Have you ever wondered what sets the best chess players a part from the rest? Or what you, as a novice of any mastery, could learn from that? If your ‘lack of talent’ is holding you back from taking up singing, carpentry or computer programming? Whether talent even exists?
“…Secrets from the new science of expertise”
Epistemic status: I have spent some time looking at expertise, starting with physical skill acquisition. Read this book thoroughly but have not yet looked into all my research leads around expertise
If you have an interest in getting better at your pursuits, skills and masteries, read on!
In Peak, Anders Ericsson and Robert Pool have set out to both study and concisely present research into the question of how do humans ‘get better at stuff’. Peak seeks to breakdown the research being conducted in the field, add in some little stories to flesh the ideas and concepts out and also wades into some of the implications.
The key concepts the book discusses are deliberate practice, mental representation, mentorship, and feedback. I found the book very engaging, due to my interest in this domain and that it was quite disgestable to me; linking to my strong priors, such as mental models, tight feedback loops, mentorship from a rites of passage frame and prior reading about practice. In essence, we build mastery most effectively by undertaking focused, limits-of-ability practice, either under direct coaching (mentorship) or after developing powerful mental representations that allow for masterful feedback. There are specific conditions attached to these general terms: you can’t just do one aspect of deliberate practice and expect to progress. So I will unpack some of those aspects.
Why should I care about deliberate practice? Two major drives for acquiring a skill include wanting to survive and, for the sheer joy of its mastery. If you really want to excel at surviving (hey, some of us are quite keen on living) or simply love getting better at the hobbies and disciplines we practice, deliberate practice is something we should be engaging in.
So I need to work at getting better at stuff, right, of course…
What one needs to take from the idea of deliberate practice is that just putting in hours of practice is not enough; there are specific conditions that need to happen for it to be fully effective. For illustrative purposes, I’ll list some of the things that lead us to ‘degraded practice’:
We go through the motions; we stop paying attention to our practice; we don’t get expert mentorship; we do not analyse our practice to get self-feedback; and … we bail on practice altogether.
If we place the two on a spectrum, what does it take to push us from the ‘degraded practice’ end of a spectrum, to the deliberate practice end?
- Having a clearly defined and well established path to mastery, with easily identifiable experts; I am labelling these “Clear expertise fields”, something not common to all fields
- Mentorship from teachers/masters of the discipline; adjusting if a teacher can no longer push you beyond your limit
- Meaningful, regular feedback
- Mental representations, that allow one to self-correct errors during practice
- Continued focus during practice
- Practice that is ‘difficult’; it continues to push your boundaries, even if this is exhausting
These fields tend to have two main qualities. 1) The field is already well-developed, so expectations about mastery and paths to achieving that are already well-defined. and 2) the field has direct competition and rankings that make it ‘easy’ to compare practitioners. Examples of this span chess, classical music, all sorts of sports but particularly individual sports, martial arts, and structured forms of dance. As the authors say on p.98, “What doesn’t qualify?”
Pretty much anything in which there is no or little direct competition, such as gardening … [being a] business manager, teacher, electrician, engineer, consultant, and so on.
That is somewhat unfortunate if you care muchly about one of those “excluded” fields, something I will explore later with software engineering. I would also argue to include aviation, medicine, civil engineering and similar as ‘clear expertise’ fields; fields that do not have competition perse but that have well defined metrics (often with life or death consequences!) by which to gauge interventions.
Mentors guide you on your path to mastery. They often set one’s curriculum, shift the focus of practice and, most importantly, they provide rapid, accurate feedback on errors. Getting accurate and speedy feedback is one of the most powerful mechanisms in human experience. It is the essential underpinning of how we learn to navigate reality overtime. Mentors greatly improve this feedback process, particularly if the discipline is well understood. Beyond their instructive guidance, a broader sense of being mentored by someone no doubt has benefits to motivation and well-being.
We are constantly predicting what will happen around us and correcting our expectations based on sensory input. The feedback we glean from practice and from our mentors is just a more built-up version of this. While mentor feedback is very valuable, to pursue excellence, one must figure out how to give oneself worthwhile feedback. This leads us to …
Ericsson and Pool place great importance on their concept of mental representations. They are important because they must be developed by practitioners for them to be able to critique themselves. Think of how a musician can glance at a score and immediate develop a ‘sense’ of how it should sound. A dancer feeling the stickiness of their last sequence and knowing that they did not quite nail it. A chess master or a professional soccer player can look at a snapshot of their respective fields of play and predict what will happen next. Mental representations also define how we structure our skills and their interrelated parts in our minds. Like how a guitarist will structure collections of notes into chords and progressions. I would add that “clear expertise” fields probably have defined mental representations formally, and have them shared across the whole discipline. Where as in other less mature/structured fields, there are a multitude of bespoke mental representations going around and it becomes unclear how easily concepts can be shared.
