2005 Conference Proceedings

Go to previous article 
Go to next article 
Return to 2005 Table of Contents 


Barry Romich, P.E.
AAC Institute
1022 Heyl Road
Wooster, OH 44691-9786
Tel: 330-262-1984 x211
Fax: 330-263-4829
Email: bromich@aacinstitute.org

Katya Hill, Ph.D., CCC-SLP
Center for Assistive Technology Education and Research (CATER)
102 Compton Hall
Edinboro University of Pennsylvania
Edinboro, PA 16444-0001
Tel: 814-732-2431
Fax: 814-732-2184
Email: khill@edinboro.edu

The goal of AAC is the most effective communication possible. Many factors influence communication performance and the achievement of fluency. Some of these factors focus on the physical interface and others focus on the method of representing language, both of which have a human factors component. The AAC assessment process should evaluate performance differences among trial systems noting the influence of human factors issues. Specific performance data can be collected and reported to support clinical decisions reflecting human factors awareness.

Human factors refers to issues regarding the interaction between a person and a machine or an object, such as a tool, appliance, vehicle, computer, etc. When a person uses an AAC system, various human factors issues come into play. Appropriate consideration of these issues is required for achievement of the most effective communication possible. This paper explores a few such issues.

Ease of use at first encounter may be contrary to most effective long term use (Norman, 1980; King, 1999). Therefore, following a short evaluation, if one chooses the AAC system that appears easiest, one may also be choosing the system that will be least effective in the long term.

Proficiency in performing a task develops over time. For even able-bodied adults with no cognitive disability, hundreds or thousands of iterations of a task may be necessary to reach the achievable level of skill (Jagacinski & Monk, 1985). Therefore, performance following a short trial on a selection method (keyboard, headpointer, etc.) or language representation method may offer little or no indication of what can be achieved. Clinical case study data illustrate how to report performance comparing performance patterns for the AAC assessment and to monitor change for evidence-based practice. The research base identifies learning curves and performance differences among various AAC systems (Hill & Spurk, 2004).

When arrays of selections change, such as with word prediction lists or pages of single meaning pictures, this constitutes a discontinuous cognitive process (Romich, 1994). A discontinuous process prevents the development of automaticity, a basic requirement of fluency. Therefore, core vocabulary may be most effectively accessed using a static (non-dynamic or non-changing) set of symbols. When the size of the vocabulary exceeds the number of selections directly available, then icon sequencing may be required to maintain automaticity.

Selection rate, measured in bits per second, is the rate at which a person can enter information into a system. The upper limit of selection rate is thought to be around 100 bits per second using physical techniques (Lucky, 1991). Selection rate directly impacts communication rate. Selection rates of people who use AAC have been measured. Different selection methods (keyboard, headpointing, eye gaze, scanning, etc.) result in different selection rates. In general, the method that yields the highest selection rate would be the preferred method. The choice of a selection rate can be based on quantitative measurement of performance.

The time required to make a selection is proportional to the logarithm of the distance to the target and inversely proportional to the logarithm of the size of the target (Fitts, 1954). Therefore, the relative locations and sizes of elements to be selected can have a profound impact on communication rate. The placement of elements in a scanning array has long been recognized as affective performance. However, the same is true, although to a lesser degree, regarding the placement of elements in a direct selection array.

Design tradeoffs are inherent in nearly everything (Petroski, 2003). Rarely does a product design address all possible interests. When a product is designed to meet the needs of a funding agent or reimbursement policy, for example, then features that can result in more effective communication may need to be omitted.

The Human Factors and Ergonomics Society (www.hfes.org) addresses issues of this nature and can be a useful resource. Through appropriate recognition of human factors issues, individuals who use AAC can achieve the most effective communication possible.


Fitts, P.M. (1954). The information capacity of the human motor system in controlling the amplitude of movement. Journal of Experimental Psychology, 47, 381-391.

Hill, K., & Spurk, E. (2004). AAC performance based on semantic organization schemes on dynamic displays. In Proceedings of the RESNA 2004 Annual Conference [CD]. Arlington, VA: RESNA Press.

Jagacinski, R.J., & Monk, D.L. (1985). Fitts' law in two dimensions with hand and head movements. Journal of Motor Behavior, 17, 77-95.

King, T. (1999). Assistive technology: essential human factors. Boston: Allyn and Bacon.

Lucky, R. (1991). Silicon dreams: information, man, and machine. New York: St. Martin's Press.

Norman, D. (1980). The psychology of everyday things. New York: Basic Books Inc.

Petroski, H. (2003). Small things considered. New York: Alfred A. Knopf.

Romich, B. (1994). Knowledge in the world vs. knowledge in the head: the psychology of AAC systems. Communication Outlook. Vol. 16 No. 2., pp. 19-21.

Go to previous article 
Go to next article 
Return to 2005 Table of Contents 

Reprinted with author(s) permission. Author(s) retain copyright.