Automaticity is an important phenomenon in everyday mental life. Most of us recognize that we perform routine activities quickly and effortlessly, with little thought and conscious awareness — in short, automatically (James, 1890). As a result, we often perform those activities on “automatic pilot” and turn our minds to other things. For example, we can drive to dinner while conversing in depth with a visiting scholar, or we can make coffee while planning dessert. However, these benefits may be offset by costs. The automatic pilot can lead us astray, causing errors and sometimes catastrophes (Reason & Myceilska, 1982). If the conversation is deep enough, we may find ourselves and the scholar arriving at the office rather than the restaurant, or we may discover that we aren’t sure whether we put two or three scoops of coffee into the pot.

Automaticity is also an important phenomenon in skill acquisition (e.g., Bryan & Hatter, 1899).

Skills are thought to consist largely of collections of automatic processes and procedures

(e.g., Chase & Simon, 1973; Logan, 1985b). For example, skilled typewriting involves automatic recognition of words, translation of words into keystrokes, and execution of keystrokes (Salthouse. 1986). Moreover, the rate of automatization is thought to place important limits on the rate of skill acquisition: LaBerge and Samuels (1974) claimed that beginning readers may not be able to learn to read for meaning until they have learned to identify words and letters automatically.

Over the last decade, considerable progress has been made in understanding the nature of automaticity and the conditions under which it may be acquired (for reviews, see Kahneman & Treisman, 1984; LaBerge, 1981; Logan, 1985b; Schneider, Dumais, & Shiffrin, 1984). There is evidence that automatic processing differs qualitatively from nonautomatic processing in several respects: Automatic processing is fast (Neely, 1977; Posner&Snyder, 1975), effortless (Logan, 1978, 1979; Schneider& Shiffrin, 1977), autonomous (Logan, 1980; Posner&Snyder, 1975; Shiffrin&Schneider, 1977; Zbrodoff & Logan, 1986), stereotypic (McLeod, McLaughlin, & Nimmo-Smith, 1985; Naveh-Benjamin & Jonides, 1984), and unavailable to conscious awareness (Carr, McCauley, Sperber, & Parmalee, 1982; Marcel, 1983). There is also evidence that automaticity is acquired only in consistent task environments, as when stimuli are mapped consistently onto the same responses throughout practice. Most of the properties of automaticity develop through practice in such environments (Logan, 1978, 1979; Schneider &Fisk, 1982; Schneider & Shiffrin, 1977; Shiffrin& Schneider, 1977).

Automaticity is commonly viewed as a special topic in the study of attention. The modal view links automaticity with a single-capacity model of attention, such as Kahneman’s (1973). It considers automatic processing to occur without attention (e.g., Hasher &Zacks, 1979; Logan, 1979,1980; Posner&Snyder, 1975; Shiffrin & Schneider, 1977), and it interprets the acquisition of automaticity as the gradual withdrawal of attention (e.g., LaBerge & Samuels, 1974; Logan, 1978; Shiffrin & Schneider, 1977). The modal view has considerable power, accounting for most of the properties of automaticity: Automatic processing is fast and effortless because it is not subject to attentional limitations. It is autonomous, obligatory, or uncontrollable because attentional control is exerted by allocating capacity;

From: Toward an Instance Theory of Automatization by Gordon D. Logan University of Illinois 1988

What is automation?

Definition – Oxford Dictionary:

“Automatic control of the manufacture of a product through a number of successive stages; the application of automatic control to any branch of industry or science; by extension, the use of electronic or mechanical devices to replace human labor”

Automation, automatization and automization? 

There are two basic forms of automation. Mechanization on one side and computerization on the other.

Mechanization is the oldest form of automation and takes care of the most tangible, production tasks like manufacturing. A lot of research has been done within the field of automation in manufacturing.

Computerization emerged with the arrival of computers (hardware) and programming (software). Computerization encompasses all the things computers can do or will be able to do. Computers help humans with their tasks. It’s all about computer-human interaction.

So basically there is a duality when tasks are allocated. Fitts (1951) presented a list of general tasks covering both humans and machines, illustrating where the performance of one category exceeds that of the other.

Will computers ever be able to complete those most typical of human tasks, like decision making, having insight or finding the ‘aha’ moment, seeing the bigger picture without losing eye for the detail, Q&A, overall sense of good and bad, empathy, personality etc?

The Level of Automation (LoA), a term originally related to manufacturing and aviation, depends strongly upon the existence of flexible tools and advanced machinery on one side and the level of (artificial) intelligence or intelligent computer systems involved when human tasks are being replaced by machines on the other side. The level of automation in the context of these expert systems is most applicable to cognitive tasks such as the ability to respond to and make decisions based on system information. The more advanced the tool and the higher the AI, the higher the level of the automation will be.

There are different ways to see the Levels of Automation concept. The Sheridan-Verplanck 10 levels of automation (Sheridan 1980) for example incorporate issues of feedback as well as the relative sharing of functions. Another way of viewing levels of computerization is from a perspective of human information processing (Wickens and Hollands 2000), which determines that human information processing consists of the following four steps: acquire the information, analyze and display the information, decide action based on the analysis, and finally implement the action based on the decision (Parasuraman, 2000).

Examples of early forms of automation (mechanization) can be taken from the 19th-century industrial revolution. Until the 19th century, shoemaking was a traditional handicraft, but by the century’s end, the process had been almost completely mechanized, with production occurring in large factories. Despite the obvious economic gains of mass production, the factory system produced shoes without the individual differentiation that the traditional shoemaker was able to provide.

The main innovation here was not the invention of the sewing machine, which happened almost 100 years before, but the introduction of it into the process of mass production in 1846, providing an alternative method for the mechanization of shoemaking. A second innovation, the McKay shoe stitching machine opened up bottlenecks within the production line. Parts were still done by hand. More and more of the manufacturing stages, such as pegging and finishing, became automated. By the 1890s, the process of mechanization was largely complete.

Research Note:

Before this shift to mass production, the design of shoes was mainly done by craftsmen, cobblers, and small family run shoe making enterprises. After the automation revolution shoes were designed specifically for mass production. Cheaper materials, simplified features for easier and faster production results. On the other hand, we also saw that due to reduced cost in production, the possibility emerges for more variation. There are more types of shoes and a larger variety of models and brands available for different and very specific purposes. 

For example, the initial football boots from the 19th century were not designed at all, they were just normal working boots that were strong and hard, so really good for kicking around. The idea of creating boots that were solely for protection was eventually dropped and a focus on the design of a boot that would be light, agile and give the user a better kick were the main factors for new boots.

In 1954 Adolf Dassler designed his football ‘boot’ with innovative new features. Screw-in studs that could be adapted to ground conditions, cut-away ankles, front lacing almost right down the front of the boot, a soft toe, and foam rubber interior. This design would change the way we look at football ‘shoes’ and it also changed how the sport is played. They were designed for a very particular use or purpose and were 100% ready for mass production.

Parallel with all this tremendous change and progress, the design and craft of making custom-made shoes, before something only available for the rich, did not change at all. The craft of bespoke manufacturing still exists although within a very small niche market.

If you want the real thing, you can still have shoes designed and made the authentic way. Authenticity becomes a commodity. (important for later – draw a parallel with web design)

A pattern:

A rare and valuable item/craft – innovation/automation/disruption + mass production = item becomes highly available and cheap/common – the rare and valuable item/craft remains within a small niche

Automation will probably occur where the outcome has to be functional, commercial and mass-produced (see matrix)