Hick’s Law: An S-Tier Behavioral Designer’s Guide

Hand someone a remote control with six buttons and they press the right one without thinking. Hand them the universal remote with fifty-five buttons and watch what happens: the eyes scan, the thumb hovers, the whole body hesitates. The task is identical. Turn up the volume. The only thing that changed is how many other things they could have done instead, and that alone added a measurable delay to a decision that should have been instant.

That delay has a name and, rare for behavioral science, a real equation behind it. Hick’s Law says the time it takes to make a choice grows with the number of options you are choosing between. Not in a straight line, which is the part everyone gets wrong, but along a logarithm. William Edmund Hick measured it in 1952, Ray Hyman nailed the math a year later, and the curve they drew has held up for more than seventy years across keyboards, menus, dashboards, cockpits, and checkout pages none of them could have imagined.

For anyone who designs behavior, this is one of the few laws you can build on. It is quiet, it sits one layer beneath motivation, and it is almost always taxing the exact decision you most want a user to make quickly. Get the choice architecture right and the good option feels obvious. Get it cynical and you can bury a cancel button inside a thicket of equally-weighted choices until people give up trying to leave. Same law, opposite ethics. Here is how it actually works, where the popular version of it is flat wrong, and how it fits inside a real motivation framework instead of floating around as a “less is more” slogan.

Speed Run Notes

• Hick’s Law: the time to choose grows with the number of options, along a logarithm, not a straight line. Two clean options are fast; a wall of them is slow. W. E. Hick, 1952; Ray Hyman, 1953.
• The formula is RT = a + b · log₂(n + 1). The log term is the choice’s information content in bits. Deciding is literally treated as transmitting information.
• It is not a Core Drive. It is a decision-cost law: it governs the cognitive cost of choosing, not whether anyone wants to choose at all. Octalysis supplies the motivation; Hick supplies the price.
• It sits upstream of Fitts’s Law. First you decide which target (Hick), then your hand moves to it (Fitts). Two different frictions, one after the other.
• The famous “reduce choices” advice is a misreading. Hick’s Law says options cost only logarithmically, so the real villain is usually search and decision-anxiety, not raw count. Organize, don’t just amputate.
• The dark-pattern version is choice flooding: drown the user-serving action in a sea of equal options so they stall out, while the company-serving button stays one big obvious click.

Table of Contents

Author Credibility: Yu-kai Chou

Yu-kai Chou created the Octalysis Framework after studying gamification since 2003 — years before the term entered mainstream vocabulary. As a Human-Systems Architect & Behavioral Designer, his framework has been applied by LEGO, Microsoft, Porsche, Coca-Cola, Salesforce, and MrBeast, impacting over 1.5 Billion Users.

Chou has taught the Octalysis methodology at Harvard, Stanford, Yale, Tesla, Google, BCG, and IDEO.

His work has been cited by Harvard, Stanford, MIT, Forbes, Wall Street Journal, Wired, US Department of Energy, NIST, NSF, NCBI, US Department of Education, ClinicalTrials.gov, and Google Scholar — with 3,700+ more academic publications. Explore his books here.

TEDxLausanne · Gamification to Improve Our World17 min

What Is Hick’s Law?

Hick’s Law, sometimes called the Hick-Hyman Law to credit both researchers who built it, describes a single relationship: the more options a person has to choose from, the longer the choice takes. It is a law about decision time, the gap between “I need to pick something” and “I picked.”

The crucial detail, the one that separates people who actually understand the law from people who quote it at design reviews, is the shape of that relationship. Decision time does not double when you double the options. It grows logarithmically. Going from two options to four adds a chunk of time. Going from four to eight adds the same chunk again. Going from eight to sixteen, the same chunk once more. Every doubling of the option set adds a roughly constant delay, which means each additional option matters less than the one before it. The first few choices are expensive; the hundredth is nearly free.

That single property is why so much popular advice built on Hick’s Law is backwards. If you believe choice time rises in a straight line, then cutting options from twenty to ten looks like a huge win. But the law says the difference between ten and twenty options is small, because it is only the difference of one bit of information. The expensive part already happened in the jump from one option to two. We will come back to this, because it is the most useful and most ignored thing about the whole law.

