I’ve not talked a lot about perceived self-efficacy on this blog, and yet it is one of the most important pre-requisites for cognitive productivity, success and happiness.
I came across this article in one of my news feeds: Discouraged by Peer Excellence: Exposure to Exemplary Peer Performance Causes Quitting. I think we can all relate to this phenomenon. Everyone has experienced being around someone who is far more competent than himself or herself, and even than one can become with practice. My Cognitive Productivity framework is all about using knowledge to become more effective. Still, genetics and early experience do (differentially) place upper limits on everyone.
I clearly remember when, many years ago, I started working at Abatis Systems. I was the first employee. I had been through many learning curves in my life without ever feeling like I was “in over my head.” But my first few months there were brutal! I had no experience in C programming or the many types of “low level” technical challenges the company needed to address (classifying packets at wire speed). I had used the Internet Protocol but never read an RFC. For the first time in my life I felt like I was cognitively “in over my head.”
The company was set to grow fast. There would be immense pressure until the company was profitable or acquired. The next rounds of employees after me all had domain expertise (in IP, other networking protocols, etc.) And of course, they were extremely smart. The new hires were great for the company, and I would learn a lot from them. But I knew that I had to make the right comparison between them and myself. If I was not to make any allowance for the facts that (a) their university training and professional experience prepared them better for the initial technical challenges and (b) I would need time to acquire the domain knowledge, then I would sink. I was very well aware of research on perceived self-efficacy. Albert Bandura and his colleagues had had clearly demonstrated many ways in which low perceived self-efficacy can cause someone to fail. I had to deliberately apply this psychological knowledge as I “inhaled” IETF RFC after RFC after RFC (and ATM standard after ATM standard). At the height of the development of our sophisticated, “multi-service”, “policy-enabled” edge router, I was given the privilege of leading the element management team, which required sufficient understanding of all the protocols supported by our router. I credit Bandura’s research on perceived self-efficacy for that.
Perceived self-efficacy is an important contributor to “effectance” (the motivation to improve one’s competence), which enables one to focus and apply one’s learning strategies.
(Within two years, Abatis was acquired for $1.3 billion for its highly innovative products, patents and employees.)
Understanding Psychological Challenges is Critical to Enhancing Cognitive Productivity
Here is an excerpt from Cognitive Productivity that tells some of the story. NB, the excerpt below uses the term “effectance”. The book describes that very important (but under-acknowledged!) concept in great detail.
I had the opportunity to learn with brilliant experts in networking technology[^104]. The following episode epitomizes the mental atmosphere. In 1998 meetings, Dr. Renwei Li, an ambulant theorem-prover, described his new algorithms aimed at our core technical challenges—to classify IP data packets at wire speed. The meetings were one-against-many intellectual ping-pong matches. The team smashed questions at Li who instantly responded. It was a mentally demanding and exhilarating experience. After several months of effectant processing, I was in my own element, writing technical specifications, developing software and at various times leading teams of developers. I experienced what people in R&D routinely experience: being thrown into high-pressure situations where they must rapidly learn and produce.
I discovered a couple of problems at Abatis that are particularly relevant to the problem of this book. First, despite the fact that we were equipped with top-end computer monitors, many of us chose to print reams of documents and read them from paper. I began to wonder why we preferred print. When I left Abatis in 2001, I set out to discover how to make it preferable to read and learn with technology. I have been on that journey ever since. While information technology has come a long way since then, many of the requirements I identified early on have yet to be widely addressed. However, one can kludge together useful workflows, as I will show in Part 3.
Second, I was struck that most technical staff were not very familiar with cognitive science. Cognitive scientists had evidently not done a good enough job of exporting their knowledge. This situation seemed analogous to building bridges without knowing Newtonian mechanics. Or had cognitive science nothing useful to offer to the brightest minds? To be sure, cognitive science is still mainly a factual discipline. Most cognitive science research is conducted on students. That population is much more variable than knowledge workers (e.g., in terms of intelligence and thinking dispositions). Still, I felt that the potential of cognitive science was left untapped.
When psychologically-oriented workshops and training are given in the workplace, they usually deal with social, affective, and communication issues rather than (classical) cognition. When cognition is presented in industry, the information is often superficial and uninformative for knowledge workers, people for whom learning is an intrinsic part of work; sometimes it is simply false. Often it is mislabeled as “brain training”. For example, the workshops sometimes make such superficial recommendations as to take frequent breaks, study actively, listen to Baroque music, eat fish and dark produce and consider oneself to be intellectually gifted (Cullen, 2010; Jones, 2005; Small, 2008). Their vague recommendations to “study actively” could be substantiated with concrete indications about how to use technology to overcome specific challenges to “active study”. However, their recommendations typically skirt core information-processing and technology challenges altogether. Carl Bereiter summed up current “thinking strategy” offerings:
The teaching of thinking strategies, although motivated by contemporary research, still relies mostly on stepwise procedures and slogans and could as easily have been designed 50 years ago. (2002, p. 349)
The road to thinking skills and creativity is lined with quacks. (2002, p. 413)
If we are to help professionals productively develop themselves with knowledge, firstly, we had better understand their challenges in relation to high-caliber findings and theories about cognition. Our information and advice should reflect the fact that professional knowledge workers are different. For example, they are of above-average intelligence. They are highly educated. Reading is a significant part of their work. Many of them enjoy learning and are effectant. They tend to use technology to research and learn. This chapter deals with their challenges. Secondly, we had better extend and apply cognitive science to their ends. Thirdly, we ought to provide specific guidance for using information technology. Parts 2 and 3 do some of this.
Later in that chapter, I talk about perceived self-efficacy, as follows.
One must strike a balance between arrogance and underconfidence.
Effectance is predicated on perceived self-efficacy. Believing one inherently is unable to succeed in a domain has been shown to affect performance in a wide variety of areas: work performance, academic performance, health, etc. (Bandura, 1997). Perceived self-efficacy is one of the most researched phenomena in psychology. It ought not to be confused with self-esteem, self-concept or “locus of control”. If a person believes she is inherently incompetent in one area (such as mathematics), it will directly affect that area without necessarily affecting another (e.g., writing). Consider, for an ironic example, the psychologist who sees herself as quite competent in helping children improve their perceived self-efficacy yet who sees herself as being inept with computers. She does not realize it, but her assumption that she is “simply not a computer person” makes it difficult for her to (want to) keep abreast of the literature.
I deliberately chose the example of perceived competence with technology because I believe it is one of the most wide-spread self-limiting attitudes people contend with, even young knowledge workers. By failing to become more proficient with technology, highly intelligent people also limit their meta-effectiveness.
The mechanisms by which perceived self-efficacy affect performance are easy to comprehend and compelling. Wood & Bandura (1991) report that perceived self-efficacy in a domain affects:
To find out more about how perceived self-efficacy, and other principles of cognitive science (including psychology) are relevant to you, please check out my book, Cognitive Productivity.