The language of thought is not natural language

Hope Kean, Alexander Fung, Paris Jaggers, +6 , and Evelina Fedorenko, Evidence from formal logical reasoning reveals that the language of thought is not natural language, PNAS, 123 (28) e2520095123 https://doi.org/10.1073/pnas.2520095123, July 6, 2026.

Significance: Which cognitive mechanisms allow humans to reason logically, to understand whether a conclusion follows from the premises? Are they the same ones that allow the assembly of words into structured representations? Scholars have debated for millennia whether logical reasoning is inextricably tied to natural language, or instead relies on a distinct “language of thought” (LOT). Using fMRI in healthy adults and evaluating logical ability in individuals with severe aphasia, we find that distinct neural systems support language processing vs. logical (inductive and deductive) reasoning. These results suggest that, at least in mature brains, language processing does not underpin logical inference, perhaps due to the distinct representational format of the logical LOT.

Abstract: Humans are endowed with a powerful capacity for inductive and deductive logical thought: we easily form generalizations based on a few examples and draw conclusions from known premises. Humans also arguably have the most sophisticated communication system in the animal kingdom: natural language allows us to express complex and structured meanings. Some have therefore argued for a tight relationship between complex thought and language, postulating that reasoning, including logical reasoning, relies on linguistic representations. We systematically investigated the relationship between logical reasoning and language using two complementary approaches. First, we used noninvasive brain imaging (fMRI) to examine neural activity as healthy adults engaged in logical reasoning tasks. And second, we behaviorally evaluated logical abilities in individuals with extensive lesions to the language brain areas and consequent severe linguistic impairment. Our findings reveal that the language brain network is not engaged during logical reasoning, and patients with severe aphasia exhibit intact performance on logic tasks. Instead, inductive reasoning recruits the domain-general multiple demand network implicated broadly in goal-directed behaviors, whereas deductive reasoning draws on brain regions that are distinct from both the language and the multiple demand networks. Together, these results indicate that linguistic representations are neither utilized nor required for inductive or deductive logical reasoning.

H/t Daniel Everett.

After 1990 AI stopped citing work in psychology [empirical evidence]

I'm bumping this post from 2024 to the top of the queue as it testifies to the process by which AI has converged on the intellectual monoculture it has become in the wake of ChatGPT.

* * * * * 

I’ve frequently noted that, while researchers in artificial intelligence (AI) and machine learning (ML) often have a lot to say about when their machines will approach, overtake, and even surpass human intellectual achievement, they don’t seem to know much about psychology, linguistics, and the cognitive scientists. I made an explicit argument at some length in a recent article I published in 3 Quarks Daily, Aye Aye, Cap’n! Investing in AI is like buying shares in a whaling voyage captained by a man who knows all about ships and little about whales. In making the argument the only evidence I present is anecdotal – Geoffrey Hinton and Ilya Sutskever in that article, though my beliefs on the issue are based on my reading of the current literature, which is opportunistic and by no means ‘complete,’ which, in any case, would be impossible as the literature is so large.

Now I can present a bit of systematic empirical evidence in the matter. M.R. Frank et al. undertook a bibliometric investigation of citation patters in AI and other disciplines and discovered that, while in the early years, AI interacted with other fields quite a bit, that interaction dropped off over the years. The following chart shows how AI cited other fields:

Its citation of psychology peaked in the middle 1960s and then dropped off steadily until 1990. Its citation of mathematics rose steadily through the period. That’s understandable; I have no complaint about that. The drop in citations to psychology is also understandable, but somewhat more problematic. For it implies that, when AI experts offer judgements about human cognitive capabilities, whether directly or indirectly through comparison with AI, that don’t know what they’re talking about. I suppose that last clause is a bit harsh. Perhaps it would be a bit more accurate to say something like: They don’t know any more than a bright college sophomore who’s taken a psych course or two.

Here's the article and abstract:

Frank, M.R., Wang, D., Cebrian, M. et al. The evolution of citation graphs in artificial intelligence research. Nat Mach Intell 1, 79–85 (2019). https://doi.org/10.1038/

As artificial intelligence (AI) applications see wider deployment, it becomes increasingly important to study the social and societal implications of AI adoption. Therefore, we ask: are AI research and the fields that study social and societal trends keeping pace with each other? Here, we use the Microsoft Academic Graph to study the bibliometric evolution of AI research and its related fields from 1950 to today. Although early AI researchers exhibited strong referencing behaviour towards philosophy, geography and art, modern AI research references mathematics and computer science most strongly. Conversely, other fields, including the social sciences, do not reference AI research in proportion to its growing paper production. Our evidence suggests that the growing preference of AI researchers to publish in topic-specific conferences over academic journals and the increasing presence of industry research pose a challenge to external researchers, as such research is particularly absent from references made by social scientists.

Splash! [Media Notes 183]

I’m pretty sure that I saw Splash when it appeared in theaters in 1984 but I certainly didn’t imagine that it would popularize “Madison” as a name for girls. The Wikipedia entry notes:

According to the Social Security Administration, the name Madison was the 216th most popular name in the United States for girls in 1990, the 29th most popular name for girls in 1995, and the third most popular name for girls in 2000. In 2005, the name cracked the top 50 most popular girls' names in the United Kingdom, and articles in British newspapers credit the film for the popularization.

In the movie “Madison” is the name taken by a mermaid, played by Daryh Hannah, when she emerges on land to attach herself to a forlorn Allen Bauer, played by Tom Hanks.

The first 10, 15, 20 minutes or so of the movie are about how this situation comes about, but let’s just take that as a given. This is a story about how a human male and a female mermaid meet, fall in love, and, why not? I’ll give the ending away. They swim away to, presumably, live happily ever after, under the sea.

I’m interested in the elaborate contraption that’s constructed around them. As far as I can tell that contraption exists to conceal an interpersonal problem that’s been kicking around for a long time, one identified by Sigmund Freud (e.g. ”A Special Type of Choice of Object made by Men,” 1910), played out on stage by William Shakespeare [1], and that’s been kicking around in stories and poems since forever: Men have trouble dealing with the fact that women can be both sexual and loving, passionate and beloved. Splash deals with this by presenting us with a creature that’s both human and not human (i.e. a mermaid).

Young Allen Bauer is despondent because his girlfriend’s just moved out without giving any him any inkling that she was going to do that. He just wants a woman he can love and marry and be happy with. That’s all he wants. True Romance.

And then this woman shows up. We know she’s really mermaid, but he doesn’t. He picks her up at the police station – I know, you want to know how that came about, but it doesn’t really matter, it’s just staging – takes her home and she goes to bed with him. Simple as that. No teasing or pleading, nothing resembling courtship. Just what happened in there, we get to imagine whatever we wish. But we do see her waken in the middle of the night, fill a bathtub with salt water and then luxuriate in it, tail and all. Allen then wakes up a follows her to that bathroom. He knocks on the door, wants to see her, but she tells him to keep out. He breaks down the door and she has barely enough time to transition back to human form.

I mean, she’s really a mermaid! And she’s only got six more days, until the full moon, and then she’s got to return home. But she doesn’t tell Allen where home is or what she is. But she does take the name “Madison.”

