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Autoshaped impulsivity: Some explorations with a neural network model.

Miguel Aguayo-Mendoza¹, Jonathan Buriticá¹, José E Burgos¹

¹Centro de Estudios e Investigación en Comportamiento, University of Guadalajara, Mexico.

Behavioural Processes

|April 28, 2024

Summary

This summary is machine-generated.

This study shows that the Diffuse Discrepancy (DD) model can simulate autoshaped impulsivity. Introducing a trace interval effectively eliminated this impulsive choice behavior in simulations.

Keywords:

Artificial neural networks Autoshaped Computational learning models Impulsivity Pavlovian contingencies Temporal discounting

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Area of Science:

Behavioral psychology
Computational neuroscience

Background:

Autoshaped impulsivity describes the preference for smaller, immediate rewards over larger, delayed ones.
Previous research (Alcalá, 2017; Burgos & García-Leal, 2015) explored this phenomenon.

Purpose of the Study:

To evaluate the impact of reinforcement delay and magnitude on Pavlovian contingencies.
To extend understanding of autoshaped impulsivity using the Diffuse Discrepancy (DD) model.
To analyze the effect of trace intervals on impulsive choice behavior.

Main Methods:

Utilized the Diffuse Discrepancy (DD) model with inhibitory units.
Trained the model on two signals with varying delays and reinforcement magnitudes.
Employed an ABA within-subject design in concurrent choice tasks without reinforcement or learning.

Main Results:

The DD model successfully simulated autoshaped impulsivity, aligning with prior studies.
The model predicted the elimination of autoshaped impulsivity when a trace interval was introduced.
Contextual and inhibitory units played a role in the model's predictions.

Conclusions:

The DD model provides a viable framework for understanding autoshaped impulsivity.
Trace intervals can mitigate impulsive choice behavior.
Findings support further investigation in both animal and human subjects.