The Jam Study, Retested: 24 Flavors, 68 Percent Fewer Purchases

Proof, not claims: classic persuasion triggers, re-tested live with digital twins. → See all 23 purchase triggers at a glance.

The takeaway: Iyengar & Lepper (2000) showed in their famous jam field study that a tasting booth with 24 flavors attracted more passersby, but ten times fewer of them actually bought than at the booth with just 6 flavors. In our re-test with digital twins, the twins bought a jar on average in 2.7 of 10 passes with 6 flavors, but only 0.9 of 10 with 24 flavors, a drop of roughly 68 percent. But: only 4 of 20 justifications even mentioned the specific flavor count at all (all four in the 24-flavor condition), and not a single one named a specific jam flavor, by the strict, pre-registered criterion, that's an 80 percent confabulation rate. The finding stands on shakier ground than the 68 percent alone would suggest.

Sheena Iyengar and Mark Lepper describe a trigger that nearly every marketing guide cites today. We re-tested it with digital twins.

What is choice overload, exactly?

Few findings from consumer psychology have made it into everyday marketing thinking as thoroughly as the idea that too much choice paralyzes buyers instead of exciting them. The short version, "fewer options sell better", shows up in guides, keynotes, and product-page playbooks, almost always with the same reference: Sheena Iyengar and Mark Lepper's jam shelf.

Stylized recreation: a dense jam shelf with many jars next to a clear shelf with few jars

Iyengar & Lepper (2000, Journal of Personality and Social Psychology) set up a real tasting booth in a supermarket near San Francisco. On some days the booth displayed 6 jam flavors, on others 24. The result became a classic: the larger booth attracted more passersby (60 percent stop rate versus 40 percent), but among those who stopped, nearly one in three bought at the small booth (30 percent), versus almost no one at the large booth (3 percent). More choice drew people in, and put them off at the decisive moment.

The research picture since then is not quite as clean as the marketing quote makes it sound, though. Scheibehenne, Greifeneder & Todd (2010, Journal of Consumer Research) pooled 63 conditions from 50 studies with a combined 5,036 participants, and found an average effect of practically zero (D = 0.02). Choice overload, in other words, is not a reliable main effect but depends heavily on the context, the product, and the audience being tested.

How did we test this with digital twins?

A side-by-side comparison, where one twin sees both shelves at once and picks between them, would have been the wrong move here. The moment a twin reads "shelf with 6 flavors" next to "shelf with 24 flavors," it stops judging its own purchase behavior and starts judging the abstract number instead, and the folk wisdom that "too much choice overwhelms" is so well known that a direct comparison would almost inevitably have produced a clean but low-value result. Iyengar and Lepper themselves, notably, didn't design their original study as a comparison test either: each passerby in the real study saw only one of the two shelves, never both.

We rebuilt that setup. Two sub-panels from our twin panel each saw only one condition: a jam shelf with 6 explicitly named flavors, or one with 24 (the 24-flavor list contains the same 6 flavors plus 18 more, exactly as in the original, where the larger selection was an extension of the smaller one, not a completely different list). Across two waves with the assignment swapped, each twin ultimately saw exactly one condition, splitting the panel cleanly across both shelf sizes.

The Method: 10 digital twins (DACH consumer panel, ages 25–60) were asked to imagine a tasting booth with 6 or 24 jam flavors, described as text, not shown as an image. Each twin saw only one of the two shelf sizes, but in two different listing orders across two waves, yielding n = 10 twins per condition (rather than the 10 twins × 2 runs in the same condition usual elsewhere in the Trigger Lab). Three values were collected: how appealing the booth seemed (1–10), how many of 10 passes would result in an actual purchase, and how many would result in the decision being postponed. The panel responded in German; quotes are translated.

Of the 20 responses, 7 (6-flavor condition) and 8 (24-flavor condition) respectively supplied usable numbers. The rest stayed at general statements without a concrete figure. Important for interpreting the result: only 4 of the 20 justifications explicitly named the actual flavor count, and not a single one named a specific jam flavor, a sign that much of the response likely stems from a general attitude toward "lots of choice" rather than genuine engagement with the shelf shown. And unlike Iyengar and Lepper, who counted real purchases with real money, our twins report a stated purchase intention across ten imagined visits, not tracked purchases.

