The first proper brain scan involving the effects of mescaline found the drug ignoring the regions where psychedelic research has spent a decade studying, settling instead on the one structure no one really expected.

Sometime around 3,700 BC, someone in what is now southwest Texas cut two tops off a peyote cactus and dried them. They spent most of the twentieth century in a drawer at the Witte Museum in San Antonio, and when chemists finally analyzed them in the early 2000s, the mescaline was still detectable — roughly 2% alkaloids by weight, 5,700 years on.

Those dried-out cactus tops, where the drug compound is concentrated, remain the oldest plant drug ever to give up a major bioactive compound to chemical analysis. And with thousands of years of use, mescaline remains a bit of a scientific mystery.

Mescaline became a Schedule 1 substance under the Controlled Substances Act in 1970, and the classification effectively ended research on it. LSD and psilocybin eventually clawed their way back into laboratories for scientific research, but mescaline, the older compound by several thousand years, doesn’t have the same level of lab testing.

Now, a new study in Neuroscience Bulletin has become the first to properly look, and the answer is not what most expected.

Researchers at Northeastern University’s Center for Translational Neuroimaging scanned 24 rats, evenly split by sex. After a single dose of mescaline, while the animals were awake (anesthesia flattens the very signals a drug study is trying to read), the drug largely ignored the forebrain. It went almost entirely for the cerebellum, which is a strange location for a psychedelic. 

The cerebellum is the lump at the back and bottom of the brain where balance and motor coordination live. It also holds more than half of all the neurons in the brain, and everything it computes leaves through just three narrow output channels.

An interpretation, proposed by neuroscientist Anna Devor in 2002, is that the cerebellum is the “Great Gate.” As the brain receives sensory information, it acts as a filter that suppresses the sensory information that the brain already predicted and passes along only what it did not expect. A working brain spends most of its time discarding what it saw coming.

Under mescaline, the gate appeared to come off its hinges.

Activity inside the cerebellum dropped sharply, while its connections outward multiplied to the hippocampus, the thalamus, the sensory cortex, and the midbrain. Quieter on the inside, wired to far more on the outside. The researchers read this as a filter that has stopped filtering and started broadcasting, pushing raw sensory information into regions that normally receive it neatly packaged.

“Wouldn’t it be devastating if you didn’t ‘gate’ all of that sensory information out?” senior author Craig Ferris explained in a university press statement.

The clearest evidence for that reading came from a smell test. Rats were given almond odor, a reliably rewarding stimulus, and in the control group, it lit up 48 of the 169 brain regions the team tracked. Under mescaline, the response was essentially abolished, flattened close to nothing across the board.

Broken down by modality, smell, touch, and hearing were all measurably blunted, while the visual system showed no significant change at all — a peculiar result for a compound whose entire cultural reputation rests on what it does to sight. 

The authors put the discrepancy down to species differences, reasonably enough given that rats navigate by nose and whisker, but it remains one of the odder findings in the paper. What makes this significant is the comparison. 

Mescaline, LSD, and psilocybin all act on the same target, the 5-HT2A serotonin receptor, and all three produce broadly similar experiences in people, which has encouraged researchers to treat findings from one as transferable to the others. However, brains don’t seem to cooperate.

Earlier work from this same lab found LSD acting on the prefrontal cortex, thalamus, and hippocampus, and psilocybin engaging cortico-striato-thalamic circuits. Mescaline’s acute signature sat in neither place.

“The power of mescaline to me is that it is doing something different than other psychedelics,” said lead author Noah Cavallaro. “Essentially, the cerebellum is being shut down internally, but it’s talking more to other parts of the brain.”

The distinctiveness is narrower than it first appears, though. The lab’s own psilocybin work also produced pronounced cerebellar hyperconnectivity, so the sprawling network changes are not unique to mescaline; what looks specific to it is the acute suppression, the cerebellum falling quiet while the rest of the brain carried on largely unaffected.

“If two compounds produce similar hallucinogenic symptoms through different neural pathways, comparing them could help us pinpoint what’s actually causing those specific experiences,” Cavallaro said.

That being said, there are some cautions offered in the paper. First, these were rats, and rat and human brains differ most in the cerebellar-cortical wiring. Second, only one dose was tested. Third, the team did not monitor heart rate or breathing during scanning, so ordinary cardiovascular effects cannot be cleanly separated from neural ones.

Lastly, the researchers also flagged recent work showing that the scanner signal itself can run opposite to a brain region’s actual oxygen use, which complicates reading any of these images as direct neural activity. The one previous attempt to image mescaline in humans, a small study decades ago, found a different pattern entirely.

With all that being said, the paper presents a key social issue. Mescaline’s status as a drug has left it largely untested, and only now are we learning about what it can do to the brain, outside of the anecdotes of its users.

After fifty years of regulatory silence, scientists are just starting to answer a question people have arguably been asking since the Neolithic.

Siri Wolfram Alpha integration helped create one of the defining moments in the early history of consumer artificial intelligence. When Apple introduced Siri with iPhone 4S in October 2011, users were not merely given another form of voice control. They could ask a spoken question and receive a calculated answer rather than a page of search results.

Questions involving mathematics, measurements, dates, nutrition, astronomy and factual comparisons could be routed to Wolfram Alpha, a computational knowledge engine launched in 2009. Siri handled the conversation, while Wolfram Alpha supplied structured information and calculations behind many of the responses.

The experience could feel remarkably advanced for its time. Asking how many days remained before a holiday, comparing the populations of two cities or converting miles into kilometers no longer required opening Safari, entering a query and selecting a website.

