{"id":1176573,"date":"2026-06-25T09:00:00","date_gmt":"2026-06-25T16:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?p=1176573"},"modified":"2026-06-23T12:11:27","modified_gmt":"2026-06-23T19:11:27","slug":"understanding-the-brain-with-ai-driven-explanations-and-experiments","status":"publish","type":"post","link":"https:\/\/www.microsoft.com\/en-us\/research\/blog\/understanding-the-brain-with-ai-driven-explanations-and-experiments\/","title":{"rendered":"Understanding the brain with AI-driven explanations and experiments"},"content":{"rendered":"\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"2560\" height=\"1441\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/06\/UnderstandingtheBrain-BlogHeroFeature-1400x788-1-scaled.jpg\" alt=\"Understanding the brain | four white line icons on an abstract purple background: brain icon, chat bubble icon, circle with a checkmark icon, search icon\" class=\"wp-image-1176579\" srcset=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/06\/UnderstandingtheBrain-BlogHeroFeature-1400x788-1-scaled.jpg 2560w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/06\/UnderstandingtheBrain-BlogHeroFeature-1400x788-1-300x169.jpg 300w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/06\/UnderstandingtheBrain-BlogHeroFeature-1400x788-1-1024x576.jpg 1024w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/06\/UnderstandingtheBrain-BlogHeroFeature-1400x788-1-768x432.jpg 768w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/06\/UnderstandingtheBrain-BlogHeroFeature-1400x788-1-1536x865.jpg 1536w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/06\/UnderstandingtheBrain-BlogHeroFeature-1400x788-1-2048x1153.jpg 2048w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/06\/UnderstandingtheBrain-BlogHeroFeature-1400x788-1-1066x600.jpg 1066w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/06\/UnderstandingtheBrain-BlogHeroFeature-1400x788-1-655x368.jpg 655w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/06\/UnderstandingtheBrain-BlogHeroFeature-1400x788-1-240x135.jpg 240w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/06\/UnderstandingtheBrain-BlogHeroFeature-1400x788-1-640x360.jpg 640w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/06\/UnderstandingtheBrain-BlogHeroFeature-1400x788-1-960x540.jpg 960w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/06\/UnderstandingtheBrain-BlogHeroFeature-1400x788-1-1280x720.jpg 1280w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/06\/UnderstandingtheBrain-BlogHeroFeature-1400x788-1-1920x1080.jpg 1920w\" sizes=\"auto, (max-width: 2560px) 100vw, 2560px\" \/><\/figure>\n\n\n\n<div style=\"padding-bottom:0;padding-top:0\" class=\"wp-block-msr-immersive-section alignfull row\">\n\t\n\t<div class=\"container\">\n\t\t<div class=\"wp-block-msr-immersive-section__inner wp-block-msr-immersive-section__inner--narrow\">\n\t\t\t<div class=\"wp-block-columns mb-10 pb-1 pr-1 is-layout-flex wp-container-core-columns-is-layout-8f761849 wp-block-columns-is-layout-flex\" style=\"box-shadow:var(--wp--preset--shadow--outlined)\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<h2 id=\"at-a-glance\" class=\"wp-block-heading h3\">At a glance<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li>LLM-based models can predict the human brain\u2019s responses to language with high accuracy. But what drives that performance is essentially unreadable: a vast collection of learned parameters, not scientific theories anyone can read.<\/li>\n\n\n\n<li>Generative causal testing (GCT), developed in a collaboration between Microsoft Research, the University of California, Berkeley, the University of California, San Francisco, and Columbia University, distills these brain-prediction models into short verbal explanations of what each patch of cortex responds to: phrases like \u201cfood preparation\u201d or \u201clocation names.\u201d<\/li>\n\n\n\n<li>GCT then closes the loop: an LLM writes new stories designed to activate a targeted brain area, subjects hear them in the scanner, and the region lights up only if the explanation is right.<\/li>\n\n\n\n<li>In experiments, GCT confirmed known selectivity, teased apart neighboring place-processing regions long thought interchangeable, and revealed tiny prefrontal \u201cmicro-regions\u201d tuned to specific concepts like dialogue, clock times, and measurements.