Prospective contingency explains behavior and dopamine signals during associative learning

Associative learning depends on contingency, the degree to which a stimulus predicts an outcome. Despite its importance, the neural mechanisms linking contingency to behavior remain elusive. In the present study, we examined the dopamine activity in the ventral striatum—a signal implicated in associative learning—in a Pavlovian contingency degradation task in mice. We show that both anticipatory licking and dopamine responses to a conditioned stimulus decreased when additional rewards were delivered uncued, but remained unchanged if additional rewards were cued. These results conflict with contingency-based accounts using a traditional definition of contingency or a new causal learning model (ANCCR), but can be explained by temporal difference (TD) learning models equipped with an appropriate intertrial interval state representation. Recurrent neural networks trained within a TD framework develop state representations akin to our best ‘handcrafted’ model. Our findings suggest that the T..., , # Prospective contingency explains behavior and dopamine signals during associative learning

## Directory Structure

```
.
├── README.md
├── Code/
│ └── Code/
│ ├── checked_figure_code/ # R code for generating figures
│ ├── matlab_simulation_code/ # MATLAB code for TD and ANCCR simulations
│ ├── photometry_data_analysis/# Python code for photometry analysis
│ ├── python_simulation_code/ # Python code for TD and RNN simulations
│ ├── r_analysis_code/ # R code for statistical analysis
│ └── value_rnn/ # Python package for value-RNN models
└── Data/
├── photo_data.parquet # Processed photometry data
├── lick_data.parquet # Licking behavior data
├── trial_data.parquet # Trial-level behavioral data
├── mouse_table.csv # Mouse metadata (group, sex, photometry)
├── Photometry Mice/ # Photometry and behavioral data
│ ├── Raw_data/ # ...,