Neural correlates of the effect of implementation intention on prospective memory

Chen, Xing Jie; Liu, Lu Lu; Wang, Ya; Yang, Tian Xiao; Huang, Jia; Li, Ke; Zeng, Ya Wei; Cheung, Eric F.C.; Shum, David; Amelsvoort, Thérèse van; Chan, Raymond C. K.

doi:10.1002/pchj.257

Cited by 6 publications

(5 citation statements)

References 41 publications

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“…In addition, the performance of time-point and time-period prospective memory of school-age children continues to develop with age. This shows that clear external time cues are more helpful in improving the performance of the time-point prospective memory of school-age children, which verifies the previous research results [ 58 , 59 ]. At the same time, the two types of prospective memory abilities of school-age children do not develop in sync, and the time-period prospective memory ability mainly concentrates on the stage of 7- to 9-year-old children.…”

Section: Discussionsupporting

confidence: 90%

The Effect of Time Monitoring on the Development of Time-Based Prospective Memory among Children Aged 7–11 Years Old

Wang,

Ren,

Yue

et al. 2024

Behavioral Sciences

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Time-based prospective memory (TBPM) refers to the ability of an individual to successfully execute an expected plan in the future at a certain time point or after a definite period of time. This study investigated the potential developmental mechanism of school-age children’s TBPM ability from the perspective of time monitoring. Experiment 1 used a between-subjects design of three ages (7, 9, 11) × two TBPM types (time point, time period) to investigate the trends and characteristics of two types of TBPM in children aged 7–11 years old. Experiment 2 used a between-subjects design of three ages (7, 9, 11) × two monitoring conditions (free monitoring, fixed monitoring) to investigate differences in two types of TBPM and monitoring behavior among school-age children under different monitoring conditions. These results showed that the age effect of TBPM was affected by the type of prospective memory (PM) and that time-point PM performance was significantly better than time-period PM performance among school-age children. These findings indicate that clear and definite external cues are helpful for school-age children in performing TBPM tasks. Moreover, there were significant differences found in the TBPM performance of school-age children under different time monitoring conditions. The performance of older children was significantly better than that of younger children. This indicates that older children can better allocate their attentional resources and use time monitoring strategies to improve their performance in PM tasks. Accordingly, this study showed that the TBPM ability of 7- to 11-year-old children is in continuous development and that the time monitoring behavior in the TBPM task is affected by task type and monitoring conditions.

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Section: Discussionsupporting

confidence: 90%

The Effect of Time Monitoring on the Development of Time-Based Prospective Memory among Children Aged 7–11 Years Old

Wang,

Ren,

Yue

et al. 2024

Behavioral Sciences

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“…This indicates that the participants could selectively and dynamically invest the attention resources into different task stages based on the time information during the implementation of MPM. Chen et al (2014) further used the event-related potential technology to verify that the processing mechanism of MPM was different from EBPM, showing the characteristics of dynamic and flexible attention allocation. According to the above evidence, MPM has additional time cues compared with EBPM.…”

Section: Introductionmentioning

confidence: 99%

The Processing Mechanisms of Two Types of Mixed Prospective Memory

2021

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Mixed prospective memory (MPM) needs to be executed when both external time and event cues appear. According to the clarity of time cues, MPM can be further divided into two types: time-point MPM and time-period MPM. There is no research on these two types of MPM. Whether existing theories of EBPM can explain its processing mechanisms is worth exploring. The current study was aimed at examining the differences in attentional allocation characteristics between these two types of MPM and EBPM under different difficult ongoing tasks. The results showed that the attention consumption of the two types of MPM groups was less than that of the EBPM group in the early and middle stages of high cognitive load, but there was no difference between the three groups in the later stage of the task. The attention distribution of time-point MPM and time-period MPM displayed dynamic changes: the time-point MPM only had attention consumption in the later stage, while the time-period MPM also existed in the early and middle stages. These results support dynamic multiprocess theory.

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“…69 BA 8R could participate in memory retrieval and could respond to related stimuli. 70 Taken together, BA 7L-BA 19L…”

Section: Cortical Functional Connectivity In Each Microstate Of Doc P...mentioning

confidence: 86%

“… 69 BA 8R could participate in memory retrieval and could respond to related stimuli. 70 Taken together, BA 7L–BA 19L and BA 10R–BA 8R may have indicated that VS patients were not unconscious when faced with pain or stimuli, but could activate relevant brain regions and respond to stimuli.…”

Section: Discussionmentioning

confidence: 98%

Temporal and spatial variability of dynamic microstate brain network in disorders of consciousness

Li,

Gao,

Yang

et al. 2024

CNS Neurosci Ther

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BackgroundAccurately diagnosing patients with the vegetative state (VS) and the minimally conscious state (MCS) reached a misdiagnosis of approximately 40%.MethodsA method combined microstate and dynamic functional connectivity (dFC) to study the spatiotemporal variability of the brain in disorders of consciousness (DOC) patients was proposed. Resting‐state EEG data were obtained from 16 patients with MCS and 16 patients with VS. Mutual information (MI) was used to assess the EEG connectivity in each microstate. MI‐based features with statistical differences were selected as the total feature subset (TFS), then the TFS was utilized to feature selection and fed into the classifier, obtaining the optimal feature subsets (OFS) in each microstate. Subsequently, an OFS‐based MI functional connectivity network (MIFCN) was constructed in the cortex.ResultsThe group‐average MI connectivity matrix focused on all channels revealed that all five microstates exhibited stronger information interaction in the MCS when comparing with the VS. While OFS‐based MIFCN, which only focused on a few channels, revealed greater MI flow in VS patients than in MCS patients under microstates A, B, C, and E, except for microstate D. Additionally, the average classification accuracy of OFS in the five microstates was 96.2%.ConclusionConstructing features based on microstates to distinguish between two categories of DOC patients had effectiveness.

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Neural correlates of the effect of implementation intention on prospective memory

Cited by 6 publications

References 41 publications

The Effect of Time Monitoring on the Development of Time-Based Prospective Memory among Children Aged 7–11 Years Old

The Effect of Time Monitoring on the Development of Time-Based Prospective Memory among Children Aged 7–11 Years Old

The Processing Mechanisms of Two Types of Mixed Prospective Memory

Temporal and spatial variability of dynamic microstate brain network in disorders of consciousness

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