<\/li>\n<\/ul>\n<\/div>\n<\/div>\t\t<\/div>\n\t<\/div>\n\n\t<\/div>\n\n\n\n<h2 id=\"the-explainability-problem-in-language-neuroscience\" class=\"wp-block-heading\">The explainability problem in language neuroscience<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Over the past decade, LLMs have become the most accurate tools we have for predicting how the human brain responds to language. Feed an LLM the same story a person hears in an fMRI scanner, and the model\u2019s internal representations can predict the activity of individual patches of cortex with remarkable fidelity. But this success comes with a catch: nobody can read these models. They are millions of inscrutable parameters that can\u2019t be directly translated into interpretations. A model that predicts brain activity tells us that a region responds to language, but not what it is actually picking up on, whether it\u2019s food, places, numbers, or something else entirely. As black-box models spread, the gap between prediction and understanding has become one of the central problems in computational neuroscience.<\/p>\n\n\n\n<h2 id=\"turning-black-boxes-into-testable-theories\" class=\"wp-block-heading\">Turning black boxes into testable theories<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">In a <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/generative-causal-testing-to-bridge-data-driven-models-and-scientific-theories-in-language-neuroscience\/\" type=\"link\" id=\"https:\/\/arxiv.org\/abs\/2410.00812\" target=\"_blank\" rel=\"noreferrer noopener\">new paper<\/a> accepted in <em>Nature Neuroscience<\/em>, Microsoft Research scientists, in collaboration with scientists at the University of California, Berkeley, University of California, San Francisco, and Columbia University, introduce a framework to overcome this explainability crisis: generative causal testing (GCT). GCT distills brain-prediction models into short, readable accounts of what each patch of cortex responds to, then tests those claims. An LLM writes new stories engineered to activate a specific brain area, subjects hear them in the scanner, and if the explanation is correct, the targeted region lights up. The result is a method that translates uninterpretable predictive models back into the currency of science: concise hypotheses that can be confirmed or refuted in a follow-up experiment. An LLM writes new stories engineered to activate a specific brain area, subjects hear them in the scanner, and if the explanation is correct, the targeted region lights up. The result is a method that translates uninterpretable predictive models back into the currency of science: concise hypotheses that can be confirmed or refuted in a follow-up experiment.<\/p>\n\n\n\n<figure class=\"wp-block-image aligncenter size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"1126\" height=\"676\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/06\/UnderstandingtheBrain-Blog-fig1.png\" alt=\"Figure 1: Diagram showing a 2-step process. At the top, in the first step a pipeline of arrows shows the progression from story ngrams to a voxel explanation that reads \u201cFood preparation\u201d. The bottom shows the second step with an AI chat and images of brain regions and line plots of their responses.\" class=\"wp-image-1176577\" srcset=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/06\/UnderstandingtheBrain-Blog-fig1.png 1126w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/06\/UnderstandingtheBrain-Blog-fig1-300x180.png 300w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/06\/UnderstandingtheBrain-Blog-fig1-1024x615.png 1024w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/06\/UnderstandingtheBrain-Blog-fig1-768x461.png 768w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/06\/UnderstandingtheBrain-Blog-fig1-240x144.png 240w\" sizes=\"auto, (max-width: 1126px) 100vw, 1126px\" \/><figcaption class=\"wp-element-caption\">Figure 1. The two steps of generative causal testing (GCT). In Step 1, the phrases that most strongly drive a brain region\u2019s predictive model are summarized by an LLM into a short candidate explanation, such as \u201cfood preparation.\u201d In Step 2, an LLM writes new stories designed to match that explanation, and the region\u2019s response to these \u201cdriving\u201d stories is measured in the scanner and compared against baseline.