At the heart of my transformative journey of learning abut learning lie several key elements; many of them present in the wonderful «How We Learn» by Stanislas Dahene. Today I´m talking about some ideas after reading the book, specially about surprise, and the role of prediction and learning so that meaningful understanding is built.
Attention, as illuminated by cognitive science, serves as the guiding force that directs our cognitive resources towards relevant stimuli. It acts as a gatekeeper, carefully selecting and amplifying information for deeper processing. Michael Posner’s taxonomy of attention systems (alert, orient, central executive) further highlights the nuanced nature of this cognitive mechanism, emphasizing its pivotal role in directing our mental focus towards pertinent tasks and stimuli.
However, attention alone is not sufficient to facilitate robust learning outcomes. Active engagement, characterized by participation, interaction, and hypothesis generation, is equally indispensable. Active engagement prompts individuals’ cognitive system to predict outcomes in what many called a Bayesian reasoning style.
Our brains constantly integrate new information with existing knowledge to form updated beliefs or predictions about the world. This process aligns closely with Bayesian inference, where prior beliefs (models of what will happen) are combined with new evidence to form posterior beliefs. For example, when we encounter new information, our brains instinctively evaluate its credibility based on our existing knowledge and adjust our beliefs accordingly. For more on this, I strongly recommend David Lagnado’s «Explaining the Evidence».
Furthermore, Bayesian reasoning allows for the incorporation of uncertainty and ambiguity, reflecting the probabilistic nature of human cognition. Our brains inherently deal with uncertainty, weighing probabilities and making decisions based on incomplete information. Bayesian inference provides a formal framework for this intuitive process, allowing us to make rational decisions in uncertain environments.
When we experience surprise or failure, whether in academic endeavors, professional pursuits, or personal challenges, we are confronted with a discrepancy between our expectations and reality. This discrepancy can be viewed as new evidence that updates our beliefs or mental models about the task at hand, similar to the process of Bayesian updating.
Bayesian reasoning allows us to incorporate this new evidence (i.e. surprise) into our existing knowledge base (prior beliefs) to form updated beliefs or hypotheses (posterior beliefs) about what went wrong and how to improve. By embracing failure as informative feedback rather than a mark of incompetence, we can iteratively refine our understanding, strategies, and approaches.
Indeed, failure, often stigmatized and feared, emerges as a crucial catalyst for growth and development. The hypothesis posited by Robert Rescorla and Allan Wagner underscores the profound significance of cognitive dissonance in the learning journey. According to their framework, learning thrives on the dissonance between expectation and reality, with surprise serving as a potent trigger for cognitive adaptation and refinement.
Far from being a deterrent, failure offers invaluable opportunities for reflection, iteration, and improvement. The Rescorla-Wagner learning rule provides a theoretical foundation for understanding how error signals drive cognitive adaptation, prompting individuals to update their internal representations to better align with reality.
What for teachers? Educators must prioritize objective, non-punitive feedback that empowers learners to identify, analyze, and rectify their mistakes. By using low-stakes quizzing, for example. After reading extensively about feedback and formative assessment, I find that these ideas may explain the principles that enable the recurrent occurrence of both as highly effective. Also, all this resonates into the 5E model develop by Bybee (click here for more on this). The 5E model is a systematic approach to science education, providing a structured framework for engaging students in the learning process. It begins with an engaging activity or question to capture students’ interest and activate prior knowledge. Through exploration, students delve into the topic, making observations and conducting experiments to develop their understanding. That is, creating a prediction of the phenomena.
After that, in the explanation phase, teachers provide guidance and explanations, helping students connect their observations to scientific concepts and unveiling the level of underlap between their predictions and the reality. This process fosters surprise and correction for error.
Finally, another idea that I linked while writing this post was the «productive failure» by Manu Kapur. This approach allow students to confront and explore new concepts before teaching them. This exploration helps them build relevant experiences with which they can later encode instruction. For example, it helps students understand how certain concepts should be applied, what they mean, and how they can be used to assemble more complex ideas.
In summary, my exploration of Stanislas Dehaene’s «How We Learn» augmented by insights underscores the multifaceted nature of effective learning. Hope this post does the same for you.
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