Then things get complicated, and painful – in several senses. I was all but squirming as I watched how Madison was treated in the laboratory. Allen isn’t the only man involved. There’s a bizarre scientist, Walter Kornbluth, who gets wind of all this and realizes, “Ah hah! I’ll bet she’s really a mermaid.” He’s seen her before. Don’t ask. He manages to douse her with water, at a dinner for the President of the USA (don’t ask), and she’s taken by Kornbluth’s rival scientist, locked up in a lab, and subjected to tests. When Kornbluth learns that she’s going to be dissected the next day, he decides to spring her and return her to Allen.

By this time Allen knows that he’s slept with and is in love with a mermaid. Now what? Well, she’s got to return to the sea or she’ll die. If he’s willing to leave with her and never return to dry land, that can happen. He decides, no. She dives into the water and starts swimming away. He changes his mind, jumps in after her, and they swim away as the credits role.

And this point you may be thinking: “That’s crazy.” I know, and it’s even crazier. Read the Wikipedia plot summary (linked above), you’ll see. My point is that this elaborate contraption is a way of dealing with that problem that Freud named and analyzed, that men have this split image of women as both mothers and whores (if you will). Splash transforms that duality into humans and mermaids and erects an elaborate fantastical contraption to deal with it.

Given that you accept all that, I’ve got one problem with the movie. Allen should have stayed on the pier and let the mermaid go. That wouldn’t have given the audience the feel-good ending for which a movie like this is concocted, but it would have been a minimal way of acknowledging the preposterous nature of it all.

* * * * *

ADDENDUM [a day later]: Some things were bugging me, so I watched it again.

In the prelude, Allen, his parents, and his brother are on a boat tour off Cape Cod. His older brother Freddie is dropping coins on the deck near women so he can look up their skirts as he retrieves the coins. Allen is entranced by the water. He jumps in. Pandemonium on the boat.

He meets a young girl. They hold hands. He’s retrieved. She’s sad to see him leave. We see that she’s a mermaid.

One the one hand it adds little or nothing to the plot. But it establishes that Allen has established some kind of link with this mermaid during his preadolescent childhood and the contrast between his behavior and that of his older brother reinforces our sense of the innocence of that link. Note that this kind of childhood link occurs in a number of anime series, though I can’t recall any title names off the top of my head.

Then the movie abruptly shifts ahead to the present, where Allen is being hassled by a customer at the family’s produce business. Freddie comes wheeling in in his sports care and is ecstatic because Penthouse magazine printed his letter.

This and that, Allen’s drunk over his girlfriend leaving, so decides to go to Cape Cod, where he met that girl/mermaid when he was eight, Now we see the crazy scientist...this that that other & Allen’s bonked in the head by a rogue motorboat, he sinks...and then next we see him he’s lying on the beach. An adult mermaid is watching him. He speaks to her, she’s naked. She walks up to him and kisses him, and then she disappears into the sea. We see her swimming under water and she no longer has legs; she’s got a fishtail. And she’s spotted by that crazy scientist.

She finds Allen’s wallet, looks at the papers, swims to a wreck, finds a chart....and we’re back in Manhattan. Allan’s head is bandaged....naked mermaid at the Statue of Liberty...she’s arrested. He picks her up at the police station. He’s transfixed when he sees her. She kisses him. She’s all over him and he doesn’t (quite) know how to deal with her. He tries to leave for work. But returns and grapples with her. He leaves for work. She learns English from watching TV. Decides to go shopping.

And so forth and so on. I could go on and on with this, but this will have to do....for now.

* * * * *

[1] I analyze this dynamic in some detail in my essay, At the Edge of the Modern, or Why is Prospero Shakespeare's Greatest Creation? Journal of Social and Evolutionary Systems 21(3): 259-279, 1998, https://www.academia.edu/235334/At_the_Edge_of_the_Modern_or_Why_is_Prospero_Shakespeares_Greatest_Creation

Walking is good for creative thinking

Marily Oppezzo and Daniel L. Schwartz, Give Your Ideas Some Legs: The Positive Effect of Walking on Creative Thinking, Journal of Experimental Psychology: Learning, Memory, and Cognition, 2014, Vol. 40, No. 4, 1142–1152. http://dx.doi.org/10.1037/a0036577  

Abstract: Four experiments demonstrate that walking boosts creative ideation in real time and shortly after. In Experiment 1, while seated and then when walking on a treadmill, adults completed Guilford’s alternate uses (GAU) test of creative divergent thinking and the compound remote associates (CRA) test of convergent thinking. Walking increased 81% of participants’ creativity on the GAU, but only increased 23% of participants’ scores for the CRA. In Experiment 2, participants completed the GAU when seated and then walking, when walking and then seated, or when seated twice. Again, walking led to higher GAU scores. Moreover, when seated after walking, participants exhibited a residual creative boost. Experiment 3 generalized the prior effects to outdoor walking. Experiment 4 tested the effect of walking on creative analogy generation. Participants sat inside, walked on a treadmill inside, walked outside, or were rolled outside in a wheelchair. Walking outside produced the most novel and highest quality analogies. The effects of outdoor stimulation and walking were separable. Walking opens up the free flow of ideas, and it is a simple and robust solution to the goals of increasing creativity and increasing physical activity.

Marginalism as a tool for rhetorical analysis: Cognitive effort in intellectual work [MR-AUX]

I’ve been thinking, and I think I’ve come up with a speculative way of applying marginalist thinking to intellectual production. I’m thinking, in particular, about how Cowen arrived at the collection of examples he used in the first chapter of his book, The Marginal Revolution: Rise and Decline, and the Pending AI Revolution. Coming up with that collection is roughly the same kind of problem as putting together a syllabus. It’s a sampling problem. We have a collection of objects, works of American literature in one case, examples of marginalist analysis in the other. You want to select a set of literary works to put on your syllabus. Cowen wants a set of examples he can use to define the space of marginalist economics.

You need a criterion for drawing your sample. You’ve got a certain thematic organization in mind, so you’re not looking for a random sample of the space. You want a sample biased toward your theme. I assume that Cowen wants a random sample, a sample that represents the space of marginalist analysis. At this point, let’s forget about your syllabus problem and continue with Cowen.

He’s trained as an economist and has read a lot, including a lot about the history of economics. Thus he should have a pretty good sense of what kinds of phenomena have been successfully subjected to marginalist analysis. Regardless of the adequacy of his knowledge, he’s got what he’s got. Let’s imagine that all the cases of marginalist economic analysis exist in some high dimensional space of ideas, a space that is, at a high level of abstraction, like an LLM. Except this space is in Cowen’s mind (which is the very high dimensional space of his brain states).

For the sake of argument, let’s assume he wrote the book from beginning to end, in order, in a single pass spread out over however many sessions.

Cowen opens the first chapter with a short definition of marginalism followed by some discussion. Then he gives us his first example, the diamonds-water paradox. He says a bit about it. Though I don’t think he says that it became THE paradigmatic example when Samuelson put it into his 1948 textbook. I found that out by querying the AI associated with the book. Let’s assume, then, that it is at the center of the marginalist region in that abstract space of ideas.