One more boundary before we go further. Hick’s Law in its strict form describes a very specific situation: a person facing a set of known, equally likely alternatives, each mapped to its own response, deciding which one to fire. That is a clean laboratory choice. Most of the choices your users face are messier than that, and the gap between the clean version and the messy reality is exactly where the law gets misapplied. Hold that thought.

The Formula, in Plain English

The equation looks like this:

RT = a + b · log₂(n + 1)

Read it left to right. RT is reaction time, the thing we are predicting. a is a fixed baseline: the time your nervous system spends on everything that is not the decision itself, like registering that a stimulus appeared and physically starting a response. b is a slope, the cost of each additional bit of choice, and it varies by person, practice, and task. n is the number of equally likely alternatives. The log₂ is the heart of it.

Why log base 2? Because Hick borrowed it straight from information theory. In the same years that Claude Shannon was defining information as the reduction of uncertainty, measured in bits, Hick realized a choice is an act of information transmission. Picking one option out of two resolves one bit of uncertainty. Picking one out of four resolves two bits. One out of eight, three bits. The number of bits is the log base 2 of the number of options, and Hick found that reaction time rises in step with the bits, not with the raw option count. The +1 inside the log accounts for the additional possibility of making no response at all, a refinement that makes the curve fit the data at the low end.

The practical translation: the unit of decision cost is not “an option,” it is “a doubling.” Every time you double the number of equally likely choices, you add one bit, and you add one increment of b to the decision time. This is why a well-organized menu of sixty-four items (six doublings, six bits) is not thirty-two times harder than a menu of two. It is six times the per-bit cost on top of the baseline. The math is generous to large sets, as long as the choice stays a clean decision and does not turn into a hunt.

The 1952 Experiments That Started It

The story starts in Cambridge, England, at the Applied Psychology Unit, with a researcher named W. E. Hick sitting in front of a ring of ten lamps. Each lamp could light at random, and Hick’s job was to press the matching key as fast as he could. He ran the experiment on himself and one colleague, varying how many lamps were “live” in a given block, from two up to ten.

What he found, published in 1952 as On the Rate of Gain of Information, was that reaction time climbed in a beautifully regular way as the number of choices grew. Not linearly. Logarithmically. Hick connected this directly to the brand-new information theory of the day and argued that the brain has a roughly constant rate of gaining information, a fixed channel capacity, so transmitting more bits simply takes proportionally more time. He even showed that when he asked himself to trade accuracy for speed, the same information-rate relationship held. The mind, he proposed, processes choices like a communication channel with a stable bandwidth.

A year later, in 1953, the American psychologist Ray Hyman tightened the screws. Hick had mostly varied the raw number of alternatives. Hyman wanted to prove it was really the information that mattered, not the option count specifically, so he manipulated uncertainty three different ways: he changed the number of stimuli, he changed how often each one appeared (making some likely and some rare), and he changed how predictably one stimulus followed another. All three are different levers on the same underlying quantity, the amount of uncertainty the person has to resolve. In every case, reaction time tracked the information in bits, not the surface count of options. Hyman’s paper, Stimulus Information as a Determinant of Reaction Time, is why the law carries both names.

This origin matters for designers because of what it implies. The law was never really about “how many things are on the screen.” It was about how much uncertainty a person has to resolve before acting. Two options that are wildly different in likelihood carry less than one full bit of uncertainty, because you can lean toward the likely one before you even look. Hyman proved that in 1953, and almost every modern misuse of Hick’s Law is a failure to remember it.

What Hick and Hyman Got Right

Three things about this seventy-year-old finding have aged remarkably well, and they are worth stating plainly before we get to the criticisms.

First, the logarithm is real and it is the whole point. The single most counterintuitive claim in the law, that the cost of choice flattens out as options grow, keeps getting confirmed. People who design as if every added option carries the same penalty are working from a mental model the data rejected in 1952. The pain of choosing is front-loaded. The leap from a blank screen to a binary decision is where most of the cognitive cost lives.

Second, information, not count, is the real variable. Hyman’s contribution is the unsung hero here. Because what taxes a decision is uncertainty, anything that reduces uncertainty reduces the cost, even if the number of visible options stays the same. Make one option clearly more likely or more recommended, and you have shaved bits off the decision without removing a single choice. This is the mathematical license behind every “recommended” badge, every pre-selected default, every “most popular” tag. They do not reduce n. They reduce the uncertainty, which is what actually costs time.

