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Aphantasia Logo
Back to all research
Aphantasia Logo

Building awareness and understanding of aphantasia through research, education, and community support.

About

  • What is Aphantasia?
  • What is Hyperphantasia?
  • Take Assessment
  • Getting Started
  • Newsletter
  • About Us
  • Contact

Community

  • Premium Membership
  • Find support
  • Discussions
  • Events
  • Visualize

For Professionals

  • Overview
  • Free Introduction
  • Counselor Training
  • Educator Training
  • List Your Practice
  • Pricing & Bundles

Resources

  • Articles & Stories
  • Videos & Interviews
  • Aphantasia Course
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Research

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  • Participate in Studies
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© 2026 Aphantasia Network. All rights reserved.

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Ask AI About This Paper

Mental imagery and perception overlap within transmodal association networks

DOI: 10.1016/j.neuron.2026.03.013
Anderson, N. L., Salvo, J. J., Smallwood, J., & Braga, R. M. (2026). Mental imagery and perception overlap within transmodal association networks. Neuron. doi:10.1016/j.neuron.2026.03.013

Abstract

Human cognition relies on two modes: a perceptually coupled mode where mental states are driven by sensory input and a perceptually decoupled mode featuring self-generated mental content. Imagined states that evoke mental imagery are thought to be supported primarily by reinstated activity in sensory cortex, but transmodal systems are also implicated in imagery-related processes like mind-wandering, recollection, and imagining the future. During a precision fMRI experiment, participants imagined different scenarios in the scanner, then rated their mental states using multi-dimensional experience sampling. Thinking involving scenes evoked activity within parts of the canonical default network, while imagining speech evoked activity within the language network. In each domain, imagining-related activity overlapped with activity evoked by viewing scenes or listening to speech, respectively; however, this overlap was predominantly within transmodal association networks, rather than adjacent unimodal sensory networks. We conclude that the engagement of transmodal networks supports self-generated mental states involving different forms of mental imagery.

Authors

  • Nathan L. Anderson1
  • Joseph J. Salvo1
  • Jonathan Smallwood1
  • Rodrigo M. Braga1
Ask AI About This Paper

Mental imagery and perception overlap within transmodal association networks

DOI: 10.1016/j.neuron.2026.03.013
Anderson, N. L., Salvo, J. J., Smallwood, J., & Braga, R. M. (2026). Mental imagery and perception overlap within transmodal association networks. Neuron. doi:10.1016/j.neuron.2026.03.013

Abstract

Human cognition relies on two modes: a perceptually coupled mode where mental states are driven by sensory input and a perceptually decoupled mode featuring self-generated mental content. Imagined states that evoke mental imagery are thought to be supported primarily by reinstated activity in sensory cortex, but transmodal systems are also implicated in imagery-related processes like mind-wandering, recollection, and imagining the future. During a precision fMRI experiment, participants imagined different scenarios in the scanner, then rated their mental states using multi-dimensional experience sampling. Thinking involving scenes evoked activity within parts of the canonical default network, while imagining speech evoked activity within the language network. In each domain, imagining-related activity overlapped with activity evoked by viewing scenes or listening to speech, respectively; however, this overlap was predominantly within transmodal association networks, rather than adjacent unimodal sensory networks. We conclude that the engagement of transmodal networks supports self-generated mental states involving different forms of mental imagery.

Authors

  • Nathan L. Anderson1
  • Joseph J. Salvo1
  • Jonathan Smallwood1
  • Rodrigo M. Braga1
Aphantasia Logo

What This Study Is About

Researchers wanted to find the exact "map" in the brain where we create mental imagery—the ability to picture things or hear sounds in your mind. They specifically wanted to see if the brain uses the same tools to *imagine* a scene as it does to actually *see* one.

How They Studied It

This was a "deep dive" study. Instead of testing hundreds of people briefly, they studied 8 adults very intensely using precision fMRI (a high-tech brain scanner).
  • The Task: Participants spent over 60 total hours in the scanner! They were given prompts like "Imagine a castle on a hill" or "Imagine the sound of a motorcycle" and had to rate how vivid (clear) the mental image or sound was.
  • The Comparison: Researchers then had the participants actually look at pictures and listen to sounds to see which brain areas overlapped.

What They Found

The team discovered that imagination is a "high-level" job.
  • Different Folders: Imagining a place (like a park) uses a different brain network than imagining speech.
  • Not Just a Replay: When you imagine a sunset, your brain doesn't just "replay" the basic light-detecting parts of your vision. Instead, it uses transmodal networks—think of these as the brain’s "command centers" that handle complex ideas rather than raw data.
  • Vividness Matters: The more vivid a participant said their mental image was, the harder these specific command centers worked.

What This Might Mean

For people with aphantasia (those who have a "blind mind's eye" and cannot visualize), this research is a big clue. It suggests that the "blindness" might not be a problem with the brain's vision centers, but rather how these high-level command centers coordinate.
However, we have to be careful: this was a very small study of only 8 people who *could* visualize. While it "suggests" how the brain works, we need more research involving aphantasic participants to "prove" if their command centers are simply wired differently.

One Interesting Detail

The participants were so dedicated that they spent about 7.6 hours each inside the MRI machine. That is like sitting perfectly still and imagining castles for the entire length of three blockbuster movies!
This summary was generated by AI and may contain errors. Always refer to the original paper for accuracy.
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