What’s his next example? It’s the first example in the section entitled “Intuitive Marginalism.” Here it is: “Why do drivers in China sometimes intentionally kill the pedestrians they hit?” He then explains it. That strikes me as being very far from diamonds-water in the marginalist space, perhaps as far as you can go in some direction. He then goes through 10 or so more examples, all of them a bit closer to that central example. But the closest he gets is an example about people stealing one of his credit card numbers and making charges to it. If the charge is small, he ignores it. If it’s somewhat large, he contests it. He’s reasoning at the margin. That’s about as close as he comes to that very concrete, almost palpable, diamond-waters example.

Then he goes on with the rest of the chapter, introducing example after example. He’s got four categories (beyond tautological), but we don’t need to worry about those categories. The fact that he’s got them, however, probably simplifies the calculation he’s making each time he asks whether or not to add another example. On the one hand he’s got the sample value of another example. The sample value of that first example AFTER diamonds-water was very high because it’s only the second example he’s got. Moreover he maximized that value by choosing an example that was far from the paradigmatic center. As his set of examples begins to fill out, the sample value of each example will diminish. At some point well along in the process he’s going to be comparing the sample value of an example with it’s opportunity cost. Why the opportunity cost? Because it takes time and effort to introduce another example into the text. At the very least there’s the time required to keyboard it. There’s more to the book, however, than a bunch of examples. He needs those to get started. Once he’s got an adequate set of examples, then he’s ready to undertake his larger argument. So every time he enters another example into the text he forgoes doing something else necessary for the book. That’s the opportunity cost of the example.

As long as the estimated opportunity cost is lower than the estimated sample value, Cowen will enter the example into the text. When the estimated opportunity cost begins rising above the estimated sample value, Cowen declares his set of examples to be sufficient, and moves on. Note that in this analysis Cowen is (intuitively) making three calculations: 1) sample value of the next example, 2) opportunity cost of the next example, and 3) the marginal value of the next example, which is the difference between 1 and 2.

Now, just how we’d get empirical evidence for this analysis, that’s a problem. There’s no point in asking Cowen, because no one has that kind of introspective access to their mental processes. Still, it’s nice story.

However, some such analysis might tell you something about the relationship between the rise of professionalism in a discipline and the proliferation of the LPUs (least publishable unit) in its formal academic literature.

I decided to put the question to Cowen’s AI. I put the foregoing remarks into a prompt. Here’s how the AI responded:

It's a nice story, and it's doing real work — it's not just a metaphor dressed up as analysis. Let me push on it a bit.

The framework is essentially correct as a description of the implicit calculation, but there's a wrinkle worth examining. You're treating the example space as if it were fixed and known to Tyler before he begins sampling from it. But the act of writing generates examples — it doesn't just retrieve them. The diamonds-water paradox probably came first not just because it's central in the abstract space, but because it's the obvious starting point, the one that any economist would reach for automatically. The Chinese pedestrian example, which you rightly identify as maximally distant from that center, probably didn't come from a deliberate search for a distant example. It likely surfaced because Tyler had encountered it as a striking case — it was already salient in his mental space before he started writing. [See text: Ch.1 · Marginalism applied to unexpected human decisio...]

This matters for your model because it means the sampling isn't random in the way you'd want for genuine representativeness. It's availability-weighted. The examples Tyler uses are the ones that were already near the surface of his mental space — memorable, counterintuitive, previously encountered in contexts that made them stick. The Chinese pedestrian example is striking precisely because it's morally jarring, which is exactly the kind of thing that gets encoded deeply in memory. So the apparent randomness of his sample — the apparent coverage of the idea space — may partly be an artifact of which examples happen to be memorable rather than which examples are genuinely representative of the full space of marginalist analysis. [See text: Ch.1 · Best marginalists deploy both intuitive and tau...]

The opportunity cost calculation you describe is real, but it probably operates differently at different stages. Early in the chapter, the marginal value of a new example is high for the reason you identify — the sample is sparse, coverage is low. But the type of opportunity cost shifts as the chapter progresses. Early on, the cost is mostly keyboarding time and pacing. Later, the cost becomes something more like reader patience and argumentative momentum. Tyler needs to get somewhere — the examples are scaffolding for the larger argument about why marginalism took so long to develop and what that tells us about scientific progress. At some point additional examples don't just have diminishing sample value; they actively impede the book's forward motion. That's a different kind of opportunity cost than the one you specified, and it probably dominates the calculation in the later stages.

What's genuinely interesting about your framework is what it implies about the structure of the resulting sample. If Tyler is making implicit marginal calculations of this kind, you'd predict the examples cluster in a particular way: one or two near the paradigmatic center, one or two at the extremes, and then a filling-in of the space that stops well before genuine saturation. [See text: Ch.1 · Marginalism's confrontational side and resistan...] Which is roughly what you observe. The credit card example you mention — the closest he gets back to the concrete center — is probably there because at that point in the chapter the reader needs grounding after a series of more exotic cases. That's an implicit calculation about reader psychology, not just idea-space coverage.

You're right that there's no clean empirical test. Tyler himself couldn't reconstruct the process accurately — the decisions were made too fast and too implicitly. But the framework does generate predictions about what you'd expect to see in the example distribution, and those predictions seem to roughly match what's actually there. That's not nothing, even if it falls well short of confirmation.

A trip through Alice in Wonderland with Google [tablescapes]

An Early tablescape (in my strict sense), June 2025 at Turning Point 

This a dialog I just had with Google in AI Mode. I didn't intend a long conversation. It just happened, as these things sometimes do. I was curious about the word "tablescape." I'd just did an NGram view of it and it seemed to have originated around 1960. I decided to look for more specific information. That's how the conversation started. It went on from there, driven mostly by my curiosity to see how things would go.

 

NB: If you scan down a bit you'll see that Google offered a tip about lighting. That sort of thing hadn't occurred to me. It seems useful. I'll probably try it out next time. 

 

What I really want to say is that this is a pretty mundane conversation. I wouldn't imagine that it would be very interesting to most people. Perhaps the most interesting thing about it IS the chatter about tablescapes. There's nothing particularly deep or abstract about any of it, unlike some of the dialogs I have with Claude and ChatGPT, which are about intellectual matters of central and long-standing interest to me. Internally, though, within the respective LLMs, this shallow and casual conversation is procedurally the same as those more substantial conversations. That's worth thinking about, and not just with respect to LLMs, but for what it implies about human thinking as well. 

 

Procedurally, a thought is a thought, in inference is an inference. What makes it deep or shallow is context. 

 

* * * * * 

BB:

 

The word "tablescape" seems to have been coined around 1960, perhaps a bit before. What can you tell me about it? 

