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Christopher Honey

@chrishoney.bsky.social

1.3K followers 510 following 55 posts

Associate Professor, Psychological & Brain Sciences, Johns Hopkins University. theoretical neuroscience; open-ended cognition; memory

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Christopher Honey @chrishoney.bsky.social · 16/09/2026

3/ The persistence of this background pattern predicted how reliably posterior medial cortex re-synced to the story after the interruption, and (along with hippocampal activity) it predicted how well listeners later recalled the story.

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Christopher Honey @chrishoney.bsky.social · 16/09/2026

2/ One known mechanism for continuity is hippocampal memory: we can snapshot mental context and later restore it. But Xian found a cortical process: a background pattern that gradually drifted over narrative time, and which persisted through interruptions in a transformed format.

A three-row illustration of (A) the timecourse of correlation between the story-phase pattern and the interruption-phase pattern immediately following, revealing negative correlation; (B) a topographic map of the PMC region that was analyzed; (C) spatial maps of voxelwise activation across different segments of the story (activation selective to each segment) and the following interruption phases.

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Christopher Honey @chrishoney.bsky.social · 16/09/2026

1/ How do we sustain mental continuity in the face of interruption? @xianl-explorer.bsky.social found that, while people listen to a story, posterior medial cortex expresses a slowly evolving "background" representation of the story's context, which survived through silence and interfering tasks.

A schematic figure showing an evolving timeline of mental context during an interrupted narrative.

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Christopher Honey @chrishoney.bsky.social · 15/06/2026

These instructions actually crash the bookshop.org checkout. I am not kidding. There is some kind of bug where, if you enter a large enough number in the quantity box, and press "Update, the site produces this:

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Christopher Honey @chrishoney.bsky.social · 15/06/2026

Fourth, Americans are more likely to expect AI to widen inequality than narrow it (52% vs 18%). And when asked "who gains the most power?", 44% name tech companies, 20% say AI systems themselves, and 8% say ordinary people.

Stacked bar titled "Will AI widen or narrow inequality?" over the next 10 years. Greatly increase 27%, somewhat increase 25%, not much 15%, somewhat decrease 10%, greatly decrease 8%, not sure 16%. In total 52% expect an increase vs 18% a decrease.

Bar chart titled "Who Americans think will gain the most power from AI," share naming each group. Large technology companies 44%, AI systems themselves 20%, governments 15%, ordinary individuals 8%, not sure 13%.

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Christopher Honey @chrishoney.bsky.social · 15/06/2026

Third, trust in AI varies greatly, both across people and across tasks, and this is somewhat independent of the population's broad support for regulation.

Bar chart titled "What Americans trust AI to do," percentage who trust AI a great deal or somewhat, ranked. Look up facts 67%, create art or music 57%, file taxes 42%,medical advice 37%, retirement savings 33%, be my co-worker 32%, teach my child 31%, emotional support or therapy 30%, drive my car 24%, decide my court case 19%.

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Christopher Honey @chrishoney.bsky.social · 15/06/2026

There is also bipartisan support for AI-related unemployment benefits and a "digital dividend", which is roughly a UBI funded by taxes on tech companies. @imarinescu.bsky.social

Both parties support taxing AI and paying displaced workers," showing percent support by political affiliation (Republicans, Democrats,
Independents). Benefits for AI-displaced workers: 65 / 72 / 65. Digital dividend, a UBI funded by a tech tax: 52 / 60 / 52.

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Christopher Honey @chrishoney.bsky.social · 15/06/2026

Second, there are specific rules that people strongly want: the right to a human and legal requirements of transparency and privacy around AI.

Grouped bar chart titled "Bipartisanship is not dead: everyone wants legal restrictions on AI," showing percent support by political affiliation (Republicans, Democrats, Independents). Human review of AI decisions: 83 / 80 / 77. Tell me when I'm talking to AI: 79 / 79 / 75. Ban AI impersonation: 75 / 76 / 74. Label AI-generated media: 71 / 71 / 69.

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Christopher Honey @chrishoney.bsky.social · 15/06/2026

Some findings from our national survey of U.S. population attitudes toward AI. First, feelings about AI are actually very similar across political parties (and many other demographics). They vary most by people's experience with AI tools. Explore the data: futurerealities.org/poll/2026/ 🧵

Bar chart titled "Positive feeling toward AI varies by experience, not by party," showing the percentage who feel positive about AI overall. By experience using AI: never used AI 12%, tried it a few times 24%, use it weekly 50%, skilled daily user 80%. By political party: Republicans 39%, Democrats 36% (nearly identical).

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