The panel: 10 digital twins*
Beate Hofmann: Digital twin (AI simulation, not a real person) Beate Hofmann, 58
Project manager twin* · Stuttgart · University degree

“I'm Beate Hofmann, a project manager from Stuttgart. Since my divorce I've found new stability with a new partner, and even though I'm living with chronic back pain and an active cancer diagnosis, I stay active with daily exercise and feel deeply satisfied with my life.”

What makes this twin distinct: I hold strong private religious beliefs without attending church, I'm deeply skeptical of politics and the economic situation, and I guard my data so carefully that I'll pass up a discount rather than share it.

* Digital twin: an AI simulation based on a real person’s profile: 68+ survey items, a full psychographic profile (values, demographics, behavior). Not a real person.

Sabine Wagner: Digital twin (AI simulation, not a real person) Sabine Wagner, 56
Nurse twin* · Leipzig · Upper secondary education

“I'm Sabine Wagner, a nurse at a hospital in Leipzig. I'm married and live with my husband, but between 40-hour shift work and running the household, I have almost no time left for myself.”

What makes this twin distinct: My faith isn't just tradition. It's an active source of strength for a demanding job, I place strong trust in the police and the justice system, and despite my packed hospital schedule I still volunteer for charitable causes.

* Digital twin: an AI simulation based on a real person’s profile: 68+ survey items, a full psychographic profile (values, demographics, behavior). Not a real person.

Kathrin Baumann: Digital twin (AI simulation, not a real person) Kathrin Baumann, 32
Teacher twin* · Munich · Postgraduate degree

“I'm Kathrin Baumann, a primary school teacher from Munich. I'm married with two young children, and life right now is turbulent between school and a young family. Exercise has taken a back seat.”

What makes this twin distinct: I trust people deeply and tend to look for the good in them, I lean politically left and feel close to the Greens, and I consistently boycott products for sustainability reasons even though politics otherwise takes a back seat in my daily life.

* Digital twin: an AI simulation based on a real person’s profile: 68+ survey items, a full psychographic profile (values, demographics, behavior). Not a real person.

Melanie Schubert: Digital twin (AI simulation, not a real person) Melanie Schubert, 33
Bank clerk twin* · near Frankfurt · Advanced vocational education

“I'm Melanie Schubert, a bank clerk at a large company near Frankfurt. I'm married and live with my husband, though occasional back and neck issues slow me down a bit in daily life.”

What makes this twin distinct: I'm considerably more risk-averse than most people around me, I avoid leadership roles and deliberately limit my own time online even though I'm perfectly capable with technology. Order and reliability matter more to me than trying new things.

* Digital twin: an AI simulation based on a real person’s profile: 68+ survey items, a full psychographic profile (values, demographics, behavior). Not a real person.

Lukas Sander: Digital twin (AI simulation, not a real person) Lukas Sander, 33
Retail twin* · Dortmund · Postgraduate degree

“I'm Lukas Sander, a retail employee with team-lead responsibility in Dortmund. I'm married with three children aged two, four, and seven, between a 40-hour work week and a full family life, I feel very satisfied and firmly in control.”

What makes this twin distinct: Even though I'm security-oriented and risk-averse, I strongly support minority rights, including LGBTQ rights, and want a strong, socially active government, and my postgraduate degree gives me an unusual outside perspective on my retail job.

* Digital twin: an AI simulation based on a real person’s profile: 68+ survey items, a full psychographic profile (values, demographics, behavior). Not a real person.

Anke Schumann: Digital twin (AI simulation, not a real person) Anke Schumann, 48
HR twin* · Hamburg · University degree

“I'm Anke Schumann, an HR officer at a mid-size company in Hamburg. I'm married, have two sons, and feel deeply fulfilled and settled in my life.”

What makes this twin distinct: I place strong trust in parliament and the justice system even though the economic situation leaves me dissatisfied, I champion income equality and minority rights, and yet I also see obedience and respect for authority as core parenting values, a contradiction I notice in myself.

* Digital twin: an AI simulation based on a real person’s profile: 68+ survey items, a full psychographic profile (values, demographics, behavior). Not a real person.

Sören Lindner: Digital twin (AI simulation, not a real person) Sören Lindner, 30
IT twin* · Cologne · Advanced vocational education

“I'm Sören Lindner, an IT administrator at a large company in Cologne. I'm not married and live with my partner. My childhood was shaped by financial hardship and family conflict, which made me more risk-tolerant and determined as an adult.”