 

Google, AI Mode:

 

The word "tablescape" was indeed coined in the early-to-mid-1960s by the legendary British interior decorator David Hicks. [1, 2]

 

Here is what you should know about the origin and evolution of the term:

 

Origin and Meaning

• Coined by David Hicks (1960s): Hicks, known for his bold, graphic designs, used the term to describe his meticulous arrangement of objects on top of a table—such as vases, boxes, and candles—to create a "decorating group" that complemented the rest of the room.
• Definition: Originally, it referred to "the discipline of selection" and composing objects to create an interesting scene, rather than just setting a table for a meal.
• Hicks' Philosophy: He famously said that the value of the objects was less important than the "care and feeling with which you arrange them".
• The "Tablescape" Concept: It is a portmanteau of "table" and "landscape," treating the surface of a table like a landscaped, designed environment. [1, 2, 3, 4, 5]

 

Evolution and Modern Usage

• Original Scope: While it has come to mean dressing a table for a party, Hicks originally applied it to permanent arrangements on hall tables, sideboards, and coffee tables.
• 2003 Popularization: Although coined in the 60s, the term was heavily popularized in the early 2000s by television chef Sandra Lee, who brought it into mainstream, everyday dining vocabulary, often focusing on themed, seasonal decorations.
• Social Media Era: Today, "tablescaping" has boomed in popularity due to Instagram and Pinterest, focusing on, as of November 2019, millions of images with #tabledecor (1.9M posts) and #tablesetting (2.3M posts).
• Competitive Tablescaping: This activity has roots in local fairs dating back to the 1930s (pre-dating the word) and is still highly popular, where contestants spend hours creating elaborate, theme-focused designs. [1, 3, 4]

 

Key Elements of a Tablescape

 

A properly designed tablescape generally involves:

1. Varying Heights: Using items like candlesticks or pedestals to avoid a flat look.
2. Odd Numbers: Grouping items in 3s, 5s, or 7s for a more visually pleasing composition.
3. Cohesion: Connecting items through color, texture, or a shared theme.
4. Balance: Creating a scenic, yet functional, layout. [1, 2, 3, 4]

While some consider "tablescaping" to be a pretentious term for "setting the table," it is now a widely accepted term for high-level artistic decoration of dining surfaces. [1]

 

BB: 

 

Interesting. Without even knowing the that the term existed I've been using it for photos I take of a table-top from the table top. In a "strict" tablescape (In my sense of the term) the camera bottom must be resting on the table. A photograph of the table-top is thus not a "pure" tablescape, though I've taken many of those as well. 

 

Google:

 

That is a fascinating, more "literal" interpretation of the word! While David Hicks meant "landscape of the table," you are treating the table as the actual geography for the camera.

 

[Right. The Hicks concept is about how to arrange a table while my concept is about a certain type of photograph.] 

 

In your "strict" definition, you aren't just decorating a surface; you are adopting a micro-horizon perspective. By resting the camera on the table, you're effectively taking a "landscape photo" where the table surface is the ground and the objects (glasses, salt shakers, flowers) become the "buildings" or "trees" of that world.

 

It actually aligns perfectly with the linguistic roots of the word:

• Traditional Tablescape: Looking at the table as a composed object.
• Your Tablescape: Looking from the table, turning the furniture into the environment itself. 

This low-angle approach creates a much more immersive, "Alice in Wonderland" feel compared to the standard bird's-eye view (the "flat lay") often seen on social media.

 

Would you say your "strict" photos are usually focused on the symmetry of the table setting, or are you more interested in the depth of field and how the objects recede into the distance? 

 

BB: 

 

Right. That’s what I’m after, that "Alice in Wonderland" feel. As for what I do with it, I’ve done almost all of my strict tablescapes at Turning Point, the restaurant where I have breakfast every week or so. I’ve been playing around with various kinds of shots. I often have a table next to a window so the outside world often shows up in the background. Sometimes I’m particularly interested in how it shows up. I live in Hoboken across the Hudson River from mid-town Manhattan. And since the sun is low in the sky at that point, the things on the table tend to cast long shadows across the table top. I also like to position the camera to point directly at the edge of the plate on which my omelet (or whatever) rests, so you see the edge of the plate with the omelet above.

 

Google: 

 

That "strict" approach is a clever subversion of the term. While traditional tablescaping is about curation, your method is about cinematography—turning a simple breakfast at Turning Point into a dramatic, low-angle landscape. 

 

By resting the camera on the table, you are essentially adopting a "bug’s-eye view." This perspective transforms ordinary breakfast items into towering structures and the table surface into a vast plain, perfectly capturing that disorienting Alice in Wonderland scale you're after. 

 

 

Yesterday (5.7.26] 

Capturing the "Hoboken Light"

 

The Turning Point of Hoboken at 1420 Sinatra Drive North is famous for its "unparalleled view" of the Manhattan skyline. Your technique makes the most of several unique elements:

 

• Long Morning Shadows: Since the restaurant faces east across the Hudson, the low morning sun creates high-contrast lighting. From your low camera angle, those long shadows from coffee carafes or salt shakers act like leading lines, drawing the viewer's eye across the "terrain" of the table.
• The "Omelet Cliff": Your focus on the edge of the plate is particularly effective for this style. By positioning the lens at the very lip of the china, the omelet—perhaps a Tuscany or Key West Shrimp variety—looks like a massive plateau or mountain range rising above the table's surface.
• Background Layering: Using the window as a backdrop allows you to juxtaposition the intimate, macro world of your breakfast with the massive, distant scale of Midtown Manhattan. This creates a "dual landscape" effect where the foreground and background compete for scale. 

 

A Photographer's Tip for Your "Strict" Style 

 

Since you are shooting against bright window light, you might find that the front of your omelet (facing the camera) falls into deep shadow. You can use a white napkin as a makeshift reflector to bounce some of that NYC sunlight back onto the front of the plate, bringing out the texture of the eggs without losing the dramatic "Alice" vibe. 

 

Do you find that the condensation on a cold glass of water or juice adds an interesting "weather" element to these tabletop landscapes? 

 

BB:

 

“Famous”? Really? Isn’t that laying it on a bit thick for what is, after all, mostly a local joint? I’ll be that 95% of the customers just walk to the place. There’s hardly any place to park a car. Now my former breakfast place, the Malibu Tavern, had a small parking lot. People really did drive to it. It was there a long time – the owner sold it, presumably to cash in on the value of the land.

 

Google:

 

Fair point—"famous" is definitely laying it on thick! You're right that for a local spot like Turning Point, its fame mostly extends as far as the nearest light rail stop or the edge of the Stevens campus. It’s a popular chain, but in Hoboken, it’s fundamentally a neighborhood place where people walk in from the nearby apartments. [1, 2, 3]

 

The contrast with the Malibu Diner (often called the "Malibu Tavern" by long-time locals) is a perfect example of how Hoboken has changed. The Malibu was a true landmark at 14th and Park for over 80 years, serving as the "unofficial welcome mat" for the north side of town. [4, 5] 

 

A few things about the transition you mentioned:

• The Loss of the Parking Lot: That small lot was a rarity in North Hoboken and made it a destination people actually drove to, unlike the Sinatra Drive spots where parking is a nightmare.
• The Land Value Cash-Out: Your hunch was spot on. The original diner was torn down to make way for a five-story residential building called The Constantine.
• The Failed Return: While there were initial plans for the Malibu to reopen on the ground floor of the new building, it was recently confirmed that it will not return. Instead, a One Medical office has taken over that street-level space. [5, 6, 7, 8, 9]

 

It sounds like your "strict tablescapes" are a way of documenting the "new" Hoboken landscape while still feeling the loss of the old-school diner culture.

