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The Intriguing Dance of Learning

Explore how explicit and implicit learning shape our daily lives.

Yonatan Stern, Ophir Netzer, Danny Koren, Yair Zvilichovsky, Uri Hertz, Roy Salomon

― 6 min read


Learning: Mind's Dual Learning: Mind's Dual Approach and instinct in learning. Discover how our brains balance thought
Table of Contents

Learning is a fascinating process that involves how we gather information and make decisions based on that information. When we learn, we often have two systems at work: one that we consciously think about and another that operates without us even realizing it. This article discusses how these two systems work together, sometimes seamlessly and sometimes in surprising ways, when we are faced with tasks in our daily lives.

The Role of Explicit and Implicit Learning

At the heart of this discussion are two types of learning: explicit and implicit. Explicit learning is the kind where we know what we are doing and can explain it to others. For example, if you learned that you should look left before crossing the street because cars come from that direction, that's explicit learning. Implicit learning, on the other hand, happens automatically without our conscious effort. This is more like a reflex—if you see a ball coming at your face, your body automatically reacts to dodge it.

Interestingly, these two systems often work together. When you prepare to cross the street, you consciously think about looking for oncoming traffic (explicitly). At the same time, your body instinctively knows how to look in the right direction (implicitly). This union of conscious thought and automatic response is what makes human learning so effective and seamless.

Instances of Divergence

However, there are times when these two systems do not align perfectly. Imagine you are traveling in a country where cars drive on the opposite side of the street. You know, explicitly, that you should look right before crossing, but your body might still look left out of habit. This is a clear example of how our implicit learning can sometimes conflict with our explicit knowledge.

This mismatch can happen in various situations, and it emphasizes how our brains are wired to adapt to different environments. The learning process is not flawless and can sometimes lead to mistakes, particularly in unfamiliar settings.

Gaze and Attention: An Unlikely Pair

One of the essential links between these two systems is through our gaze and attention. When we learn new things or make predictions, our eyes tend to look where we think something will happen. For instance, if you predict that the target is going to appear on the right side of the table, your eyes will naturally drift towards the right. This tendency to look where we expect something to be is a powerful tool our brains use to prepare for action.

Researchers have found that our gaze can reveal much about our expectations and decisions. The way we look at things is not random; it often reflects what we think is going to happen next. For example, if we're shown an image and are asked to make a choice, we might glance more at the option we believe will be correct. This behavior is a mix of both thought and instinct.

The Link Between Gaze and Confidence

But why does our gaze matter? Well, it can tell us about the confidence we have in our predictions. When we are sure about our choices, our gaze may be steadier and more focused on the target. In contrast, if we're uncertain, we might look around more, seeming a little lost. This natural behavior not only shows where our focus is but also gives insight into how confident we feel about our decision.

Many studies have examined this interaction between gaze and confidence. One finding is that when people feel confident about their choices, their gaze patterns align closely with their predictions. If they think they’re making the right choice, their eyes will reflect that confidence by staying locked on the target. On the other hand, if there is doubt, their gaze may wander.

The Learning Curve: How We Improve

Learning isn't a straight path, and it often comes with its ups and downs. When people practice a task, they tend to get better over time. This is known as the learning curve. For example, think about learning to ride a bike. At first, you might wobble and fall, but with practice, you become more stable and confident in your ability to stay upright.

In the context of explicit and implicit learning, this curve showcases how both systems adapt. Over repeated trials of an activity, individuals learn to refine their predictions and adjust their gaze in line with their expectations. When a task is well learned, both the explicit thought and the implicit sensorimotor reaction become smoother and more aligned, resulting in better performance.

Exploring the Benefits of Divergence

While convergence between these two systems is useful, divergence also has its merits. Sometimes, our implicit system can explore options even when our explicit mind is set on a particular choice. This can lead to what is often called "hedging"—where we hold back a little to see what happens, just to ensure we're not making any rash decisions.