Third, it generalizes across input methods because it is about the mind, not the hand. Hick used lamps and keys. The relationship reappears in spoken responses, in eye movements, in button presses, in touchscreen taps. That portability is the signature of a law that describes something fundamental about cognition rather than an artifact of one apparatus. When a principle survives every change of medium for seven decades, it is telling you something true about people.

Where Hick’s Law Falls Apart

Here is where I part ways with most of the design blogs that cite this law. Hick’s Law is real, but the version that circulates in product teams, “more choices always means slower and worse, so cut options ruthlessly,” is closer to folklore than to what Hick and Hyman actually found. The law has hard boundaries, and crossing them is how good designers end up making confident, wrong decisions.

It assumes a clean decision, not a visual hunt

The strict law describes a person choosing among known alternatives, each already mapped to a response. The lamps were learned. Hick knew which key went with which light. That is a decision, and decisions follow the log.

But the first time a user opens an unfamiliar menu, they are not deciding. They are searching. Their eyes scan item by item, reading and rejecting, until they find the thing they want. Visual search through an unfamiliar, unsorted list is roughly linear in the number of items, not logarithmic, because there is no shortcut: you might have to look at all of them. Andy Cockburn, Carl Gutwin, and Saul Greenberg modeled exactly this in their 2007 study of menu performance, and the structure is elegant. A novice pays a linear visual-search cost; an expert who already knows where the item lives pays the logarithmic Hick-Hyman decision cost. Same menu, two completely different time curves, depending on whether the user is hunting or choosing.

So when someone invokes “Hick’s Law” to justify trimming a navigation menu, ask which user they mean. For the first-timer, you are fighting linear search, and the fix is sorting, grouping, and labeling, not deletion. For the veteran, you are in true Hick territory, and the cost was already small.

It assumes options are equally likely, and they almost never are

Hyman’s whole point was that information, not count, drives the time. Real option sets are wildly unequal in probability. On a checkout page, most people pick the same one or two payment methods; the long tail of options is almost never chosen. Those rare options add almost nothing to the decision cost, because a user who wants the common path barely registers them. Counting visible options and plugging that into the formula overstates the real cost badly, because the formula in its simple form assumes a uniform distribution that real interfaces never have.

It does not actually say “less is more”

This is the big one, and a team of researchers led by Wanyu Liu argued it sharply in a 2020 paper provocatively titled How Relevant is Hick’s Law for HCI? Their reading is that Hick’s Law, taken literally, argues against the “less is better” dogma, not for it. Because the cost of options is logarithmic, adding more of them is cheap. A law that says “the tenth option costs almost nothing” is a strange thing to cite when you want to delete options. They go further: the rigid stimulus-response setup Hick used rarely matches real interface tasks at all, where the harder and more interesting questions are about visual search and decision-making, not the bare reaction-time curve.

I do not read this as “Hick’s Law is useless.” I read it as a correction to the cargo-cult version. The law is a precise statement about decision time under specific conditions. It is not a license to amputate features. The honest takeaway is that you should reduce the cost of the right decision, which is sometimes achieved by fewer options and far more often achieved by better structure.

What’s Actually Happening in the Brain

Hick’s own explanation was that the brain is a communication channel with fixed bandwidth, so more bits take more time at a constant rate. That framing is clean, and for sixty years it was the textbook story. Modern cognitive science offers a more mechanical picture that sits comfortably underneath it.

The dominant model of simple choice today is evidence accumulation, often formalized as a drift-diffusion process. When you face a decision, your brain does not flip instantly to an answer. It gathers evidence over time, a noisy signal climbing toward a threshold, and the moment the accumulated evidence for one option crosses that threshold, you commit and act. More options means the evidence has to distinguish between more competing possibilities, which takes more samples, which takes more time. Raise the bar for how sure you need to be and you get slower but more accurate choices; lower it and you get the opposite. That speed-accuracy trade-off is exactly the one Hick observed when he pushed himself to respond faster, and the diffusion model reproduces the logarithmic-style slowing as the option set grows.