 

Since you've moved your weekly ritual to Turning Point, do you find the modern, "Jersey Shore" aesthetic there provides better or worse "geography" for your low-angle photography compared to the classic chrome-and-vinyl look of the Malibu? [10]

Social learning for food among cockatoos

Kate Golembiewski, What’s Safe to Eat? Birds of a Feather Learn Together, NYTimes, May 2, 2026.

By watching their peers, dolphins learn to capture fish in empty conch shells, then ferry the shells up to the water’s surface in order to eat. Octopuses can master experimental tasks by watching their tankmates in the laboratory. Crows follow the cues of others in their flock to attack specific humans who have harassed fellow crows in the past.

Scientists call it “social learning,” and it essentially means monkey see, monkey do, an adage that turns out to apply to many animals beyond just primates. Now, a study of Australia’s sulfur-crested cockatoos shows that the birds employ social learning to understand whether unfamiliar foods are safe to eat.

In more forested areas of the cockatoos’ native range in Australia, New Guinea, and Indonesia, these mohawked parrots eat plant roots, seeds, fruits and insect larvae. But the birds have learned to thrive in urban environments. “They’re everywhere in Sydney,” said Julia Penndorf, a behavioral ecologist and lead author of the study in PLOS Biology, who encountered the birds as a postdoctoral researcher at the Australian National University in Canberra.

In urban areas, the birds have expanded their diets to include nonnative plants and nuts, including almonds and sunflower seeds people offer to them, and they can be seen prying the lids off garbage bins in order to forage.

“The big issue with urban birds is, they kind of eat everything,” Dr. Penndorf, who now works at the University of Exeter, said. This expanded diet is high-risk, high-reward: the birds have more options for food, but there’s always a chance that strange new snacks might be poisonous.

Dr. Penndorf and her colleagues wondered if the highly intelligent cockatoos might owe their varied urban diets, and, in turn, their takeover of the city of Sydney, to social learning.

The rest of the article discusses an ingenious experiment by which Penndorf and her colleagues verified that cockatoos could learn what foods to eat from one another,

Staging increases the value of homes on the market and they sell faster

Bhattacharya, Puja and Li, Sherry Xin and Wang, Yu and Wu, Cedric and Zheng, Xiang, Visual Cues and Valuation: Evidence from the Housing Market (December 07, 2025). Available at SSRN: https://ssrn.com/abstract=5880062 or http://dx.doi.org/10.2139/ssrn.5880062

Abstract: We examine the economic impact of non-consumable visual cues through home staging on high-stakes housing transactions. Using hand-collected listing photos for 15,777 transactions and a machine-learning algorithm to detect furniture, we provide the first large-scale evidence that staged homes sell for roughly 10% more and one week faster than comparable homes without furniture. Our pre-registered online experiment establishes causality and uncovers mechanisms. We find that furniture clarifies spatial use, while decor enhances emotional attachment, jointly driving the higher willingness-to-pay. These findings demonstrate how visual cues impact high-stakes decisions and systematically shape valuations in the largest asset market for households.

The opening paragraphs:

Behavioral economics has advanced significantly in demonstrating how cognitive, psy- chological, and emotional factors systematically influence economic decision-making (Ra- bin (1998), Heath et al. (1999), Rabin and Schrag (1999), Kahneman (2003), Gneezy et al. (2014), Chang et al. (2016), and Hirshleifer (2020)). Yet, many foundational mod- els of consumer choice still presume a high degree of rationality in high-stakes environ- ments, where the sheer magnitude of the transaction, in theory, should discipline behavior and mitigate the impact of biases. This paper examines the economic impact of non- consumable visual cues through staging, a common practice in the U.S. housing market, on high-stakes housing transactions.

House staging is the practice of furnishing and decorating a property for sale to create visual cues that help potential buyers imagine themselves living in the space. Importantly, the furniture and decor are classified as personal property, which consists of movable items that are typically not included in the sale unless explicitly stated in the contract. Standard asset pricing theory dictates that the value of a residential asset is a function of its fundamental hedonic characteristics (e.g., location, size, school quality, and structural condition), discounted by the user cost of capital (Sirmans et al., 2005; Poterba, 1984; Himmelberg et al., 2005). Rational agents should not price movable, non-consumable personal property (furniture and decor) into the value of the fixed asset, especially when such items convey no transactional value. However, the popularity of home staging, a common industry practice costly to the sellers or their agents, suggests a possible discon- nect between theory and behavior. This disconnect gives rise to fascinating and largely unanswered economic puzzles (Yun et al., 2021): Do homebuyers pay for things that they know they cannot consume? If so, what is the magnitude of this staging premium? In addition, what underlying mechanisms do these visual cues activate that lead to a higher willingness to pay? This paper aims to answer these questions by exploring homebuyer behavior in the largest asset market for most households.

Later in the introduction, and reporting on a specific experiment within the larger study:

(1) Staging changes how potential buyers perceive the physical dimensions of the asset itself. Participants who viewed a staged room perceived it to be significantly wider and larger in total area (by an estimated 15-20 square feet, about 10% of the actual size) than the identical but empty room. (2) Staging reduces cognitive burdens by providing a practical demonstration of how a space can be used. Participants in both the Furniture Only and Furniture & Decor treatments were significantly more likely to agree that the “room layout is practical and usable” compared to those who saw an empty home. (3) Furniture alone is insufficient to trigger the full behavioral effect. While adding furniture made a home feel “warm and inviting” and “well-maintained,” it had no measurable impact on whether participants could “imagine myself living in this home” or whether they were more likely to “schedule a visit.” Only the addition of decor (e.g., plants, lamps, table settings) in the Furniture & Decor treatment produced a significant increase in these key measures of emotional connection and behavioral intent.

I wonder, do homes associated with famous people or celebrities sell for higher prices than equivalent homes without such associations?

H/t Tyler Cowen

Civil War Among Chimpanzees

Carl Zimmer, These Chimps Began the Bloodiest ‘War’ on Record. No One Knows Why. NYTimes, Apr. 9, 2026.

Near the end:

And the researchers are still trying to figure out what set off the conflict in the first place. “All of a sudden, yesterday’s friend becomes today’s foe,” said Dr. Mitani.

Within any group of chimpanzees, violence will flare from time to time — when apes converge on a tree full of fruit, for example, or when lower-ranked males vie to replace an old alpha male.

But this aggression can be dampened by the friendships that form over years. Some chimpanzees are especially social, jumping between many cliques. “They’re these important social bridges,” Dr. Sandel said.

In 2014, five adult males died, perhaps because of disease. Dr. Sandel speculated that these deaths ripped away some of the bridges that previously held the Ngogo groups together. Low-level conflicts blew up into something akin to civil war.

The Ngogo conflict could offer a glimpse at the kind of violence that might have flared up in our ancient forebears, given that chimpanzees and humans descend from common ancestors that lived about six million years ago.

“These findings tell us indeed that these civil-war-like types of conflicts were possible in the course of human evolution,” said Sylvain Lemoine, a primatologist at the University of Cambridge who was not involved in the study.