&nbsp;<\/figcaption><\/figure>\n\n\n\n<h2 id=\"how-gct-works\" class=\"wp-block-heading\">How GCT works<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">GCT has two steps: explanation, then verification. To generate an explanation, the method starts from a predictive model for a single voxel or region and identifies the short phrases that most strongly drive its predicted response. An LLM then summarizes those words into a concise verbal explanation, often a single phrase such as \u201cfood preparation\u201d or \u201clocation names.\u201d<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The crucial second stage closes the loop. To build trust in the explanation, GCT uses an LLM to write new stories in which each paragraph is carefully constructed to drive a brain region according to its explanation. Three subjects returned to the scanner to read these synthetic stories. If a region\u2019s activity to its \u201cdriving\u201d paragraphs was significantly greater than to baseline text, the explanation passed a genuine causal test, not just a correlational one.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Across all three subjects, the core approach held up: the synthetic stories reliably drove their target regions above baseline, confirming that GCT\u2019s short explanations capture something the cortex genuinely responds to. The explanations were also most trustworthy where the underlying brain-prediction models were strongest (the more stable the model, the more reliably its explanation could be confirmed in the scanner). With the method validated on regions whose selectivity was already known, the researchers turned GCT on harder questions.<\/p>\n\n\n\n<figure class=\"wp-block-image aligncenter size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"1442\" height=\"677\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/06\/UnderstandingtheBrain-Blog-fig2.png\" alt=\"Figure 2: Six visualizations of brain surfaces show the normalized bold response for different categories including Locations and Food Preparation.\" class=\"wp-image-1176578\" srcset=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/06\/UnderstandingtheBrain-Blog-fig2.png 1442w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/06\/UnderstandingtheBrain-Blog-fig2-300x141.png 300w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/06\/UnderstandingtheBrain-Blog-fig2-1024x481.png 1024w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/06\/UnderstandingtheBrain-Blog-fig2-768x361.png 768w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/06\/UnderstandingtheBrain-Blog-fig2-240x113.png 240w\" sizes=\"auto, (max-width: 1442px) 100vw, 1442px\" \/><figcaption class=\"wp-element-caption\">Figure 2. Brain response maps to GCT stories for different topics. Some maps recover well-established findings: the explanation \u201cLocations\u201d produces strong responses in the place areas RSC, OPA, and PPA. Others independently confirm newer hypotheses: \u201cFood Preparation\u201d activates a region in ventral occipital cortex near the fusiform face area (FFA). Some like (\u201cBirthdays\u201d) do not map cleanly onto any known result, pointing toward directions for future research. <\/figcaption><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">GCT also proved sharp enough to settle long-standing ambiguities. Three neighboring regions involved in processing places have often been treated as functionally similar: the retrosplenial cortex (RSC), the parahippocampal place area (PPA), and the occipital place area (OPA). At first, stories written for one region also activated the others. But by generating differential stimuli (stories designed to switch one region on while keeping its neighbors quiet), GCT teased the three apart. For example, RSC responds more strongly to proper noun location names, like Tokyo or Connecticut, rather than general location. This is the kind of nuanced, region-specific theory that a raw predictive model cannot provide on its own.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Beyond known regions, the authors discovered new prefrontal \u201cmicro-regions.\u201d By scanning a grid of candidate locations and keeping only the most stable ones, GCT surfaced these previously unmapped regions tuned to remarkably specific concepts: one selective for dialogue between people (words like \u201csaid\u201d or \u201ctold\u201d), one for mentions of clock times (\u201cone o\u2019clock\u201d), and one for numeric measurements (\u201c50 feet\u201d). These are distinctions no one had gone looking for; they emerged because the method could propose a hypothesis and immediately test it.