There is a second layer worth naming, because it is where the cold math meets the warm reality of users. Pure reaction-time studies strip out emotion. Real choices do not. When a person faces a genuinely consequential set of options, a retirement plan, a health-insurance tier, a checkout with eight upsells, the cost is not only the milliseconds of evidence accumulation. It is the anxiety of possibly choosing wrong. That anxiety is not in Hick’s equation, but it rides on top of the same structure, and for a behavioral designer it is often the larger cost. The lab measures the decision. The product has to survive the dread.

Hick’s Law vs the Other Laws

Hick’s Law is one member of a small family of quantitative laws that govern interaction. Knowing where it sits relative to its cousins keeps you from using the wrong tool.

Hick’s Law vs Fitts’s Law

These two are partners, not rivals, and they fire in sequence. Fitts’s Law predicts the time to move to a target based on its size and distance. Hick’s Law predicts the time to decide which target to move to based on how many options there are. The full path of a single action is: decide, then move. Hick governs the first half, Fitts the second. A designer who only optimizes button size and placement (Fitts) while leaving the user staring at forty equally-weighted choices (Hick) has fixed the cheap half of the problem and ignored the expensive one. The two laws also pull in opposite directions on one decision: making every option a big, easy-to-hit target is good for Fitts but can be bad for Hick, because more equally-prominent targets can mean a harder decision. The art is sequencing, not maximizing either one alone.

Hick’s Law vs Miller’s Law (7±2)

George Miller’s famous 1956 finding, that working memory holds about seven chunks, gets conflated with Hick’s Law constantly, and they are different things. Miller is about memory capacity, how many items you can hold in mind at once. Hick is about decision speed, how long a choice takes. You can see all twenty options on a screen without holding them in memory, so Miller’s limit may not even bind; but you still pay Hick’s decision cost. They overlap in one important place: chunking. Miller observed that we beat the memory limit by grouping items into chunks, and grouping is also the single most powerful way to cut Hick’s cost, because a well-labeled category lets you make one coarse decision (which group?) followed by one fine decision (which item in the group?), turning a flat search into a fast two-step hierarchy. Same trick, two different problems solved.

Hick’s Law vs the Paradox of Choice

This pairing is where most confusion lives. Barry Schwartz’s Paradox of Choice and Sheena Iyengar and Mark Lepper’s 2000 jam study, in which a display of twenty-four jams drew more shoppers but produced far fewer purchases than a display of six, are about choice overload: the satisfaction and conversion damage of too many options. Hick’s Law is narrower and colder. It is only about the milliseconds of decision time, and it says options are cheap. So which is right? Both, because they describe different costs. Hick measures the time to choose, which scales gently. Choice overload measures the emotional and behavioral cost, the anxiety, the regret, the deferral, the walking away, which can spike hard with abundance and which Hick’s clean reaction-time apparatus was never built to capture. When people cite “Hick’s Law” to explain why huge menus hurt conversion, they usually mean choice overload. The honest move is to name both and use the right one.

Hick’s Law in the Real World

Once you can separate decision cost from search cost from emotional cost, real interfaces start to read like x-rays.

Remote controls, menus, and the cluttered dashboard

The fifty-five-button remote is the canonical villain, but the deeper lesson is about what kind of cost it imposes. For the owner who uses it daily, most buttons are learned and the common ones are found by feel: low Hick cost, low search cost. For a guest, every button is an unfamiliar item to scan: high search cost. The fix that actually works is not always fewer buttons. It is making the five buttons people use ninety percent of the time obviously different in size, position, and color, so the common decision collapses to near-zero uncertainty while the rare options recede. That is Hyman’s lesson applied: change the probabilities, not just the count.

E-commerce checkout and the paradox of the upsell

Every added field, payment option, and “would you also like” toggle on a checkout page is a small decision tax, and checkout is the worst possible place to levy it, because the user has already decided to buy and any added friction risks the sale. The strongest checkouts do not just have fewer options; they have a clear default path with a single obvious primary action, and they push the genuine choices (shipping speed, gift wrap) into clearly-labeled, skippable moments. The decision cost is real, but the conversion killer is usually the anxiety spike of a sudden wall of choices appearing between the user and the thing they already wanted.