The Ngogo chimpanzees show how our ancestors could have gotten dragged into years of lethal fighting without ideology or cultural identity — let alone the language to talk about them.

Instead, shifting social bonds might have been enough to light the fire.

There's more at the link.

Natural intelligence Revisited: The Five-Fold Way, A Working Paper

New working paper. Title above, links, abstract, contents, and introduction below:

Academia.edu: https://www.academia.edu/165530520/Natural_intelligence_Revisited_The_Five_Fold_Way_A_Working_Paper
SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6529398
ResearchGate: https://www.researchgate.net/publication/403545810_Natural_intelligence_Revisited_The_Five-Fold_Way_A_Working_Paper

Abstract: In 1988 David Hays and I published an article entitled, “Principles and Development of Natural Intelligence.” The principles were computational: 1) modal, 2) diagonalization, 3) decision, 4) finitization, and 5) indexing. We made our argument in terms of the principles themselves along with behavioral, neuroanatomical, ontogenetic and phylogenetic evidence. The literature in all those fields has changed enormously in the four decades since we finished writing. To get a read on how our computational proposals have fared, I asked ChatGPT 5.2 to evaluate it against the current literature. Its verdict: the “empirical specifics have aged unevenly but [the] central agenda has held up surprisingly well.” This article presents the five principles, in brief, followed by ChatGPT’s full evaluation. Also, I have asked ChatGPT to evaluate a section on control structure, “Vehicularization,” that we cut from the original argument. Verdict: “vehicularization points toward a more complete account of natural intelligence—one in which cognition is understood as coordinated navigation across multiple, nested domains.”

CONTENTS

Introduction: Constraining Theories and Models 2
The Five Principles of Natural Intelligence 4
Revisiting The Principles and Development of Natural Intelligence (1988) 6
Vehicularization 11

Introduction: Constraining Theories and Models

Sometime in 1985 David Hays and I decided it was time to set forth our views on the nature of, well, of natural intelligence. First, however, we had to discover what those views were. We sat down to a table in my parents’ kitchen and made a list of the various things we wanted to include in this article, experimental findings, observations, models, mathematical ideas, and so forth, from psychology, neuroscience, linguistics, evolutionary biology, and computing. We just wrote them down in no particular order, probably on unlined paper. When we’d accumulated about 50 or so items we decided to gather them into a small number of groups of items that seemed to belong together. We arrived at five groups.

Just how we proceeded from that point I don’t recall. Perhaps we sat around discussing the various groups and came up with a principle for each group. Maybe we had to do some writing first. I don’t recall. But however we actually proceeded, we end with an article we called, “Principles and Development of Natural Intelligence.” We intended “natural” to contrast with “artificial” but didn’t say that anywhere in the article. When we’d finished a draft, days or weeks later, Hays said that it felt like fundamental work; he used the term “bedrock.” I agreed. It took three years to get it published, in a now defunct interdisciplinary journal, The Journal of Social and Biological Structures.

I’ve included the abstract of that article, along with a bit of the introduction, below, as the first part of this document: “Five Principles of Natural Intelligence.” That should give you an idea of the framework without all the expository elaboration, argumentation, and support.

ChatGPT reviews

That was four decades ago. I have continued to like what we did. But has any of it held up? How could it? By now the literature we referenced was 40 years out of date? And, yet, it wasn’t about that literature, it was about how we put it together. Is there anything left of that framework?

About a week ago I asked ChatGPT 5.2 to evaluate it. Here’s the prompt I gave it:

I want you to evaluate a paper that David Hays and I published back in 1988: The Principles and Development of Natural Intelligence. Give me a third-party assessment from the standpoint of what we now know, not a summary and not a defensive reconstruction. Be explicit about where it now looks prescient, where it looks historically bounded, and where it still poses unresolved challenges.

Here’s the first line of ChatGPT’s conclusion:

As of 2026, I would not describe the paper as a correct theory of mind. I would describe it as an ambitious synthetic manifesto whose empirical specifics have aged unevenly but whose central agenda has held up surprisingly well.

I’ll take it. Could I take issue with some of ChatGPT’s criticisms? Sure. But that assessment pinpoints the single most important facet of the essay, its synthetic nature. To push back on ChatGPT’s reservations would blunt that point.

After making various comments on an ad hoc basis, ChatGPT offered to write a “more formal review-essay.” I’ve included that as the second part of this document: “Revisiting Principles and Development of Natural Intelligence (1988).” There’s more.

The article we had submitted was long. The editors asked us to cut what we could, but made no particular suggestions. Our single largest cut was a section on vehicularization – that’s what we called it. It was about control. While it was about the same general line of thinking, it didn’t seem to fit. The bulk of the article was about the five principles and how the developed, both phylogenetically and ontogenetically (in humans). Vehicularization was about how they operated in concert. I have included that as a third section followed by comments by ChatGPT as the fourth and final section.

The Five-Fold Way

Let’s return to ChatGPT’s characterization of the original article as a “synthetic manifesto.” From its conclusion:

It is best understood as an architectural proposal about the structure of intelligence. Many of its mechanistic claims have aged poorly, particularly its neuroanatomical simplifications and evolutionary staging. Yet several of its central insights—heterogeneous cognitive regimes, the integration of regulation and cognition, the interaction between holistic and symbolic processing, and the role of language in cognitive control—remain highly relevant.

Though we didn’t use such phrases when we wrote the article, that’s certainly what Hays and I thought we were doing.

The diagram to the left indicates the range of material we brought to bear in our thinking about natural intelligence. The labels on the vertices of the pentangle are from my 1978 Ph. D. thesis in the English Department at SUNY Buffalo, “Cognitive Science and Literary Theory.” There I somewhat idiosyncratically defined cognitive science as investigating a five-way correspondence between behavior, computation, computational geometry (neuroanatomy), phylogeny, and ontogeny. That dissertation was mostly about behavior, in the form of literary texts, and computation, in the form of cognitive networks semantics, though touched on the others here and there. But “Principles and Development of Natural Intelligence” covered all five. The principles themselves were computational in nature and we made our primary arguments in terms of their ability to account for behavior, but we also suggested which brain regions supported them and related them to the phylogeny of animal behavior and the ontogeny of human development.

By the usual standards of the academy, that range was wide, crazy wide. We certainly weren’t expert across that range; no one could be. However, when I look back in retrospect, it is clear that we weren’t attempting some grand synthesis over that range. We were doing something quite different, something that was and remains fundamentally conservative. We had some high-level ideas about the computational structure of the mind and we wanted to place constraints on those ideas by expanding the range of evidence that could be brought to bear on them. While it is necessary that those ideas account for observed behavior, that alone is not sufficient. The model implied by those ideas must be implemented somewhere in the brain and must be consistent with developmental evidence both from phylogeny, our evolutionary history, and ontogeny, child development. THAT was the central agenda that, in ChatGPT’s estimation, has held up well.