Being able to stick with your practice, and deeply drop into the now of it. Beyond emphasizing its importance, Peak doesn’t go deep into how to cultivate focus. That is left to the reader. Much has been said in contemporary media about the economy of attention/distraction urbanites exist in. I won’t delve into that issue here but to ask: If it is true that we are being distracted more so than ever before, are we at risk of diminishing society’s (and our own) ability to acquire mastery?
Deliberate practice requires constant tuning and shifting; we get less and less value out of revising the same material/pattern over and over again (spaced repetition learning details how to maximise that). Peak is very clear about one aspect related to this: Deliberate practice is uncomfortable. It is frustrating. We learn some aspect of a discipline. It is difficult at first. We get the hang of it. Perhaps even a little feeling of accomplishment comes our way. But then we need to do something different, harder. We probably mess up and get frustrated again. Just to keep making progress. This process can be literally fatiguing. Peak noted this fatigue in the high performing classical music students; putting in huge reps of deliberate practice and integrating afternoon naps to compensate. Obviously there is a very real skill in itself of balancing how exhausting, challenging and difficult practice is, with also making sure we are staying focused, healthy and ‘well-rounded’.
Hopefully you now have a reasonable grounding in the key concepts of Peak. Next, I will focus on implications inspired by reading Peak or referenced in the book itself.
Education, notionally oriented to improving expertise, is the first field that the authors examine regarding the implications of Peak. Specifically, in chapter 9, they detail a trial conducted at the University of British Columbia, at the undergraduate level. Splitting a large physics course into two cohort, the study only shifted their twelfth week of instruction, on the topic of electromagnetic waves.
In the study cohort, the goal was not to just feed the students the required information for that week but to instead prompt them to practice thinking like physicists. Before each class both cohorts were prompted to read short additional material. The first difference was that the study cohort was also asked a battery of true/false online questions, that would give them immediate feedback about whether they were employing the same mental representations that a physicist ought to be. The next difference was the study cohort dropped the classic lecture style. Instead, the cohort was broken up into small groups and asked ‘clicker’ questions. These questions could be answered electronically, the answers sent straight to the instructor. Before answering, the students would be given a chance to discuss the question within their group. After the answers were submitted, the instructor would display the answers and discuss, and then answer additional questions students had. Several of these questions were asked and if a decent number of students struggled with a particular concept, the lecturer would take that as a cue to offer a mini-lecture on that topic. This question-discuss-answer-feedback loop was replicated by another ‘active learning’ task, where students wrote up short answers and the lecturers moved around the groups, dropping into different discussions. This allowed a tighter feedback loop than the traditional lecture: students were regularly tested on their concepts (without the pressure of being ‘graded’), got feedback, with some level of expert mentorship and ostensibly built up better mental representations of electromagnetic waves.
So, what was the tested difference between the study and control cohorts after this? Both cohorts received multiple-choice quiz. The control cohort averaged 41% and the study cohort averaged 74% (Random guessing would net you about 23%). That is a huge improvement. The study authors determined that it produced outcomes better than two standard deviations above the mean, overcoming Bloom’s 2 sigma problem. Certainly a result that I would like to see replicated in a wide cross-section of studies. This study was published in 2011, and I would be curious to know to what extent these techniques have been adopted across higher education. On p.254, the authors do touch on this subject, saying that the techniques rapidly spread across science departments at UBC… but we hear nothing about their wider dissemination from the 5-6 years between the referenced study and Peak’s publication.
The other main line of study I would like to see more of is: would these educational models also hold for younger learners. The ‘test-and-adjust’ frame of Wieman and co’s work reminds me of another simple educational intervention, “Teaching At The Right Level”; testing kids on specific abilities and moving them into different grade classes based on their results. The JPAL studies I just linked to are not the first time I have heard of the effectiveness of avoiding age-class bracketing: The Finnish Lesson cites the idea of ‘teaching to the right level’ as one of the important aspects of why the Finns performed so well by international educational measures. Wieman and co’s study included a micro-level loop of ‘testing to the right level’, by testing students and slowing down when material was not understood.
Software engineering is not a “clear expertise” field. It is an immature discipline, relative to other technical industries like aviation, construction, and medicine. There is not a clear path to overall mastery; more so there are many paths to incomplete masteries in different sub-domains and environments. At its worst (hacker news, twitter, blog comments), this is evident in vitriolic arguments, vigilante gatekeeping, and ‘bikeshedding’ discussions. This is because it is a relatively new discipline, is rapidly growing and changing, and tends to forget about its history. The industry’s rapid growth means it is both difficult to determine what to be mentored on, and where to even find mentors.
