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New ‘Mind-Reading’ AI Predicts What Humans Will Do Next, And It’s Shockingly Accurate

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(Image by metamorworks on Shutterstock)

In a nutshell

  • Scientists created an AI called Centaur that can predict human behavior across any psychological experiment with unprecedented accuracy
  • The AI outperformed decades-old specialized models and successfully predicted behavior in completely new scenarios it had never seen before
  • Centaur’s internal workings became more aligned with human brain activity just by learning to predict our choices, potentially revolutionizing our understanding of cognition

MUNICH — An artificial intelligence system can now predict your next move before you make it. We’re not just talking about whether you’ll click “buy now” on that Amazon cart, but rather how you’ll navigate complex decisions, learn new skills, or explore uncharted territory.

Researchers have developed an AI called Centaur that accurately predicts human behavior across virtually any psychological experiment. It even outperforms the specialized computer models scientists have been using for decades. Trained on data from more than 60,000 people making over 10 million decisions, Centaur captures the underlying patterns of how we think, learn, and make choices.

“The human mind is remarkably general,” the researchers write in their paper, published in Nature. “Not only do we routinely make mundane decisions, such as choosing a breakfast cereal or selecting an outfit, but we also tackle complex challenges, such as figuring out how to cure cancer or explore outer space.”

An AI that truly understands human cognition could revolutionize marketing, education, mental health treatment, and product design. But it also raises uncomfortable questions about privacy and manipulation when our digital footprints reveal more about us than ever before.

How Scientists Built a Digital Mind Reader AI

The research team started with an ambitious goal: create a single AI model that could predict human behavior in any psychological experiment. Their approach was surprisingly straightforward but required massive scale.

Scientists assembled a dataset called Psych-101 containing 160 experiments covering memory tests, learning games, risk-taking scenarios, and moral dilemmas. Each experiment was converted into plain English descriptions that an AI could understand.

Rather than building from scratch, researchers took Meta’s Llama 3.1 language model (the same type powering ChatGPT) and gave it specialized training on human behavior. They used a technique that allows them to modify only a tiny fraction of the AI’s programming while keeping most of it unchanged. The entire training process took only five days on a high-end computer processor.

Image depicting a human brain connected to artificial intelligence
Centaur could mark a new turning point in AI in its unprecedented ability to understand the human mind. (Image by Shutterstock AI Generator)

Centaur Dominates Traditional Cognitive Models

When tested, Centaur completely crushed the competition. In head-to-head comparisons with specialized cognitive models that scientists spent decades perfecting, Centaur won in almost every single experiment.

The real breakthrough came when researchers tested Centaur on completely new scenarios. The AI successfully predicted human behavior even when the experiment’s story changed (turning a space treasure hunt into a magic carpet adventure), when the structure was modified (adding a third option to a two-choice task), and when entirely new domains were introduced (logical reasoning tests that weren’t in its training data).

Centaur could also generate realistic human-like behavior when running simulations. In one test involving exploration strategies, the AI achieved performance comparable to actual human participants and showed the same type of uncertainty-guided decision-making that characterizes how people behave.

Neural Alignment: Centaur Mimics Human Brain Activity

In a surprising discovery, Centaur’s internal workings had become more aligned with human brain activity, even though it was never explicitly trained to match neural data. When researchers compared the AI’s internal states to brain scans of people performing the same tasks, they found stronger correlations than with the original, untrained model.

Learning to predict human behavior apparently forced the AI to develop internal representations that mirror how our brains actually process information. The AI essentially reverse-engineered aspects of human cognition just by studying our choices.

The team also demonstrated how Centaur could accelerate scientific discovery. They used the AI to analyze human behavior patterns, leading to the discovery of a new decision-making strategy that outperformed existing psychological theories.

What’s Next for Human Behavior AI?

While impressive, this research represents just the beginning. The current version focuses primarily on learning and decision-making, with limited coverage of areas like social psychology or cross-cultural differences. The dataset also skews toward Western, educated populations, a common limitation in psychological research.

