Category: Learning

Learning Effectively From Experience: Distinguishing High from Low Performers

High performers learn from both success and failure making small adjustments. Conversely, low performers learned more from success.

Learning effectively from experience is a daunting task for any organism. For every good or bad outcome, there are an immense number of potential causes and associations to be considered. For many decisions, it can be nearly impossible to pick out the few relevant factors from the many irrelevant factors, even with extensive experience. A major stumbling block for learning in these multi-dimensional environments is the tendency to form spurious beliefs: i.e., to attribute a causal role to factors that have no actual bearing on the outcome.

The formation of spurious beliefs is universal, from Skinner's observations of superstitious pigeons [1] to an athlete's belief in a lucky hat. In some situations, these beliefs are essentially harmless; by-products of learning mechanisms, but in other settings their impact can be severe. For example, spurious associations can have literal life-or-death consequences when they affect the complex decisions made by physicians. These expert decision-makers must extract and distill relevant features from a myriad of tests, symptoms, and personal histories, and employ these features to make critical medical decisions. Consequently, it is important to understand how spurious associations form and how they can bias subsequent decisions.

Spurious learning and false belief formation happens when the dorsolateral prefrontal cortex fails to distinguish correctly between important and unimportant associations.

The authors conclude:

High performers learned from both successes and failures, and made smaller rule adjustments after feedback. Conversely, low performers learned disproportionately from successes, and made larger rule adjustments. …

Taken together, the behavioral and neuroimaging results suggest that success-chasing and confirmation bias may underlie the relative pervasiveness of premature, asymmetric learning and the resultant poor performance of the majority of physician subjects in the present study. The general human bias towards confirmation over disconfirmation in hypothesis-testing has been extensively documented in a variety of non-medical contexts, such as the Wason Card Task. Conversely, the necessity for disconfirmation learning in empirical investigations is a key principle identified by the philosopher of science, Karl Popper [28]. Conceivably, providing medical professionals with formal training in disconfirmation learning could improve their ability to learn effectively from clinical experience in real-world settings. Exploring this possibility would be an important area for future research.

In conclusion, the results of this study show distinct patterns of learning, both behaviorally and neurally, between effective and ineffective learners among physicians making decisions in a medically framed learning task. The tendency to chase successes and ignore failures provides a simple computational model of how spurious beliefs might be formed, and how different individuals seeing similar data might learn very different sets of associations. The neural differences observed could conceivably be developed into useful biomarkers for essential differences in individual learning styles. These may in turn prove useful in identifying those individuals who can resist the impulse to chase successes, and hence learn most effectively from experience. Finally, we note that although this study focused upon the specific case of medical decision-making, the findings may be also be relevant to many other fields in which experts must make high-stakes decisions by drawing upon personal experience.

Abstract:

Accurate associative learning is often hindered by confirmation bias and success-chasing, which together can conspire to produce or solidify false beliefs in the decision-maker. We performed functional magnetic resonance imaging in 35 experienced physicians, while they learned to choose between two treatments in a series of virtual patient encounters. We estimated a learning model for each subject based on their observed behavior and this model divided clearly into high performers and low performers. The high performers showed small, but equal learning rates for both successes (positive outcomes) and failures (no response to the drug). In contrast, low performers showed very large and asymmetric learning rates, learning significantly more from successes than failures; a tendency that led to sub-optimal treatment choices. Consistently with these behavioral findings, high performers showed larger, more sustained BOLD responses to failed vs. successful outcomes in the dorsolateral prefrontal cortex and inferior parietal lobule while low performers displayed the opposite response profile. Furthermore, participants' learning asymmetry correlated with anticipatory activation in the nucleus accumbens at trial onset, well before outcome presentation. Subjects with anticipatory activation in the nucleus accumbens showed more success-chasing during learning. These results suggest that high performers' brains achieve better outcomes by attending to informative failures during training, rather than chasing the reward value of successes. The differential brain activations between high and low performers could potentially be developed into biomarkers to identify efficient learners on novel decision tasks, in medical or other contexts.

Source

(via Deric Bownds)

Why are some people so much more effective at learning from their mistakes?

Jonah Lehrer comments on a new study forthcoming in Psychological Science led by Jason Moser at Michigan State that helps explain why some people are more effective at learning from their mistakes than others.

