How is the brain a prediction machine?

Our brain works a bit like the autocomplete function on your phone – it is constantly trying to guess the next word when we are listening to a book, reading or conducting a conversation.

ru.nl - Our brain is a prediction machine that is always active | Radboud University

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Our brain works a bit like the autocomplete function on your phone – it is constantly trying to guess the next word when we are listening to a book, reading or conducting a conversation. By contrast with speech recognition computers, our brains are constantly making predictions at different levels, from meaning and grammar to specific sounds. This is what researchers at Radboud University’s Donders Institute and the Max Planck Institute for Psycholinguistics discovered in a new study. They are publishing their results in PNAS today. This is a recent theory on how our brain works: it is a predictive machine, which continuously the compares sensory information that we pick up (such as images, sounds and language) with internal predictions. "This theoretical idea is extremely popular in neuroscience, but the existing evidence for it is often restricted to artificial situations," says lead author Micha Heilbron of the Donders Institute (University) and the Max Planck Institute for Psycholinguistics. "I would really like to understand precisely how this works and test it in different situations." Brain research into this phenomenon is usually done in an artificial setting, Heilbron reveals. Subjects are told to stare at dots for half an hour, or they listen to abstract noises that sound like 'bib bib boop'. "Studies of this kind do in fact reveal that our brain can make predictions, but not that this always happens in the complexity of everyday life as well. We are trying to take it out of the lab setting. We are studying the same type of phenomenon, how the brain deals with unexpected information, but then in natural situations that are much less predictable."

Hemingway and Holmes

The researchers analysed the brain activity of people listening to stories by Hemingway or about Sherlock Holmes. At the same time, they analysed the texts of the books using computer models (deep neural networks). By this means, they were able to calculate for each word how unpredictable it was. For each word or sound, the brain makes detailed statistical expectations and turns out to be extremely sensitive to the degree of unpredictability: the brain response is stronger whenever a word is unexpected in the context. "That is not very surprising: after all, everyone knows that you can sometimes predict language. For example, you complete sentences yourself if someone starts to speak very slowly, stutters or is unable to think of a word. But what we have shown here is that this happens continuously. Our brain is constantly guessing at words; the predictive machinery is always turned on."

More than software

"In fact, our brain does something comparable to speech recognition software. Speech recognisers using artificial intelligence are also constantly making predictions and are allowing themselves to be guided by their expectations, just like the autocomplete function on your phone. Nevertheless, we observed a big difference: brains predict not only words, but make predictions on many different levels, from abstract meaning and grammar to specific sounds." There is good reason for the ongoing interest from tech companies who would like to use new insights of this kind to build better language and image recognition software, for example. But these sorts of applications are not the main aim for Heilbron. "I would really like to understand how our predictive machinery works and when at a fundamental level. I’m now working with the same research setup, but for visual and auditive perceptions, like music."

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What is a best fit graph?

A line of best fit, also called a trend line or linear regression, is a straight line drawn on a graph that best represents the data on a plot. This line passes through some of the points, all of the points, or none of the points. It can be used to make predictions or to show trends in data.

plotly.com - Make a Line of Best Fit in Chart Studio - Plotly

After your data's been added, go to GRAPH on the left-hand side, then 'Create'. Choose your 'Chart type', and add your traces using the X and Y dropdown (this section is different depending on the chart type).

Step 4 Select Your Fit

When you've finished plotting your data, visit the ANALYSIS section on the left-hand side of your workspace. Click on the blue '+ Analysis' button, then click on the dropdown menu and select 'Fit'. Select your 'Target Trace' (remember, these are the traces you've set up earlier). You'll then have to select the function you want under 'Function Family'. The dropdown menu has a few to choose from including linear, quadratic, polynomial, and exponential to name a few. Alternatively, you have the option of adding your own fit function by selecting 'Custom fit function' and entering it in the field. You can also enter your parameters in the 'Constant Estimates' fields. Your advanced options include fitting to a specific range and/or displaying your fit over a specific range. The blue 'More' button on the bottom right will display two output options. You have the choice of setting your output with evenly spaced points and using the slider to enter your points, or your points matching your X data. The last thing to do is click 'RUN' at the bottom. We've chosen the 'Linear' function, and this is what our plot looks like with the line of best fit. Don't forget to save your graph and show it off to your friends by sharing it. For more information on how to save, share, and export, visit this page!

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