1. 1. INTODUCTION ○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. 2. SUMMARY 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. 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. 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 – Langolf, Chaffin, and Foulke (1976) – IP decreases as the limb changes from the finger to the wrist to the arm Fitts’ Law

5. 4.Derivation From a Theory of Movement ○deterministic iterative-correction model (Crossman and Goodeve, 1963/1983) Fitts’ Law

6. 3. D ETAILED A NALYSIS 1.The Original Experiments ○Fitts’ paradigm – the reciprocal tapping task Fitts’ Law

7. ○MT = 12.8 + 94.7 ID (r = 0.9831) ○IP = 1/b = 10.6 bits/s ○Difference due to a positive intercept vs. zero intercept Fitts’ Law

8. 2.Problem Emerge Fitts’ Law  Upward curvature of MT away from the regression line for low values of ID – impulse-driven ballistic control (Gan & Hoffmann, 1988)  relative contributions of A & W in the prediction equation

9. 3.Variations on Fitts’ Law Fitts’ Law

10. 7.Targets and Angles ○two aspects of dimensionality: the shape of target s and the direction of movement ○1D movement (back and forth) – target height only a slight main effect ○rectangular targets in 2-D from 0°to 90°-- the role of target width and height reverse Fitts’ Law

13. 5. A PPLICATIONS O F F ITTS ’ L AW 1.The Generality of Fitts’ Law Fitts’ Law ○ higher IP (13.5 bits/s) than serial tasks (IP=10.6) because they exclude time on target ○ the role of visual feedback  movements under approx. 200ms are ballistic

14. 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