Tag Archives: fitts law

The Human Processor Model & Fitts Law

Fitts Law
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Self Notes:


This tool is probably one of the best ways to learn the fundamentals of Fitt’s law. It’s a fantastic way to understand the different variables that make up this law.

The Fitt’s law depends on 3 factors:

  • T is the average time taken to complete the movement.
  • D is the distance from the starting point to the target’s center.
  • W is the width of the target (measured along the axis of motion).

What are the implications of this?

  • While designing a UI, the designer needs to makes sure the buttons are large enough and close proximity for the action to be completed quickly.
  • If the actionable object has been put far away, it would be important to compensate it by making the object larger.
  • It helps us determine which is the best pointing device that be used in order to achieve tasks quickly and efficiently.
  • While we are using pointers – the edge of the screens are the easiest to target. This is because they have infinite width and height. While designing a UI, if the user needs quick access to something, one should make sure that  the quick access icons/menus should be towards the edges for quick access.
  • Although the right click menu is effective – the pie menu can be more effective than the right click menu.
  • The pixel you are pointing at is the easiest to click. But the corners of the screen are almost as easy to point.

Disadvantages of FITTs Law:

  • Predicts only one dimensional movement. Doesn’t account for z axis.
  • Absence of a consistent technique to deal with errors

Is an attempt to understand the goals of task analysis, calculation and approximation. It enables us to predict user performance to a certain degree.
The model works on two principles:

  1. Set of processors, memories and their interconnection
  2. Set of principles of operation

Once complete, the calculations can then be used to determine the probability of a user remembering an item that may have been encountered in the process. The probability could then be used to determine whether or not a user would be likely to recall an important piece of information they were presented with while doing an activity.

Factors affecting the Principles of Operation:

  • P1: Variable Perceptual Processor Rate Principle: The Perceptual Processor Cycle Time varies inversely with stimulus intensity i.e. Th Perceptual Processor Cycle Time will be lesser if the stimulus intensity will be high and vice-versa.
  • P2: Encoding Specificity Principle: Specific encoding operations performed on what is perceived determine what is stored, and what is stored determines what retrieval cues are effective in providing access to what is stored. The principle states that memory is most effective when information available at encoding is also present at retrieval.
  • P3: Discrimination Principle: The difficulty of memory retrieval is determined by candidates that exist in the memory, relative to retrieval cues.
  • P4: Variable Cognitive Processor Rate: The cognitive processor cycle time is shorter for greater task demands and increased information loads; it also diminishes with practice
  • P5: Fitts Law: The time (t) to move the size of the target (s) which lies at a distance (d).
  • P6: Power Law of Practice: The power law of practice states that the logarithm of the reaction time for a particular task decreases linearly with the logarithm of the number of practice trials taken.
  • P7: The uncertainty Principle: Decision time increases with uncertainty of decision or judgement to be made.
  • P8: Rationality Principle: A person acts to attain his goals through rational actions, given the structure of the tasks and his inputs of information and bounded by his limitations on his knowledge and processing ability. Goal + Task + Operators + Input + Knowledge + Process Limit  = Behaviours
  • P9: The Problem Space Principle: The rational activity in which people engage to solve a problem can be described in terms of 1) a set state of knowledge, 2) operators for changing one state to another, 3) constraints on operator movement and 4) control knowledge for deciding which operator to apply next.

Some applications of CMN Model:

  • Moving Picture Rate
  • Determining Perceptual Causality
  • Reading Rate
  • Reaching a button