Neural Basis of Serial Dependence: Retention of Prior Information and Its Influence on Perception
Poster No:
831
Submission Type:
Abstract Submission
Authors:
Yujie Liu1,2, Liqin Zhou3, Jinhua Tian4, Ke Zhou3, Zhentao Zuo1,2
Institutions:
1Institute of Biophysics, Chinese Academy of Sciences, Beijing, China, 2Sino-Danish College, University of Chinese Academy of Sciences, Beijing, China, 3Faculty of Psychology, Beijing Normal University, Beijing, China, 4School of Systems Science, Beijing Normal University, Beijing, China
First Author:
Yujie Liu
Institute of Biophysics, Chinese Academy of Sciences|Sino-Danish College, University of Chinese Academy of Sciences
Beijing, China|Beijing, China
Institute of Biophysics, Chinese Academy of Sciences|Sino-Danish College, University of Chinese Academy of Sciences
Beijing, China|Beijing, China
Co-Author(s):
Zhentao Zuo
Institute of Biophysics, Chinese Academy of Sciences|Sino-Danish College, University of Chinese Academy of Sciences
Beijing, China|Beijing, China
Institute of Biophysics, Chinese Academy of Sciences|Sino-Danish College, University of Chinese Academy of Sciences
Beijing, China|Beijing, China
Introduction:
The perceptual system leverages the redundancy of sensory information, using recent history as a prior to guide current perceptual judgments-a widespread phenomenon known as serial dependence, where prior stimuli systematically bias current perceptual judgments across various features (Fischer & Whitney, 2014; Manassi et al., 2023). Behavioral studies have demonstrated that feature-based attention can amplify the influence of prior stimuli and even determine their presence or absence (Fritsche & de Lange, 2019; Togoli et al., 2021). This study investigates neural correlates of serial dependence when attention is selectively guided to separate features. Using fMRI, we explored how past information is retained and influences the representation of current information in numerosity and size judgment tasks.
Methods:
Data from 25 participants (22±2.4 years, 7 males) were analyzed. Each participant completed 9–10 runs with 4 blocks per run, each block corresponding to either a Number or Field Area task (Figure 1). Blocks included 90 sample trials and 10 match trials. Figure 1A displayed the trial paradigm. Green fixation indicated sample trials (no response), while red fixation signaled match trials requiring participants to compare the current stimulus to the previous one and respond via button press. Stimuli varied systematically across numerosity (8, 16, 32) and field area (10, 16, 25.6 visual degrees), resulting in 9 conditions (Figure 1). MRI data were collected on a Siemens Prisma 3T scanner, preprocessed using SPM12, and single-trial BOLD responses were estimated with GLMsingle.
·Figure 1.
Results:
To identify brain regions representing features, we conducted a searchlight-based support vector machine (SVM) analysis. Group-level significant clusters (voxel-wise p = 0.005-0.001, FPR = 0.01) were identified via permutation. We decoded task-relevant and task-irrelevant features from the current and previous trial in two tasks. For current trial features, brain regions processing current stimuli overlapped with those reported in prior studies on numerosity and spatial size (Harvey & Dumoulin, 2017; Kadosh et al., 2005).
For previous trial features, in the Number task, previous numerosity was decoded in the bilateral visual cortex and left frontal lobe (Figure 2). In contrast, in the FA task, previous numerosity was decodable only in the right insula. No significant clusters were found for previous field area in either task under stringent statistical criteria.
To investigate how past trial history influences neural representations of the current trial, we developed a novel "neural shift" analysis. Using SVM, we classified current features and quantified how prior features affected prediction probability distributions. A positive shift bias indicated an attractive influence, while a negative bias suggested repulsion. Voxel-wise neural shift bias maps were generated via searchlight analysis.
Figure 2B showed a stronger attractive influence of previous numerosity on current numerosity in the Number task, with significant effects in bilateral visual areas and the inferior and superior parietal lobes. In the FA task, this effect was weaker and localized to the left V2 and bilateral superior parietal regions. In contrast, the attractive influence of previous field area on current field area was consistent across tasks, primarily observed in bilateral V1-V4.