This paints a rather glum outlook for the aspiring engineer or developer, wanting to pursue deliberate practice in their field. However, despite these legitimate frustrations, the industry does have several bright spots. Partial tight feedback loops are very achievable in software development. Just getting a program to run and getting some output can provide immediate and clear feedback about your efforts. I say it is partial however, since there are many pitfalls and bad habits one might not be alerted with this simple level of feedback loop. Another advantage (relative to the industries mentioned at the beginning of this section), is the bewildering array of open source and free tooling, tutorials, forums, chat channels and help available online. In part these are a direct response to the disorganized nature of computer engineering mastery; and it is certainly challenging to determine the value of feedback and advice garnered from across this mass.
Specifically for my own circumstances, it reinforces the need to practice coding regularly (partially fulfilled by this blog), continue to seek mentorship (especially since it is lacking at work) where and when I can (Hello Bradfield!) and try to understand what mental representations I am operating with when coding.
Touching on talent, drive, motivation, opportunity, IQ and privilege
In all this discussion on deliberate practice and its importance to building expertise, I have not yet talked about the place of ‘innate talent’ (Addressed in chapter 8 of Peak). As a society, we place a great deal of focus on natural ability (or lack thereof); we tend to operate under a mental model whereby some folks just have incredible talent and many do not have it. We pigeon-hole ourselves; “I don’t have a brain for math”, “I just can’t hold a tune”, “I’m not a computer person”, “I’m just not good with people”, etc. However, one of the consistent themes of Peak is that ‘innate talent’ and ‘natural ability’ play a negligible or very limited role in expertise (to be clear, this is not to suggest that ‘talented’ people don’t exist, in the sense that ‘talented’ people are good in their domain because of their history of practice and not as some genetic blessing).
The authors reference the importance of deliberate practice over innate talent through examples including:
- The Polgar Family creating-chess-genius experiment (generating three female chess geniuses in an era when women’s minds weren’t considered suitable for chess);
- Experiments into whether folks can develop perfect pitch, widely considered as something you either had or didn’t have (In the study, some folk were successful in developing perfect pitch but not all participants);
- A British study done on chess-playing children of a range of ability and IQs. It found two interesting results: for the whole cohort, practice was the biggest determiner of ability, with IQ having some impact. But for the elite playing part of the cohort, IQ played no role in ability.
The Peak authors consciously avoid detailing the depths of the debate about what IQ actually measures on page 227, while highlighting how IQ intersects with expertise. Seeing as IQ did have a positive correlation for the non-elite chess-kids, they posit that a higher IQ might aid someone slightly in picking up a mental representation or strategy. However, at the elite level, this slight advantage seemed to fade away; deliberate practice reigned supreme when it came to ability. Consequently, from this perspective, we should view the place of education not as some sort of unearthing of innate talent or high IQ individuals, and more as a scaffold for building up mastery across all students.
If IQ is not a decisive accompaniment to deliberate practice, what then is? Peak does not go into this at length but the authors do make references to a range of internal and external factors that matter for building expertise. On the internal side, practitioners need to cultivate motivation to do practice, grit at sticking with mastery challenges, and a willingness to pursue a specific mastery. These may well be meta-skills that can also be cultivated, or innate; this is not really addressed in Peak.
External factors include access to mentorship, privilege and supportive community. If you cannot convince someone to mentor you, progress in your mastery will be tougher. When I say privilege in this context, I am essentially asking: how free is an individual to pursue their mastery? Do they live a monastic lifestyle, free from distractions, provided all required tools and materials to practice, fed and sheltered? Or do they have to work, be a care-giver to others, scrape together funds to buy supplies and tools? As for the presence or absence of a supportive community; these can provide external motivation (practicing with peers, encouragement from family), mentorship opportunities and resources. The book title includes a glowing quote from the Economist:
“[Peak] offers an optimistic anti-determinism that ought to influence how people educate children, manage employees, and spend their time. The good news is that to excel one need only look within.”
It is that last sentence that I think is a misrepresentation of the sum of Peak’s findings. If we miss the importance of the factors that support and sustain deliberate practice, we risk being blind to systemic biases against some groups and their pursuit of mastery.
Implications for me!
Peak feels like the coalescing of learning moments from my past. It has been a fantastic reminder that I need to put more care and time into my masteries. While I disparaged the Economist quote, I do strongly agree with its view that Peak offers an optimistic anti-determinism for humanity. Collectively, it is worth investing in building skills and education, if we heed the lessons on the research into expertise. We are not wholly the result of some genetic and circumstantial lottery. I do feel the pull between several disciplines, and I must acknowledge that splitting my time does ‘slow me down’ in any one mastery. But that feels like balance for me.
I hope this review has given you a fair and accurate understanding of what Peak has to offer and perhaps, a little fire under you to re-engage with your masteries with some deliberate practice.