  While action anticipation has garnered a lot of research interest recently,
most of the works focus on anticipating future action directly through observed
visual cues only. In this work, we take a step back to analyze how the human
capability to anticipate the future can be transferred to machine learning
algorithms. To incorporate this ability in intelligent systems a question worth
pondering upon is how exactly do we anticipate? Is it by anticipating future
actions from past experiences? Or is it by simulating possible scenarios based
on cues from the present? A recent study on human psychology explains that, in
anticipating an occurrence, the human brain counts on both systems. In this
work, we study the impact of each system for the task of action anticipation
and introduce a paradigm to integrate them in a learning framework. We believe
that intelligent systems designed by leveraging the psychological anticipation
models will do a more nuanced job at the task of human action prediction.
Furthermore, we introduce cyclic transformation in the temporal dimension in
feature and semantic label space to instill the human ability of reasoning of
past actions based on the predicted future. Experiments on Epic-Kitchen,
Breakfast, and 50Salads dataset demonstrate that the action anticipation model
learned using a combination of the two systems along with the cycle
transformation performs favorably against various state-of-the-art approaches.