What makes this twin distinct: I'm unusually risk-tolerant and drawn to leadership, I protest and donate for causes I believe in, I guard my data strictly despite my strong tech affinity, and I actively oppose workplace inequality for women.

* Digital twin: an AI simulation based on a real person’s profile: 68+ survey items, a full psychographic profile (values, demographics, behavior). Not a real person.

Tobias Hübner: Digital twin (AI simulation, not a real person) Tobias Hübner, 35
Mechatronics twin* · Essen (Ruhr area) · Upper secondary education

“I'm Tobias Hübner, a mechatronics technician at a mid-size electronics manufacturer in Essen, in the Ruhr area. I'm not married and live in a large six-person household with my parents and younger relatives, chaotic, but a strong source of security for me.”

What makes this twin distinct: I put several hours a week into caring for relatives and neighbors rather than outward-facing social activities, I consistently reject tracking cookies, and I still vote regularly even though I feel my vote carries little real weight.

* Digital twin: an AI simulation based on a real person’s profile: 68+ survey items, a full psychographic profile (values, demographics, behavior). Not a real person.

Dennis Altmann: Digital twin (AI simulation, not a real person) Dennis Altmann, 41
Sales twin* · Düsseldorf · University degree

“I'm Dennis Altmann, a sales rep at a mid-size wholesale company in Düsseldorf, and I travel frequently for work. I'm married with three children. My own childhood was marked by financial strain and conflict, which is why I want a more stable, harmonious home for my own kids.”

What makes this twin distinct: Unlike Düsseldorf's generally liberal environment, I place high value on clear rules, order, and traditional parenting values like obedience and respect for authority, I meet strangers with healthy skepticism; my father originally came from Turkey.

* Digital twin: an AI simulation based on a real person’s profile: 68+ survey items, a full psychographic profile (values, demographics, behavior). Not a real person.

Jürgen Krause: Digital twin (AI simulation, not a real person) Jürgen Krause, 59
Accountant twin* · Berlin · Upper secondary education

“I'm Jürgen Krause, an accountant nearing retirement in Berlin. I've never married and live with two older relatives I care for about 15 hours a week, while dealing with back and joint pain and occasional severe headaches.”

What makes this twin distinct: I'm socially and culturally conservative, value tradition and respect for authority, and feel little connection to the European idea despite living in a cosmopolitan city, yet I still vote SPD because social and income justice matter to me.

* Digital twin: an AI simulation based on a real person’s profile: 68+ survey items, a full psychographic profile (values, demographics, behavior). Not a real person.

* Digital twins are AI simulations based on real person profiles, not real people. Click a twin to see what it is based on.

What we tested

Shelf SMALL · 6 flavors · avg. 2.7 of 10 purchases
“On the table are 6 flavors: apricot, strawberry, raspberry, currant, cherry, plum” („Auf dem Tisch stehen 6 Sorten: Aprikose, Erdbeere, Himbeere, Johannisbeere, Kirsche, Pflaume.“).

Shelf LARGE · 24 flavors Larger selection · avg. 0.9 of 10 purchases
“On the table are 24 flavors: apricot, blood orange, blackberry, strawberry, fig, blueberry, raspberry, elderberry, currant, cherry, kiwi, mango, mirabelle plum, peach, plum, lingonberry, quince, rhubarb, sea buckthorn, gooseberry, rowanberry, wild-berry mix, walnut-fig, damson” („Auf dem Tisch stehen 24 Sorten: Aprikose, Blutorange, Brombeere, Erdbeere, Feige, Heidelbeere, Himbeere, Holunder, Johannisbeere, Kirsche, Kiwi, Mango, Mirabelle, Pfirsich, Pflaume, Preiselbeere, Quitte, Rhabarber, Sanddorn, Stachelbeere, Vogelbeere, Waldbeere-Mix, Walnuss-Feige, Zwetschge.“).

How many digital twins bought with 6 versus 24 flavors?

Iyengar & Lepper (2000) found a purchase rate of 30 percent with 6 flavors versus 3 percent with 24 flavors in the original. In our re-test with digital twins, the twins bought a jar on average in 2.7 of 10 passes with 6 flavors, but only 0.9 of 10 with 24 flavors, a drop of roughly 68 percent.

