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Bailu Si

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4 papers

2 author rows

YNIMG Journal 2025 Journal Article

Precision-dependent modulation of social attention

Wenhui Gao
Changbo Zhu
Bailu Si
Liqin Zhou
Ke Zhou

Social attention, guided by cues like gaze direction, is crucial for effective social interactions. However, how dynamic environmental context modulates this process remains unclear. Integrating a hierarchical Bayesian model with fMRI, this study investigated how individuals adjusted attention based on the predictions about cue validity (CV). Thirty-three participants performed a modified Posner location-cueing task with varying CV. Behaviorally, individuals' allocation of social attention was finely tuned to the precision (inverse variance) of CV predictions, with the predictions updated by precision-weighted prediction errors (PEs) about the occurrence of target locations. Neuroimaging results revealed that the interaction between allocation of social attention and CV influenced activity in regions involved in spatial attention and/or social perception. Precision-weighted PEs about target locations specifically modulated activity in the temporoparietal junction (TPJ), superior temporal sulcus (STS), and primary visual cortex (V1), underscoring their roles in refining attentional predictions. Dynamic causal modeling (DCM) further demonstrated that enhanced absolute precision-weighted PEs about target locations strengthened the effective connectivity from V1 and STS to TPJ, emphasizing their roles in conveying residual error signals upwards to high-level critical attention areas. These findings emphasized the pivotal role of precision in attentional modulation, enhancing our understanding of context-dependent social attention.

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YNIMG Journal 2025 Journal Article

Temporal dynamics of quantity processing: distinct time course and representational patterns revealed by multivariate pattern analysis

Jinhua Tian
Wei Xu
Bailu Si
Guochen Sun
Ke Zhou

People employ both discrete and continuous quantities to quantify aspects of their environment. However, the temporal dynamics and interactions underlying the processing of these quantitative information remain insufficiently understood. Our study aimed to address this gap by employing a one-back task in conjunction with magnetoencephalography (MEG) to investigate neural responses to dot stimuli representing both discrete (e.g., number of dots) and continuous (e.g., distribution of dots in space) quantities. Our primary finding, derived from representational similarity analysis (RSA), was that processing of field area and numerosity information preceded that of individual information (e.g., individual area and shape), suggesting different timing in the processing of these visual dimensions. Furthermore, within-dimensional temporal generalization analysis revealed distinct temporal patterns of these two different information: numerosity and field area exhibited a combination of chain-like (sequential, non-overlapping processes) and reactivated (initially active, then silent, then reactivated) patterns. Notably, an intermediate 'silent' phase emerged between the initial generalizable representation and subsequent trials, indicating the retrieval of early information to meet subsequent task demands (e.g., the one-back response). In contrast, individual area and shape predominantly followed a chain-like pattern. Furthermore, cross-dimensional temporal generalization analysis showed that numerosity and individual area representations could generalize to each other point-to-point in time, and that early numerosity representations and late individual area representations also generalized to each other, implying both parallel and sequential shared representation of these quantities. Field area showed limited generalization to numerosity and individual area, suggesting that they are processed independently. In summary, our results suggest that quantity processing involves temporally distinct operations with different processing timings and a shared encoding pattern of numerosity and individual area that links these temporally distinct processes.

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AAAI Conference 2024 Conference Paper

Learning Visual Abstract Reasoning through Dual-Stream Networks

Kai Zhao
Chang Xu
Bailu Si

Visual abstract reasoning tasks present challenges for deep neural networks, exposing limitations in their capabilities. In this work, we present a neural network model that addresses the challenges posed by Raven’s Progressive Matrices (RPM). Inspired by the two-stream hypothesis of visual processing, we introduce the Dual-stream Reasoning Network (DRNet), which utilizes two parallel branches to capture image features. On top of the two streams, a reasoning module first learns to merge the high-level features of the same image. Then, it employs a rule extractor to handle combinations involving the eight context images and each candidate image, extracting discrete abstract rules and utilizing an multilayer perceptron (MLP) to make predictions. Empirical results demonstrate that the proposed DRNet achieves state-of-the-art average performance across multiple RPM benchmarks. Furthermore, DRNet demonstrates robust generalization capabilities, even extending to various out-of-distribution scenarios. The dual streams within DRNet serve distinct functions by addressing local or spatial information. They are then integrated into the reasoning module, leveraging abstract rules to facilitate the execution of visual reasoning tasks. These findings indicate that the dual-stream architecture could play a crucial role in visual abstract reasoning.

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ICRA Conference 2017 Conference Paper

A prey-predator model for efficient robot tracking

Fengzhen Tang
Bailu Si
Daxiong Ji

Tracking is a common topic in various areas of robotics research. Motivated by the hunting behavior of predators in nature, we propose a prey-predator model for efficient robot tracking. The head direction and speed of the pursuer is automatically adjusted according to the position and velocity of the prey. Under the situation with perception uncertainty, where the actual location of the prey is not observable, the pursuer predicts the location of the prey according to simple inference, an online adaptive autoregressive model, or an online adaptive echo state network. Simulation results demonstrate that the proposed prey-predator model is able to control the pursuer and to track the prey efficiently, even under perception uncertainty. Simple inference gives better results when the motion of the target is piecewise linear, while echo state network is more suitable when the dynamics of the target are more complex. The proposed prey-predator model thus provides an efficient method tracking targets with various statistical nature of trajectories for applications such as underwater robot tracking, human tracking and team formation.

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