Related Experiment Video
Updated: May 28, 2026

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
Published on: January 23, 2017
Distinguishing anticipation from causality: anticipatory bias in the estimation of information flow
Daniel W Hahs1, Shawn D Pethel
1U.S. Army RDECOM, RDMR-WSS, Redstone Arsenal, Alabama 35898, USA.
Abstract:
We report that transfer entropy estimates obtained from low-resolution and/or small data sets show net information flow away from a purely anticipatory element whereas transfer entropy calculated using exact distributions show the flow towards it. This means that for real-world data sets anticipatory elements can appear to be strongly driving the network dynamics even when there is no possibility of such an influence. Furthermore, we show that in the low-resolution limit there is no statistic that can distinguish anticipatory elements from causal ones.
More Related Videos
Related Concept Videos
Hindsight Biases
The Anchoring-and-Adjustment Heuristic
Cause and Effect
The Availability Heuristic
Reason and Intuition
Theory of Attribution I: Correspondent Inference Theory

