Cognitive Science > Cognitive Neuroscience >
Supervised learning

Definition:

Supervised learning is a machine learning task where an algorithm learns to map input data to output labels, based on a set of training examples that are labeled with the correct answers. The algorithm learns to make predictions by generalizing patterns from the input data and the corresponding labels provided during training. This form of learning is guided by a teacher or supervisor providing feedback on the algorithm's predictions, allowing it to improve its accuracy over time.

The Fundamentals of Supervised Learning

Supervised learning is a foundational concept in the field of cognitive science and cognitive neuroscience, playing a vital role in understanding how humans and machines learn from labeled data.

What is Supervised Learning?

Supervised learning is a type of machine learning where the algorithm is trained on a labeled dataset, meaning that each input data point is paired with the correct output. The goal of supervised learning is to learn a mapping function from the input to the output data.

How Does Supervised Learning Work?

In supervised learning, the algorithm learns to make predictions based on examples. It adjusts its internal parameters to improve the mapping between the input and output data. Through this process, the algorithm can generalize its learning to never-before-seen data.

Applications of Supervised Learning

Supervised learning is widely used across various domains, including natural language processing, computer vision, speech recognition, and medical diagnosis. In cognitive neuroscience, supervised learning models help researchers understand how the brain processes and learns from incoming sensory information.

Challenges in Supervised Learning

Although supervised learning is powerful, it comes with challenges such as overfitting, where the model performs well on the training data but poorly on unseen data. Researchers in cognitive science are exploring ways to mitigate these challenges and improve the robustness of supervised learning models.

Supervised learning is a cornerstone in both cognitive science and cognitive neuroscience, providing insights into how humans and machines learn from labeled data. As researchers delve deeper into this concept, the potential applications and implications for understanding learning and cognition continue to expand.

If you want to learn more about this subject, we recommend these books.

You may also be interested in the following topics:

• Unsupervised learning
• Reinforcement learning
• Coding of information