Food Choices after Cognitive Load: An Affective Computing Approach

Abstract

Psychology and nutritional science research has highlighted the impact of negative emotions and cognitive load on calorie consumption behaviour using subjective questionnaires. Isolated studies in other domains objectively assess cognitive load without considering its effects on eating behaviour. This study aims to explore the potential for developing an integrated eating behaviour assistant system that incorporates cognitive load factors. Two experimental sessions were conducted using custom-developed experimentation software to induce different stimuli. During these sessions, we collected 30 h of physiological, food consumption, and affective states questionnaires data to automatically detect cognitive load and analyse its effect on food choice. Utilising grid search optimisation and leave-one-subject-out cross-validation, a support vector machine model achieved a mean classification accuracy of 85.12% for the two cognitive load tasks using eight relevant features. Statistical analysis was performed on calorie consumption and questionnaire data. Furthermore, 75% of the subjects with higher negative affect significantly increased consumption of specific foods after high-cognitive-load tasks. These findings offer insights into the intricate relationship between cognitive load, affective states, and food choice, paving the way for an eating behaviour assistant system to manage food choices during cognitive load. Future research should enhance system capabilities and explore real-world applications.

NeurostimML: A machine learning model for predicting neurostimulation-induced tissue damage

“…In supervised learning (e.g., k-nearest neighbours or multilayer perceptron), the non-linear relationship between input variables (features) and output targets (labels) is uncovered using training instances, which can be subsequently used for prediction on new instances (testing instances) 29 . Supervised learning algorithms have been widely used in areas of neuroscience and neural engineering research including brain imaging analysis [30][31][32] , neuroinformatics [33][34][35] , and behavioural analysis [36][37][38] .…”

NeurostimML: a machine learning model for predicting neurostimulation-induced tissue damage

“…Subsequently the trained algorithm may be used for prediction on new, untested stimulation parameters (testing instances) [29]. The machine learning approach has been widely used in areas of neuroscience and neural engineering research including brain imaging analysis [30][31][32], neuroinformatics [33][34][35], and behavioral analysis [36][37][38]. Herein, we extend the application of machine learning models to predict electrical stimulation-induced neural tissue damage.…”