The Shape of Choice: What Hick’s Law Really Reveals About Decision Time

What happens when your design asks users to make too many choices? In this solo episode, we explore a deceptively simple principle with massive implications for user experience: Hick’s Law.

This law explains why more options mean more decision time—and why that’s not always a good thing.

From cluttered navigation to bloated dropdowns, we’ll break down how cognitive overload quietly slows users down. You'll learn when reducing choices helps, when it hurts, and how to use psychological insights to guide your interface design decisions.

By understanding Hick’s Law, you’ll learn how to make your interface feel faster, smarter, and more intuitive to use.

Transcript

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Have you ever found yourself trying to select one thing, but there were too

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many options and so it took a long time?

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Think of a restaurant menu or the toothbrush aisle at a store.

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A time where you saw something right away that you liked, but it still took

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a long time to make a choice with all those options in front of you.

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Now, even though the reason might feel obvious, more choices equals more time,

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what if there's a more nuanced relationship at play, maybe a mathematical one,

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between how many options are

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sitting in front of you and how long it takes for you to make a choice?

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And how exactly does that calculation change when the options are presented differently?

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What are the other ways available to us where we can bring ease and clarity

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into the process of making a choice, even when we're faced with too many options?

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This episode is about Hick's Law, a key principle of human decision-making.

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We'll explore how it works. We'll even pit a robot's logic against a human's

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logic and see which one wins on efficiency.

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And then we'll dive into how we as design psychologists can create smoother

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user experiences even when the complexity of choices is unavoidable.

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Okay, to start off this episode, imagine that you're participating in an experiment.

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You're standing in a room in front of a table and on the table there are four

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options of fruit that you can eat.

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There's an apple, an orange, a peach and a plum.

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You quickly pick one and the experimenter clicks their stopwatch and the amount

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of time it took you has been recorded.

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Now imagine that instead you have eight options in front of you.

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There are the first four fruits plus berries, bananas, kiwis, and mangoes.

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The decision won't be as quick and if you're considering them all,

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it will take longer by a certain amount of time.

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But why is that? What is it about simply having more choices that slows us down?

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How does the mere number of options contribute to decision-making time?

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Let's break this down with a simple graph that I'll ask you to imagine.

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Picture a graph with two axes.

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On the x-axis, running from left to right, we have the number of choices,

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starting from zero on the left and increasing in the number of choices as you move further right.

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On the y-axis, running from bottom to top, we have decision time,

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faster at the bottom, small amount of time, and slower as you move to the top, more time.

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Now to really wrap our minds around this concept, let's look at three different

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potential relationships we might have between the number of options and decision time.

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Let's look at these potential relationships as three different slopes on our

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graph that show how decision-making time could be related to the number of options.

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We'll start with a simple one, a linear slope, a straight slanted line that

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climbs from the bottom left to the top right.

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This represents a system where each additional option adds one additional unit

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of decision time. To be clear, the straight line is not what Hicks law gives

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us. We'll get to Hicks law a little later.

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So let's apply this function to our four fruits, the apple, orange, peach, and plum.

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We're going to compare algorithms here. So instead of us selecting the fruit

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this time, we're going to use a small robot.

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It's been placed on the table in front of you, and it's been programmed to pick for us.

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So to test our robot, we're going to try to get it to pick the plum.

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We're going to tell the robot, pick the fruit that is smooth and purple.

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So first it rolls up to the apple and asks itself, is this fruit smooth and

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purple? For the apple, that's a no.

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So then the robot scoots back and rolls over to the orange and asks itself,

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is this fruit smooth and purple? And for the orange, that's another no.

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Then it tries the peach, and the answer is no again. And finally,

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it tries the plum, and that's a yes. the robot has found the plum.

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In this example, the robot's approach is the straight line on our graph.

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Each choice adds a constant amount of time.

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So, let's say for easy math that it took one second for the robot to evaluate each fruit.

