Exploring Cognitive Variability in Interactive Museum Games
Abstract
1. Introduction
2. Background, Related Work, and Research Objective
2.1. Theoretical Background
2.2. Related Works
2.2.1. Games in the Cultural Heritage Domain
2.2.2. Cognitive Differences in Games and Cultural Heritage
- Cognitive styles. The FD-I cognitive style has been one of the most frequently studied constructs, particularly due to its implications for visual exploration and information processing. FI visitors tend to engage in analytical and structured processing, while FD visitors typically adopt a more holistic, context-sensitive approach. These styles have been shown to influence interaction behavior, game navigation, and information recall in cultural heritage games [2]. Empirical studies reveal that FD and FI users demonstrate distinct gaze behaviors during gameplay. For example, FI individuals produce longer and more targeted fixations on salient areas. At the same time, FDs exhibit more dispersed and shorter scanpaths [2]. These visual behavior differences are mirrored in interaction patterns. FIs are more likely to explore detailed game elements and perform better in tasks requiring focused attention and cognitive restructuring. In contrast, FDs may benefit more from structured gameplay or socially-driven scenarios [2]. Such distinctions are particularly evident in tasks involving visual search or item identification, which are common in heritage games.
- Cognitive characteristics. Analytic reasoning and cognitive flexibility have been increasingly used to differentiate styles of game playing. For example, strategic players often display an analytic cognitive style and a high need for cognition, preferring games that require planning and deliberate decision-making. In contrast, non-strategic or impulsive players may rely more on intuitive thinking and seek immediate rewards, reflecting a faster, less reflective game approach [18]. Studies on collaborative games have shown that players’ performance and interaction styles vary significantly with their cognitive profiles, influencing how they process contextual and visual information [19].
- Cognitive levels. They influence how users interact with educational games and the benefits they derive. At the early cognitive level, educational games that focus on basic classification, color and shape recognition, and simple number concepts have been shown to enhance foundational thinking skills [20]. Games that require memory, sequencing, and logical thinking are more suitable at the developing stage, helping to deepen understanding of abstract concepts and improve critical thinking abilities [21]. At the advanced cognitive level, games that include strategic complexity, adaptive challenges, and narrative engagement provide optimal stimulation. Advanced systems that dynamically adjust game difficulty and content based on the learner’s performance (e.g., those using fuzzy reasoning models) could further enhance learning outcomes by tailoring content to individual cognitive profiles [22]. Moreover, research has shown that different cognitive levels shape how they interpret, interact with, and internalize digital museum content, in game-based exploration (e.g., character-driven guides or VR-based tasks), influencing their ability to process narrative content, navigate interactive tasks, and sustain attention across multimodal experiences [23] and enhancing meaning-making and memory for specific cognitive levels (e.g., less cognitively advanced audiences) [24]. Recent research has expanded these investigations to XR and multimodal settings in recent years, emphasizing the need for inclusive design strategies that account for cognitive variability [25]. Emerging work highlights the need to bridge traditional user modeling approaches with real-time multimodal sensing to enable responsive and personalized learning pathways. As such, integrating cognitive characteristics into adaptive frameworks is expected to grow, fostering personalized engagement with cultural content across diverse audiences and platforms.
2.3. Research Objective
3. Study
3.1. Methodology
3.1.1. Null Hypotheses
3.1.2. Dataset and Data
- User interaction data capture aspects of player behavior during the game: Time_Spent represents the total time (in seconds) a participant engaged with the game and is stored as a continuous numeric variable, Total_Actions record the number of interactions performed, Correct_Responses_Ratio represents the ratio of correctly answered challenges, and Hint_Usage represents the number of hints used by the participant.
- Affective and performance states represent high-level behavioral and emotional indicators during gameplay: Engagement_Level reflects the participant’s inferred engagement and is recorded as a nominal variable with categories such as Low, Medium, and High; Game_Completion_Status is a nominal variable indicating whether the participant completed the game session; Facial_Expression_Sentiment captures affective state using emotion recognition categories (e.g., Neutral, Frustrated), and Performance_Level is a nominal classification of overall task performance based on predefined success criteria. We note that the dataset does not provide technical documentation about the emotion recognition method used to derive these sentiment labels (e.g., model type, training data, or accuracy), which limits our ability to assess their validity within a digital museum context.
- Sensor-based interaction measures include continuous metrics derived from real-time user interaction and physiological responses: Eye_Tracking_Focus_Duration measures the total duration (in seconds) of gaze focus on relevant game elements; Touch_Interactions counts the number of physical touch-based inputs recorded during the session, and Reaction_Time captures the average time (in seconds) the participant took to respond to in-game prompts or tasks.
3.1.3. Procedure
3.2. Results
3.2.1. User Interaction
3.2.2. Affection and Performance
3.2.3. Sensor-Based Interaction
4. Discussion
4.1. Contribution
4.2. Theoretical and Practical Implications
4.3. Limitations
4.4. Future Research Directions
5. Conclusions
Funding
Data Availability Statement
Conflicts of Interest
1 | Dataset URL: https://www.kaggle.com/datasets/ziya07/museum-game-interaction-dataset/data (accessed on 20 June 2025) |
2 | See note 1 above |
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Raptis, G.E. Exploring Cognitive Variability in Interactive Museum Games. Heritage 2025, 8, 267. https://doi.org/10.3390/heritage8070267
Raptis GE. Exploring Cognitive Variability in Interactive Museum Games. Heritage. 2025; 8(7):267. https://doi.org/10.3390/heritage8070267
Chicago/Turabian StyleRaptis, George E. 2025. "Exploring Cognitive Variability in Interactive Museum Games" Heritage 8, no. 7: 267. https://doi.org/10.3390/heritage8070267
APA StyleRaptis, G. E. (2025). Exploring Cognitive Variability in Interactive Museum Games. Heritage, 8(7), 267. https://doi.org/10.3390/heritage8070267