The team plans to expand their dataset to include more diverse domains and populations, envisioning a comprehensive model that could serve as a unified theory of human cognition. They’ve made both their dataset and AI model publicly available for other researchers to build upon.

Centaur represents more than another AI breakthrough: it’s a powerful new tool for understanding ourselves. For the first time, we have an artificial system that can predict human behavior across the full spectrum of psychological research with unprecedented accuracy. Whether that development excites or concerns you may depend on how confidently we can ensure such tools are used responsibly.

Paper Summary

Methodology

The researchers created Centaur by fine-tuning Meta’s Llama 3.1 70B language model on a dataset called Psych-101, which contains trial-by-trial behavioral data from 160 psychological experiments involving over 60,000 participants making more than 10 million choices. They converted all experiments into natural language format and used a parameter-efficient training technique called QLoRA that modified only 0.15% of the model’s parameters. The training focused specifically on predicting human responses while masking out other parts of the experimental instructions.

Results

Centaur outperformed existing domain-specific cognitive models in almost every experiment when predicting behavior of held-out participants. The AI also successfully generalized to modified cover stories, structural task changes, and entirely new domains like logical reasoning. In open-loop simulations, Centaur generated realistic human-like behavior patterns and achieved comparable performance to actual humans in exploration tasks. Additionally, the model’s internal representations became more aligned with human neural activity compared to the base model.

Limitations

The current dataset focuses primarily on learning and decision-making domains, with limited coverage of social psychology, cross-cultural studies, and individual differences. The participant pool skews toward Western, educated populations typical of psychological research. The natural language format also introduces selection bias against experiments that cannot be easily expressed in text, and the researchers note the need for eventual expansion to multimodal data formats.

Funding and Disclosures

Research was supported by the Max Planck Society, the Humboldt Foundation, the Volkswagen Foundation, and the NOMIS Foundation. One author has consulting relationships and ownership interests in several biotech companies. The researchers have made their dataset and model publicly available for scientific use.

Publication Information

“A foundation model to predict and capture human cognition” was published in Nature on July 2, 2025. The study was led by Marcel Binz at the Institute for Human-Centered AI, Helmholtz Center Munich, with collaborators from institutions including Princeton University, University of Tübingen, Max Planck Institute for Biological Cybernetics, and others.



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The Greatest First Basemen of All Time According to Artificial Intelligence

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July 7, 2025

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In the intricate dance of Major League Baseball, the first baseman stands as a unique blend of offensive powerhouse and defensive anchor. They are the receivers of throws, the stretchers for outs, and often, the most prolific sluggers in the lineup. But who among these giants of the diamond truly represents the pinnacle of the position? Leveraging vast datasets of offensive metrics, defensive prowess, awards, and historical impact, Artificial Intelligence has meticulously analyzed the MLB careers of baseball’s greatest first basemen. The result is a definitive ranking of the top, based on an impartial assessment of their unparalleled contributions to the game.



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I asked ChatGPT to help me pack for my vacation – try this awesome AI prompt that makes planning your travel checklist stress-free

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2 hours ago

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July 7, 2025

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It’s that time of year again, when those of us in the northern hemisphere pack our sunscreen and get ready to venture to hotter climates in search of some much-needed Vitamin D.

Every year, I book a vacation, and every year I get stressed as the big day gets closer, usually forgetting to pack something essential, like a charger for my Nintendo Switch 2, or dare I say it, my passport.



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Denodo Announces Plans to Further Support AI Innovation by Releasing Denodo DeepQuery, a Deep Research Capability — TradingView News

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3 hours ago

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July 7, 2025

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PALO ALTO, Calif., July 07, 2025 (GLOBE NEWSWIRE) — Denodo, a leader in data management, announced the availability of the Denodo DeepQuery capability, now as a private preview, and generally available soon, enabling generative AI (GenAI) to go beyond retrieving facts to investigating, synthesizing, and explaining its reasoning. Denodo also announced the availability of Model Context Protocol (MCP) support as part of the Denodo AI SDK.