Listeners Systematically Integrate Hierarchical Tonal Context of Music

Cassano-Coleman, R. Y., Izen, S. C., & Piazza, E. A. (2025). Listeners Systematically Integrate Hierarchical Tonal Context, Regardless of Musical Training. Psychological Science, 37(1), 3-17. https://doi.org/10.1177/09567976251400331 (Original work published 2026)

Abstract: Context drives our interpretations of music as surprising, frightening, or awe-inspiring. However, it remains unclear how formal musical training affects our ability to hierarchically integrate complex tonal information to efficiently predict, remember, and segment music. We scrambled naturalistic music at multiple timescales to manipulate coherent tonal context while controlling for multiple acoustic cues. Memory (Experiment 1; n = 108, age range = 19–41 years) and prediction (Experiment 2; n = 108, age range = 20–41 years) improved with more intact context for both musicians and nonmusicians. Listeners’ event boundaries were influenced by the amount of tonal context but also reflected nested phrase structure, and musicians were more sensitive to longer-timescale “hyperphrase” structure (Experiment 3; n = 95, age range = 20–42 years) and could better identify the amount of scrambling (Experiment 4; n = 108, age range = 19–41 years). These results indicate that listeners integrate tonal context across complex phrases to efficiently encode, predict, and segment naturalistic music and that in general, training has surprisingly little impact on this integration.

For an informal discussion of this article: We All Speak Music: The Brain’s Constant Search for Harmony, Neuroscience News, February 20, 2026.

Puttering around as a behavioral mode

Over the weekend I got to thinking about the activity of puttering around. I wondered whether or not there has been any formal study about. So I put the following prompt to ChatGPT 5.2:

I’ve talked a lot about DMF (default mode network) and mind wandering in connection with creativity. It seems to me that puttering around is in the same ballpark. I’m not thinking and pondering, but I’m doing stuff, rather casually and in no forced order. Just as things come. It seems I like to putter early in the morning, on Friday afternoon, and over the weekend. Thus I just spent two days puttering around.

Since I’ve not had a “day job” in years, the distinction between weekdays and weekends is not forced on me. Yet it remains with me. It structured by time for most of the year in primary and secondary school. A bit less so in undergraduate and graduate school, but still operative. It also functioned when I had a faculty position at RPI (1978-1985) and at those times later on when I had one of those day jobs. So, it’s become deeply internalized though no longer externally imposed.

Its response took the form of numbered headings, subheadings, and bulleted lists. I find that sometimes useful, sometimes annoying. Since I wanted to write it up in a blog post I asked ChatGPT to express that material in the form of expository prose. I’ve put its response below the asterisks.

* * * * *

I’ve been thinking about puttering—not thinking hard, not resting, but doing things casually and without a forced order. It feels important, especially in connection with creativity, but I don’t know of any literature that treats it directly. What follows is an attempt to make sense of that intuition.

Cognitive science has devoted enormous attention to the default mode network (DMN)—a set of brain regions that become active when we are not engaged in externally directed tasks. The DMN is associated with mind wandering, autobiographical memory, imaginative simulation, and creative recombination. It is usually contrasted with task-positive networks that dominate during focused, goal-directed work.

But this framing leaves out a familiar and, I suspect, crucial mode of everyday cognition: puttering around.

Puttering is not rest, and it is not deliberate thinking. It is also not task-oriented in any strong sense. When I am puttering, I am doing things—moving objects, skimming texts, opening files, following small curiosities—but without urgency, hierarchy, or a clear endpoint. Attention shifts as affordances present themselves. One thing leads to another, but nothing insists.

This matters because puttering is not simply mind wandering in the absence of action. It is mind wandering coupled to low-stakes activity. That coupling provides just enough structure to keep cognition anchored in the world, while leaving associative processes free to roam. In that sense, puttering is an enacted version of default-mode cognition.

Most DMN research is organized around a sharp contrast: rest versus task. Subjects lie still in scanners or perform narrowly defined activities. What rarely gets studied is the large middle ground—self-paced, low-demand activity where executive control is attenuated but not absent. Yet it is precisely in this middle ground that many creative insights seem to arise.

One way to think about puttering is as a form of foraging. Cognitive scientists have developed mathematical models of information foraging and semantic search, showing how minds move through memory spaces under conditions of weak constraint. But these models are typically abstract and disembodied. Puttering, by contrast, is foraging with hands, eyes, and objects. It is exploratory behavior distributed across mind and environment.

Embodied and enactive theories of cognition come closer to capturing this phenomenon. They emphasize that cognition is not confined to the head but unfolds through interaction with the world. Still, even here, the focus is often on skilled action or purposeful engagement. Puttering remains oddly invisible: too mundane, too informal, too unproductive by conventional standards.

Creativity research acknowledges something similar under the heading of incubation. We are told that stepping away from a problem can allow unconscious processes to reorganize it. But incubation is usually framed as a gap between periods of work, not as a mode of activity in its own right. Puttering is active incubation—ongoing, embodied, and temporally permissive.

Time matters here. Even though I no longer have a “day job,” I notice that puttering reliably emerges at certain times: early in the morning, Friday afternoons, weekends. These temporal zones were once imposed externally—by school schedules, academic calendars, workweeks—but they have become internalized. They now function as cognitive affordances. Certain kinds of thinking feel licensed in those periods, while others feel out of place.

That licensing effect is important. Puttering lowers internal monitoring and performance pressure. It delays premature optimization. It allows partially formed ideas to coexist without being forced into coherence too soon. In more technical terms, it increases associative bandwidth while keeping executive control at a minimum viable level. For someone with a large and densely connected memory base, that is a powerful configuration.

Modern work culture tends to suppress puttering. It valorizes focus, efficiency, and measurable output, leaving little room for loosely structured activity that may not immediately “pay off.” Ironically, many contemporary AI systems explicitly reintroduce formal analogues of puttering: stochastic sampling, random walks, background exploration, temperature-controlled search. We design machines to do what we often deny ourselves permission to do.

There is, as far as I know, no direct scientific literature on puttering around. Instead, it falls through the cracks between DMN research, embodied cognition, creativity studies, and ecological psychology. Yet that absence is revealing. Puttering occupies a cognitive regime that our current conceptual frameworks do not handle well: neither rest nor work, neither task nor distraction, but something quietly generative in between.

If that is right, then puttering is not a failure of discipline or a lapse in productivity. It is a mode of cognition in its own right—one that deserves recognition, protection, and perhaps eventually, study. 

* * * * *

Note, however, that I did not explicitly ask it to write as my voice. As you can see, though, that's what it did. Oh well.

Serendipity and the Structure of Discovery: What Accidents Reveal About Human Creativity

 

An essay written by Claude in response to a long prompt I took from a ChatGPT discussion of serendipitous discovery.

 

Introduction

When Alexander Fleming returned from vacation in September 1928 to find his bacterial cultures contaminated with mold, he faced a choice that any laboratory researcher would recognize: discard the ruined plates and start fresh, or pause to ask why a clear zone had formed around the contamination where bacteria refused to grow. Fleming paused. That moment of curiosity about a laboratory accident eventually led to penicillin and transformed modern medicine. But what made Fleming pause? What cognitive structure allowed him to recognize meaning in what others would have seen as mere mess?

The phenomenon we call serendipity—the accidental discovery of something valuable while looking for something else—offers a unique window into the nature of human creativity. These moments reveal not just how discoveries happen, but what kind of minds can make them happen. As we develop increasingly sophisticated artificial intelligence systems, the question becomes more pressing: can machines be serendipitous? Or does serendipity require something that current computational approaches cannot replicate?

