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Variable Precision

This repository contains the DataJoint pipeline for the publication Shen and Ma, 2019

Link to the publication: https://psycnet.apa.org/record/2018-51674-001

This repository used DataJoint Matlab to implement the data pipeline, for an introduction to DataJoint, please visit https://datajoint.io/

Here is the Diagram of the data pipeline, containing the major tables used in this pipeline. Diagram

  • Tables corresponding to the experiments and behavioral data and statiscis: Experiment, Subject, Recording, Data, DataStats, DataStats2D, Performance.

  • Tables corresponding to the model and model fits: Model, InitialPoints, ParameterRange, RunBps, FitParsEvi*, FitPrediction*.

  • Green squares are manual tables, blue dots are imported tables, red stars are computed tables.

The code that define the above tables and their computations is in the package +varprecision.

The orignal behavioral data was stored in the directory VP_data/.

Decision rules used in this publication are in the directory +varprecision/+decisionrule_bps.

Directory scripts/ provides scripts that help run the data pipeline.

Directory utils/ provides some utility functions used in this project.

Directory +varprecision/+plots provides some plotting functions.

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source paper: Shen, S., & Ma, W. J. (2019). Variable precision in visual perception. Psychological review, 126(1), 89.

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  • MATLAB 98.7%
  • Objective-C 1.3%