Sat, Jul 11, 2026
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Volume 27, Issue 4 (winter 2026)
Advances in Cognitive Sciences 2026, 27(4): 59-76 |
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Jamaloo A, Hasani J, Mohammadkhani S. The interactive effects of complexity and measurement repetition on relational networks: A hyper-dimensional multi level approach. Advances in Cognitive Sciences 2026; 27 (4) :59-76
URL: http://icssjournal.ir/article-1-1880-en.html
URL: http://icssjournal.ir/article-1-1880-en.html
1- PhD Student in Clinical Psychology, Faculty of Psychology and Educational Sciences, Kharazmi University, Karaj, Iran
2- Professor of Clinical Psychology, Faculty of Psychology and Educational Sciences, Kharazmi University, Karaj, Iran
2- Professor of Clinical Psychology, Faculty of Psychology and Educational Sciences, Kharazmi University, Karaj, Iran
Abstract: (363 Views)
Introduction: Relational Frame Theory (RFT) posits Arbitrary Applicable Relational Responding (AARR) as the foundation of language and cognition. The Hyper-Dimensional Multi Level (HDML) framework operationalizes complexity as a key dimension influencing AARR. This study examined the interaction between complexity and repeated measurement on AARR performance within a dynamic paradigm.
Methods: Sixty participants were tested using Implicit Relational Assessment Procedure (IRAP), including symbolic stimuli (e.g.,“)))” and “!!!”) and contextual cues (OST, UNR, ALB), in a between-subjects design. Task complexity (Low vs. High) was manipulated, and performance was assessed across two consecutive measurement points (Set1, Set2). Data from 5,077 trials were analyzed using Generalized Linear Mixed Models (GLMM) in SPSS.
Results: For Response Time (RT), a significant Measurement Point×Complexity interaction was observed (β=0.260, P<0.001). Simple slopes analysis revealed a crossover pattern: RT increased from Set1 to Set2 in low-complexity trials (3.89 ms to 4.28 ms) and decreased in high-complexity trials (4.12 ms to 3.49 ms). For accuracy, only the main effect of Measurement Point was significant (β=-1.243, OR=0.288, P<0.001), indicating a general decline at the second measurement. The main effect of Complexity (P=0.837) and the interaction (P=0.291) were not significant for accuracy.
Conclusion: The crossover interaction for RT indicates that task complexity moderates the effect of repeated measurement on processing efficiency. These findings suggest that complexity shapes cognitive demand and adaptive potential, and that simple versus complex relational networks engage distinct adaptive processes over time.
Methods: Sixty participants were tested using Implicit Relational Assessment Procedure (IRAP), including symbolic stimuli (e.g.,“)))” and “!!!”) and contextual cues (OST, UNR, ALB), in a between-subjects design. Task complexity (Low vs. High) was manipulated, and performance was assessed across two consecutive measurement points (Set1, Set2). Data from 5,077 trials were analyzed using Generalized Linear Mixed Models (GLMM) in SPSS.
Results: For Response Time (RT), a significant Measurement Point×Complexity interaction was observed (β=0.260, P<0.001). Simple slopes analysis revealed a crossover pattern: RT increased from Set1 to Set2 in low-complexity trials (3.89 ms to 4.28 ms) and decreased in high-complexity trials (4.12 ms to 3.49 ms). For accuracy, only the main effect of Measurement Point was significant (β=-1.243, OR=0.288, P<0.001), indicating a general decline at the second measurement. The main effect of Complexity (P=0.837) and the interaction (P=0.291) were not significant for accuracy.
Conclusion: The crossover interaction for RT indicates that task complexity moderates the effect of repeated measurement on processing efficiency. These findings suggest that complexity shapes cognitive demand and adaptive potential, and that simple versus complex relational networks engage distinct adaptive processes over time.
Keywords: Relational frame theory, Arbitrary applicable relational responding, Hyper-dimensional multi level framework, Complexity
Type of Study: Research |
Received: 2026/01/3 | Accepted: 2026/04/19 | Published: 2026/06/10
Received: 2026/01/3 | Accepted: 2026/04/19 | Published: 2026/06/10
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