The School of Graduate Studies (GS) at Lingnan University launched the Postgraduate Summer School 2026. This year's programme focuses on interdisciplinary methodological approaches, including the integration of artificial intelligence (AI) and innovative mixed method designs. Lingnan is providing scholarship for each of the 18 outstanding postgraduate students, which includes sending them to the University of Oxford for an intensive UK session to further expand their horizons.
This prestigious annual flagship event has been co-organised with Hertford College, University of Oxford, since 2018. It serves as a platform for advancing postgraduate students' international exposure, global research collaboration, and cross-cultural learning. This year, over 260 students from Lingnan University and other local and overseas institutions participated in the Hong Kong SAR session, held from 24 to 26 June in a hybrid format (online and in-person).
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The School of Graduate Studies at Lingnan University hosts the opening ceremony of the Postgraduate Summer School 2026, which focuses on AI and interdisciplinary global research.
Prof Sam Kwong Tak-wu, Associate Vice-President (Strategic Research), J.K. Lee Chair Professor of Computational Intelligence and Dean of the School of Graduate Studies, delivers the welcome remarks.
The programme features several international scholars who will lead innovative research workshops on methodologies and technological integration.
Zhou Ying, a student from the Master of Science in Health Analytics and Management, offered by the GS, looks forward to seizing this rare opportunity to explore advanced AI applications and pursue academic growth at Oxford.
In his welcoming remarks at the opening ceremony of the Hong Kong SAR session, Prof Sam Kwong Tak-wu, Associate Vice-President (Strategic Research), J.K. Lee Chair Professor of Computational Intelligence and Dean of the School of Graduate Studies, explained how global exposure and technology empower future scholars. "For today's postgraduate students, AI serves as a critical research catalyst to accelerate data analysis and drive interdisciplinary breakthroughs. By organising this global exchange and supporting our students, Lingnan ensures young talents can master advanced tools without financial barriers. Students get a strategic opportunity to improve their understanding of research ethics and AI capabilities, educating them to handle the complexities of the digital age."
The School of Graduate Studies at Lingnan University hosts the opening ceremony of the Postgraduate Summer School 2026, which focuses on AI and interdisciplinary global research.
Prof Sam Kwong Tak-wu, Associate Vice-President (Strategic Research), J.K. Lee Chair Professor of Computational Intelligence and Dean of the School of Graduate Studies, delivers the welcome remarks.
The Hong Kong SAR session of the summer school involved several interactive workshops led by renowned scholars invited from Australia, Germany, the Hong Kong SAR, Switzerland, and the UK. The workshops were led by Prof Kingsley Agho from Western Sydney University, Australia; Prof Alessio D'Angelo from the University of Derby, UK; Prof Tinashe Dune from Charles Darwin University, Australia; Prof Tobia Fattore from the University of Fribourg, Switzerland; Prof Susann Fegter from the Technical University Berlin, Germany; Prof Gleb Papyshev from the Department of Government and International Affairs (GOV) and Division of Artificial Intelligence of the School of Data Science (SDS), and Ms Angel Fan Yim-hung from the Centre for English and Additional Languages (CEAL) at Lingnan University. The topics spanned advanced research methodologies, academic communication, technologically-driven research and interdisciplinary research skills.
The programme features several international scholars who will lead innovative research workshops on methodologies and technological integration.
For instance, a workshop titled "Understanding the Role of Large Language Models in Interdisciplinary Research", led by Prof Gleb Papyshev, Assistant Professor of the Department of Government and International Affairs and Division of Artificial Intelligence of Lingnan University, taught students on how Large Language Models (LLMs) can be applied in social science research to simulate human behaviour, generate synthetic data, and address issues such as bias and research ethics. The UK Session expanded on this workshop by offering additional training on how advanced technologies and creative thinking are shaping the future of research and education, with specific topics on "AI in Healthcare" and "AI in the Classroom". The programme also served as an opportunity for peer exchange on research practices across different universities and for identifying ways to develop new research clusters that promote cross-border collaboration and international publications.
Upon completion of the Hong Kong SAR session of the summer school, 18 outstanding postgraduate students who received scholarship from Lingnan University participated in the UK session of the summer school at the University of Oxford from 28 June to 12 July 2026.
Zhou Ying, a student from the Master of Science in Health Analytics and Management programme offered by the GS and a participant in the UK Session, said "Experiencing Oxford's world-class academic environment and rich cultural heritage is an invaluable opportunity made possible by the University's subsidy. It allows me to make friends from diverse backgrounds and broaden my horizons. Beyond my own academic training, I look forward to getting to know peers and scholars from different disciplines, and exchanging perspectives on healthcare, technology, education, and social issues. I believe this interdisciplinary exposure will inspire valuable new ways of thinking in both my academic growth and future professional development."
Zhou Ying, a student from the Master of Science in Health Analytics and Management, offered by the GS, looks forward to seizing this rare opportunity to explore advanced AI applications and pursue academic growth at Oxford.
