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A fast implementation of the temporal context model (TCM).
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README.md

TCM

A fast and flexible implementation of the Temporal Context Model/Context Maintenance and Retrieval model of free recall.

This toolbox is designed to fit data from free recall studies, where partipants study a list and are then asked to recall the items in any order. The model fits not just statistics of free recall, such as the percentage of items recalled in each list, but instead uses the exact sequence of recalls made on each list to estimate different properties of each individual's memory system. The fitted model can then be used to generate new, simulated data, which can be analyzed and compared to the real data.

example fit Top row: serial position curve, probability of first recall, temporal organization, and semantic organization for data from Morton et al. (2013). Bottom row: data simulated using TCM with maximum likelihood parameters estimated for each individual subject. See Morton & Polyn (2016) for model details.

The main code is implemented in Matlab for ease of use, but the most computationally intensive work is implemented in compiled code written in c++. This makes evaluating a model about 8-70X faster (depending on the version of the model used) than is possible using pure Matlab code, making data fitting much faster. For example, fitting 373 recall events from free recall of 30 lists, using a model with 11 parameters, takes about 30 seconds on a fast desktop computer. See tcm/tests/run_logl_fit.m for details.

Features

The current implementation includes the following features for simulating recall data:

  • free parameters that control how context evolves during learning and recall
  • two different mechanisms for fitting the primacy effect (enhanced learning and start-of-list context reinstatement)
  • a number of mechanisms for simulating the effects of semantic similarity between items on a list, including incorporation of semantic features into context
  • mechanisms for simulating distraction during a list and between the end of a list and recall

The following features are part of the CMR framework, but are not currently implemented here:

  • support for different subregions of context that evolve at different rates
  • support for context disruption at specific boundaries between items
  • support for simulating multiple lists at a time (currently, all individual lists are simulated independently, with the model state reset between lists)

Installation

Download or clone the code project to some local project_directory. If cloning, you may need to first install git-lfs to get the sample data files, which are used to run tests of the code. In Matlab:

cd project_directory
init_tcm

This will add the necessary directories to your Matlab path. To compile the c++ code for your local machine:

cd project_directory/src
mex tcm_matlab.cc parameters.cc paramArray.cc recall.cc network.cc weights.cc context.cc 

You may need to first specify some settings for your compiler. To test your installation, run:

result = run_tests_tcm;
table(result)

This will run a set of tests on sample data and show the results. If any of the tests in test_logl failed, this may be due to a problem calling the binary version of TCM.

To analyze real or simulated free recall data, get a copy of EMBAM. EMBAM is not required for running simulations or parameter fits, but is required to run some of the analysis code in exp/cfrl. Download or clone a copy, then cd to that directory and run init_embam to set your path to include all subdirectories.

Getting Started

To get an idea of how to run a fit of the model to some free recall data, look at tcm/tests/run_logl_fit.m. It will fit a relatively simple version of TCM to some sample data (or other free recall data), determine the set of parameters that maximizes the likelihood of the data, and generate simulated data based on the best-fitting parameters. These simulated data can then be analyzed in a similar way to actual data, for example to calculate a serial position curve for both the data and the model.

References

Morton, N. W., Kahana, M. J., Rosenberg, E. A., Baltuch, G. H., Litt, B. B., Sharan, A. D., et al. (2013). Category-specific neural oscillations predict recall organization during memory search. Cerebral Cortex, 23(10), 2407–2422. http://doi.org/10.1093/cercor/bhs229

Kragel, J. E., Morton, N. W., & Polyn, S. M. (2015). Neural activity in the medial temporal lobe reveals the fidelity of mental time travel. The Journal of Neuroscience : the Official Journal of the Society for Neuroscience, 35(7), 2914–2926. http://doi.org/10.1523/JNEUROSCI.3378-14.2015

Morton, N. W., & Polyn, S. M. (2016). A predictive framework for evaluating models of semantic organization in free recall. Journal of Memory and Language, 86, 119–140. http://doi.org/10.1016/j.jml.2015.10.002

Morton, N. W., & Polyn, S. M. (2017). Beta-band activity represents the recent past during episodic encoding. NeuroImage, 147, 692–702. http://doi.org/10.1016/j.neuroimage.2016.12.049

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