<\/p>\n\n\n\n\t<div class=\"border-bottom border-top border-gray-300 mt-5 mb-5 msr-promo text-center text-md-left alignwide\" data-bi-aN=\"promo\" data-bi-id=\"999693\">\n\t\t\n\n\t\t<p class=\"msr-promo__label text-gray-800 text-center text-uppercase\">\n\t\t<span class=\"px-4 bg-white display-inline-block font-weight-semibold small\">Spotlight: Event Series<\/span>\n\t<\/p>\n\t\n\t<div class=\"row pt-3 pb-4 align-items-center\">\n\t\t\t\t\t\t<div class=\"msr-promo__media col-12 col-md-5\">\n\t\t\t\t<a class=\"bg-gray-300 display-block\" href=\"https:\/\/www.microsoft.com\/en-us\/research\/event\/microsoft-research-forum\/past-episodes\/?OCID=msr_researchforum_MCR_Blog_Promo\" aria-label=\"Microsoft Research Forum\" data-bi-cn=\"Microsoft Research Forum\" target=\"_blank\">\n\t\t\t\t\t<img decoding=\"async\" class=\"w-100 display-block\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2025\/05\/Research-Forum-hero_1400x788.jpg\" alt=\"Research Forum | abstract background with colorful hexagons\" \/>\n\t\t\t\t<\/a>\n\t\t\t<\/div>\n\t\t\t\n\t\t\t<div class=\"msr-promo__content p-3 px-5 col-12 col-md\">\n\n\t\t\t\t\t\t\t\t\t<h2 class=\"h4\">Microsoft Research Forum<\/h2>\n\t\t\t\t\n\t\t\t\t\t\t\t\t<p id=\"microsoft-research-forum\" class=\"large\">Join us for a continuous exchange of ideas about research in the era of general AI. Watch the latest episodes on demand.<\/p>\n\t\t\t\t\n\t\t\t\t\t\t\t\t<div class=\"wp-block-buttons justify-content-center justify-content-md-start\">\n\t\t\t\t\t<div class=\"wp-block-button\">\n\t\t\t\t\t\t<a href=\"https:\/\/www.microsoft.com\/en-us\/research\/event\/microsoft-research-forum\/past-episodes\/?OCID=msr_researchforum_MCR_Blog_Promo\" aria-describedby=\"microsoft-research-forum\" class=\"btn btn-brand glyph-append glyph-append-chevron-right\" data-bi-cn=\"Microsoft Research Forum\" target=\"_blank\">\n\t\t\t\t\t\t\tWatch on-demand\t\t\t\t\t\t<\/a>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div><!--\/.msr-promo__content-->\n\t<\/div><!--\/.msr-promo__inner-wrap-->\n\t<\/div><!--\/.msr-promo-->\n\t\n\n\n<h2 id=\"implications-and-looking-forward\" class=\"wp-block-heading\">Implications and looking forward<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">The significance of GCT reaches well beyond neuroscience. Researchers increasingly face the same dilemma: a model that predicts beautifully but explains nothing. GCT shows that a data-driven model need not be the end of inquiry; it can be distilled into a readable, experimentally testable theory, and that theory can be checked against reality by generating new experiments on demand.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">For neuroscience specifically, GCT points toward a faster, more hypothesis-rich way of mapping the cortex\u2014one where an AI system proposes what a brain region might encode and a closed-loop experiment confirms or rejects it within a single study. The same generate-and-verify philosophy could extend to other domains where powerful predictive models have outrun our ability to understand them. The broader lesson is hopeful: the rise of black-box models in science does not necessarily mean the retreat of human-readable theory. With the right framework, the two can advance together.<\/p>\n\n\n\n<h2 id=\"acknowledgements\" class=\"wp-block-heading\">Acknowledgements<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">This work was a collaboration across Microsoft Research, UC Berkeley (Alex Huth, Bin Yu, Sihang Guo, and Aliyah Hsu), Columbia University (RJ Antonello, co-lead), and UCSF (Shailee Jain). We also thank the study participants and the broader language-neuroscience community whose tools and datasets made this research possible.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Read <a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/arxiv.org\/abs\/2410.00812\" type=\"link\" id=\"https:\/\/arxiv.org\/abs\/2410.00812\" target=\"_blank\" rel=\"noopener noreferrer\">the paper<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>: \u201cGenerative causal testing to bridge data-driven models and scientific theories in language neuroscience,\u201d accepted in <em>Nature Neuroscience<\/em> and <a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/github.com\/microsoft\/automated-brain-explanations\" type=\"link\" id=\"https:\/\/github.com\/microsoft\/automated-brain-explanations\" target=\"_blank\" rel=\"noopener noreferrer\">the code on Github<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Researchers introduce generative causal testing, which translates black box models into clear hypotheses and verifies them in the scanner, revealing what specific brain regions respond to in language.