FITTS’ LAW○ 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

SUMMARY1. Information Theory Foundation

○ Fitts’ idea1. the difficulty of a task could be measured using the information

metric, bits2. 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)

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Fitts’ Law

SUMMARY2. 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 = 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

SUMMARY3. 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

DETAILED ANALYSIS1. 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

Fitts’ Law

Fitts’ Law

DETAILED ANALYSIS2. 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

Fitts’ Law

DETAILED ANALYSIS3. Variations on Fitts’ Law

○ Welford’s variation○ MT = a + b log2(A/W + 0.5)○ Higher correlation between MT and ID

○ MT = a + b log2(A/W + 1)○ A negative rating for task difficulty when the targets overlap

○ MT = a + b1log2A – b2log2W○ b1log2A -- Initial open-loop impulse toward a target

○ b2log2W -- Feedback-guided final adjustment

Fitts’ Law

Fitts’ Law

DETAILED ANALYSIS4. 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 = log2[sqrt(2e)*] = log2[4.133]

Fitts’ Law

Fitts’ Law

APPLICATIONS OF FITTS’ LAW1. 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