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For the four fruits together, then it took four seconds.

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But now let's add four more fruits, a strawberry, a blueberry,

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a blackberry, and a raspberry. If the plum is at the end, forcing the robot

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to go through all the options, it's going to take 8 seconds.

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So there's our straight line on the graph. Each fruit adds a constant amount of time.

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So now we're going to do something cool. We're going to pick up the robot,

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press a few buttons, and now the robot is using a different algorithm.

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This new algorithm is for maximizing thoroughness.

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This time, it's going to ask more questions to make absolutely sure of its choice.

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So it starts with the apple. Is it purple? No.

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Is it red? Yes, but it doesn't stop there. It keeps ongoing.

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Is this fruit fuzzy, checking for a peach? Is this fruit lumpy,

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checking for a berry, and so on?

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With each new fruit, the robot adds more questions. In this example,

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each additional fruit adds more factors to the algorithm.

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Since this robot has to consider more factors, decision making time rapidly

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increases with this thorough algorithm.

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When plotted on our graph now, this slope is not a simple straight line. It curves upward.

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With each additional option, the slope gets steeper and steeper.

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And with this algorithm, the act of adding choices sharply punishes decision-making time.

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So what about the human brain?

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What algorithm does the human brain run on?

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Hicks law warns us that more options means longer, slower decision-making times,

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which can imply that humans cannot handle lots of choices. But is that true?

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Are we more or less efficient than the robot, either in the first or second example?

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And what exactly is the relationship between the number of options and our decision-making time?

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So to think about this more holistically, I want to add one more dimension to

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this discussion, and that's information theory.

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So we have bits, which are units to measure information.

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So just as we have grams to measure weight or centimeters to measure length,

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we have bits to measure information.

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So in the examples we're using with our robot, each question that is asked is a bit.

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It's a no or yes, zero or one outcome.

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So when it asked the first four fruits questions, that was four bits.

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And when we added the four more fruits, the berries, that was eight bits in total.

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But when we had the upward curving curve, that was much worse.

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The system got punished, the algorithm got worse, and it was asking more questions

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every time more fruits were added to the set.

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So what is the human algorithm?

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Looking through the lens of information theory, how many bits does it take for

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the human to solve this problem?

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So just as a reminder of an important caveat,

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for simplicity, we are ignoring the actual complexity of this decision-making

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process, and we're only looking at what does the number of options add to decision-making time.

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So we already know that according to Hicks law, the decision-making process

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is punished in terms of time by adding extra choices, but by how much and in what way.

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Okay, so to test this out, we've downloaded into our robot a human-like algorithm.

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Okay, so let's watch it and see what it does.

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The robot rolls up and it takes one look at the fruits and it asks, do I want a berry?

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No. And it cuts the set in half. And then now with the remaining set,

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it asks, do I want to have an ordinary fruit that I see all the time,

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like an apple or an orange? And the answer is no.

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So now the set has been cut in half again. And now it looks at the final two

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and it says, well, I want the juicier fruit.

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So that rules out the peach and it grabs the plum.

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So that was three decisions and it only used three bits for this set of eight options.

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So to be clear, the way the robots used its bits is that when starting with

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eight options, it chopped it immediately in half with one decision.

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And with four options left, it chopped it in half again. And with only two options

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left, it chose a final option.

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So when we plot this on our original graph, we get a logarithmic curve.

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It starts out angled up, but then it flattens out underneath the linear straight line.

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It gets less and less steep as you add more options.

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So in other words, as the penalty increases because you're adding more options,

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it does so by less and less as you're adding those options.

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So it's efficient, unlike what it could have been.

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It could have been that with our very intelligent human minds that we're considering

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so many options and that every time you add options, it goes up and up at a steep slope.

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Or it could have been that it's constant and every time you add an option,

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we're forced to consider that with an equal weight as all of the other options.

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But instead, we are efficient with how we deal with increased options.

