How expectation influences perception

These prior beliefs help us make sense of what we are perceiving in the present.

how our expectations influence perception

For decades, research has shown that our perception of the world is influenced by our expectations. These expectations, also called "prior beliefs," help us make sense of what we are perceiving in the present, based on similar past experiences.


Consider, for instance, how a shadow on a patient's X-ray image, easily missed by a less experienced intern, jumps out at a seasoned physician. The physician's prior experience helps her arrive at the most probable interpretation of a weak signal.

The process of combining prior knowledge with uncertain evidence is known as Bayesian integration and is believed to widely impact our perceptions, thoughts, and actions. Now, MIT neuroscientists have discovered distinctive brain signals that encode these prior beliefs. They have also found how the brain uses these signals to make judicious decisions in the face of uncertainty.

"How these beliefs come to influence brain activity and bias our perceptions was the question we wanted to answer," says Mehrdad Jazayeri, the Robert A. Swanson Career Development Professor of Life Sciences, a member of MIT's McGovern Institute for Brain Research, and the senior author of the study.

The researchers trained animals to perform a timing task in which they had to reproduce different time intervals. Performing this task is challenging because our sense of time is imperfect and can go too fast or too slow. However, when intervals are consistently within a fixed range, the best strategy is to bias responses toward the middle of the range. This is exactly what animals did. Moreover, recording from neurons in the frontal cortex revealed a simple mechanism for Bayesian integration: Prior experience warped the representation of time in the brain so that patterns of neural activity associated with different intervals were biased toward those that were within the expected range.

MIT postdoc Hansem Sohn, former postdoc Devika Narain, and graduate student Nicolas Meirhaeghe are the lead authors of the study, which appears in the July 15 issue of Neuron.

Ready, set, go

Statisticians have known for centuries that Bayesian integration is the optimal strategy for handling uncertain information. When we are uncertain about something, we automatically rely on our prior experiences to optimize behavior.

"If you can't quite tell what something is, but from your prior experience you have some expectation of what it ought to be, then you will use that information to guide your judgment," Jazayeri says. "We do this all the time."

In this new study, Jazayeri and his team wanted to understand how the brain encodes prior beliefs, and put those beliefs to use in the control of behavior. To that end, the researchers trained animals to reproduce a time interval, using a task called "ready-set-go." In this task, animals measure the time between two flashes of light ("ready" and "set") and then generate a "go" signal by making a delayed response after the same amount of time has elapsed.

They trained the animals to perform this task in two contexts. In the "Short" scenario, intervals varied between 480 and 800 milliseconds, and in the "Long" context, intervals were between 800 and 1,200 milliseconds. At the beginning of the task, the animals were given the information about the context (via a visual cue), and therefore knew to expect intervals from either the shorter or longer range.

Jazayeri had previously shown that humans performing this task tend to bias their responses toward the middle of the range. Here, they found that animals do the same. For example, if animals believed the interval would be short, and were given an interval of 800 milliseconds, the interval they produced was a little shorter than 800 milliseconds. Conversely, if they believed it would be longer, and were given the same 800-millisecond interval, they produced an interval a bit longer than 800 milliseconds.

"Trials that were identical in almost every possible way, except the animal's belief led to different behaviors," Jazayeri says. "That was compelling experimental evidence that the animal is relying on its own belief."

Once they had established that the animals relied on their prior beliefs, the researchers set out to find how the brain encodes prior beliefs to guide behavior. They recorded activity from about 1,400 neurons in a region of the frontal cortex, which they have previously shown is involved in timing.

During the "ready-set" epoch, the activity profile of each neuron evolved in its own way, and about 60 percent of the neurons had different activity patterns depending on the context (Short versus Long). To make sense of these signals, the researchers analyzed the evolution of neural activity across the entire population over time, and found that prior beliefs bias behavioral responses by warping the neural representation of time toward the middle of the expected range.

"We have never seen such a concrete example of how the brain uses prior experience to modify the neural dynamics by which it generates sequences of neural activities, to correct for its own imprecision. This is the unique strength of this paper: bringing together perception, neural dynamics, and Bayesian computation into a coherent framework, supported by both theory and measurements of behavior and neural activities," says Mate Lengyel, a professor of computational neuroscience at Cambridge University, who was not involved in the study.

