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Dandan Liu

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7 papers

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YNIMG Journal 2026 Journal Article

Learning from feedback is independent from feedback visibility, but supported by aperiodic neural activity

Dandan Liu
Shiwei Jia
Yanliang Sun
Lorenza Colzato
Bernhard Hommel

Humans and other animals learn from feedback, by tending to repeat rewarded behavior and change behavior that receives negative feedback. Previous findings suggest that feedback does not need to be consciously perceived in order to be effective. Using continuous flash suppression, we presented participants with visible and invisible positive and negative feedback during a time estimation task while recording EEG. Behavioral results showed that both visible and invisible feedback significantly influenced time estimation error and adjustment in trial N+1, suggesting that subliminal reward information can be effectively utilized. Electrophysiological indicators (reward positivity, P3a, theta activity, aperiodic exponent) showed feedback-valence effects, but only when the feedback was visible. Performance in trial N+1 was successfully predicted by the aperiodic exponent only. These findings suggest that (1) behavioral control is independent from conscious perception of feedback signals; (2) the valence sensitivity of electrophysiological indicators is not informative for effective learning or the impact of feedback on behavioral control; and (3) the general neural state, as characterized by the aperiodic exponent, is predictive of the quality of learning from feedback, with steeper exponents providing the most supportive conditions for learning.

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AAAI Conference 2026 Conference Paper

Mind the Gap: Predicting, Explaining and Reducing Time-to-First-Comment (Reply Gap) in Online Mental-Health Communities

Guangrui Fan
Dandan Liu
Lihu Pan

Online peer-support communities are vital for mental health, but their therapeutic benefit hinges on receiving a timely and helpful first reply. Posts that languish unanswered can exacerbate feelings of distress and abandonment. This paper develops and validates an integrated framework to predict, explain, and reduce this ``reply gap" on Reddit. First, using survival analysis on over 91,000 posts (2018–2025), we show that a deep learning model (DySurv) can accurately predict reply times (C-Index = 0.742), with a post's lexico-semantic content being a far stronger predictor than author history. Second, moving from correlation to causation, we use a causal inference framework on 48,612 posts to estimate the effect of different support types. We find that initial replies providing emotional support are most effective, increasing the odds of a positive user response by 49% (OR=1.49), an effect most pronounced for high-risk users. Third, we operationalize these insights in RiskMatch, a recommender system that routes at-risk posts to historically effective helpers. Rigorous counterfactual evaluation using inverse propensity scoring (IPS)—a method that corrects for biases in historical data—demonstrates that our system reduces the median wait time by 26 minutes for the highest-risk quintile. This work provides a validated, data-driven methodology to build more responsive and effective peer-support ecosystems, offering a concrete pathway to ensure fewer calls for help go unanswered.

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AAAI Conference 2026 Conference Paper

ST-VLM: A Spatial-to-Image Multimodal Spatial-Temporal Prediction Framework with Vision-Language Model

Tong Zhao
Junping Du
Zhe Xue
Meiyu Liang
Aijing Li
Xiaolong Meng
Dandan Liu

Spatial-temporal prediction plays a crucial role in various domains, including intelligent transportation and environmental monitoring. Although large language model has shown advantages in long-range dependency modeling and excellent generalization ability for forecasting, it has limited understanding of spatial-temporal features. Especially for spatial features, most existing methods still simplify the spatial-temporal prediction task into multiple independent temporal prediction tasks, failing to effectively encode the dynamic evolution of spatial relations. To address these problems, we propose ST-VLM (Spatial-Temporal Forecasting with Vision-Language Model), a novel framework that leverages visual representations to encode the dynamic spatial dependencies within spatial-temporal data and integrates multi-modal information to enhance prediction. This framework transforms spatial-temporal features into three modalities: vision, text, and time series, enhances cross-modal fusion through an attention-aware fusion mechanism in the first-layer of Vision-Language Model (VLM), optimizes multi-modal feature interaction via adaptive fine-tuning strategies. After fusion, the multi-modal embeddings are subsequently used for the final spatial-temporal prediction task. Extensive experiments demonstrate that ST-VLM achieves state-of-the-art performance across various datasets. In particular, the framework exhibits promising results in few-shot scenarios, verifying its strong generalization ability.

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IJCAI Conference 2025 Conference Paper

Creative Momentum Transfer: How Timing and Labeling of AI Suggestions Shape Iterative Human Ideation

