ALT The structure and training process of a DNN. A. The basic components of a DNN. B. The computations performed inside a neuron. C. The training process for minimizing loss (prediction error) using stochastic gradient descent via backpropagation.

ALT Applications of DNNs for advancing naturalistic affective research. A. DNNs provide a more scalable way to quantify behavior in naturalistic contexts. B. DNN-based quantifications can support better experimentation by facilitating naturalistic stimulus selection and manipulation. C. DNNs can serve as cognitive/neural models for many types of affective processes, capable of capturing interactive and nonlinear effects.