LogRT-model for vigor learning in the monetary incentive delay task

Model overview from Willinger et al. (2021).

The functions provided in this repository provide the log-response time model for vigor learning in the monetary incentive delay task as used in Willinger et al. (2021) and Willinger et al. (2022). The scripts work for the TAPAS toolbox in MATLAB.

Installation

Copy the new model files *.m into you HGF/ directory of the TAPAS toolbox.

Instructions

For the model I used, you will need 3 trial-wise variables for each partipant:

• The (log) response time for the target (of each outcome!)
• The cue value (in your case +1/+5/-1/-5/0)
• The monetary outcome of the trial (+1/+5/-1/-5/0)

There are perception models with one single learning rate (tapas_rw_binary_dw.m), two learning rates (tapas_rw_binary_2lr_dw.m) for reward and loss, and the observation model of logRTs tapas_logrt_linear_binary_dw.m.

For each subject you should be able to fit the model using

estim = tapas_fitModel(log(rt),...
        [cue_value outcome],...
        'tapas_rw_binary_dw_config',...
        'tapas_logrt_linear_binary_dw_config',...
        'tapas_quasinewton_optim_config');

With your data looking like this

rt cue_value outcome
251 1 1
280 -5 0
240 -1 -1
.
.
.

Where rt is the vector of response times, and cue_val is a vector with cue values and outcome is the vector of, well, the outcome of each trial. Also consider cleaning your behavioral data before fitting the data (e.g., remove unlikely "real" response times like 10ms).

Credits

This software is based on the work of Chris Mathys (HGF Toolbox) that is part of the software collection TAPAS (Frässle et al., 2021).

References

• Frässle, S., et al. (2021). TAPAS: An Open-Source Software Package for Translational Neuromodeling and Computational Psychiatry. Frontiers in Psychiatry, 12:680811. https://doi.org/10.3389/fpsyt.2021.680811

• Willinger, D., Karipidis, I. I., Dimanova, P., Walitza, S., & Brem, S. (2021). Neurodevelopment of the incentive network facilitates motivated behaviour from adolescence to adulthood. NeuroImage, 237, 118186. https://doi.org/10.1016/j.neuroimage.2021.118186

• Willinger, D., Karipidis, I. I., Neuer, S., Emery, S., Rauch, C., Häberling, I., ... & Brem, S. (2022). Maladaptive avoidance learning in the orbitofrontal cortex in adolescents with major depression. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 7 (3), 293-301. https://doi.org/10.1016/j.bpsc.2021.06.005