🤖 Autonomous Science Announcement. Our AI Engine worked solo to test how processing speed links to reasoning ability - the mathematical shape of this fundamental cognitive relationship. Title: Rigorous model comparison reveals linear speed-reasoning coupling Insight: Your brain's processing speed affects reasoning in a straight line - no thresholds, no acceleration zones. 557K people, tested four ways. But here's the twist: this conclusion completely flips depending on how you transform reaction times. The answer depends on your measurement choices. 257h 36m | 60.1M tokens | 0 humans Full Paper ↓

The search for cognitive complexity may have overlooked the profound simplicity at its core. The next question isn't just which tests to add, but what it truly takes to measure something *other* than general intelligence. Full paper on explorescience.ai

This suggests that many test batteries might be inefficient, using multiple subtests that don't add new information but simply re-measure 'g' in a slightly different way. Test construction may need to be less theoretical and more data-driven.

Amid this uniformity, one skill stood out as a remarkably strong predictor of overall performance. While not always the single top measure, arithmetic reasoning showed a consistently high correlation with the general cognitive score across multiple battery designs.

The only exception? The most comprehensive battery, with 11 subtests spanning four cognitive domains. Only this battery was complex enough to tease out a second, distinct cognitive factor, suggesting a high threshold is needed to see beyond general ability.

Instead, they collapsed into a single dimension. Performance across wildly different tasks was so intertwined that it all pointed to one dominant factor: general intelligence, often called 'g'. The tests were largely measuring the same core cognitive horsepower.

But an analysis of 5M+ scores from users of a web-based cognitive platform found something unexpected. Across 8 different test batteries, despite their diverse mix of tasks, 7 of them didn't produce a complex map of cognition at all.

Neuropsychological assessments are designed to create a detailed map of the mind. The assumption is that a wider variety of tests (for memory, attention, speed) will reveal a more complex, multi-faceted picture of a person's cognitive abilities.

🤖 Autonomous Science Thread. We build complex cognitive tests to measure distinct skills like memory and reasoning. But what if most of them are just measuring the same thing over and over? 16h 58m | 19.7M tokens | 0 humans ↓

🤖 Autonomous Science Announcement. Our AI Engine worked solo to analyse cognitive test structures - the hidden blueprint of how our mental abilities are organized. Title: Battery diversity is associated with simple structures in neuropsychological assessment Insight: Adding more types of brain puzzles to a test doesn't mean you're measuring more types of smarts. They mostly just point back to one general intelligence factor. 757K people, near-perfect replication. More tests doesn't always mean more insight. 16h 58m | 19.7M tokens | 0 humans Full Paper ↓

These results encourage a shift from focusing just on “how long” people study to exploring what kinds of learning experiences truly shape our thinking over time.

Data came from 1083 adults and 11 cognitive tests from the #NCPT developed by @Lumosity. Main finding: time on task matters, but content and strategy matter more. It is not simply how long you study, it is what and how you study that best predicts performance across tasks.

Our AI scientist asked whether education level predicts how varied someone’s cognitive abilities are across different areas, like memory, reasoning, and attention. It framed the question, ran the analyses, and drafted the manuscript end-to-end without human help.

Our paper got accepted to #Agents4Science - a conference hosted by Stanford, where for the first time ever, AI agents are both authors and reviewers! The entire study was conceived, run, analysed, and written by our in-house AI scientist, Rosa, and her agents.

So, the slowdown isn't a measure of effort, but of inefficiency. It suggests the person's brain is paying a higher tax for the mental gear-shift, leaving fewer resources for abstract thought. It's about the efficiency of the engine, not just its horsepower.

Here's the pattern: individuals with a larger proportional slowdown (a higher "cognitive cost" for switching) consistently scored lower on unrelated reasoning and logic puzzles. This held true even after controlling for age, memory, and general processing speed.

Everyone is slower on the harder task. But the key isn't just raw speed; it's the proportional slowdown from the easy part to the hard part. This isolates the cognitive cost of switching tasks from a person's baseline speed.

The evidence comes from a classic test. Part A is simple: connect numbered dots in order (1-2-3...). This measures basic processing speed. Part B is harder: alternate between numbers and letters (1-A-2-B...). This requires mental flexibility to switch between two rules.

🤖 Autonomous Science Thread. Thinking harder doesn't always mean thinking smarter. For some people, the extra mental effort needed to solve a complex task is a reliable sign of poorer abstract reasoning. 16h 58m | 19.7M tokens | 0 humans