Open Source Python Psychology Software

IRAMUTEQ : Interface de R pour les Analyses Multidimensionnelles de Textes et de Questionnaires. Logiciel de traitement de données pour des corpus texte ou de type individus/caractères. Permet notamment de réaliser des analyses de type "ALCESTE"

Python and pygame cross platform program to exercise the brain with the intention of enhancing useful field of view, also known as speed of responsiveness. This yields benefits in driving, motility, and even depressive symptoms, as described in research papers of randomized, double blind trials. Whether ufov exercise the brain to yield those benefits is unknown, since there are no research studies using it. The research is conducted with a commercial product that I have not seen or used. I used the description of what useful field of view is supposed to do to create this program for experimental purposes. I notice personal changes from using the program, but that is only anecdotal evidence. I was especially struck by the research that found a significant decrease in dementia for those who used such a program to exercise their brain. For a full description of the program, and links to some information about useful field of view, see the project web page.

psignifit is a toolbox to fit psychometric functions and test hypotheses on psychometric data. This is version 3 which will now predominantly support python.

The program will convert to text an image, such as a photo , with the purpose of analyzing it to spot intelligible words. Use the program with photos of clouds, sea, soil, vegetation or any other photo of natural or man-made semi-homogeneous configuration, to reveal the hidden universal-philosophical messages of the image. You can also use it on photos of people or art pieces to have a psychological insight of the person portrayed or of the image author. The resulting text will be a long string of mostly repeating characters but, every so often, you will notice an intelligible word, either spelled normally or with repeated letters, missing letters or anagrammed letters.

Program to help with the procedure called theWork of ByronKatie. It is not standalone executable on windows or osx. It will require the installation of python, wxPython, and pygame, all free. Links on the website.

The Model Zoo of Cognitive Diagnosis Models, including classic Item Response Ranking (IRT), Multidimensional Item Response Ranking (MIRT), Deterministic Input, Noisy "And" model(DINA), and advanced Fuzzy Cognitive Diagnosis Framework (FuzzyCDF), Neural Cognitive Diagnosis Model (NCDM), Item Response Ranking framework (IRR), Incremental Cognitive Diagnosis (ICD) and Knowledge-association baesd extension of NeuralCD (KaNCD). Cognitive diagnosis model (CDM) for intelligent educational systems is a type of model that infers students' knowledge states from their learning behaviors (especially exercise response logs). Typically, the input of a CDM could be the students' response logs of items (i.e., exercises/questions), the Q-matrix that denotes the correlation between items and knowledge concepts (skills). The output is the diagnosed student knowledge states, such as students' abilities and students' proficiencies on each knowledge concepts.

Datasets in Education and convenient interface for downloading and preprocessing dataset in education. The CLI tools to quickly convert the "raw" data of the dataset into "mature" data for knowledge tracing task. The "mature" data is in json sequence format and can be modeled by XKT and TKT(TBA) The analysis dataset tool only supports the json sequence format. To check the following statical indexes of the dataset. In order to better verify the effectiveness of the model, the dataset is usually divided into train/valid/test or using kfold method. Each item in the sequence represents one interaction. The first element of the item is the exercise id (in some works, the exercise id is not one-to-one mapped to one knowledge unit(ku)/concept, but in junyi, one exercise contains one ku) and the second one indicates whether the learner correctly answers the exercise, 0 for wrongly while 1 for correctly.

FreeBrain will be an application for the analysis of 2D neurophysiological signals, like heart-rate, EEG or eyemovements. It is aimed at those who want intuitive or free software and those who want a customizeble tool that can handle mixed datasets.

NeuroKiwi is the application of the Associative Model (AM, previously dubbed AMDroid), being a model storing relational change-detections. The application tries to emulate ground-up knowledge accumulation with limited or no prior knowledge of shapes or patterns.

With this Python module (based on C++ code) it is possible to create a Brain Comp. Interface (BCI) and herewith to get EEG data online and evaluate the data while receiving it. - Up to now, this tool relies on the Brain Vision Rec. as EEG software -

Convert a sound file to text you can analyze to spot hidden words for psychological or universal-philosophical interpretation. The program will convert a sound file to text, with the purpose of analyzing it to spot intelligible words. Use the program with songs, movie or TV dialogue, private recordings, to reveal the hidden text messages of the sound. The resulting text will be a long string of mostly repeating characters but, every so often, you will notice an intelligible word, either spelled normally or with repeated letters, missing letters or anagrammed letters. It takes a little allegoric-Freudian psychological exegetic ability to elaborate the right chain of mind associations to infer a convincing interpretation of the resulting text. You may want to share the text with a psychologist to get a professional opinion on your interpretation, but sometimes the meaning will be surprisingly obvious.

The app reads content from a text file and converts it to a BMP image. You can have fun trying to spot recognizable shapes (pareidolias) in the resulting image.

Allows a dominant to set a task for a submissive, keeping him/ner busy and under control

The program can read a text file and count the occurrences of single words and clusters of 2 and 3 words. The resulting list will be sorted in descending order (highest frequency on top).