…the scientists applied a dichotomy first proposed by Carol Dweck, a psychologist at Stanford. In her influential research, Dweck distinguishes between people with a fixed mindset — they tend to agree with statements such as “You have a certain amount of intelligence and cannot do much to change it” — and those with a growth mindset, who believe that we can get better at almost anything, provided we invest the necessary time and energy. While people with a fixed mindset see mistakes as a dismal failure — a sign that we aren’t talented enough for the task in question — those with a growth mindset see mistakes as an essential precursor of knowledge, the engine of education.

On the Moser study, Lehrer comments, “It turned out that those subjects with a growth mindset were significantly better at learning from their mistakes. As a result, they showed a spike in accuracy immediately following an error. … implying that the extra awareness was paying dividends in performance. Because the subjects were thinking about what they got wrong, they learned how to get it right.”

Dweck's research, found mindsets have important practical implications. She debunked the commonly held belief that praise for ability encouraged motivation, concluding that “that praise for intelligence had more negative consequences for students' achievement motivation than praise for effort.” How you approach the problem makes a difference. “According to Dweck, praising kids for intelligence encourages them to “look” smart, which means that they shouldn’t risk making a mistake.”

So, praising for innate intelligence encourages kids to avoid learning activities where they are likely to fail. And unless we experience the unpleasantness of being wrong and direct our attention to the very thing we'd like to ignore the mind will never become effective at learning from mistakes. As Lehrer concludes, we'll keep making the same mistakes, “forsaking self-improvement for the sake of self-confidence.”

If you want to learn more, read Dweck's book Mindset: The New Psychology of Success.

Continue Reading.

Jonah Lehrer is the author of How We Decide and Proust Was a Neuroscientist.

James March: The Ambiguities of Experience

“Since experience in organizations often suffers from weak signals,
substantial noise, and small samples, it is quite likely that realized history
will deviate considerably from underlying reality.”
— James March

***

In his book, The Ambiguities of Experience, James March explores the role of experience in creating intelligence.

Folk wisdom both trumpets the significance of experience and warns of its inadequacies.

On one hand, experience is thought to be the best teacher. On the other hand, experience is described as the teacher of fools, of those unable or unwilling to learn from accumulated knowledge.

The disagreement between those folk aphorisms reflects profound questions about the human pursuit of intelligence through learning from experience.

March convincingly argues that although individuals and organizations are eager to derive intelligence from experience, the inferences stemming from that eagerness are often misguided.

The problems lie partly in errors in how people think, but even more so in properties of experience that confound learning from it. ‘Experience,' March concludes, ‘may possibly be the best teacher, but it is not a particularly good teacher.'

Here are some of my notes from the book:

  • Intelligence normally entails two interrelated but somewhat different components. The first involves effective adaptation to an environment. The second: the elegance of interpretations of the experiences of life.
  • Since experience in organizations often suffers from weak signals, substantial noise, and small samples, it is quite likely that realized history will deviate considerably from the underlying reality.
  • Agencies write standards because experience is a poor teacher.
  • Constant exposure to danger without its realization leaves human beings less concerned about what once terrified them, and therefore experience can have the paradoxical effect of having people learn to feel more immune than they should to the unlikely dangers that surround them.
  • Generating an explanation of history involves transforming the ambiguities and complexities of experience into a form that is elaborate enough to elicit interest, simple enough to be understood, and credible enough to be accepted. The art of storytelling involves a delicate balancing of those three criteria
  • Humans have limited capabilities to store and recall history. They are sensitive to reconstructed memories that serve current beliefs and desires. They conserve belief by being less critical of evidence that seems to confirm prior beliefs than of evidence that seems to disconfirm them. They destroy both observations and beliefs in order to make them consistent. They prefer simple causalities, ideas that place causes and effects close to one another and that match big effects with big causes. 
  • The key effort is to link experience with a pre-existent accepted storyline so as to achieve a subjective sense of the understanding.
  • Experience is rooted in a complicated causal system that can be described adequately only by a description that is too complex for the human mind. The more accurately reality is reflected, the less comprehensible the story, and the more comprehensible the story, the less realistic it is.
  • Storytellers have their individual sources and biases, but they have to gain acceptance of their stories by catering to their audiences.
  • Despite the complications in extracting reality from experience, or perhaps because of them, there is a tendency for the themes of stories of management to converge over time.
  • Organizational stories and models are built particularly around four main mythic themes: rationality (the idea that the human spirit finds definitive expression through taking and justifying action in terms of its future consequences for prior values); hierarchy (the ideas that problems and actions can be decomposed into nested sets of subproblems and sub-actions such that interactions among them can be organized within a hierarchy); individual leader significance (the idea that any story of history must be related to a human project in order to be meaningful and that organizational human history is produced by the intentions of specific human leaders); and historical efficiency (the idea that history follows a path leading to a unique equilibrium defined by antecedent conditions and produced by competition.
  • Underlying many of these myths is a grand myth of human significance: the idea that humans can, through their individual and collective intelligence actions, influence the course of history to their advantage.
  • The myth of human significance produces the cruelties and generosities stemming from the human inclination to assign credit and blame for events to human intention.
  • There is an overwhelming tendency in American life to lionize or pillory the people who stand at the helms of our large institutions -to offer praise or level blame for outcomes over which they may have little control.
  • An experienced scholar is less inclined to claim originality than is a beginner.
  • …processes of adaptation can eliminate sources of error but are inefficient in doing so.
  • Knowledge is lost through turnover, forgetting, and misfiling, which assure that at any point there is considerable ignorance. Something that was once known is no longer known. In addition, knowledge is lost through its incomplete accessibility.
  • A history of success leads managers to a systematic overestimation of the prospects for success in novel endeavors. If managers attribute their success to talent when they are, in fact, a joint consequence of talent and good fortune, successful managers will come to believe that they have capabilities for beating the odds in the future as they apparently have had in the past.
  • In a competitive world of promises, winning projects are systematically projects in which hopes exceed reality
  • The history of organizations cycling between centralization and decentralization is a tribute, in part, to the engineering difficulty of finding an enduring balance between the short-run and local costs and the long-run and more global benefits of boundaries.
  • The vividness of direct experience leads learners to exaggerate the information content of personal experience relative to other information.
  • The ambiguities of experience take many forms but can be summarized in terms of five attributes: 1) the causal structure of experience is complex; 2) experience is noisy; 3) history includes numerous examples of endogeneity, causes in which the properties of the world are affected by actions adapting to it; 4) history as it is known is constructed by participants and observers; 5) history is miserly in providing experience. It offers only small samples and thus large sampling error in the inferences formed.
  • Experience often appears to increase significantly the confidence of successful managers in their capabilities without greatly expanding their understanding.

***

Still interested? Want to know more? Buy the book. Read Does Experience Make you an Expert? next. 

Making A Mistake: Fienberg and Buffett

making a mistake

Knowing when you've made a mistake and learning from that experience can be a very rewarding and profitable undertaking.

If you can admit your mistake, you can learn from it. However, an inability to learn from mistakes can mean you make the exact same mistake again.

Consider Steve Feinberg, the onetime owner of Chrysler, and CEO of Cerberus Capital Management reflecting on his ‘mistake'

…even now, Mr. Feinberg, a man who can play a decent game of chess while blindfolded, is hard-pressed to pinpoint many mistakes. Sitting in his office on Park Avenue, far away from the detritus that surrounds Detroit, he grows pensive when asked what he has learned from his audacious — and failed — effort to privatize and resurrect the legendary and deeply troubled auto giant. “I don’t know what we could have done differently,” he says, crossing his arms on his chest. “From the day we bought it, we worked hard to improve it.” He pauses, pondering, as the clock ticks away. Then he shakes his head. “We were too optimistic on timing,” he says. “Maybe what we should have done was not bought it.”

Compare that with Warren Buffett talking about his oft-cited U.S. Airways purchase in hindsight:

I liked and admired Ed Colodny, the company’s then-CEO, and I still do. But my analysis of USAir’s business was both superficial and wrong. I was so beguiled by the company’s long history of profitable operations, and by the protection that ownership of a senior security seemingly offered me, that I overlooked the crucial point: USAir’s revenues would increasingly feel the effects of an unregulated, fiercely-competitive market whereas its cost structure was a holdover from the days when regulation protected profits. These costs, if left unchecked, portended disaster, however reassuring the airline’s past record might be.

Words matter. To learn more about decoding CEOs read Investing Between the Lines: How to Make Smarter Decisions By Decoding CEO Communications.