Built to address complex, open-ended business questions, DeepQuery will leverage live access to a wide spectrum of governed enterprise data across systems, departments, and formats. Unlike traditional GenAI solutions, which rephrase existing content, DeepQuery, a deep research capability, will analyze complex, open questions and search across multiple systems and sources to deliver well-structured, explainable answers rooted in real-time information. To help users operate this new capability to better understand complex current events and situations, DeepQuery will also leverage external data sources to extend and enrich enterprise data with publicly available data, external applications, and data from trading partners.

DeepQuery, beyond what’s possible using traditional generative AI (GenAI) chat or retrieval augmented generation (RAG), will enable users to ask complex, cross-functional questions that would typically take analysts days to answer—questions like, “Why did fund outflows spike last quarter?” or “What’s driving changes in customer retention across regions?” Rather than piecing together reports and data exports, DeepQuery will connect to live, governed data across different systems, apply expert-level reasoning, and deliver answers in minutes.

Slated to be packaged with the Denodo AI SDK, which streamlines AI application development with pre-built APIs, DeepQuery is being developed as a fully extensible component of the Denodo Platform, enabling developers and AI teams to build, experiment with, and integrate deep research capabilities into their own agents, copilots, or domain-specific applications.

“With DeepQuery, Denodo is demonstrating forward-thinking in advancing the capabilities of AI,” said Stewart Bond, Research VP, Data Intelligence and Integration Software at IDC. “DeepQuery, driven by deep research advances, will deliver more accurate AI responses that will also be fully explainable.”

Large language models (LLMs), business intelligence tools, and other applications are beginning to offer deep research capabilities based on public Web data; pre-indexed, data-lakehouse-specific data; or document-based retrieval, but only Denodo is developing deep research capabilities, in the form of DeepQuery, that are grounded in enterprise data across all systems, data that is delivered in real-time, structured, and governed. These capabilities are enabled by the Denodo Platform’s logical approach to data management, supported by a strong data virtualization foundation.

Denodo DeepQuery is currently available in a private preview mode. Denodo is inviting select organizations to join its AI Accelerator Program, which offers early access to DeepQuery capabilities, as well as the opportunity to collaborate with our product team to shape the future of enterprise GenAI.

“As a Denodo partner, we’re always looking for ways to provide our clients with a competitive edge,” said Nagaraj Sastry, Senior Vice President, Data and Analytics at Encora. “Denodo DeepQuery gives us exactly that. Its ability to leverage real-time, governed enterprise data for deep, contextualized insights sets it apart. This means we can help our customers move beyond general AI queries to truly intelligent analysis, empowering them to make faster, more informed decisions and accelerating their AI journey.”

Denodo also announced support of Model Context Protocol (MCP), and an MCP Server implementation is now included in the latest version of the Denodo AI SDK. As a result, all AI agents and apps based on the Denodo AI SDK can be integrated with any MCP-compliant client, providing customers with a trusted data foundation for their agentic AI ecosystems based on open standards.

“AI’s true potential in the enterprise lies not just in generating responses, but in understanding the full context behind them,” said Angel Viña, CEO and Founder of Denodo. “With DeepQuery, we’re unlocking that potential by combining generative AI with real-time, governed access to the entire corporate data ecosystem, no matter where that data resides. Unlike siloed solutions tied to a single store, DeepQuery leverages enriched, unified semantics across distributed sources, allowing AI to reason, explain, and act on data with unprecedented depth and accuracy.”

Additional Information

  • Denodo Platform: What’s New
  • Blog Post: Smarter AI Starts Here: Why DeepQuery Is the Next Step in GenAI Maturity
  • Demo: Watch a short video of this capability in action.

About Denodo

Denodo is a leader in data management. The award-winning Denodo Platform is the leading logical data management platform for transforming data into trustworthy insights and outcomes for all data-related initiatives across the enterprise, including AI and self-service. Denodo’s customers in all industries all over the world have delivered trusted AI-ready and business-ready data in a third of the time and with 10x better performance than with lakehouses and other mainstream data platforms alone. For more information, visit denodo.com.

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pr@denodo.com



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