Guangrui Fan
Dandan Liu
Lihu Pan
Yishan Huang

Human–AI collaboration is increasingly integral to a variety of domains where creative ideation unfolds in iterative cycles, yet most existing studies evaluate AI-generated concepts in a single step. This paper addresses the gap by investigating “Creative Momentum Transfer”—how the timing (early vs. late) and labeling (AI-labeled vs. unlabeled) of AI prompts shape multi-round human ideation. In a between-subjects experiment (N = 247), participants proposed solutions for plastic pollution over two rounds, with AI suggestions introduced either at the outset or mid-process and labeled explicitly or not. Results reveal that early AI prompts increase overall creativity but induce stronger anchoring, whereas late AI prompts trigger a mid-round pivot that fosters more divergent thinking yet still boosts final outcomes compared to a no-AI control. Labeling amplifies both subjective and objective adoption of AI ideas, although most participants could detect AI sources even when unlabeled. Furthermore, qualitative interviews highlight nuanced perspectives on perceived ownership, authenticity, and the ways in which labeling triggers deeper scrutiny of the AI’s style. By demonstrating that baseline creativity moderates these effects more robustly than trust in AI, this study advances our theoretical understanding of multi-round human–AI synergy while offering design guidelines for next-generation creativity support systems. We discuss how user-centered design can balance rapid convergence (via early AI) with strategic pivot opportunities (via late AI) and weigh transparent labeling against ethical considerations of authorship and user autonomy.

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IJCAI Conference 2025 Conference Paper

CSAHFL: Clustered Semi-Asynchronous Hierarchical Federated Learning for Dual-layer Non-IID in Heterogeneous Edge Computing Networks

Aijing Li
Junping Du
Dandan Liu
Yingxia Shao
Tong Zhao
Guanhua Ye

Federated Learning (FL) enables collaborative model training across distributed devices without sharing raw data. Hierarchical Federated Learning (HFL) is a new paradigm of FL that leverages the Edge Servers (ESs) layer as an intermediary to perform partial local model aggregation in proximity, reducing core network transmission overhead. However, HFL faces new challenges: (1) The two-stage aggregation process between client-edge and edge-cloud results in a dual-layer non-IID issue, which may significantly compromise model training accuracy. (2) The heterogeneity and mobility of clients further impact model training efficiency. To address these challenges, we propose a novel Clustered Semi-Asynchronous Hierarchical Federated Learning (CSAHFL) framework that integrates adaptive semi-asynchronous intra-cluster aggregation at client-edge layer and dynamic distribution-aware inter-cluster aggregation at edge-cloud layer, collaboratively enhancing model performance and scalability in heterogeneous and mobile environments. We conducte experiments under varying degrees of dual-layer non-IID in both static and high-mobility scenarios. The results demonstrate significant advantages of CSAHFL over representative state-of-the-art methods.

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YNIMG Journal 2025 Journal Article

Periodic and aperiodic neural activity contribute to the microgenesis of learning from mistakes

Shiwei Jia
Dandan Liu
Yangming Yue
Lorenza Colzato
Bernhard Hommel
Christian Beste

The ability to adapt behavior to situational changes is a central aspect of human cognitive control or executive functioning. Through trial and error, individuals can then infer the correct rules and adapt their behavior or response strategy accordingly. Prior research has mostly linked feedback-related negativity (FRN) and theta band activity to feedback-related behavioral adaptation. Based on neurophysiological and cognitive science considerations and using an extended sample of N = 226 healthy individuals, we asked whether other neural activities (i.e., aperiodic activity and alpha band activity) are important elements enabling adaptive behavior. In an EEG-based Wisconsin Card Sorting Task, we examined the chain of processes triggered by the feedback presentation up to the next trial to see how people adjusted. Our findings highlight the distinctive role of alpha band activity, particularly after adjusting for aperiodic activity. Alpha activity, modulated throughout feedback stages, increased following negative feedback and strongly predicted improved performance in subsequent trials. This predictive relationship emerged after controlling for aperiodic activity, revealing alpha's role in inhibitory gating and categorization processes critical for adaptive behavior, especially under conditions requiring suppression of task-irrelevant information. Although not directly predictive of performance, aperiodic activity influenced alpha processes during feedback, suggesting a metacontrol mechanism that supports feedback-related behavioral adaptation. These findings integrate alpha and aperiodic activity with established FRN and theta band processes, offering novel insights into the neural basis of behavioral adaptation.

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YNICL Journal 2023 Journal Article

Baseline grey matter volumes and white matter hyperintensities predict decline in functional activities in older adults over a 5-year follow-up period

Corey J. Bolton
Omair A. Khan
Elizabeth E. Moore
Kimberly R. Pechman
L. Taylor Davis
Dandan Liu
Bennett A. Landman
Katherine A. Gifford

INTRODUCTION: Functional independence is an essential predictor of quality of life in aging, yet few accessible predictors of functional decline have been identified. This study examined associations between baseline structural neuroimaging markers and longitudinal functional status. METHODS: Linear mixed effects models with follow-up time interaction terms related baseline grey matter volume and white matter hyperintensities (WMHs) to functional trajectory, adjusting for demographic and medical covariates. Subsequent models assessed interactions with cognitive status and apolipoprotein E (APOE) ε4 status. RESULTS: Smaller baseline grey matter volumes, particularly in regions commonly affected by Alzheimer's disease (AD), and greater baseline WMHs were associated with faster functional decline over a mean 5-year follow-up. Effects were stronger in APOE-ε4 carriers on grey matter variables. Cognitive status interacted with most MRI variables. DISCUSSION: Greater atrophy in AD-related regions and higher WMH burden at study entry were associated with faster functional decline, particularly among participants at increased risk of AD.

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