Scoring Graphical Responses in TIMSS 2019 Using Artificial Neural Networks.

Automated scoring of free drawings or images as responses has yet to be used in large-scale assessments of student achievement. In this study, we propose artificial neural networks to classify these types of graphical responses from a TIMSS 2019 item. We are comparing classification accuracy of convolutional and feed-forward approaches.

Sorry, baby: Infant faces reach awareness more slowly than adult faces.

Past research has found an attentional bias for positive relative to neutral stimuli, with a greater attentional bias for stimuli that are more motivationally relevant. Baby faces are an example of a motivationally relevant stimulus because they elicit caretaking behaviors. Building on previous work demonstrating that baby faces capture attention, the current study used breaking continuous flash suppression (bCFS) to investigate whether infant faces are prioritized for access to awareness.