revealed early clusters with increased parietal theta power for non-conflicting compared to conflicting trials, followed by increased midfrontal theta power for both stimulus and response conflict. Our results suggest that conflict resolution in the flanker task relies on a combination of target amplification, depicted by parietal theta power increase, and distractor inhibition, indexed by midfrontal theta power increase, for both stimulus and response conflicts. Attentional amplification of sensory target features is discussed with regard to a domain-general conflict monitoring account.

Neural mechanisms underlying distractor inhibition on the basis of feature and/or spatial expectations

A rapidly growing body of research indicates that inhibition of distracting information may not be under flexible, top-down control, but instead heavily relies on expectations derived from past experience about the likelihood of events. Yet, how expectations about distracting information influence distractor inhibition at the neural level remains unclear. To determine how expectations induced by distractor features and/or location regularities modulate distractor processing, we measured EEG while participants performed two variants of the additional singleton paradigm. Critically, in these different variants, target and distractor features either randomly swapped across trials, or were fixed, allowing for the development of distractor feature-based expectations. Moreover, the task was initially performed without any spatial regularities, after which a high probability distractor location was introduced. Our results show that both distractor feature- and location regularities contributed to distractor inhibition, as indicated by corresponding reductions in distractor costs during visual search and an earlier distractor-evoked Pd ERP. Yet, control analyses showed that while observers were sensitive to regularities across longer time scales, the observed effects to a large extent reflected intertrial repetition. Large individual differences further suggest a functional dissociation between early and late Pd components, with the former reflecting early sensory suppression related to intertrial priming and the latter reflecting suppression sensitive to expectations derived over a longer time scale. Also, counter to some previous findings, no increase in anticipatory alpha-band activity was observed over visual regions representing the expected distractor location, although this effect should be interpreted with caution as the effect of spatial statistical learning was also less pronounced than in other studies. Together, these findings suggest that intertrial priming and statistical learning may both contribute to distractor suppression and reveal the underlying neural mechanisms.