Date of Award
Bachelor of Arts
As a dual behavioral and computational neuroscience research project, this study first tested reproductive effects on attention, learning, and decision making using the Attentional Set-Shifting Task (AS-ST) and then a Machine Learning model was constructed to simulate perceptual judgments and decision making through reinforced learning. In the behavioral task, response times and errors from 5 primiparous (one-time mothers) and 4 nulliparous (never pregnant) Sprague-Dawley rats were recorded during four increasingly complex attention modulation and paired associative learning tasks. The Machine Learning model reconstructed each task's decision problems through representation of internal and external conditions, valuation, action, and outcome evaluation to complete the task optimally. Behavioral results indicated that maternal experience significantly improved task performance response time and accuracy. Results from training the artificial model to complete the task indicate the potential decision making and learning processes of the rodents. Behavioral results were compared to the model pre and post-training, suggesting that maternal experience is associated with increased learning and optimal decision making as seen in the post-trained model.
Freeman, Katie Coren, "From mind to machine : parity affects in the attentional set-shifting task in animal and machine models" (2013). Honors Theses. 1352.