Onboarding and the first five minutes

The first session in any product is where Hick’s costs are highest, because the user is a pure novice: everything is an unfamiliar item, so they are paying the linear visual-search cost, not the cheap logarithmic one, on every screen. This is the strongest argument for progressive disclosure. Show a tiny, curated set of choices at first, when the user has zero ability to navigate, and reveal depth as they gain the expertise that converts search into fast recognition. Front-load simplicity, back-load power. The same interface that should feel almost empty on day one can feel rich and capable on day thirty, and Hick’s Law tells you why the order matters.

Forms, settings pages, and the toggle wall

A web form is a sequence of decisions wearing the costume of data entry. Every field is a micro-choice, and a long unstructured form is a stack of them with no breathing room, which is why progress bars, step-grouping, and “we’ll fill this in for you” defaults raise completion rates so reliably: they cut both the decision cost and the dread that piles up alongside it. Settings pages are the same problem at higher stakes. A privacy or notification page with three dozen equally-weighted toggles is, technically, a low-per-bit Hick environment, but it is the textbook habitat of choice flooding, because a designer who wants you to leave the data-sharing toggles on can simply make turning them all off a thirty-decision chore. The user-protective move is the mirror image: group the toggles, give them honest defaults, and offer a single “use recommended privacy settings” decision that collapses thirty choices into one. The number of toggles barely changed; the cost of the right decision fell off a cliff.

High-stakes, high-speed decisions

Hick’s Law leaves the comfort of product design entirely when you reach domains where a slow choice is dangerous. Emergency dispatchers, pilots, surgeons, and police are all subject to the same curve, and good training in those fields is partly an exercise in cutting decision cost: drilling responses until they become near-automatic recognition rather than deliberate choice, and pre-organizing options into checklists and protocols so the live decision set is as small and as unequal in likelihood as possible. The aviation checklist is, among other things, a Hick’s Law intervention. It shrinks the live decision to the next single confirmed step.

Applying Hick’s Law with the Octalysis Framework

This is where most articles stop, having told you to count your menu items and cut a few. The more interesting question for a behavioral designer is structural: where does a decision-cost law like this sit relative to a motivation framework, and what does that placement tell you about how to use it?

The Octalysis Framework maps all human motivation onto eight Core Drives, the underlying reasons a person wants to do anything at all. The first thing to say about Hick’s Law is what it is not: it is not a ninth Core Drive. Nobody is motivated by having few options. There is no appetite being fed when a menu is short. Hick’s Law does not make anyone want to act.

Instead, Hick’s Law is a decision-cost law. It governs the cognitive price of choosing, once the desire to choose already exists. Picture two separate axes. One axis is motivation: how badly does the user want to do this? That axis is what the eight Core Drives describe. The other axis is friction: how much does the act cost to perform? Friction itself splits into two layers. There is the motor cost of executing a chosen action, which is where Fitts’s Law lives, and there is the cognitive cost of choosing which action in the first place, which is where Hick lives. Hick sits one step earlier in time than Fitts. You decide, then you move.

Decision cost and motivation trade off against each other

The reason this framing earns its keep is that the two axes trade. A user with roaring motivation will push through a punishing decision. They will read all forty options, compare specs across six tabs, and agonize for ten minutes, because the wanting overpowers the cost. A user with weak motivation abandons the choice the instant it gets even slightly heavy. This is the same logic behind behavior models that multiply motivation by ability: when the decision is cheap, even faint motivation converts; when the decision is expensive, you need overwhelming motivation to compensate. So lowering Hick’s cost lowers the motivation threshold an action needs in order to fire. Cutting decision cost is not a substitute for motivation. It is a multiplier on whatever motivation you already have.

How Hick’s Law touches the Core Drives

Because it is a cost and not a drive, Hick’s Law shows up as a tax or a relief on the motivation the Core Drives generate. Three relationships matter most.

It can serve Core Drive 3 (CD3): Empowerment of Creativity & Feedback. Meaningful choice is one of the most empowering things an interface can offer; the feeling of “I get to decide, and I can see what my decision did” is pure CD3. But that empowerment only survives if the decision cost stays manageable. A creative tool with rich options is empowering; the same tool with those options dumped in one flat, unsorted panel is paralyzing. The craft is to deliver the feeling of abundant choice (CD3) while keeping the cost of any individual decision (Hick) low, through categories, presets, and smart defaults. Done right, the user feels powerful, not taxed.

