The rationale for Fitts’ law is that pointing tasks have the information-theoretic analogy of sending a signal over a noisy channel, thereby matching Shannon’s capacity formula. Yet, the currently received analysis is incomplete and unsatisfactory: There is no explicit communication model for pointing; there is a confusion between central concepts of capacity (a mathematical limit), throughput (an average performance measure), and bandwidth (a physical quantity); and there is also a confusion between source and channel coding so that Shannon’s Theorem 17 can be misinterpreted. We develop an information-theoretic model for pointing tasks where the index of difficulty (ID) is the expression of both a source entropy and a zero-error channel capacity. Then, we extend the model to include misses at rate ε and prove that ID should be adjusted to (1−ε)ID. Finally, we reflect on Shannon’s channel coding theorem and argue that only minimum movement times, not performance averages, should be considered.
A regression method to estimate a linear bound in the presence of outliers is discussed. An exponentially-modified Gaussian (EMG) noise model is proposed, based on a maximum entropy argument. The resulting "EMG regression" method is shown to encompass the classical linear regression (with Gaussian noise) and a minimum regression (with exponential noise) as special cases. Simulations are performed to assess the consistency of the regression as well as its resilience to model mismatch. We conclude with an example taken from a real-world study of human performance in rapid aiming with application to humancomputer interaction.
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