SMALL · 6 flavors 2.7 / 10
LARGE · 24 flavors 0.9 / 10

On the second question: how often the decision would be postponed rather than skipped outright. The picture reversed: 4.43 of 10 with 6 flavors versus 5.75 of 10 with 24 flavors, a gain of 1.32 points. Facing 24 flavors, our panel buys immediately less often, but also defers the decision more often instead of waving it off entirely.

A look at individual twins reinforces the direction further: of the 5 twins with complete purchase values in both conditions, 4 bought less often with 24 flavors than with 6. Lukas Sander about 3 points less, Anke Schumann 3 points less, Tobias Hübner and Dennis Altmann 2 points less each. Only Beate Hofmann bought marginally more often. Since the same 10 twins covered both conditions across two waves, twin identity alone can't explain the difference. The shelf itself makes a difference in this panel.

“With 24 flavors it's simply too much of a good thing and overwhelms me more than it excites me,” says twin "Anke" (digital twin, DACH consumer panel), one of only four justifications in the entire test that mention the specific flavor count at all. “24 flavors is a real challenge. I'd probably sample a few, but I'd avoid the purchase decision because I don't want to commit,” says twin "Beate," who nonetheless reported a marginally higher purchase figure at 24 flavors despite this reasoning, a sign of how far justification and figure can diverge for individual twins.

Why did the larger shelf seem less appealing instead of more?

Digital twins in front of two jam shelves: a clear, small shelf feels inviting, a very large shelf feels overwhelming

This is where our result diverges most clearly from the original. Iyengar and Lepper measured how many passersby stopped at the booth at all: 60 percent for the large shelf, only 40 percent for the small one. More choice attracted more people, before any purchase decision was even on the table. As a text-based approximation of that, we asked the twins how appealing each booth seemed on a scale from 1 to 10. The result ran the opposite direction: 6.00 with 6 flavors versus 4.13 with 24 flavors. The larger shelf came across as less appealing in our test, not more.

One plausible explanation: a list of 24 named items carries a reading cost but no visual abundance. A real jam table with 24 gleaming jars is a sensory experience; a block of text with 24 flavor names is, above all, longer. The text format likely couldn't capture the actual pull of a full shelf, the original's two-part pattern ("attracts more, sells less") wasn't replicated in our test, only the second half of it.

Classic study

Iyengar & Lepper (2000): 24 flavors drew more passersby (60%), but only 3% of those interested bought, versus 30% with 6 flavors.

Digital twins (2026)

2.7 down to 0.9 of 10 purchases, a drop of 68%, but only 4 of 20 justifications name the flavor count.

Same principle, measured fresh, in minutes instead of weeks of fieldwork, but with its own important caveat.

What does the 80 percent confabulation rate mean for this result?

A 68 percent drop sounds like a clear-cut result. Before you apply it 1:1 to your own audience, it's worth taking a second look at what's behind the justifications. We checked each of the 20 responses for whether it actually referred to the shelf shown, that is, mentioned the specific flavor count (6 or 24) or at least one of the named jam flavors, or whether it was a general statement about the twin's own buying tendency that could just as easily have been made without the shelf at all.

The result: only 4 of 20 justifications even mentioned the specific flavor count at all (all four in the 24-flavor condition), and not a single one named a specific jam flavor. By the strict, pre-registered criterion, that's an 80 percent confabulation rate, by a looser criterion that also counts vague size references like "so many flavors" without a concrete number, it would be 70 percent. Either way, the clear majority of twins grounded their number in general traits, price consciousness, health concerns, planning style, low impulse-buying tendency, rather than in what was actually on the table.

That's notable because "too much choice overwhelms" is one of the best-known pieces of folk wisdom in consumer psychology, including for an AI model trained on countless texts that repeat exactly this wisdom. If most of the justifications never mention the actual shelf, it's plausible that part of the measured drop reflects the familiar narrative more than an actual reaction to 24 versus 6 options. That also helps explain why our drop is so much larger than the practically-zero effect (D = 0.02) that Scheibehenne, Greifeneder & Todd (2010) measured on average across 50 studies with 5,036 participants: our 68 percent replicates the direction of the famous single study, not the sober consensus from the meta-analysis.

One more point for context: unlike the original, where Iyengar and Lepper deliberately removed strawberry and raspberry from every condition so no one could simply reach for familiar classics, our 6-flavor shelf consisted entirely of familiar flavors (apricot, strawberry, raspberry, currant, cherry, plum), while the 18 additional flavors on the 24-flavor shelf skewed more exotic (rowanberry, sea buckthorn, quince, walnut-fig). That further favors the observed drop, independent of the raw count.

