Machine Learning for Computational Psychology
Authors: Sarah Brown
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | I assess candidate solutions to these problems using two test datasets representing different areas of psychology: the first aiming to build more objective Post-Traumatic Stress Disorder (PTSD) diagnostic tools using virtual reality and peripheral physiology, the second aiming to verify theoretical tenets of the new paradigm in a study of basic affect using functional Magnetic Resonance Imaging (f MRI). |
| Researcher Affiliation | Collaboration | Sarah M. Brown Northeastern University, Boston, MA Charles Stark Draper Laboratory, Cambridge, MA |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access (link or explicit statement of release) to the source code for the methodology described. |
| Open Datasets | No | The paper mentions 'two test datasets' for PTSD diagnosis and fMRI but does not provide concrete access information (links, DOIs, formal citations with author/year, or repository names) for them to be considered publicly available. |
| Dataset Splits | No | The paper mentions using 'two test datasets' but does not provide specific details on how these datasets were split into training, validation, or test sets (e.g., percentages, sample counts, or cross-validation specifics). |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., CPU/GPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions various methods and frameworks (e.g., SCRC, Bayesian nonparametric models, Gaussian Processes) and cites papers for them, but does not provide specific version numbers for any software dependencies used in the implementation. |
| Experiment Setup | No | The paper does not provide specific experimental setup details such as hyperparameter values, optimizer settings, or training configurations. |