It can suppress Core Drive 2 (CD2): Development & Accomplishment. CD2 is the drive to make progress and feel competent. When a decision is so heavy that the user stalls before reaching the rewarding action, the accomplishment never arrives. Every step of decision cost between a user and a win is a chance for them to quit before the win. High Hick cost on the path to progress is one of the quietest killers of CD2 momentum, because the user does not rage-quit; they just drift off mid-decision and never come back.

It gets weaponized through Core Drive 8 (CD8): Loss & Avoidance and Core Drive 6 (CD6): Scarcity & Impatience. This is the dark side. A heavy decision set breeds the fear of choosing wrong, which is a CD8 anxiety, and a cynical designer can engineer that paralysis on purpose. Drown the cancel option in a settings page with thirty equally-weighted toggles and most users give up and stay subscribed, not because they decided to, but because the decision cost defeated them. Stack a time-pressured countdown (CD6) on top of an overloaded choice and you get a user who is too rushed to think and too overwhelmed to choose well, which is precisely the state a manipulative funnel wants them in.

The ethical fork: decision-cost asymmetry

Here is the cleanest test for whether a design uses Hick’s Law for the user or against them, and it mirrors the friction-asymmetry test from Fitts’s Law exactly, one layer up. Look at the two paths that matter: the action that serves the user and the action that serves the company. Then ask which one carries the heavier decision cost.

White Hat design lowers the decision cost on the user-serving path. It uses a clear primary action, sensible defaults, organized categories, and recommended options to collapse uncertainty exactly where the user’s interest and the company’s interest align. It adds meaningful CD3 choice where choosing is itself the value, and it removes choice where choosing is just a tax. It may also add a deliberate decision step in front of a destructive or irreversible action, because a confirm dialog is friction working for the user.

Black Hat design inflates the decision cost on the user-serving path. The single giant “Subscribe” button, frictionless and obvious, sits one click away, while “Cancel” is buried inside a maze of equally-weighted choices, confirm-shaming prompts, and downsell offers, each one a fresh decision designed to exhaust you. That deliberate asymmetry, an effortless company-serving choice and an overwhelming user-serving one, is one of the clearest signatures of a dark pattern, and Hick’s Law is the engine underneath it.

Decision cost across the four Experience Phases

Hick’s cost is not constant over a user’s lifetime with your product, and the Octalysis four Experience Phases tell you how it moves. In Discovery and Onboarding, the user is a pure novice paying the steep, linear, visual-search version of the cost, because nothing is yet familiar. This is when you must be most ruthless about limiting live choices. By the Scaffolding and Endgame phases, the experienced user has converted search into recognition and pays only the cheap, logarithmic decision cost, which is when you can safely reveal the depth and density that would have buried them on day one. The interface should grow more option-rich as the user grows more capable, which is the opposite of a fixed design, and Hick’s Law is the reason the sequence matters.

The Practical Playbook

If you design anything where people choose, here is how to use Hick’s Law without falling into the cargo-cult version of it.

1. Cut decision cost, not option count, as the goal. The objective is never “fewer options” for its own sake. It is “make the right decision cheap.” Sometimes that means fewer options; far more often it means better organization. Measure the cost, not the count.
2. Organize before you amputate. Grouping items into labeled categories turns a flat search into a fast two-step decision (which group, then which item). Divide and conquer converts a linear hunt into a logarithmic one, which is usually a bigger win than deleting features your power users rely on.
3. Make one option obviously more likely. A default, a “recommended” badge, a “most popular” tag, or a visually dominant primary button all reduce uncertainty without removing a single choice. Remember Hyman: it is information, not count, that costs time. Bias the probabilities and you shrink the decision.
4. Use progressive disclosure for novices. Show a small curated set when the user is new and paying the steep search cost, then reveal depth as they gain expertise. Front-load simplicity; back-load power.
5. Protect the moment of progress. Audit the path to your product’s core “win” and strip every avoidable decision between the user and that accomplishment. Each one is a chance for them to drift off before the reward (CD2) ever lands.
6. Add friction only where it protects the user. A confirm step before a destructive or irreversible action is decision cost working for the user. Use it deliberately, and never let it leak onto the everyday path.
7. Run the asymmetry test before you ship. Compare the decision cost of the user-serving action against the company-serving one. If “cancel” or “no thanks” is meaningfully harder to decide than “buy” or “subscribe,” you have built a dark pattern, whether you meant to or not. Fix the asymmetry.

