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Automated Visual Cognitive Tasks for Recording Neural Activity Using a Floor Projection Maze

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Empirically validating a computational model of automatic behavior shaping.

Vincent Berardi¹, Tian Lan², Ariane Guirguis²

¹Department of Psychology, Crean College of Health and Behavioral Sciences, Chapman University, Orange, California, United States of America.

|January 17, 2025

Summary

Concave-up shaping functions most effectively guide behavior, aligning with computational models. This finding supports the use of automated shaping routines in health interventions for exercise, smoking cessation, and diet.

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Area of Science:

Behavioral science
Human-computer interaction
Computational modeling

Background:

Automated behavior shaping routines can be integrated into health interventions using mobile sensing technology.
A prior computational model explored optimal arrangements for automatic behavior shaping procedures.
The accuracy of this computational model in reflecting actual human behavior remained unverified.

Purpose of the Study:

To experimentally validate a computational model for automatic behavior shaping.
To translate computational findings into a practical experimental setting.

Main Methods:

Fifty-four participants attempted to click a hidden target using a computer mouse, receiving auditory reinforcement.
A shaping function progressively narrowed the click-to-target distance threshold.
Linear mixed-effects models analyzed outcomes across concave-up, concave-down, and linear shaping functions, using Area Under Trajectory Curves and Time Until 10 Consecutive Target Clicks as metrics.

Main Results:

Concave-up shaping functions demonstrated the highest efficacy in shaping participant behavior.
Linear shaping functions yielded the second-best results, followed by concave-down functions.

Conclusions:

Concave-up shaping routines are most effective for generating target behavior, warranting further investigation in health behavior trials.
Developed automatic shaping routines have broad applicability in areas such as exercise, smoking, caloric intake, and screen time management.