Mapping decision states in the human brain during naturalistic gaming behavior
Poster No:
715
Submission Type:
Abstract Submission
Authors:
Ozan Vardal1, Hao Li1, Ruoguang Si2, Tianming Yang2, Deniz Vatansever1
Institutions:
1Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, Shanghai, 2Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, Shanghai
First Author:
Ozan Vardal
Institute of Science and Technology for Brain-inspired Intelligence, Fudan University
Shanghai, Shanghai
Institute of Science and Technology for Brain-inspired Intelligence, Fudan University
Shanghai, Shanghai
Co-Author(s):
Hao Li
Institute of Science and Technology for Brain-inspired Intelligence, Fudan University
Shanghai, Shanghai
Institute of Science and Technology for Brain-inspired Intelligence, Fudan University
Shanghai, Shanghai
Ruoguang Si
Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences
Shanghai, Shanghai
Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences
Shanghai, Shanghai
Tianming Yang
Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences
Shanghai, Shanghai
Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences
Shanghai, Shanghai
Deniz Vatansever
Institute of Science and Technology for Brain-inspired Intelligence, Fudan University
Shanghai, Shanghai
Institute of Science and Technology for Brain-inspired Intelligence, Fudan University
Shanghai, Shanghai
Introduction:
Cognitive neuroscience has traditionally relied on abstract laboratory tasks that are limited in capturing real-world dynamic cognition and behavior. Recent perspectives suggest that video games may offer a more promising tool for investigating cognitive processes like decision-making and planning, as they combine multiple cognitive processes within dynamic, ecologically-valid environments [1][5]. Meanwhile, advances in high-field MRI and individual-specific approaches have enabled precise functional mapping of cognitive processes during complex behaviors [2]. Here, we combine dense sampling at high-resolution 5T fMRI with a laboratory version of Pac-Man, previously validated in non-human primates [6], to investigate neural representations of decision-making during naturalistic gaming behavior.
Methods:
Five participants (mean age = 30.4 years, SD = 4.7, 2F/3M,) completed ten 10-minute runs of a laboratory Pac-Man task during 5T fMRI scanning (TR = 1.01s, TE = 22.2ms, 1.6mm iso voxels). In each game, participants navigated Pac-Man through a maze using a four-button box, collecting pellets while either fleeing from or chasing ghosts depending on their energized state. Each game allowed two attempts before resetting the maze with a random pellet layout. Participants completed an average of seven games per run, with only fully competed games included in the analysis. High-resolution frame-level behavioral data was recorded, including player button presses, positions, and ghost states. Behavioral analyses employed linear mixed-effects models to assess differences between chase and flee conditions while accounting for the nested data structure (multiple events within runs within participants). The neuroimaging data were preprocessed using HCP pipelines (QuNex [4]) and registered to the fsLR_32k cifti space. For statistical analysis we used a GLM, contrasting chase versus flee periods while controlling for visual and motor confounds. Group-level analysis averaged individual beta maps across participants for both dense (vertex-wise) and parcellated (Glasser 360-region [3]) cortical representations. The parcellated results were transformed to z-scores using a one-sample t-test across participants to identify regions showing consistent activation differences between chase and flee conditions.
Results:
Behavioral analyses revealed systematic differences between chase and flee conditions. During chase periods, participants showed significantly higher button press rates (0.86 vs 0.63 button presses per second, p = 0.006) and scored more points (2.61 vs 1.40 points per second, p < 0.001). The magnitude of these effects varied across participants, with greater individual variability observed in points scored (σ² = 0.437) compared to input rates (σ² = 0.034) during chase periods. At the neural level, the chase versus flee contrast revealed a distributed set of regions supporting strategic goal-directed behavior. Key activations (Z > 2.8) included left premotor regions supporting motor planning, premotor eye field supporting visual attention, and right superior parietal cortex supporting visuomotor integration. Additional engagement of temporo-parietal junction and fronto-opercular regions suggested a role for spatial awareness and executive control during chase behavior. Conversely, greater activity was centered on the anterior insula and cingulate cortices, indicating more pronounced salience processing during flee periods.
·Figure 1
·Figure 2
Conclusions:
Our findings reveal a distributed network of brain regions that supports distinct behavioral strategies during naturalistic gaming behavior. Taken together, this work demonstrates the potential of combining rich, naturalistic tasks with high-field imaging to reveal the neural basis of complex, goal-directed behavior in dynamic environments.
Higher Cognitive Functions:
Decision Making 1
Executive Function, Cognitive Control and Decision Making
Modeling and Analysis Methods:
Activation (eg. BOLD task-fMRI)
Motor Behavior:
Motor Planning and Execution
Novel Imaging Acquisition Methods:
BOLD fMRI 2
Keywords:
Cognition
Cortex
FUNCTIONAL MRI
Motor
1|2Indicates the priority used for review
Abstract Information
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3. Glasser, M. F. et al. A multi-modal parcellation of human cerebral cortex. Nature, 536, 171–178 (2016).
4. Ji, J. L. et al. QuNex—An integrative platform for reproducible neuroimaging analytics. Front. Neuroinform. 17, 1104508 (2023).
5. Wise, T., Emery, K., & Radulescu, A. (2024). Naturalistic reinforcement learning. Trends in Cognitive Sciences, 28(2), 144-158.
6. Yang, Q., Lin, Z., Zhang, W., Li, J., Chen, X., Zhang, J., & Yang, T. (2022). Monkey plays Pac-Man with compositional strategies and hierarchical decision-making. eLife, 11.