<\/p>\n","protected":false},"author":43868,"featured_media":1176579,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":[{"type":"user_nicename","value":"Chandan Singh","user_id":"42126"},{"type":"user_nicename","value":"Jianfeng Gao","user_id":"32246"}],"msr_hide_image_in_river":0,"footnotes":""},"categories":[1],"tags":[],"research-area":[13556,13545,13553],"msr-region":[],"msr-event-type":[],"msr-locale":[268875],"msr-post-option":[243984],"msr-impact-theme":[],"msr-promo-type":[],"msr-podcast-series":[],"class_list":["post-1176573","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-research-blog","msr-research-area-artificial-intelligence","msr-research-area-human-language-technologies","msr-research-area-medical-health-genomics","msr-locale-en_us","msr-post-option-blog-homepage-featured"],"msr_event_details":{"start":"","end":"","location":""},"podcast_url":"","podcast_episode":"","msr_research_lab":[199565],"msr_impact_theme":[],"related-publications":[],"related-downloads":[],"related-videos":[],"related-academic-programs":[],"related-groups":[144931],"related-projects":[],"related-events":[],"related-researchers":[{"type":"user_nicename","value":"Jianfeng Gao","user_id":32246,"display_name":"Jianfeng Gao","author_link":"<a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/jfgao\/\" aria-label=\"Visit the profile page for Jianfeng Gao\">Jianfeng Gao<\/a>","is_active":false,"last_first":"Gao, Jianfeng","people_section":0,"alias":"jfgao"}],"msr_type":"Post","featured_image_thumbnail":"<img width=\"960\" height=\"540\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/06\/UnderstandingtheBrain-BlogHeroFeature-1400x788-1-960x540.jpg\" class=\"img-object-cover\" alt=\"Understanding the brain | four white line icons on an abstract purple background: brain icon, chat bubble icon, circle with a checkmark icon, search icon\" decoding=\"async\" loading=\"lazy\" srcset=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/06\/UnderstandingtheBrain-BlogHeroFeature-1400x788-1-960x540.jpg 960w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/06\/UnderstandingtheBrain-BlogHeroFeature-1400x788-1-300x169.jpg 300w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/06\/UnderstandingtheBrain-BlogHeroFeature-1400x788-1-1024x576.jpg 1024w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/06\/UnderstandingtheBrain-BlogHeroFeature-1400x788-1-768x432.jpg 768w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/06\/UnderstandingtheBrain-BlogHeroFeature-1400x788-1-1536x865.jpg 1536w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/06\/UnderstandingtheBrain-BlogHeroFeature-1400x788-1-2048x1153.jpg 2048w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/06\/UnderstandingtheBrain-BlogHeroFeature-1400x788-1-1066x600.jpg 1066w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/06\/UnderstandingtheBrain-BlogHeroFeature-1400x788-1-655x368.jpg 655w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/06\/UnderstandingtheBrain-BlogHeroFeature-1400x788-1-240x135.jpg 240w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/06\/UnderstandingtheBrain-BlogHeroFeature-1400x788-1-640x360.jpg 640w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/06\/UnderstandingtheBrain-BlogHeroFeature-1400x788-1-1280x720.jpg 1280w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/06\/UnderstandingtheBrain-BlogHeroFeature-1400x788-1-1920x1080.jpg 1920w\" sizes=\"auto, (max-width: 960px) 100vw, 960px\" \/>","byline":"Chandan Singh and <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/jfgao\/\" title=\"Go to researcher profile for Jianfeng Gao\" aria-label=\"Go to researcher profile for Jianfeng Gao\" data-bi-type=\"byline author\" data-bi-cN=\"Jianfeng Gao\">Jianfeng Gao<\/a>","formattedDate":"June 25, 2026","formattedExcerpt":"Researchers introduce generative causal testing, which translates black box models into clear hypotheses and verifies them in the scanner, revealing what specific brain regions respond to in 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