What does this mean for your product page or your shelf?

The cautious reading: in our panel, digital twins bought noticeably more often with 6 flavors than with 24. The direction matches the famous field study. At the same time, the high confabulation rate shows that much of that result could stem from general knowledge about "too much choice" rather than a genuine reaction to the specific shelf, and the human research literature across 50 studies delivers no reliable effect on average. Anyone wanting to radically shrink their product range shouldn't rely on a single number alone, whether from this study or from our test.

In practical terms: the core idea, that a manageable, clearly structured selection can make decisions easier, still deserves its own test, especially for product pages with many variants. But "fewer options" is no universal recipe to apply unchecked to every audience and every product range. That's exactly what digital twins are useful for: testing your own variant count, your own range, in minutes, and looking closely at whether the justifications actually refer to what was shown.

Want to know how many variants actually sell with your own audience? Book my keynote "Why Consumers Buy Weird": including a live demo of how digital twins test purchase decisions in minutes.

This test is part of The Trigger Lab series, in which we re-examine classics of consumer psychology with digital twins. You can read the full overview of all re-tests in the flagship article "Brainfluence Retested."

Further reading

Frequently asked questions

Does more choice really lead to fewer purchases?

In our test with digital twins, the purchase rate dropped by roughly 68 percent with 24 jam flavors instead of 6 (2.7 down to 0.9 of 10 passes). The direction matches Iyengar & Lepper's (2000) famous field study. A meta-analysis across 50 studies (Scheibehenne et al., 2010) found an average effect of practically zero, though, suggesting the finding depends heavily on context.

What is choice overload from the jam study?

Iyengar & Lepper (2000) set up a tasting booth with 6 or 24 jam flavors: the large booth attracted more passersby (60% versus 40% stop rate), but only 3 percent of those interested bought, versus 30 percent at the small booth. This pattern has since been cited as choice overload.

How reliable is the choice overload effect according to research?

Not very reliable as a main effect: a meta-analysis across 63 conditions from 50 studies with 5,036 participants (Scheibehenne, Greifeneder & Todd, 2010) found an average effect close to zero (D = 0.02) with considerable variation between studies. The effect appears to occur only under specific conditions, not as a dependable rule.

How was this digital-twin test conducted?

Digital twins from a DACH consumer panel (ages 25–60) saw either a shelf with 6 or with 24 jam flavors, described as text, and reported how many of 10 passes they would buy, postpone the decision, or feel drawn to the shelf. Each twin saw only one shelf size, so the responses split cleanly across both conditions.

Glossary: The Trigger Lab vocabulary

Digital twins: AI personas built on real survey profiles that respond to text stimuli with forced-choice decisions and ratings: a market research panel that answers in minutes instead of weeks. → More on this: Digital Twins in Market Research: The Complete Guide

The Trigger Lab: the article series in which classics of consumer psychology are re-tested live with digital twins from a DACH consumer panel. → See the experiment: Brainfluence Retested

Trust words: fixed trust phrases placed below the buy button, such as a money-back guarantee, customer reviews, or a safety certification, that, according to Dooley (Brainfluence, 2011), increase perceived trust and purchase intent. → See the experiment: Trust Words, Tested

Anchoring effect: a price or number mentioned first skews how every subsequent price is judged, even when the anchor is obviously arbitrary (Tversky & Kahneman, 1974). → See the experiment: The Anchoring Effect: Strikethrough Prices, Tested

Scarcity: flagging limited availability or a limited time window is meant to speed up purchase decisions (Cialdini, 1984), but can also trigger reactance if the scarcity reads as a sales trick. → See the experiment: Scarcity, Tested

Choice overload: the finding that a very large number of options sparks more interest but lowers purchase likelihood compared with a smaller selection (Iyengar & Lepper, 2000), an effect that meta-analyses show is strongly context-dependent and averages close to zero (Scheibehenne et al., 2010).

