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Undergraduate researcher with interests in applied Machine Learning in Psychology and Neuroscience Research. Member of 1step2life lab (Dr. Nancy Darling) and CASH lab (Dr. Kenneth Allen). Past summer fellow TarrLab (Dr. Michael Tarr) www.mkramerpsych.com
June 2018 - August 2018
Validation of the Emotional Stop Signal Task. The ESST serves as an objective measure of emotion dysregulation, eschewing the need to rely on potentially biased self-report measures.
Chronic pain affects between 15 to 40% of adolescents worldwide. The impact and prevalence of chronic pain can be felt every day in terms of missed school days, strained familial relationships, and financial stress. While rehabilitation programs specifically designed for chronic pain management exist, they cannot always adapt to the idiosyncratic n...
A Residual Neural Network model was employed on a subset of the VGGFace2 dataset to examine the role that color plays as a cue to sex classification. Results from Yip & Sinha (2002) were extended to a deep learning framework and preliminary analysis into the entanglement of color and sex in CNN representations was conducted.
Ohio Undergraduate Psychology Research Conference 2019 Presentation by Max Kramer In collaboration with Nancy Darling, Rich Salter, and Keith Downing
Ohio Undergraduate Psychology Research Conference 2018 Presentation Presented by Max Kramer In collaboration with Nancy Darling, Rich Salter
Bayesian Parameter Estimation of the reaction time results of the Emotional Stop Signal Task (Allen & Hooley, 2015). Predictive modeling of output parameters and examination of the impact of valence coding in responses to the modified stimuli.
Examine the independent and interactive effects of emotion reactivity (measured in multiple modalities) and incentive sensitivity to determine whether these factors additively contribute to the classification of specific forms of psychopathology. This project involves secondary analyses across several combined datasets including diverse clinical and non-clinical populations.