HUMANIST divides the research activity into three directions:

Leveraging Individual Differences in Information Visualization: Researchers have increasingly applied Psychology theories to the Human-Computer Interaction (HCI) field to inform design choices and understand how individual differences affect technology use. Among psychological constructs, personality traits and cognitive abilities have shown promise in Information Visualization (InfoVis). I will investigate how these psychological traits influence users’ interaction with and comprehension of data visualizations. In particular, I aim to identify how these individual differences affect visualization preferences, interpretation, and decision-making. The goal is to develop adaptive visualization strategies that personalize visual content based on user profiles to enhance effectiveness and user experience.

Cognitive Biases in Information Visualization: While human perception has been well-studied, cognitive biases (i.e., systematic deviations from rational thinking) remain underexplored in InfoVis. I plan to explore the presence and mechanisms of cognitive biases within visualization contexts. Through controlled experiments and user studies, I will assess how different visualization techniques trigger or mitigate these biases, and examine the extent to which such effects interact with user characteristics.

Trust in Information Visualization: Trust in InfoVis is crucial yet understudied. Few empirical studies offer guidance on how trust is built through visualizations. Measurement tools are still being developed, and definitions of trust in this context remain inconsistent. Building on the need for empirical foundations, I will examine how trust is formed and maintained in visualization-based communication. My work will focus on how visual design choices, data framing, and individual traits influence both cognitive and affective dimensions of trust. I aim to contribute to the development of reliable measurement tools and conceptual clarity in this area, ultimately supporting the design of trustworthy visual narratives and decision support systems.

Despite promising results, the study of individual differences in InfoVis remains underdeveloped compared to related fields like perception theory. The same applies to the exploration of cognitive biases and trust in InfoVis: while critical, these areas suffer from a lack of empirical evidence, standardized measures, and clear definitions. Furthermore, digital transformation initiatives often overlook user diversity, leading to one-size-fits-all solutions that may fail to account for how different users interpret and trust visual data. This gap presents a pressing need for a user-centric, evidence-based research agenda that can inform the design of visualization systems in high-stakes decision-making environments.

To address these challenges, over the next five years, I will conduct and supervise a series of user studies that combine surveys and questionnaires, controlled experiments, and semi-structured interviews.

Data will be analyzed through statistical methods and thematic analysis. I will use validated instruments to collect data and develop experimental setups that account for individual differences. The methodology includes building detailed user profiles, designing adaptive visualization mechanisms, and creating new measurement apparatus.

To support this research, I will rely on four pillars:

•      Collaborations: Established partnerships with the HUMAN Lab, ITI/LARSyS, GAIPS/INESC-ID, and CICPSI.

•      Funding: Applying for national and international research grants.

•      Dissemination: Publishing in top-tier venues and engaging with public and academic audiences.

•      Human Resources: Supervising MSc students and collaborating with interdisciplinary teams.

This research contributes to foundational knowledge in HCI, InfoVis, and Psychology by:

•      Introducing personality and cognitive profiles into visualization design.

•      Pioneering the study of cognitive biases in InfoVis.

•      Providing the first measurable effect of certain personality traits and biases on visualization interpretation and decision-making.

•      Proposing the first steps toward a validated definition and measurement of trust in InfoVis.

 Results will bridge gaps in current research, particularly the literature gaps outlined by Liu et al. (2020), and inform future studies in decision support systems, educational tools, and adaptive visualization platforms.

The broader societal impact of this research lies in its alignment with digital transformation and public policy goals:

•      Trust and fairness: By identifying and mitigating cognitive biases and fostering trust, this work supports equitable digital systems that avoid misleading citizens or manipulating democratic processes.

•      Personalization: Tailoring visualizations to user profiles improves the quality of decisions in healthcare, public administration, marketing, and finance.

•      Public service and information disorder: By focusing on how individuals process and trust information, the project aids in combating dis- and misinformation and improving public communication.

My experience and planned contributions position me to strengthen ISCTE's international competitiveness and support the SocioDigitalLab for Public Policy's thematic goals, especially those related to citizen trust, digital education, and public-sector innovation.

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With the objective to increase the research activity directed towards the achievement of the United Nations 2030 Sustainable Development Goals, the possibility of associating scientific projects with the Sustainable Development Goals is now available in Ciência_Iscte. These are the Sustainable Development Goals identified for this project. For more detailed information on the Sustainable Development Goals, click here.