The Elephant in the Room

The uncomfortable truth about Hick’s Law is that the version most designers carry in their heads is wrong, and it has been wrong for a long time, and it keeps producing bad decisions dressed up as science.

“Hick’s Law says reduce choices” is repeated in design reviews as if it were settled fact, when the actual law says the opposite about cost: options are logarithmically cheap, so the eleventh option barely matters. The researchers who study this most closely have pointed out, politely, that the law as literally stated argues against the “less is better” slogan it is most often used to justify. When a team rips features out of a product and cites Hick’s Law, they are usually solving a different problem, choice overload, the emotional and behavioral cost that Iyengar and Lepper’s jam study captured, and using the wrong law’s name to do it.

I am not interested in being pedantic for its own sake. The reason this matters is that the two problems have different fixes. If the real issue is decision time (Hick), the fix is organization, defaults, and reducing uncertainty. If the real issue is choice overload (emotional cost), the fix may genuinely be fewer options, or it may be better curation and confidence-building so abundance stops feeling like a threat. Diagnose which cost you are actually paying before you reach for the scalpel. A designer who deletes features to fix what was really an anxiety problem has thrown away capability and not even solved the thing they were worried about. The law is a precision instrument. Used as a slogan, it does real damage.

Hick’s Law Was the Beginning, Not the End

Hick gave us something rare: a clean, quantitative handle on one slice of how people choose. Seventy years later the equation still fits, which is more than most of psychology can say. But the equation describes a single cost, decision time under specific conditions, and a real user choosing in a real product is paying several costs at once. There is the motor cost of moving to the option (Fitts). There is the memory cost of holding options in mind (Miller). There is the search cost of finding the right one when it is unfamiliar (linear, not log). And there is the emotional cost of fearing a wrong choice (CD8), which no reaction-time apparatus was built to measure.

The mistake is to treat Hick’s Law as the whole story of choice. The opportunity is to treat it as one well-measured layer inside a fuller model, sitting beneath motivation and beside the other interaction laws, each describing a different cost on the path from wanting something to doing it. Get all of those costs onto one map, weigh them against the motivation the eight Core Drives generate, and you can finally answer the question Hick could not: not just how long a choice takes, but whether the person will make it at all, and whether you helped them or trapped them in the process. That is the work. The next time someone in a meeting says “Hick’s Law, so we should cut options,” you will know to ask the better question: which cost are we actually paying, and which way is the asymmetry pointing?

Frequently Asked Questions

What is Hick’s Law in simple terms?

Hick’s Law says the time it takes to make a choice grows with the number of options you are choosing between. The key detail is that it grows logarithmically, not in a straight line: each doubling of the options adds roughly the same fixed delay, so the first few choices are expensive and additional ones get progressively cheaper. It was measured by William Edmund Hick in 1952 and refined by Ray Hyman in 1953.

What is the formula for Hick’s Law?

The formula is RT = a + b · log₂(n + 1), where RT is reaction time, a is a fixed baseline for non-decision processing, b is the time cost per bit of choice, and n is the number of equally likely options. The log base 2 comes from information theory: choosing one option out of n resolves log₂(n) bits of uncertainty, and the +1 accounts for the option of making no response.

Does Hick’s Law mean I should always reduce the number of options?

No, and this is the most common misuse. Because the cost of options is logarithmic, adding more of them is cheap, so the law does not actually support “less is always better.” The real goal is to reduce the cost of the right decision, which is usually achieved by organizing options into categories, adding sensible defaults, and highlighting a recommended choice, rather than by deleting features. Reach for deletion only when the real problem is choice overload, which is a separate, more emotional effect.

What is the difference between Hick’s Law and Fitts’s Law?

They describe two different frictions that happen in sequence. Hick’s Law predicts the time to decide which option to pick, based on how many options there are. Fitts’s Law predicts the time to physically move to the chosen target, based on its size and distance. First you decide (Hick), then you move (Fitts). A good interface optimizes both: a clear, low-cost decision followed by an easy-to-hit target.

