Research Program
How the past shapes perception and decisions, studied with computational models, neuroimaging, and data from multiple species
What I Study
Perception is not a snapshot. What we saw and judged a moment ago biases what we perceive now, a phenomenon called serial dependence. My research asks how the brain carries this history: which parts of the past are kept, how strongly they weigh on the present, and how their readout is controlled by the current task.
I build generative models, Kalman filters and latent dynamics models in which dependence on history emerges from Bayesian inference, and test their predictions in human behavior, fMRI and EEG, in mouse calcium imaging, and in brain-computer interfaces that run in real time.
Research Framework
Four stages, one modeling pipeline
Integration
New input is weighted by its reliability
Retention
Which parts of the past are kept, and for how long
Readout
How the task at hand controls what history is expressed
Monitoring
How confidence tracks the uncertainty of the readout
Computational Model: The Kalman Filter
The core working model has three latent states: a perception of the current input, a memory that integrates past observations, and a decision that generates the response. In this model each update is weighted by its precision, which reproduces serial dependence across sensory and contextual transitions, without any ad hoc learning rule.
- 1. Perception state: current sensory input
- 2. Memory state: integration of past information
- 3. Decision state: response generation
Across Species: Mouse Calcium Imaging
Do neurons in mouse cortex carry the same history from trial to trial? With the Max Planck Institute for Biological Intelligence, I analyze calcium imaging and EMG data, motion correction, ROI segmentation, and neuronal activity visualization, to test how sequential effects look at the neural level, across species.
Status: submitted to Neuron | Partner: MPI for Biological Intelligence
Application: Brain-Computer Interfaces
The same inference runs in real time. During my internship at the Munich Institute of Biomedical Engineering (TUM), I built hybrid fNIRS + EEG brain-computer interfaces with visual stimulation in virtual reality, decoding neural signals on the fly, where every millisecond of history matters.
Internship: MIBE, Technical University of Munich | Duration: 05/2024 - 11/2024