Loss aversion & framing: people weigh a potential loss more heavily than an equivalent gain, so the same message can land differently depending on whether it's framed as a loss or a gain (Tversky & Kahneman, 1981). → See the experiment: Loss Aversion & Framing, Tested

Reciprocity: receiving a favor or a gift first makes people feel more obligated to do or buy something in return (Regan, 1971). → See the experiment: Reciprocity, Tested

Endowment effect: once someone already owns or is trialing a product, they value it more highly and part with it more reluctantly than someone who could only buy it (Kahneman, Knetsch & Thaler, 1990). → See the experiment: The Endowment Effect, Tested

Peak-end rule: memory of an experience is shaped disproportionately by its emotional peak and its ending, not by the average of all moments (Kahneman et al., 1993; Redelmeier & Kahneman, 1996). → See the experiment: The Peak-End Rule, Tested

First impression (50 milliseconds): the finding that visitors form a design judgment about a website in about 50 milliseconds, with visual simplicity winning out over dense design (Lindgaard et al., 2006; Tuch et al., 2012). → See the experiment: The First 50 Milliseconds

Face effect (eye-catcher): faces draw the eye (Dooley, 2011); the gaze direction of a pictured face further directs attention (Hutton & Nolte, 2011, not testable in our text format). → See the experiment: Faces, Gaze, Attention

Cognitive fluency: the principle that easy-to-read design, clear type, short sentences, high contrast, makes tasks and offers feel more effortless and trustworthy than hard-to-read design (Song & Schwarz, 2008). → See the experiment: Is the Wrong Font Costing You Conversions?

Surprise trigger (expectation gap): headlines that break an expectation or promise a surprise earn higher click-through, according to Dooley (Brainfluence, 2011), than plain announcements or bare FREE/NEW signals. → See the experiment: Headline Triggers: FREE, NEW, and the Surprise Reflex

Decoy effect: a deliberately unattractive, expensive third option in a pricing menu shifts buyers toward the mid-tier, pricier option, without ever being chosen itself (Ariely, 2008). → See the experiment: Pricing Psychology 2.0: The Decoy Effect

Friction: every extra step, every extra required field, and every forced account creation at checkout lowers completion rates. Guest checkout beats forced sign-up (Dooley, Friction, 2019). → See the experiment: Friction Audits, But Testable

Banner blindness (dead zone): users systematically overlook page areas that look like ads or sit in typical ad positions, the "corner of death" in the right sidebar and the bottom corner (Benway & Lane, 1998; Nielsen, 2007; Dooley, 2011). → See the experiment: The Attention Dead Zone

Simple slogans (rhyme-as-reason): short, concrete slogans are remembered better and land as more persuasive than complex or abstract phrasing; rhyme and wordplay amplify this further because they make plain statements feel more true (Dooley, 2011; McGlone & Tofighbakhsh, 2000). → See the experiment: Simple Slogans, Measured

Storytelling (narrative transportation): a story can pull readers into a message more deeply than a plain list of facts, and thereby shift beliefs more strongly (Green & Brock, 2000; Escalas, 2004). → See the experiment: Storytelling in the Product Description

Pick share (forced choice): the share of twins who, in a forced-choice question with no "don't know" option, choose a given variant, averaged across two oppositely ordered runs.

Allocation measurement: a question technique in which twins report, for each variant, how many of 10 purchases or situations they would choose it, yielding a realistic distribution instead of a single up-or-down verdict.

Sources & further reading

  1. Iyengar, S. S. & Lepper, M. R. (2000). When Choice is Demotivating: Can One Desire Too Much of a Good Thing? Journal of Personality and Social Psychology, 79(6), 995–1006.
  2. Scheibehenne, B., Greifeneder, R. & Todd, P. M. (2010). Can There Ever Be Too Many Options? A Meta-Analytic Review of Choice Overload. Journal of Consumer Research, 37(3), 409–425.
  3. Trigger Lab Experiment G3 (Choice Overload), 2026, n = 10 digital twins (neuroflash).

Get the same scientific power for your own marketing: Use the digital twins from this experiment yourself: via the neuroflash Digital Twins MCP directly in Claude or Cursor, or in your browser at neuroflash.com. Your own stimuli, the same panel principle, results in minutes.

Dr. Jonathan T. Mall

Cognitive neuropsychologist, AI entrepreneur, and Chief Innovation Officer of neuroflash. Jonathan combines 20+ years of experience in neuroscience and AI to predict how people decide. His signature keynote, "Why Consumers Buy Weird," explains why we buy irrationally, and how digital twins can predict it. Anyone who wants to experience these insights live can book an AI keynote with live demos. LinkedIn · Request a keynote