That direct path from natural language to a useful answer established an expectation that continues shaping AI products today.

Siri Began as an Action-Oriented Assistant

Siri did not begin inside Apple.

Its technological roots extend to research conducted at SRI International, including work associated with the DARPA-funded CALO project. The objective was to develop systems capable of learning, organizing information and helping people complete everyday tasks.

SRI later spun the technology into Siri Inc., founded by a team including Dag Kittlaus, Adam Cheyer and Tom Gruber. The company released a standalone Siri app for iPhone in early 2010.

The original product was designed around actions rather than ordinary web search. A user could ask for a restaurant reservation, request a taxi, check entertainment listings or search for local services. Siri attempted to understand the intent and connect the request with an appropriate online provider.

This approach was ambitious because the assistant needed to coordinate several different systems. Speech had to be converted into text, the request had to be interpreted, context had to be considered and an external service had to return something useful.

Apple acquired Siri in April 2010. Steve Jobs had personally recognized the potential of moving beyond a collection of apps toward a conversational layer capable of reaching them on behalf of the user.

When the assistant returned as an integrated feature of iPhone 4S, Apple had narrowed some aspects of the original startup vision while making the technology accessible to a much larger audience.

Siri could send messages, create reminders, place calls, manage calendar events, check weather conditions and answer questions. Apple initially labeled the feature as a beta, an unusual decision for a prominent part of a new iPhone launch.

The beta label acknowledged that natural-language interaction remained unpredictable. Accents, ambiguous phrasing, network conditions and external data providers could all influence whether a request succeeded.

Siri Wolfram Alpha Made Answers Feel Immediate

Siri Wolfram Alpha became especially memorable because it showed the difference between retrieving information and computing an answer.

A conventional search engine could locate pages containing a formula. Wolfram Alpha could apply that formula to the values supplied by the user. It could interpret units, compare quantities, calculate probabilities and organize factual data into a structured response.

That distinction allowed Siri to appear more knowledgeable than a voice interface simply reading search results aloud.

A request such as “What is 15% of $86?” could return the calculation directly. A question about the distance between planets could produce a value without requiring the user to inspect several websites. Wolfram Alpha could also answer unusually specific requests, including information about aircraft flying overhead when the necessary location and aviation data were available.

The system was not a large language model. It did not generate open-ended prose in the way current AI assistants do. Wolfram Alpha relied on curated data, symbolic computation, algorithms and formal representations of knowledge.

That structure provided an advantage that remains relevant: many answers could be calculated and traced through defined information rather than improvised statistically.

Early Siri combined several specialized capabilities instead of depending on one system to do everything. One provider might supply restaurant information, another could handle maps and Wolfram Alpha could process computational questions.

The assistant acted as the conversational coordinator.

This architecture made Siri seem like one intelligence even though the response could come from a network of separate services. Modern AI platforms have returned to a similar idea through tools, agents and external data connections. A language model may manage the conversation while calling a calculator, calendar, search engine or business system when a task requires reliable external action.

Siri demonstrated that model on a consumer smartphone more than a decade earlier.

A Small Integration Created a Large Expectation

The Wolfram Alpha partnership influenced how people began judging digital assistants.

Before Siri, voice features on phones were commonly associated with rigid commands such as calling a contact or selecting music. Siri invited users to speak more naturally and attempt questions that would previously have been typed into a browser.

The results were inconsistent, but successful interactions changed perceptions immediately. The iPhone appeared capable of understanding intent, reaching into a knowledge system and returning a concise answer through one conversational interface.

That apparent simplicity concealed substantial infrastructure. Speech recognition, language interpretation, location, personal context and external databases all had to work together quickly enough to preserve the feeling of a conversation.

Wolfram Alpha also gave Apple access to expertise that would have taken years to reproduce internally. Stephen Wolfram had spent decades building Mathematica and computational systems before launching the public knowledge engine.

Following the Siri announcement, Wolfram described the integration as a characteristically direct Steve Jobs decision: users wanted access to knowledge and actions without unnecessary intermediate steps.

That idea became more influential than any individual answer Siri provided.

The assistant did not need to show how many services had participated. It needed to understand the request and return the result.

Early Siri Also Revealed the Limits of the Model

The first version of Siri could feel futuristic one moment and frustrating the next.

Some questions reached Wolfram Alpha correctly, while similar wording could trigger a web search or an unrelated response. Siri occasionally struggled to determine whether the user wanted a calculation, a local result or an action inside the phone.

Wolfram Alpha responses were also better suited to some subjects than others. Mathematical and scientific questions matched its computational structure, while casual conversation, current events and subjective requests required different sources.

Network dependence created another weakness. Early Siri processing relied heavily on remote servers, so delays or outages could make a basic request fail even when the iPhone itself was functioning normally.

Those limitations foreshadowed the central challenge surrounding modern assistants. A convincing interface encourages people to ask almost anything, but no single technical system performs equally well across every type of request.

Current generative AI can produce more natural language and handle a wider range of topics. It can also create confident errors when factual grounding is weak. The computational approach represented by Wolfram Alpha remains valuable precisely because some questions require verified data and exact calculation rather than fluent speculation.

The story behind the first Siri steps is therefore not only about an old iPhone feature. It shows that a powerful assistant needs more than a conversational personality. It requires access to dependable tools, structured knowledge and the ability to recognize when another system should provide the answer.

Siri Wolfram Alpha offered that lesson in 2011, long before AI assistants became capable of writing essays, interpreting screens and acting across apps.

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