Imagine a chef trying out a new recipe. They may have a clear idea of what they're doing (explicit knowledge), but they might also leave room for adjusting flavors based on what they taste while cooking (implicit learning). This blend of certainty and exploration can lead to deeper learning and better outcomes in various activities.

Real-World Applications of This Knowledge

Understanding how these two systems interact can have a practical impact in many fields. In education, for example, instructors can use this knowledge to design lessons that not only engage students' explicit knowledge but also cater to their implicit learning.

In sports, coaches can leverage these insights to improve players' performance by training them to trust their instincts while honing their explicit decision-making skills. This combination can help athletes perform better under pressure, where both quick reflexes and clear thinking are essential.

In Closing: The Complexity of Learning

The interplay between explicit predictions and implicit expectations is more than just a scientific curiosity; it is a fundamental part of how we learn and adapt to our environment. While things may occasionally get tangled up, causing us to look the wrong way or make a wrong prediction, these systems together create a resilient and adaptable learning process.

So the next time you find yourself learning something new, just remember: your brain is hard at work, weaving together what you consciously know with what you instinctively feel. And maybe, just maybe, your gaze is secretly guiding you towards the right path, even when you think you might be lost.

Original Source

Title: Gaze reflects confidence in explicit decisions while relying on a distinct computational mechanism

Abstract: The eyes are considered a window into the mind, shedding light on the cognitive processes leading to explicit decisions. Yet, eye movements are also a distinct oculomotor decision that is the outcome of an independent cognitive system. When exploring novel environments, the convergence and divergence of gaze and explicit decisions highlights the eyes dual role. However, the computational mechanisms underlying this interplay and its contribution to adaptative behavior remains unclear. Combining virtual reality based associative learning paradigms with computational modeling across one exploratory and two pre-registered experiments (Total N = 115), we examine learning as multi-system process encompassing explicit decisions and ocular expectations. The two exhibited a partial overlap in their learning. On the one hand, participants explicit predictions about target location and their subsequent anticipatory gaze direction robustly converged, and gaze reveals the latent probability of the explicit choice. Moreover, gaze exhibited computational hallmarks of confidence, reflecting an oculomotor assessment of the explicit prediction. On the other hand, ocular and explicit decisions consistently diverged indicating an independent computation reflected in gaze. Although the two systems learn and ascribe value similarly, they utilize different decision mechanisms. Oculomotor decisions were less driven by choice consistency across trials enabling increased exploration. Under uncertainty explicit and oculomotor decisions tended to diverge, enabling a hedging of ones decision. These findings highlight how together, explicit and oculomotor systems learning enables adaptive embodied behavior. Significance StatementThe eyes often reveal additional information beyond ones explicit decision, offering a window into the mind (e.g., leading poker players to wear sunglasses). Yet, eye movements are the product of a distinct system, occasionally diverging from explicit decisions. However, what additional information is conveyed by gaze, and what leads to its occasional divergence remain unclear. Using virtual reality and computational modeling, this study demonstrates a partial overlap between the two. Generally, they robustly converge, and gaze reflects a confidence-like assessment of explicit decisions. Yet they also diverge due to distinct decision mechanisms. Their divergence enables a hedging of ones decision under uncertainty. This demonstrates a multimodal decision process, where different action systems make overlapping but independent choices.

Authors: Yonatan Stern, Ophir Netzer, Danny Koren, Yair Zvilichovsky, Uri Hertz, Roy Salomon

Last Update: 2024-12-16 00:00:00

Language: English

Source URL: https://www.biorxiv.org/content/10.1101/2024.08.28.610145

Source PDF: https://www.biorxiv.org/content/10.1101/2024.08.28.610145.full.pdf

Licence: https://creativecommons.org/licenses/by-nc/4.0/

Changes: This summary was created with assistance from AI and may have inaccuracies. For accurate information, please refer to the original source documents linked here.

Thank you to biorxiv for use of its open access interoperability.

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