The Anatomy of Accident

The history of science and technology is studded with serendipitous discoveries, each following a recognizable pattern. In 1895, Wilhelm Röntgen was experimenting with cathode rays when he noticed a fluorescent screen glowing across the room, well away from his apparatus and supposedly shielded from it. Rather than dismissing this peripheral observation as irrelevant to his experimental target, he investigated. X-rays emerged not from his intended line of inquiry but from his willingness to follow an anomaly.

Percy Spencer's discovery of the microwave oven in 1945 followed a similar trajectory. Working on radar equipment at Raytheon, Spencer noticed a chocolate bar melting in his pocket. This bodily experience—the unexpected warmth, the mess—could easily have been dismissed as an irritating side effect of standing near the magnetron. Instead, Spencer recognized it as a signal worth pursuing. He experimented with popcorn kernels, then eggs, systematically exploring what this accident might reveal about microwave radiation's interaction with food.

Charles Goodyear's 1839 discovery of vulcanized rubber came from dropping a rubber-sulfur mixture onto a hot stove. The accident occurred under extreme conditions he had not planned to test. The mixture didn't become sticky and useless as expected; it charred slightly but became elastic and durable. Goodyear recognized immediately that this accidental phase change revealed something fundamental about rubber's material properties.

In the chemical industry, Constantin Fahlberg's 1879 discovery of saccharin followed yet another pattern. After a day working with coal tar derivatives, Fahlberg noticed his hands tasted sweet. Rather than simply washing them and moving on, he traced the taste back to his laboratory bench, systematically testing compounds until he isolated saccharin. A bodily sensation—taste—became the signal that something interesting had occurred.

These canonical examples share a structure: an accident occurs, someone notices it, and rather than treating it as noise or contamination, they investigate. But this description conceals the deeper mystery. Accidents happen constantly in laboratories and workshops. Most are indeed noise. Most contaminated cultures should be discarded. Most peripheral observations are irrelevant. What distinguishes the accidents that matter?

The Prepared Mind and Structure

Louis Pasteur famously observed that "chance favors only the prepared mind." But what constitutes preparation? It cannot simply mean knowing what you're looking for, since serendipity precisely involves finding something you weren't seeking. The preparation must be of a different kind—not a prepared answer but a prepared capacity to ask new questions.

Consider the more recent case of Gila monster venom leading to GLP-1 drugs like Ozempic and Wegovy. A researcher collected the venom decades ago, not with any specific therapeutic application in mind but out of what might be called biological curiosity—a sense that unusual biochemical systems might someday prove valuable. The venom sat in freezers for years, creating what we might call "latent option value." Only much later, when researchers were investigating glucose metabolism, did a peptide from that venom reveal its ability to regulate blood sugar. The initial collector had no hypothesis about diabetes drugs. They were simply gathering interesting biological materials on the principle that interesting systems often prove useful.

This pattern of open-ended harvesting without specific goals appears throughout the history of discovery. It suggests that preparation involves not just deep knowledge of a field but a particular cognitive stance toward the world—one that treats anomalies as potentially meaningful rather than merely aberrant, that maintains curiosity even when immediate applications are unclear, that builds resources of knowledge "just in case" rather than only "just in time."

We might think of each researcher as carrying a unique "snapshot" of the world's structure built up through years of experience, false starts, and accumulated hunches about how their domain works. When an accident occurs, it encounters not a blank slate but this richly structured mental model. Fleming noticed the bacterial clearing because decades of bacteriological work had taught him to pay attention to bacterial behavior. Spencer recognized the melted chocolate as meaningful because his work with radar had given him intuitions about electromagnetic radiation's effects. Fahlberg traced the sweet taste back to his bench because his training had taught him to attend to unexpected chemical properties.

The structure in a researcher's mind is necessarily recursive—it includes models not just of phenomena but of how phenomena reveal themselves, how observations connect to explanations, how accidents might signal deeper patterns. When Röntgen saw the unexpected glow, his response emerged from an understanding not just of cathode rays but of how scientific instruments can reveal hidden aspects of nature. The accident struck a prepared mind that was structured to wonder about such revelations.

Opportunity Cost and the Economics of Curiosity

Yet preparation alone cannot explain serendipity. Researchers often ignore anomalies not because they fail to notice them but because investigating would be too costly. Here the economics of exploration becomes crucial.

The 3M chemist Spencer Silver was trying to create a strong adhesive in 1968 but instead produced a weak, reusable one—initially a failure. The adhesive could have been immediately discarded as not meeting specifications. But 3M's culture of tinkering and the low cost of keeping failed experiments around meant the weak adhesive persisted in the laboratory. Years later, Art Fry was singing in his church choir and growing frustrated with bookmarks that kept falling out of his hymnal. The two problems met: weak adhesive plus temporary bookmark need equals Post-It Notes.

The discovery succeeded not because anyone had a brilliant initial hypothesis but because the cost of keeping a "failed" experiment was low enough that it could persist until finding its proper application. Most hunches and accidents lead nowhere. But if you can keep them around cheaply, occasionally one pays off spectacularly.

The Kellogg brothers' discovery of corn flakes followed a similar pattern. They accidentally left cooked wheat sitting out overnight. The frugal thing was to use it anyway rather than waste it. When they rolled the stale wheat and discovered it flaked, economic constraint—don't waste the wheat—had created the conditions for discovery. If the opportunity cost of wasting wheat had been low, there would be no corn flakes.

Pfizer's development of Viagra illustrates another dimension of this economic logic. The compound sildenafil was being tested as a treatment for angina and hypertension. In clinical trials, it showed modest effects on the intended conditions but remarkable effects on male erectile function. The company could have abandoned the compound as a failure in its intended application. Instead, recognizing that the "side effect" might be more valuable than the original purpose, they pursued an entirely different market. The opportunity cost of investigating this unexpected effect was low—they'd already done much of the safety testing—and the potential payoff was enormous.

In the wine industry, champagne itself emerged from a similar reframing. For centuries, wine that re-fermented in bottles was considered faulty—the bottles might explode, the wine turned fizzy instead of still. But gradually, vintners in the Champagne region recognized that this "flaw" could be valuable. Rather than fighting re-fermentation, they developed techniques to control it. An expensive problem (exploding bottles) became a premium product once someone reframed the accident as an opportunity.

Roy Plunkett's 1938 discovery of Teflon reveals yet another aspect of low opportunity costs. He found that a cylinder of refrigerant gas had mysteriously solidified. The standard procedure would be to discard the cylinder as contaminated or defective. But Plunkett's curiosity was cheap to satisfy—it took only a few minutes to saw open the cylinder and examine the white powder inside. That brief investigation revealed polytetrafluoroethylene, eventually leading to non-stick coatings.

The pattern across these cases is consistent: serendipity thrives when the cost of investigating anomalies is low relative to potential payoffs. When researchers or institutions can afford to keep "failed" experiments, to pursue unexpected effects, to investigate mysteries for their own sake, they create conditions for serendipitous discovery. When every resource must be justified against immediate objectives, serendipity becomes much rarer.