Relational integration training modulated the fluid intelligence: An EEG microstates study
Poster No:
785
Submission Type:
Abstract Submission
Authors:
Zhidong Wang1, Tie Sun2, Feng Xiao3
Institutions:
1Beijing Normal University, Beijing, China, 2Zhejiang Normal University, Jinhua, China, 3Guizhou Normal University, Guiyang, China
First Author:
Co-Author(s):
Introduction:
Relational integration, a critical component of both working memory and higher-order reasoning, involves constructing new relations while considering multiple representations (Oberauer et al., 2018; Oberauer et al., 2008). This process recruits the frontoparietal network, which is also considered the neural basis of fluid intelligence (Jung & Haier, 2007). The current study aimed to compare the effects of relational integration training and a quiz task on the frontoparietal network underlying fluid intelligence.
Methods:
Sixty participants, recruited from college students, were randomly assigned to a relational integration training group (25 females, mean age = 19.64, SD = 0.91) or a quiz active control group (24 females, mean age = 19.5, SD = 0.93). The training group underwent relational integration training for one month, while the control group participated in quiz training for the same duration. The relational integration task combined numerical inductive reasoning with an n-back task, requiring participants to judge whether the difference between current and previous numbers had changed. The quiz task items were drawn from the general knowledge section of China's National Civil Service Examination, and participants selected one answer from four possible choices. Pre- and post-tests involved the Sandia Matrices task, an effective measure of intelligence based on Raven's original Standard Progressive Matrices (SPM) (Matzen et al., 2010). This task included three levels of relational complexity. Additionally, resting EEG data were recorded during eye-closed periods at pre- and post-tests.
A three-way repeated-measures ANOVA (Time × Group × Relational Complexity) was conducted to analyze behavioral performance (accuracy and reaction times [RTs]). Topographical analysis of variance (TANOVA) was utilized to detect group and time differences for each microstate class in Ragu software. Microstate parameters (global variance, duration, occurrence, and contribution) and transfer possibilities were analyzed using a two-way repeated-measures ANOVA (Time × Group).
A three-way repeated-measures ANOVA (Time × Group × Relational Complexity) was conducted to analyze behavioral performance (accuracy and reaction times [RTs]). Topographical analysis of variance (TANOVA) was utilized to detect group and time differences for each microstate class in Ragu software. Microstate parameters (global variance, duration, occurrence, and contribution) and transfer possibilities were analyzed using a two-way repeated-measures ANOVA (Time × Group).
·The experimental material and procedure
Results:
Microstate results showed that for microstate D, the training group exhibited a significant increase in occurrence and contribution after the intervention. Additionally, the occurrence of microstate D was negatively associated with RTs. The training group also demonstrated a smaller occurrence and contribution for microstate C, relative to the control group, following training. Regarding transfer possibilities, the training group showed a decrease in transfer possibility between microstates A and B, and an increase between microstates C and D. In contrast, the control group showed an increase in transfer possibility between microstates A, B, and C, and a decrease in transfer possibility between microstate D and other microstates (B and A).
·The result of behavior and microstate analysis
Conclusions:
In summary, the current study provides evidence that relational integration training modulates large-scale brain networks associated with fluid intelligence. The association between the occurrence of microstate D and behavioral performance illustrates the effect of relational integration training. Microstate analysis also offers new insights into exploring the association between intelligence and spontaneous EEG activity. However, further evidence from microstate analysis is required to fully validate the effect of relational integration training in the future.
Higher Cognitive Functions:
Reasoning and Problem Solving 1
Novel Imaging Acquisition Methods:
EEG 2
Physiology, Metabolism and Neurotransmission:
Neurophysiology of Imaging Signals
Keywords:
Electroencephaolography (EEG)
Other - fluid intelligence; relational integration; training; frontoparietal network;microstate
1|2Indicates the priority used for review
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Provide references using APA citation style.
Oberauer, K., Süβ, H.-M., Wilhelm, O., & Wittmann, W. W. (2008). Which working memory functions predict intelligence? Intelligence, 36(6), 641-652.
Jung, R. E., & Haier, R. J. (2007). The Parieto-Frontal Integration Theory (P-FIT) of intelligence: converging neuroimaging evidence. Behavioral and brain sciences, 30(2), 135-154.
Matzen, L. E., Benz, Z. O., Dixon, K. R., Posey, J., Kroger, J. K., & Speed, A. E. (2010). Recreating Raven’s: Software for systematically generating large numbers of Raven-like matrix problems with normed properties. Behavior research methods, 42(2), 525-541.