Tutorial : applying machine learning in behavioral research

Machine-learning algorithms hold promise for revolutionizing how educators and clinicians make decisions. However, researchers in behavior analysis have been slow to adopt this methodology to further develop their understanding of human behavior and improve the application of the science to problems of applied significance. One potential explanation for the scarcity of research is that machine learning is not typically taught as part of training programs in behavior analysis. This tutorial aims to address this barrier by promoting increased research using machine learning in behavior analysis. We present how to apply the random forest, support vector machine, stochastic gradient descent, and k-nearest neighbors algorithms on a small dataset to better identify parents of children with autism who would benefit from a behavior analytic interactive web training. These step-by-step applications should allow researchers to implement machine-learning algorithms with novel research questions and datasets

Stereotypic head twirls, but not pacing, are related to a ‘pessimistic’-like judgment bias among captive tufted capuchins (Cebus apella)

Abnormal stereotypic behaviour is widespread among captive non-human primates and is generally associated with jeopardized well-being. However, attributing the same significance to all of these repetitive, unvarying and apparently functionless behaviours may be misleading, as some behaviours may be better indicators of stress than others. Previous studies have demonstrated that the affective state of the individual can be inferred from its bias in appraising neutral stimuli in its environment. Therefore, in the present study, in order to assess the emotional state of stereotyping individuals, 16 captive tufted capuchins (Cebus apella) were tested on a judgment bias paradigm and their faecal corticoid levels were measured in order to assess the intensity of the emotional state. Capuchins with higher levels of stereotypic head twirls exhibited a negative bias while judging ambiguous stimuli and had higher levels of faecal corticoids compared to subjects with lower levels of head twirls. Levels of stereotypic pacing, however, were not correlated with the monkeys’ emotional state. This study is the first to reveal a positive correlation between levels of stereotypic behaviour and a ‘pessimistic’-like judgment bias in a non-human primate by employing a recently developed cognitive approach. Combining cognitive tests that evaluate the animals’ affective valence (positive or negative) with hormonal measurements that provide information on the strength of the emotional state conduces to a better understanding of the animals’ affective state and therefore to their well-being