A quantitative measure for information transfer in human-machine control systems

This study is an exploration in measuring quantities of information transferred in the course of interaction in human-machine systems, which deemed necessary as fundamental support for interface design. We put forwards several possible measures found in previous research works, including the infamous Hick's law, and validate their applicability with dedicated experiment with 33 users performing selection tasks. The results suggest that interface “effective information capacity” factor, incorporating number of alternative targets (N) and logarithm of their vocabulary size (K, possible different symbols), provided better fit to the observed performance time, while Hick's law model and “interface message length” (IML) based models were clearly inferior. ANOVA suggested strong effect of interaction between N, K, and IML, but building corresponding conclusive model would require further theoretical research. We also calculated selection tasks throughput, which amounted to 59.0 bit/s in the experiment, steadily increasing per K and decreasing with age. Our findings might aid interface designers, in particular ones developing human-machine control systems that seek to maximize overall operating efficiency and minimize costly human errors.