1. F ITTS ’ L AW ○A model of human psychomotor behavior ○Human movement is analogous to the transmission of information ○Movements are assigned indices of difficulty (bits) ○In carrying out a movement task, the human motor system is said to transmit so many “bits of information” ○Human as a information processor ○One of the most robust, highly cited, and widely adopted models Fitts’ Law

2. S UMMARY 1.Information Theory Foundation ○Fitts’ idea 1.the difficulty of a task could be measured using the information metric, bits 2.In carrying out a movement task, information is transmitted through a human channel ○Shannon’s Theorem 17 ○C: information capacity (bits/s) ○B: channel bandwidth (1/s or Hz) Fitts’ Law

3. S UMMARY 2.Equation by Parts ○information capacity of the human motor system – index of performance (IP) – channel capacity (C) ○IP = ID/MT  MT = ID/IP ○Electronic signals analogous to movement distance or amplitude (A) and the noise analogous to the tolerance or width (W) of the target ○ID = log 2 (2A/W) ○By the regression line equation ○MT = a + b ID (1/b corresponds to IP) ○MT = a + b log 2 (2A/W) Fitts’ Law

4. S UMMARY 3.Physical Interpretation ○Predict movement time as a function of a task’s index of difficulty ○ID increases by 1 bit if target distance is doubled or if the size is halved ○a nonzero but usually substantial positive intercept – the presence of an additive factor unrelated to the ID ○ID as the number of bits of information transmitted ○IP as the rate of transmission ○IP is constant across a range of values for ID Fitts’ Law

5. DETAILED ANALYSIS 1.The Original Experiments ○Fitts’ paradigm – the reciprocal tapping task ○The rate of information processing is constant – IP = 10.1 bits/s (SD = 1.33 bits/s) ○MT = 12.8 + 94.7 ID with r = 0.9831 (IP = 10.6 bits/s) ○Difference due to a least-squares regression equation with a positive intercept ○A positive intercept reduces the slope of the line, thus increasing IP. Fitts’ Law

8. DETAILED ANALYSIS 2.Problem Emerge ○Upward curvature of MT away from the regression line for low values of ID ○The relative contribution of A & W in the prediction equation – the effect should be equal but inverse ○Reductions in target width cause a disproportionate increase in movement when compared to similar increase in target amplitude ○When ID is less than around 3 buts, movements are brief and feedback mechanisms yield to impulse-driven ballistic control Fitts’ Law

10. DETAILED ANALYSIS 3.Variations on Fitts’ Law ○Welford’s variation ○MT = a + b log 2 (A/W + 0.5) ○Higher correlation between MT and ID ○MT = a + b log 2 (A/W + 1) ○A negative rating for task difficulty when the targets overlap ○MT = a + b 1 log 2 A – b 2 log 2 W ○b 1 log 2 A -- Initial open-loop impulse toward a target ○b 2 log 2 W -- Feedback-guided final adjustment Fitts’ Law

12. DETAILED ANALYSIS 4.Effective Target Size ○Derived from the distribution of “hits” ○Information-theoretic metaphor ○The movement amplitudes are analogous to “signal” and that endpoint variability (viz., target width) is analogous to “noise” ○entrophy = log 2 [sqrt(2  e)*  ] = log 2 [4.133  ] Fitts’ Law

14. A PPLICATIONS O F F ITTS ’ L AW 1.The Generality of Fitts’ Law ○The rate of human information processing is constant across a range of task difficulties ○Higher IP for discrete tasks over serial tasks 4.Sources of Variation ○Device Differences ○ Task Differences ○Selection Technique ○Range of Conditions and Choice of Model ○Approach Angle & Target Width ○Error Handling ○ Learning Effects Fitts’ Law