Purpose:
To investigate cognitive and language processes through participants’ behavioral responses in controlled tasks.

My Experience:
I developed and implemented experiments such as lexical decision tasks, change detection tasks, and hybrid-maze sentence processing. In these studies, I measured reaction times and accuracy to assess language processing strategies.

Tools:

• Conducting Experiments: E-prime 2.0/3.0, PsychoPy

• Stimulus Extraction: Python, R

• Preprocessing Voice Stimulus: Praat

• Data Analysis and Visualization: R

Purpose:
To record real-time brain activity and analyze the neural basis of cognitive functions

My Experience:
I conducted EEG studies examining event-related potentials (ERPs) during lexical decision tasks, focusing on the neural correlates of syllable frequency. I also investigated the memory enhancement effect of tACS during change-detection tasks.

Tools:

• Conducting Experiments: E-prime 2.0/3.0

• Data preprocessing: MATLAB (EEGLAB, FieldTrip)

• Data Analysis and Visualization: R, MNE-Python

Data Analysis:

• Event-related potential (ERP) analysis

• Time-frequency-based analysis

• Cluster-based permutation tests

• EEG decoding (Support Vector Machine, SVM)

Purpose:
To measure brain activity using Blood Oxygenation Level Dependent (BOLD) signals with high spatial resolution, enabling the investigation of neural mechanisms underlying language processing.

My Experience:
I participated in fMRI studies examining neural activation patterns during syllable processing and working memory tasks. My responsibilities included participant preparation and basic preprocessing of imaging data.

Tools:

• Conducting Experiments: E-prime 2.0

• Data preprocessing: MATLAB (SPM)

Purpose:
To non-invasively modulate brain oscillatory activity, enabling causal investigation of neural dynamics involved in cognitive processing.

My Experience:
I conducted tACS experiments targeting working memory and language-related brain regions, especially the dorsolateral prefrontal cortex. My responsibilities included electrode placement, stimulation protocol setups, monitoring participant safety during sessions, and data analysis.

Tools:

• Conducting Experiments: E-prime 3.0

• Data preprocessing: MATLAB (EEGLAB, FieldTrip)

• Data Analysis and Visualization: R, MNE-Python

Purpose:
To develop and validate a syllable network-based Korean lexical recognition model that integrates computational modeling with behavioral and neural data to predict human language processing mechanisms.

My Experience:
I designed and assisted this research through the following stages:

• Network Construction:
  Built orthographic/phonological syllable networks using Korean lexical databases. 

• Model Development:
  Created an Interactive Activation (IA) framework-based model incorporating psycholinguistic variables and network metrics. 

• Validation:
  Tested model predictions against behavioral data (reaction times, accuracy) and EEG metrics (ERP components, time-frequency analyses) from lexical decision tasks.

Tools:

• Computational Modeling: Python (networkx), R (igraph)

• Validation (planned): Python (PyTorch)