How is Hick’s Law different from the Paradox of Choice?

Hick’s Law is narrow and about milliseconds: it measures how long a decision takes, and it says options are cheap. The Paradox of Choice, illustrated by Iyengar and Lepper’s jam study, is about choice overload: the satisfaction, regret, and conversion damage that abundance can cause. They are different costs. Hick measures decision time; choice overload measures the emotional and behavioral toll. People often say “Hick’s Law” when they actually mean choice overload.

Why does Hick’s Law use a logarithm?

Because Hick borrowed the idea from information theory. A choice is an act of resolving uncertainty, and uncertainty is measured in bits, which is the log base 2 of the number of equally likely possibilities. Hick found that reaction time tracks the number of bits, not the raw option count, which is why doubling the options (adding one bit) adds a constant increment of time rather than doubling it.

Does Hick’s Law apply to navigation menus and visual search?

Not cleanly. The strict law describes choosing among known options that are already learned. A first-time user scanning an unfamiliar menu is doing visual search, which tends to be linear in the number of items, not logarithmic. Research on menu performance shows novices pay a linear search cost while experts pay the cheaper logarithmic decision cost. So for unfamiliar interfaces, the fix is sorting and grouping, not just cutting items.

How do designers misuse Hick’s Law?

The two big misuses are treating the relationship as linear (so they overvalue trimming options) and citing it to justify “less is more” when the law actually implies options are cheap. The darkest misuse is choice flooding: deliberately surrounding a user-serving action like “cancel” or “unsubscribe” with a wall of equally-weighted options and extra decisions so the user gives up, while the company-serving action stays a single obvious click. That decision-cost asymmetry is a dark pattern.

Who discovered Hick’s Law?

William Edmund Hick published the core finding in 1952 in a paper titled On the Rate of Gain of Information, based on experiments with a ring of lamps and matching keys. Ray Hyman published a complementary study in 1953 showing that it is the information content of the choice, not just the number of options, that determines reaction time. Because both contributions are essential, the relationship is often called the Hick-Hyman Law.

References

• Hick, W. E. (1952). On the rate of gain of information. Quarterly Journal of Experimental Psychology, 4(1), 11–26. doi:10.1080/17470215208416600
• Hyman, R. (1953). Stimulus information as a determinant of reaction time. Journal of Experimental Psychology, 45(3), 188–196. doi:10.1037/h0056940
• Shannon, C. E. (1948). A mathematical theory of communication. Bell System Technical Journal, 27(3), 379–423.
• Hyman / Hick channel-capacity context: Proctor, R. W., & Schneider, D. W. (2018). Hick’s law for choice reaction time: A review. Quarterly Journal of Experimental Psychology, 71(6), 1281–1299. doi:10.1080/17470218.2017.1322622
• Liu, W., Gori, J., Rioul, O., Beaudouin-Lafon, M., & Guiard, Y. (2020). How relevant is Hick’s law for HCI? Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. doi:10.1145/3313831.3376878
• Cockburn, A., Gutwin, C., & Greenberg, S. (2007). A predictive model of menu performance. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 627–636. doi:10.1145/1240624.1240723
• Miller, G. A. (1956). The magical number seven, plus or minus two. Psychological Review, 63(2), 81–97.
• Fitts, P. M. (1954). The information capacity of the human motor system in controlling the amplitude of movement. Journal of Experimental Psychology, 47(6), 381–391.
• 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.
• Schwartz, B. (2004). The Paradox of Choice: Why More Is Less. Harper Perennial.

Related Reading

• Fitts’s Law: An S-Tier Behavioral Designer’s Guide — the partner law that governs the motor cost of reaching the target you just decided on.
• The Octalysis Framework — the eight Core Drives that supply the motivation Hick’s decision cost is taxing.
• The Behavioral Framework Library — every psychological model behind great behavioral design, in one place.
• Status Quo Bias — why a well-chosen default beats Hick’s cost entirely by letting the user skip the decision.
• Books by Yu-kai Chou — go deeper on motivation, friction, and ethical behavioral design.

WOULD YOU LIKE YU-KAI CHOU TO WORK WITH YOUR ORGANIZATION?