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During the last two decades, there has been a widespread interest in working memory (WM) training among researchers and the public. As current WM training studies have been criticized for the lack of theory-based approaches explaining the mechanism that underlies WM and transfer, recently, Gathercole et al. (2019) presented a new framework conceptualizing training-induced changes in WM as the acquisition of novel cognitive routines similar to learning a new skill. From this new framework, we can infer that WM training might not always result in positive outcomes when considering the variability of newly formed routines and the impact that routines bring on one’s intelligence. Thus, the primary goal of this study is to demonstrate the negative effect of working memory training. We have already conducted Study 1 fully online using a two-phase training paradigm, which has developed by Susan Gathercole and Dennis Norris at Cambridge. The results indicated that training of circle span backward task transferred negatively to subsequent training of letter span backward task. It is inferred that the impaired performance of letter span backward training may reflect disruptions by incompatible cognitive routines generated in the former training phase. Using a reversed design corresponding to Study 1, Study 2 will be conducted online to further examine the above hypotheses emerged from the results of Study 1.

Although working memory (WM) is usually defined as a cognitive system coordinating processing and storage in the short term, in most WM models, memory aspects have been developed more fully than processing systems, and many studies of WM tasks have tended to focus on memory performance. The present study investigated WM functioning without focusing exclusively on short-term memory performance by presenting participants with an n-back task on letters, n varying from 0 to 2, each letter being followed by a tone discrimination task involving from one to three tones. Predictions regarding the reciprocal effects of these tasks on each other were motivated by the time-based resource-sharing (TBRS) theoretical framework for WM that assumes the temporal sharing of attention between processing and memory. Although, as predicted, increasing the n value had a detrimental effect on tone discrimination in terms of accuracy and response times, and increasing the number of tones disrupted speed and accuracy on n-back performance, the overall pattern of results did not perfectly fit the TBRS predictions. Nonetheless, the main alternative models of WM do not seem to offer a complete account. The present findings point toward the need to use a larger range of tasks and situations in designing and testing models of WM.

Our pilot study indicated a positive correlation between working memory (WM) performance and social network size. This project aims to replicate findings of the pilot study.

オープンプラクティスとは，オープンサイエンスの理念を実現するための個別具体的な研究実践である．心理学におけるオープンプラクティスには，オープンアクセスやオープンデータなどの研究完了後に行われるものだけでなく，事前登録，事前審査，オープンレビュー，プレプリント公開など，一連の研究活動の中に組み込まれているものも多い．それぞれにその目的と期待される役割があるため，独自の展開をたどっている側面があるが，今後は，複数のオープンプラクティスが，1つのパッケージとしてまとめられ，研究の開始から成果の公開まで，ごく自然に透明性とアカウンタビリティが高められるような仕組みが構築されていくことが望まれる., Open practice is specific research action that is aimed to promote principles of open science. In psychology, there is a variety of such practices that are integral parts of a series of research activities, including preregistration, registered report, open review, and preprint upload, as well as those that are conducted after completion of the research, e.g., open access and open data. Each open practice is characterised by its specific goals and expected rules, and therefore, has been developed through its respective trajectory. It is desirable to combine a range of open practices within a coherent procedural package in order to advance transparency and accountability of scientific research., 第26回情報知識学フォーラム予稿

A series of our previous experiments (Exp. 1 – 3) with an interference task (the Multi-Source Interference Task, Bush & Shin, 2006) have been conducted to investigate control schedule learning, that is adjustment of executive function based on temporal regularity. This planed experiment (Exp. 4) aims at replicating the results of Exp. 2. Temporal position of interference stimuli in successive five trials was manipulated. In a uniform group, interference stimuli were presented uniformly on 2 – 5 trials. In a consistent group(s), they were presented on a fixed position (e.g., position 2 for the consistent 2 group or position 3 for the consistent 3 group). In a biased group, they were presented on the position 4 with the probability of 80%. In other words, interference stimuli were likely to appear on a specific position but did not always appear on that position. Exp. 1 had three consistent groups (the consistent 2, 3, 4 groups) and a uniform group with 4 blocks. Exp. 2 had a biased group and a uniform group with 4 blocks. Exp. 3 had the three groups (consistent, biased, and uniform) with 12 blocks. The sample size for previous experiments (Exp. 1 – 3) was determined in a heuristic manner: For Exp. 1 and 2 it was set to secure a statistical power of 80% by presuming a small effect size (f = .10) and for Exp. 3 it was arranged by a “rule of thumb” of 1,600 observations per condition in analysis with mixed effects models (Brysbaert & Stevens, 2018). The results of Exp. 1 – 3 were inconsistent with each other. Exp. 1 showed that accuracy on interference stimuli was higher for the three consistent groups than for the uniform group (the contrast of the three consistent groups vs. the uniform group). We did not find such a pattern in response times. For accuracy of interference stimuli on position 4, Exp. 2 did not indicate an advantage of the biased group over the uniform group but showed the interaction between group and block. The pattern of this interaction indicated that the advantage for the biased group over the uniform group became more pronounced as the block progressed, suggesting a learning effect. For response times, we did not find either an advantage for the biased group or interaction between group and block. For performance for interference stimuli on position 4, Exp. 3 did not demonstrate either the effect of groups or interaction on accuracy performance although it showed the interaction between group and stimulus type (interference vs. control stimuli on position 4) on RT performance. Given the inconsistency of results in Exp. 1 – 3, post-hoc power analyses were conducted regarding accuracy (i.e., observed power calculation), using simr package in R. For Exp. 1 the estimated power was approximately 70.50% for the contrast of the three consistent groups vs. the uniform group. For Exp. 2 it was approximately 36.90% and 55.80% for the main effect (group) and the interaction (group x block). For Exp. 3 it was approximately 33.60% and 22.90% for the contrast (consistent & biased vs. uniform) and interaction (the contrast x block). The estimated powers indicate that the previous experiments were under powered (< 80%). Therefore, Exp. 4 was designed to provide conclusive evidence that would resolve the inconsistency between the previous experiments. It aims to directly replicate Exp. 2 by following the procedure of Exp. 2 but increases the sample size to secure the statistical power of 80%. The sample size was determined as 700 participants to secure statistical power of 80% for the main effect of group (i.e., biased vs. uniform) and that of 93% for the interaction between group x block based on effect sizes calculated by the results of Exp. 2.

Children engage cognitive control reactively when they encounter conflicts; however, they can also resolve conflicts proactively. Recent studies have begun to clarify the mechanisms that support the use of proactive control in children; nonetheless, sufficient knowledge has not been accumulated regarding these mechanisms. Using behavioral and pupillometric measures, we tested the novel possibility that 5-year-old children (N = 58) learn to use proactive control via the acquisition of abstract task knowledge that captures regularities of the task. Participants were assigned to either a proactive training group or a control training group. In the proactive training group, participants engaged in a training phase where using proactive control was encouraged, followed by a test phase using different stimuli where both proactive and reactive control could be used. In the control training group, participants engaged in a training phase where both cognitive control strategies could be used, followed by a similarly-structured test phase using different stimuli. We demonstrated children in the control training group responded more quickly and accurately and showed greater cue-related pupil dilation in the test phase than in the training phase. However, there were no differences in response times, accuracies, and pupil dilation between the proactive and control training groups in the training and test phases. These findings suggest that prior task experience, that goes beyond specific knowledge about the timing of task goal activation, can lead children to engage more proactive control endogenously, even if they are not directly encouraged to do so., Special Issue: Development of self-regulation, cognitive control, and executive function (Part 1)