Indeed, through this long-standing partnership, the Postgraduate Summer School continues to foster meaningful academic exchange and strengthen Lingnan's involvement in the global research community. To learn more about the Postgraduate Summer School 2026, please visit: Postgraduate Summer School 2026 | School of Graduate Studies
Does a depressive mood inevitably lead to more pessimistic thinking or over-analysing? A global meta-analysis, the largest of its kind examining the relationship between a depressive mood and reality judgment, co-conducted by the Department of Psychology at Lingnan University has found that the key lies in the nature of the judgment. Overall, individuals in a depressive mood generally make more accurate judgments when handling self-referent tasks or complex issues requiring deep analysis. However, their accuracy is impaired as regards understanding others and interpreting interpersonal relationships. Researchers noted that the findings clarify a decades-long academic debate in psychology regarding whether a depressive mood allows individuals to perceive reality more objectively, and will aid in designing more targeted intervention strategies. The paper was published in Clinical Psychology Review, a top international academic journal in clinical psychology.
A global meta-analysis co-conducted by the Department of Psychology at Lingnan University finds that individuals in a depressive mood can make more accurate judgments in self-referent tasks requiring deep analysis.
The research team, comprising scholars from Lingnan University, the Polish Academy of Sciences in Poland, and The Chinese University of Hong Kong, aggregated psychological and clinical studies published globally between 1971 and November 2025 from three leading international academic databases: Web of Science, PsycINFO, and PubMed. Synthesising empirical data from 32,914 participants, the study examined the relationship between a depressive mood and judgmental accuracy across three distinct groups: non-depressed healthy controls, individuals with a self-reported depressive mood via questionnaires, and clinically diagnosed depressed patients, using known objective outcomes as the baseline for comparison.
The team integrated multiple classic psychological behavioural experiments in the study. The first type of experiment was the "green light test", which assessed judgment of control. Participants sat in front of a computer and chose whether or not to press a button to see if a green lightbulb would light up. In reality, the light was entirely randomised by a computer programme. The results showed that the healthy control group tended to believe they had a significant ability to control the light, exhibiting an optimistic bias. Conversely, individuals in a depressive mood understood that they had absolutely no control over the outcome.
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The second type of experiment was the "deception detection task" to test complex analytical capabilities. Participants watched multiple video clips of real people speaking and had to identify who was telling the truth and who was lying. Spotting deception requires multi-step logical deconstruction, representing a complex issue that demands deep analysis. The results indicated that in these complex tasks, individuals in a depressive mood achieved a higher level of analytical accuracy compared to the healthy control group.
The third type of experiment evaluated "other-referent tasks" testing the participants' ability to observe and decode the behaviours, emotional states, or social interactions of others, such as evaluating the actual emotional states of individuals in audio or video clips. The results revealed that the judgmental accuracy of individuals in a depressive mood lagged significantly behind. The study suggested that depressed individuals are more prone to misinterpret others' behaviour and reactions.
The research team explained that the first and second types of experiments involved self-referent judgments, such as evaluating one's own performance, assessing one's ability to influence outcomes, or facing complex tasks requiring multi-step analysis. Individuals in a depressive mood made slightly more accurate judgments than healthy controls because the non-depressed control group commonly exhibited an "optimistic bias". This bias acts as a healthy psychological defence mechanism that maintains self-esteem through over-optimism, causing people to overestimate the extent to which they can control outcomes.
However, the third type of experiment involved other-referent tasks, such as understanding the behaviour of others and interpreting interpersonal relationships. In these scenarios, participants with severe but not moderate or mild depressive symptoms were more prone to judgmental bias and demonstrated lower accuracy. This shows that the relationship between a depressive mood and judgmental accuracy varies significantly depending on the task and context; hence, a blanket assumption that a "depressive mood allows people to see reality more objectively" is inaccurate, especially for those in severe emotional distress, or with sleep problems, difficulty concentrating, or fatigue – all symptoms of clinical depression.
Prof Hodar Lam, lead and corresponding author of the study and Research Assistant Professor at Lingnan University.
Prof Hodar Lam, lead and corresponding author of the study and Research Assistant Professor of the Department of Psychology and Associate Programme Director of the MSc in Work and Organisational Psychology Programme at Lingnan University, stated that this global big-data study spanning nearly half a century provides a vital reference for Hong Kong citizens who face a fast-paced and stressful lifestyle. He said "From an evolutionary perspective, all emotions, positive and negative, help humans to survive. A depressive mood could trigger more analytical, problem-solving rumination and learnings from the negative emotions. A transient depressive mood in daily life is fundamentally different from clinical depression. Experiencing mild, short-term depressive or negative emotions in daily life does not necessarily mean a decline in cognitive capabilities. In tasks involving self-assessment, deep analysis, or complex judgments, individuals in a depressive mood are actually less susceptible to the ‘optimistic bias’ common to the healthy public, allowing them to make a more objective appraisal of their own situation and capabilities."
Prof Lam went on to explain "Society should avoid stereotyping and categorising all depressive moods as a lack of rational judgment. Equally, we must not misunderstand a depressive mood as an inherent advantage, thereby ignoring its potential risks. Since research shows that a depressive mood impairs accuracy in understanding others and interpreting interpersonal relationships, the judgmental bias of participants with more severe symptoms will increase. Therefore, people must take emotional health seriously. This area could become a key focus for future psychological interventions to design more targeted treatment and support strategies."
Prof Lam emphasised that to help others experiencing persistent emotional distress, first show empathy and validation instead of asking them to “think positively or rationally”, because their perceptions could be right. People with deteriorating depressive symptoms, or who find that their work, interpersonal relationships, or daily lives are being affected, are encouraged to seek professional help as a brave and responsible act of self-care.
The study was co-first authored by Dr June Yeung of the Polish Academy of Sciences and an alumna of Lingnan University. To read the full research paper, please visit: Depression and accuracy of judgment: A meta-analysis – ScienceDirect