Embedded knowledge

Researchers believe that prior experiences change the strength of connections between neurons. The strength of these connections, also known as synapses, determines how neurons act upon one another and constrains the patterns of activity that a network of interconnected neurons can generate. The finding that prior experiences warp the patterns of neural activity provides a window onto how experience alters synaptic connections. "The brain seems to embed prior experiences into synaptic connections so that patterns of brain activity are appropriately biased," Jazayeri says.

As an independent test of these ideas, the researchers developed a computer model consisting of a network of neurons that could perform the same ready-set-go task. Using techniques borrowed from machine learning, they were able to modify the synaptic connections and create a model that behaved like the animals.

These models are extremely valuable as they provide a substrate for the detailed analysis of the underlying mechanisms, a procedure that is known as "reverse-engineering." Remarkably, reverse-engineering the model revealed that it solved the task the same way the monkeys' brain did. The model also had a warped representation of time according to prior experience.

The researchers used the computer model to further dissect the underlying mechanisms using perturbation experiments that are currently impossible to do in the brain. Using this approach, they were able to show that unwarping the neural representations removes the bias in the behavior. This important finding validated the critical role of warping in Bayesian integration of prior knowledge.

The researchers now plan to study how the brain builds up and slowly fine-tunes the synaptic connections that encode prior beliefs as an animal is learning to perform the timing task.

The research was funded by the Center for Sensorimotor Neural Engineering, the Netherlands Scientific Organization, the Marie Sklodowska Curie Reintegration Grant, the National Institutes of Health, the Sloan Foundation, the Klingenstein Foundation, the Simons Foundation, the McKnight Foundation, and the McGovern Institute.

Reprinted with permission of MIT News. Read the original article.

How New York's largest hospital system is predicting COVID-19 spikes

Northwell Health is using insights from website traffic to forecast COVID-19 hospitalizations two weeks in the future.

Credit: Getty Images
Sponsored by Northwell Health
  • The machine-learning algorithm works by analyzing the online behavior of visitors to the Northwell Health website and comparing that data to future COVID-19 hospitalizations.
  • The tool, which uses anonymized data, has so far predicted hospitalizations with an accuracy rate of 80 percent.
  • Machine-learning tools are helping health-care professionals worldwide better constrain and treat COVID-19.
Keep reading Show less

Listen: Scientists re-create voice of 3,000-year-old Egyptian mummy

Scientists used CT scanning and 3D-printing technology to re-create the voice of Nesyamun, an ancient Egyptian priest.

Surprising Science
  • Scientists printed a 3D replica of the vocal tract of Nesyamun, an Egyptian priest whose mummified corpse has been on display in the UK for two centuries.
  • With the help of an electronic device, the reproduced voice is able to "speak" a vowel noise.
  • The team behind the "Voices of the Past" project suggest reproducing ancient voices could make museum experiences more dynamic.
Keep reading Show less

Put on a happy face? “Deep acting” associated with improved work life

New research suggests you can't fake your emotional state to improve your work life — you have to feel it.

Credit: Columbia Pictures
Personal Growth
  • Deep acting is the work strategy of regulating your emotions to match a desired state.
  • New research suggests that deep acting reduces fatigue, improves trust, and advances goal progress over other regulation strategies.
  • Further research suggests learning to attune our emotions for deep acting is a beneficial work-life strategy.
  • Keep reading Show less

    World's oldest work of art found in a hidden Indonesian valley

    Archaeologists discover a cave painting of a wild pig that is now the world's oldest dated work of representational art.

    Pig painting at Leang Tedongnge in Indonesia, made at 45,500 years ago.

    Credit: Maxime Aubert
    Surprising Science
    • Archaeologists find a cave painting of a wild pig that is at least 45,500 years old.
    • The painting is the earliest known work of representational art.
    • The discovery was made in a remote valley on the Indonesian island of Sulawesi.
    Keep reading Show less
    Mind & Brain

    What can Avicenna teach us about the mind-body problem?

    The Persian polymath and philosopher of the Islamic Golden Age teaches us about self-awareness.

    Scroll down to load more…
    Quantcast