Steven J. Luck

This study evaluates how SVM regularization and cross-validation parameters affect EEG decoding performance in binary and multiclass tasks, finding optimal settings for accuracy and effect size.

This paper introduces a population vector model of visual working memory designed for real-world scenes, showing it can predict behavior and brain activity in memory tasks.

This paper uses model-based EEG analysis to identify distinct neurocomputational mechanisms of learning impairments in MDD, BP, and SCZ compared to controls.

This study presents a rate-based neural network model with dendritic bistability to maintain novel graded patterns in working memory, showing robustness to perturbations and distinct functional neuron groups.

This paper introduces a working memory model using dendritic bistability to robustly maintain both stimulus location and amplitude without parameter fine-tuning.

This study evaluates how artifact correction and rejection affect SVM-based EEG decoding performance, finding that their combination rarely improves results but correction may still be necessary to avoid confounds.

This study investigates whether rare events enhance working memory representations by examining P3b responses and behavioral accuracy in an oddball paradigm.

The paper applies multivariate pattern analysis to ERP data to increase effect sizes and statistical power compared to traditional univariate methods.

The paper develops a method to select optimal filter settings for ERP data by maximizing signal-to-noise ratio while minimizing waveform distortion.

The paper recommends optimal filter cutoffs for seven common ERP components to balance signal-to-noise ratio and waveform distortion across different noise levels.

This paper evaluates artifact correction and rejection methods in ERP research, finding that correction reduces confounds and rejection reduces noise, improving data quality.

This study compares working memory dynamics in schizophrenia patients and healthy controls, finding distinct drift patterns in memory representations during delayed-response tasks.

The paper assesses ERP data quality across paradigms, participants, and scoring procedures using the SME metric, finding large variations in quality and highlighting the impact of quantification algorithms.

The paper re-analyzes ERP data from Bae & Luck (2018) using a more appropriate multiple comparison correction method, finding that major clusters remain significant while some minor clusters lose significance.

This study examines how reward influences selective attention in schizophrenia, finding that reaction times are slower in patients but reward and probability effects remain intact.