Now Reading
Our capacity to recognize patterns might be attributed to the brain’s drive to describe things in the simplest feasible way

Our capacity to recognize patterns might be attributed to the brain’s drive to describe things in the simplest feasible way

Infants may recognize regular sound sequences during their first year of life. As we grow older, we gain the capacity to recognize increasingly complicated patterns in streams of words and musical sounds. Traditionally, cognitive scientists thought that the brain used a complex algorithm to discover connections between dissimilar concepts, resulting in a higher-level comprehension.

Christopher Lynn, Ari Kahn, and Danielle Bassett of the University of Pennsylvania are developing an altogether new model, showing that our capacity to identify patterns may be influenced in part by the brain’s drive to encode things in the simplest way possible.

The brain does more than just process incoming information, said Lynn, a physics graduate student. “It constantly tries to predict what’s coming next. If, for instance, you’re attending a lecture on a subject you know something about, you already have some grasp of the higher-order structure. That helps you connect ideas together and anticipate what you’ll hear next.”

Sacrificing accuracy to see the big picture
A. Example sequence of visual stimuli (left) representing a random walk on an underlying transition network (right). B. For each stimulus, subjects are asked to respond by pressing a combination of one or two buttons on a keyboard. C. Each of the 15 possible button combinations corresponds to a node in the transition network. We only consider networks with nodes of uniform degree k = 4 and edges with uniform transition probability 0.25. D. Subjects were asked to respond to sequences of 1500 such nodes drawn from two different transition architectures: a modular graph (left) and a lattice graph (right). E. Average reaction times across all subjects for the different button combinations, where the diagonal elements rep- resent single-button presses and the off-diagonal elements represent two-button presses. F. Average reaction times as a function of trial number, characterized by a steep drop-off in the first 500 trials followed by a gradual decline in the remaining 1,000 trials. Credit: Lynn et al.

The new model provides fascinating insights into human cognition, implying that individuals may and do make errors in recognizing specific components of a pattern in order to see the broader picture. Lynn, for example, stated, “When you look closely at a pointillist painting, you can distinguish every dot. When you take a 20-foot step back, the details blur, but you get a greater understanding of the overall structure.” According to him, the brain may use a similar method.

To put its idea to the test, the researchers devised an experiment in which participants view a computer screen with a row of five squares and then push one or two keys to match the display. The researchers timed the replies and concluded that people press the proper keys faster when they anticipate what’s going to happen next.

Each stimulus provided to a subject may be regarded as a node in a network as part of the experimental design, with one of four neighboring nodes indicating the next stimulus. The networks are divided into two types: a “modular graph” made up of three connected pentagons and a “lattice graph” made up of five linked triangles. When subjects were shown the modular graph, they reacted faster, suggesting that they could better identify its underlying structure and, as a result, better predict the visual that would follow.

Finally, the experiment is intended to assess a quantity known as beta (β), which varies from subject to subject, with lower levels in those prone to errors and larger values in those prone to precision. The Pennsylvania group intends to obtain brain pictures via fMRI scans later this year to determine if the brains of persons with varied levels of β are, in fact, “wired differently.”

See Also

Generally, our results highlight the important role of mental errors in shaping abstract representations, and directly inspire new physically-motivated models of human behavior.

Structure from noise: Mental errors yield abstract representations of events, Christopher Lynn, Ari E Kahn, Danielle Bassett

Presented: 2019
https://meetings.aps.org/Meeting/MAR19/Session/H66.4

© 2021 GEOMETRY MATTERS. ALL RIGHTS RESERVED.
Scroll To Top