1. INTODUCTION○ A model of human psychomotor behavior○ Human movement is analogous to the transmission of in-

formation○ 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. 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)

NNSBC

2log

Fitts’ Law

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 ampli-

tude (A) and the noise analogous to the tolerance or width (W) of the target

○ ID = log2 (2A/W)○ By the regression line equation

○ MT = a + b ID (1/b corresponds to IP)○ MT = a + b log2(2A/W)

Fitts’ Law

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

4. Derivation From a Theory of Movement○ deterministic iterative-correction model (Crossman and

Goodeve, 1963/1983)

Fitts’ Law

3. DETAILED ANALYSIS

1. The Original Experiments○ Fitts’ paradigm – the reciprocal tapping task

Fitts’ Law

○ 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

2. Problem Emerge

Fitts’ Law

Upward curvature of MT away from the regression line for low values of ID – impulse-driven ballistic con-trol (Gan & Hoffmann, 1988)

relative contributions of A & W in the prediction equa-tion

3. Variations on Fitts’ Law

Fitts’ Law

Welford’s (1960, 1968) variation

○ Higher correlation between MT and ID

MacKenzie○ MT = a + b log2(A/W + 1)○ A negative rating for task difficulty

when the targets overlap

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 tar-

get width and height reverse

Fitts’ Law

4. COMPETING MODELS

1. Linear Speed-Accuracy Tradeoff○ Schmidt et al. (1978, 1979) ○ the relationship is linear rather than logarithm and the in-

formation analogy is absent○ superior to Fitts’ law for “temporally constraints” tasks○ move as accurately as possible rather than as quickly as

possible for spatially constrained tasks

Fitts’ Law

2. Power Functions○ Kvalseth (1980)

○ Sheridan & Ferrell (1963)

○ Meyer et al. (1988) – stochastic optimized-submovement

model

○ a unified conceptual framework for both the linear speed-accuracy model and Fitts’ log model

Fitts’ Law

5. APPLICATIONS OF FITTS’ LAW1. The Generality of Fitts’ Law

Fitts’ Law

○ higher IP (13.5 bits/s) than se-rial tasks (IP=10.6) because they exclude time on target

○ the role of visual feedback movements under approx. 200ms are ballistic

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