# Brain-Model Evaluations Need the NeuroAI Turing Test arXiv: https://arxiv.org/abs/2502.16238 ยท HTML: https://mlsanity.com/paper/2502.16238/ ## Key facts - Cited by 13 papers in the map (data through 14 Oct 2026). - Builds on 25 of its 120 references that are in the map. - First posted on arXiv 22 Feb 2025. ## Builds on (most cited first, 25 of 25) - [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020) - [Similarity of Neural Network Representations Revisited](https://mlsanity.com/paper/1905.00414/) (2019) - [Ego4D: Around the World in 3,000 Hours of Egocentric Video](https://mlsanity.com/paper/2110.07058/) (2021) - [Curiosity-driven Exploration by Self-supervised Prediction](https://mlsanity.com/paper/1705.05363/) (2017) - [Convergent Learning: Do different neural networks learn the same representations?](https://mlsanity.com/paper/1511.07543/) (2015) - [Where are we in the search for an Artificial Visual Cortex for Embodied Intelligence?](https://mlsanity.com/paper/2303.18240/) (2023) - [Partial success in closing the gap between human and machine vision](https://mlsanity.com/paper/2106.07411/) (2021) - [Physion: Evaluating Physical Prediction from Vision in Humans and Machines](https://mlsanity.com/paper/2106.08261/) (2021) - [Generalized Shape Metrics on Neural Representations](https://mlsanity.com/paper/2110.14739/) (2021) - [Toward Next-Generation Artificial Intelligence: Catalyzing the NeuroAI Revolution](https://mlsanity.com/paper/2210.08340/) (2022) - [Neither hype nor gloom do DNNs justice](https://mlsanity.com/paper/2312.05355/) (2023) - [Deep Learning Models of the Retinal Response to Natural Scenes](https://mlsanity.com/paper/1702.01825/) (2017) - [Task-Driven Convolutional Recurrent Models of the Visual System](https://mlsanity.com/paper/1807.00053/) (2018) - [Unifying (Machine) Vision via Counterfactual World Modeling](https://mlsanity.com/paper/2306.01828/) (2023) - [Performance-optimized deep neural networks are evolving into worse models of inferotemporal visual cortex](https://mlsanity.com/paper/2306.03779/) (2023) - [Soft Matching Distance: A metric on neural representations that captures single-neuron tuning](https://mlsanity.com/paper/2311.09466/) (2023) - [A Spectral Theory of Neural Prediction and Alignment](https://mlsanity.com/paper/2309.12821/) (2023) - [Neural Foundations of Mental Simulation: Future Prediction of Latent Representations on Dynamic Scenes](https://mlsanity.com/paper/2305.11772/) (2023) - [Scaling Laws for Task-Optimized Models of the Primate Visual Ventral Stream](https://mlsanity.com/paper/2411.05712/) (2024) - [Neural Population Geometry Reveals the Role of Stochasticity in Robust Perception](https://mlsanity.com/paper/2111.06979/) (2021) - [From deep learning to mechanistic understanding in neuroscience: the structure of retinal prediction](https://mlsanity.com/paper/1912.06207/) (2019) - [TopoNets: High Performing Vision and Language Models with Brain-Like Topography](https://mlsanity.com/paper/2501.16396/) (2025) - [Balanced Excitation and Inhibition are Required for High-Capacity, Noise-Robust Neuronal Selectivity](https://mlsanity.com/paper/1705.01502/) (2017) - [A newborn embodied Turing test for view-invariant object recognition](https://mlsanity.com/paper/2306.05582/) (2023) - [A Goal-Driven Approach to Systems Neuroscience](https://mlsanity.com/paper/2311.02704/) (2023) ## Built on by (most cited first, 13 of 13) - [From Language to Cognition: How LLMs Outgrow the Human Language Network](https://mlsanity.com/paper/2503.01830/) (2025) - [Representation Potentials of Foundation Models for Multimodal Alignment: A Survey](https://mlsanity.com/paper/2510.05184/) (2025) - [Better artificial intelligence does not mean better models of biology](https://mlsanity.com/paper/2504.16940/) (2025) - [Representation biases: will we achieve complete understanding by analyzing representations?](https://mlsanity.com/paper/2507.22216/) (2025) - [Intrinsic Goals for Autonomous Agents: Model-Based Exploration in Virtual Zebrafish Predicts Ethological Behavior and Whole-Brain Dynamics](https://mlsanity.com/paper/2506.00138/) (2025) - [What Capable Agents Must Know: Selection Theorems for Robust Decision-Making under Uncertainty](https://mlsanity.com/paper/2603.02491/) (2026) - [Task-Optimized Convolutional Recurrent Networks Align with Tactile Processing in the Rodent Brain](https://mlsanity.com/paper/2505.18361/) (2025) - [Do Language Models Align with Brains? Prediction Scores Are Not Enough](https://mlsanity.com/paper/2605.14025/) (2026) - [Robusto-1 Dataset: Comparing Humans and VLMs on real out-of-distribution Autonomous Driving VQA from Peru](https://mlsanity.com/paper/2503.07587/) (2025) - [The Umwelt Representation Hypothesis: Rethinking Universality](https://mlsanity.com/paper/2604.17960/) (2026) - [Beyond Prediction Accuracy: Target-Space Recovery Profiles for Evaluating Model-Brain Alignment](https://mlsanity.com/paper/2605.20127/) (2026) - [Bidirectional representational alignment between biological and artificial neural networks](https://mlsanity.com/paper/2608.18244/) (2026) - [Parameter symmetries determine representational geometry in overparameterized nonlinear networks](https://mlsanity.com/paper/2609.39078/) (2026) ## Abstract (arXiv) What makes an artificial system a good model of intelligence? The classical test proposed by Alan Turing focuses on behavior, requiring that an artificial agent's behavior be indistinguishable from that of a human. While behavioral similarity provides a strong starting point, two systems with very different internal representations can produce the same outputs. Thus, in modeling biological intelligence, the field of NeuroAI often aims to go beyond behavioral similarity and achieve representational convergence between a model's activations and the measured activity of a biological system. This position paper argues that the standard definition of the Turing Test is incomplete for NeuroAI, and proposes a stronger framework called the ``NeuroAI Turing Test'', a benchmark that extends beyond behavior alone and additionally requires models to produce internal neural representations that are empirically indistinguishable from those of a brain up to measured individual variability, i.e. the differences between a computational model and the brain is no more than the difference between one brain and another brain. While the brain is not necessarily the ceiling of intelligence, it remains the only universally agreed-upon example, making it a natural reference point for evaluating computational models. By proposing this framework, we aim to shift the discourse from loosely defined notions of brain inspiration to a systematic and testable standard centered on both behavior and internal representations, providing a clear benchmark for neuroscientific modeling and AI development.