A computational model for automated tracking of socially-interacting animals

Review written by Andy Jones (COS, GS)

Understanding the link between neural activity and behavior is one of the long-running goals of neuroscience. In the information age, it is becoming more and more common for neuroscientists to take a data-driven approach to studying animal behavior in order to gain insight into the brain. Under this approach, scientists collect hours’ or days’ worth of video recordings of an animal, relying on modern machine learning (ML) systems to automatically identify exact locations of body parts and classify behavior types. These methods have opened the door for more expansive studies of the relationship between brain activity and behavior, without relying on laborious manual annotations of animal movements. 

Contine reading

The false duality of habitual versus goal-directed behavior

Written by Anika Maskara ‘23 & Thiago Tarraf Varella (PSY GS)

It is common in popular culture to imagine human decision making as a clash of two distinct choices. There is a “good option” and a “bad option,” an angel or a devil sitting on our shoulders. Like many dichotomies, though, that view of decision making is misleading. It is true that research suggests we have two different decision-making systems that sometimes disagree about which action to take, but neither is better or worse than the other; they simply use different algorithms to help us decide what to do.

Continue reading

How can human uncertainty improve machine image classification?

Review written by Shanka Subhra Mondal (ELE)

In recent years, machine learning (ML) has become commonplace in our software and devices. Its applications are varied, ranging from finance and marketing to healthcare and computer vision. ML already has the ability to out-perform humans on many tasks, such as video game competitions and image/object recognition, to name a few. At a high-level, ML comprises a set of algorithms that rely heavily on data (training data) to make decisions about another set of data (testing data) that it has not previously encountered. One of the sub-fields of computer vision where machine learning has proven particularly useful is in image classification, where the goal is to categorize objects in a given image. While this task might sound easy for humans, it can be challenging for an algorithm, particularly when the picture is blurred, not properly illuminated, or noisy. Robust image classification is not an easy task.

Continue reading