For previous trial features, in the Number task, previous numerosity was decoded in the bilateral visual cortex and left frontal lobe (Figure 2). In contrast, in the FA task, previous numerosity was decodable only in the right insula. No significant clusters were found for previous field area in either task under stringent statistical criteria.
To investigate how past trial history influences neural representations of the current trial, we developed a novel "neural shift" analysis. Using SVM, we classified current features and quantified how prior features affected prediction probability distributions. A positive shift bias indicated an attractive influence, while a negative bias suggested repulsion. Voxel-wise neural shift bias maps were generated via searchlight analysis.
Figure 2B showed a stronger attractive influence of previous numerosity on current numerosity in the Number task, with significant effects in bilateral visual areas and the inferior and superior parietal lobes. In the FA task, this effect was weaker and localized to the left V2 and bilateral superior parietal regions. In contrast, the attractive influence of previous field area on current field area was consistent across tasks, primarily observed in bilateral V1-V4.
·Figure 2.
Conclusions:
The influence of prior numerosity was more strongly modulated by feature-based attention. Historical numerosity was mainly decoded and showed stronger attractive effects in the Number task. Brain regions involved in maintaining historical numerosity and those mediating its influence partially overlapped in the occipital lobe, with lateral PFC linked to retention and the parietal lobe reflecting attraction effects.
For historical field area, though not decodable, it still influenced current field area processing in the bilateral occipital cortex, with similar effects across tasks.
For historical field area, though not decodable, it still influenced current field area processing in the bilateral occipital cortex, with similar effects across tasks.
Higher Cognitive Functions:
Space, Time and Number Coding
Learning and Memory:
Learning and Memory Other 1
Modeling and Analysis Methods:
Multivariate Approaches
Perception, Attention and Motor Behavior:
Attention: Visual
Perception: Visual 2
Keywords:
FUNCTIONAL MRI
Multivariate
Other - Serial dependence; Numerosity; Task relevance; Visual perception
1|2Indicates the priority used for review
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Provide references using APA citation style.
2.Fischer, J., & Whitney, D. (2014). Serial dependence in visual perception. Nature Neuroscience, 17(5), Article 5. https://doi.org/10.1038/nn.3689
3.Fritsche, M., & de Lange, F. P. (2019). The role of feature-based attention in visual serial dependence. Journal of Vision, 19(13), 21. https://doi.org/10.1167/19.13.21
4.Harvey, B. M., & Dumoulin, S. O. (2017). A network of topographic numerosity maps in human association cortex. Nature Human Behaviour, 1(2), 0036. https://doi.org/10.1038/s41562-016-0036
5.Kadosh, R. C., Henik, A., Rubinsten, O., Mohr, H., Dori, H., van de Ven, V., Zorzi, M., Hendler, T., Goebel, R., & Linden, D. E. J. (2005). Are numbers special?: The comparison systems of the human brain investigated by fMRI. Neuropsychologia, 43(9), 1238–1248. https://doi.org/10.1016/j.neuropsychologia.2004.12.017
6.Kiyonaga, A., Scimeca, J. M., Bliss, D. P., & Whitney, D. (2017). Serial dependence across perception, attention, and memory. Trends in Cognitive Sciences, 21(7), 493–497. https://doi.org/10.1016/j.tics.2017.04.011
7.Manassi, M., Murai, Y., & Whitney, D. (2023). Serial dependence in visual perception: A meta-analysis and review. Journal of Vision, 23(8), 18. https://doi.org/10.1167/jov.23.8.18
8.Togoli, I., Fedele, M., Fornaciai, M., & Bueti, D. (2021). Serial dependence in time and numerosity perception is dimension-specific. Journal of Vision, 21(5), 6. https://doi.org/10.1167/jov.21.5.6