To visualize licks around reward delivery times (ex, Fig. 2b), load a

trialData.mat
``` file into the workspace and run

plotLicks

Move all the collected data from a mouse (one

trialData.mat
``` file per day of behavior) into the same subfolder (ex, called ‘Data’).

Run the Matlab code

licksRelativeAnalysisDualCondExt_NI

Can run on different days during behavior, and the code will add to the analysis performed for each newly added

trialData
``` file.

Saves two data structures:

Cohort_mouse_analysis_tSchultz.mat
``` – anticipatory licking, time to first lick, and trial-based locomotor data.



* 

Cohort_mouse_analysis_running.mat

Also calculates and prints probe and anticipatory licking information.

• Exclude any mouse with

Mean full probe day ant licking to tone A response
``` of less than 0.2 (20% of the anticipatory window spent licking on the last day of training).

* Exclude any mouse with

Mean probe response/ Mean full probe day ant licking to tone A response

Copy the

analysis_tSchultz.mat
``` and 

analysis_running.mat

Run the Matlab code

licksAcrossMiceWithTreadmill
``` to combine and extract anticipatory licking and trial-based locomotor data across all the mice (from the 

analysis_tSchultz.mat

This saves the data structure

 cohort_grouped_analysis_n.mat
``` with the resized data for each mouse.

Also creates a variety of plots for visualization of mean and SEM.

• Figure 3 is used to create the plots seen in Fig. 2d-f.
• Figure 4 is used to create the plots seen in Ext. Fig. 3a.

Run the Matlab code

getAUCs
``` on the saved 

grouped_analysis.mat

Run the Matlab code

runningAcrossMice
``` to combine and extract total locomotor data across mice (from the 

analysis_running.mat