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Volume 27, Issue 1 (spring 2025)
Advances in Cognitive Sciences 2025, 27(1): 105-120 |
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Haj-Reza Jafarabadi A, Pedram M, Abolghasemi Dehaqhani M, Abdolrahmani M, Pasha E. Computational Modeling of Disparity Pair Discrimination in Visual Area V4. Advances in Cognitive Sciences 2025; 27 (1) :105-120
URL: http://icssjournal.ir/article-1-1802-en.html
URL: http://icssjournal.ir/article-1-1802-en.html
Ahmad Haj-Reza Jafarabadi1 
, Mir-Mohsen Pedram *2 , Mohammad-Reza Abolghasemi Dehaqhani3 , Mohammad Abdolrahmani4 , Einollah Pasha5
, Mir-Mohsen Pedram *2 , Mohammad-Reza Abolghasemi Dehaqhani3 , Mohammad Abdolrahmani4 , Einollah Pasha5
1- PhD Student in Cognitive Modeling, Institute for Cognitive Science Studies, Department of Cognitive Modeling, Tehran, Iran
2- . Associate Professor, Kharazmi University, Faculty of Engineering, Department of Electrical and Computer Engineering, Tehran, Iran & Institute for Cognitive Science Studies, Department of Cognitive Modeling, Tehran, Iran
3- Assistant Professor, University of Tehran, Faculty of Electrical and Computer Engineering, Department of Artificial Intelligence and Robotics, Tehran, Iran
4- Institute for Cognitive Science Studies, Department of Cognitive Modeling, Tehran, Iran
5- Professor Emeritus, Kharazmi University, Faculty of Mathematical Sciences and Computer, Department of Statistics, Tehran, Iran
2- . Associate Professor, Kharazmi University, Faculty of Engineering, Department of Electrical and Computer Engineering, Tehran, Iran & Institute for Cognitive Science Studies, Department of Cognitive Modeling, Tehran, Iran
3- Assistant Professor, University of Tehran, Faculty of Electrical and Computer Engineering, Department of Artificial Intelligence and Robotics, Tehran, Iran
4- Institute for Cognitive Science Studies, Department of Cognitive Modeling, Tehran, Iran
5- Professor Emeritus, Kharazmi University, Faculty of Mathematical Sciences and Computer, Department of Statistics, Tehran, Iran
Abstract: (398 Views)
Introduction: This study investigates the computational mechanisms underlying binocular disparity discrimination in visual area V4 through a novel modeling approach that examines the ability of V4 neurons to discriminate between fine disparities near the fixation plane and develops a comprehensive computational framework to characterize this process. This study’s central hypothesis posits that disparity discrimination ability in V4 depends on two key factors: The absolute difference between disparity pairs and their proximity to zero disparity.
Methods: The present study analyzed electrophysiological data from 156 V4 neurons recorded from macaque monkeys during presentation of random-dot stereograms with varying binocular disparities (±1.2°, ±0.6°, ±0.3°, 0°) and correlation levels. A novel population-level metric, the Disparity Discrimination Ability Index (DDAI), was introduced to quantify the neural population’s capacity to distinguish between disparity pairs using receiver operating characteristic-based (ROC-based) analysis. The DDAI was computed as the average normalized area under the ROC curve across all neurons for each stimulus pair.
Results: This study’s results confirmed that V4 neurons exhibit specialized tuning for near-zero disparities, with response variability (coefficient of variation) being minimal for stimuli close to zero disparity and increasing with absolute disparity magnitude. The computational model DCM (DDAI Computational Model) successfully predicted experimental DDAI patterns with high accuracy (Pearson r=0.969, Spearman P=0.887). Statistical equivalence testing (TOST) confirmed that model predictions were practically equivalent to experimental data (mean difference=0.00018, 90% CI within ±0.03).
Conclusion: The obtained findings demonstrate that V4 employs an efficient coding strategy prioritizing precision near the fixation depth while maintaining coarser encoding for larger disparities. The DCM framework provides a robust foundation for understanding population-level disparity processing and offers potential applications in artificial vision systems and clinical assessment of stereoscopic vision disorders.
Methods: The present study analyzed electrophysiological data from 156 V4 neurons recorded from macaque monkeys during presentation of random-dot stereograms with varying binocular disparities (±1.2°, ±0.6°, ±0.3°, 0°) and correlation levels. A novel population-level metric, the Disparity Discrimination Ability Index (DDAI), was introduced to quantify the neural population’s capacity to distinguish between disparity pairs using receiver operating characteristic-based (ROC-based) analysis. The DDAI was computed as the average normalized area under the ROC curve across all neurons for each stimulus pair.
Results: This study’s results confirmed that V4 neurons exhibit specialized tuning for near-zero disparities, with response variability (coefficient of variation) being minimal for stimuli close to zero disparity and increasing with absolute disparity magnitude. The computational model DCM (DDAI Computational Model) successfully predicted experimental DDAI patterns with high accuracy (Pearson r=0.969, Spearman P=0.887). Statistical equivalence testing (TOST) confirmed that model predictions were practically equivalent to experimental data (mean difference=0.00018, 90% CI within ±0.03).
Conclusion: The obtained findings demonstrate that V4 employs an efficient coding strategy prioritizing precision near the fixation depth while maintaining coarser encoding for larger disparities. The DCM framework provides a robust foundation for understanding population-level disparity processing and offers potential applications in artificial vision systems and clinical assessment of stereoscopic vision disorders.
Keywords: Binocular disparity, V4 area, Computational modeling, Depth perception, Disparity discrimination ability
Type of Study: Research |
Received: 2025/06/8 | Accepted: 2025/07/22 | Published: 2025/08/18
Received: 2025/06/8 | Accepted: 2025/07/22 | Published: 2025/08/18
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