&lt;code&gt;prepdat&lt;/code&gt;- An &lt;code&gt;R&lt;/code&gt; Package for Preparing Experimental Data for Statistical Analysis

The ability to efficiently perform actions immediately following instructions and without prior practice has previously been termed Rapid Instructed Task Learning (RITL). In addition, it was found that instructions are so powerful that they can produce automatic effects, reflected in activation of the instructions in an inappropriate task context. RITL is hypothesized to rely on limited working memory (WM) resources for holding not-yet implemented task rules. Similarly, automatic effects of instructions presumably reflect the operation of task rules kept in WM. Therefore, both were predicted to be influenced by WM load. However, while the involvement of WM in RITL is implicated from prior studies, evidence regarding WM involvement in instructions-based automaticity is mixed. In the current study, we manipulated WM load by increasing the number of novel task rules to be held in WM towards performance in the NEXT paradigm. In this task, participants performed a series of novel tasks presented in mini-blocks, each comprising a) instructions of novel task rules; b) a NEXT phase measuring the automatic activation of these instructed rules, in which participants advance the screen using a key-press; and c) a GO phase in which the new rules are first implemented and RITL is measured. In three experiments, we show a dissociation: While RITL (rule implementation) was impaired by increased WM load, the automatic effects of instructions were not robustly influenced by WM load. Theoretical implications are discussed.

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“…The “prepdat” R package [31] was used for preprocessing. GO trials with an error were omitted from all RT analyses.…”

Section: Methodsmentioning

confidence: 99%

Rapid instructed task learning (but not automatic effects of instructions) is influenced by working memory load

Pereg

Meiran

2019

PLoS ONE

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“…Then, mean accuracy rates were calculated for each participant and for each combination of the independent variables to allow statistical analysis. This data preparation was conducted using prepdat— an R package for preparing experimental data for statistical analysis (Allon & Luria, ).…”

Section: Methodsmentioning

confidence: 99%

Filtering performance in visual working memory is improved by reducing early spatial attention to the distractors

Allon

Luria

2019

Psychophysiology

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We investigated the underlying processes that enable improving filtering irrelevant items from entering visual working memory (WM). To this end, participants performed a bilateral change‐detection task in which either targets or targets along with distractors (i.e., the filtering condition) appeared in the memory array while ERPs were recorded. In the cue‐present condition, we provided a spatial cue coupled with a temporal cue regarding where and when the distractors would appear. On some of the filtering trials, after the offset of the memory array, task‐irrelevant probes were briefly flashed either at the locations of the targets or at the locations of the distractors. This enabled measuring whether reactivating the filtering settings resulted in reducing spatial attentional resources to the distractors, allocating additional spatial attentional resources to the targets, or both, as was measured by the P1/N1 amplitude. Results revealed that, relative to the cue‐absent condition, in the cue‐present condition the P1/N1 amplitude was reduced for probes at the distractors and was similar for probes at the targets. In addition, the reduction in the P1/N1 amplitude was accompanied by a reduced filtering cost in accuracy performance in the cue‐present condition relative to the cue‐absent condition. These findings suggest that reactivating the distractor filtering settings improved filtering performance in visual WM by reducing the allocation of spatial attention to the distractors already at early processing stages, and not by allocating additional spatial attentional resources to the targets.

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“…The data were first submitted to a descriptive analysis, and all error trials were excluded from further process (21% for the Stroop task and 15% for the Simon task). The responses were then submitted to a trimming procedure with a cutoff criterion of 2.5 SD using the “prepdat” R Package (Allon & Luria, 2016). For the remaining RTs, we calculated mean RTs and performed two separated analyses of variance (ANOVAs) for the training and the control group.…”

Section: Methodsmentioning

confidence: 99%

Beta Neurofeedback Training Improves Attentional Control in the Elderly

Bielas

Michalczyk

2020

Psychol Rep

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One of the well-documented behavioral changes that occur with advancing age is a decline in executive functioning, for example, attentional control. Age-related executive deficits are said to be associated with a deterioration of the frontal lobes. Neurofeedback is a training method which aims at acquiring self-control over certain brain activity patterns. It is considered as an effective approach to help improve attentional and self-management capabilities. However, studies evaluating the efficacy of neurofeedback training to boost executive functioning in an elderly population are still relatively rare and controversial. The aim of our study was to contribute to the assessment of the efficacy of neurofeedback as a method for enhancing executive functioning in the elderly. We provided a group of seniors with beta up-training (12–22 Hz), consisting of 20 sessions (30 minutes each), on the Cz site and tested its possible beneficiary influence on attentional control assessed by means of the Stroop and Simon tasks. The analysis of the subjects’ mean reaction times during consecutive tasks in the test and the retest, after implementation of neurofeedback training, showed a significant improvement. In contrast, the difference in reaction times between the test and the retest in the control group who had not been submitted to neurofeedback training was not significant.

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<code>prepdat</code>- An <code>R</code> Package for Preparing Experimental Data for Statistical Analysis

Cited by 16 publications

References 10 publications

Rapid instructed task learning (but not automatic effects of instructions) is influenced by working memory load

Rapid instructed task learning (but not automatic effects of instructions) is influenced by working memory load

Filtering performance in visual working memory is improved by reducing early spatial attention to the distractors

Beta Neurofeedback Training Improves Attentional Control in the Elderly

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