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That's the mathematical relationship illustrated by Hicks law.

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So that's interesting, but the

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applied and practical reality is still that more options adds more time.

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But I think it's important that we take some time to understand what Hicks law

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is actually saying and what it implies by our decision-making process.

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So the easy answer for this problem is to remove options whenever you can.

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But what if there's just a lot of options? And what if you can't just remove options?

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You might be in a design scenario where that's not a thing that you can do.

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Plus, removing options should not be the only tool in our toolbox as design psychologists.

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We want to achieve simplicity through good design and not just merely relying

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on the strategy of minimalism.

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In addition to that, sometimes removing options creates other problems,

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like maybe there's really important options that become invisible or unavailable.

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Or maybe the user can get to an option, but it's unclear how to.

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So solutions may vary because design is not a silver bullet,

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but we're going to walk through some specific examples to see what can we do

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when we're faced with a lot of options in an interface.

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Okay, so for these examples, we want to design things in such a way that we

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are somehow reducing the burden of lots of options.

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And so for this discussion, we'll use the example of a restaurant menu.

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Okay, so our first strategy will be chunking and grouping.

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So, on our restaurant menu, instead of just printing out a long list of dishes,

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we're going to group the dishes into categories like appetizers,

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rice dishes, noodle dishes, desserts, and drinks.

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So, this creates a different decision landscape where the user doesn't have

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to face everything at once.

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An earlier decision at the category level can help with deciding what to pay

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attention to and whatnot.

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Or how about the strategy of progressive disclosure?

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This time you're using an interactive digital menu, and you only see the next

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option after making a previous choice.

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So you choose a burger, and because you chose a burger, it asks you,

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what type of patty do you want?

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And then what type of bun do you want? and these options become available only when they're relevant.

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In this system, you'll probably never even be exposed to all the options.

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Another strategy would be pre-filling the interface with good defaults.

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So let's say we've got our interactive menu at the restaurant,

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and the dishes have popular choices that most people choose.

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So when you choose the most popular dish, it might be pre-filled with the most

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popular choices. So let's say you picked pad thai, and then it pre-fills with

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medium spicy and with chicken.

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Or better yet, it fills with your own choices from a previous time.

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Since that's the best guess of how you're going to choose a dish now,

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it might be a good default.

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So pre-filling options reduces the number of choices you have to make.

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And when a person sees each choice, they simply verify that the choice is correct.

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And once the person realizes that the system is filling in with their previous

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options, they might even trust the system enough to not even look at the rest of the options.

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Another strategy would be providing recommendations. What if you're in a new

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restaurant and you're not feeling particularly decisive, and all you know is

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that you're hungry and you see the chef's recommendation at the top,

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and the chef has a great reputation here, so you trust their judgment,

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and you just go with that.

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This is another example where you are shielded from all of the options that

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you could go through and you can bypass them altogether.

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Yet another strategy is choosing good visual hierarchy and creating a visual

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pattern that helps guide the user through looking at the options.

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So imagine that the restaurant menu is laid out in a visually well-organized

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manner, and the prices are on the right,

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and the customer rating is in the same place for every dish,

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and that there are icons that help point out different features of the dish,

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and you find that this helps you focus your attention on the aspects of the

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choice that you actually care about,

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reducing the cognitive load.

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So this is similar to chunking and grouping, except that the layout allows you

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to direct your attention to where it needs to be directed, which is features

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of each dish that is relevant to your decision.

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The last example that I'll give here is a guided flow.

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So imagine that you're being guided through a set of steps.

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And so you're still making choices, but they're split out into steps.

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So let's say you're at a barbecue spot and the menu starts with asking,

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which regional flavor profile do you want?

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Carolina, Memphis, Kansas City, Texas.

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And then you choose your type of protein, and then you choose sauce versus dry

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rub, and then you choose sides.

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So the entire complexity of the task is now rendered down into easy steps that

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on their own are easier to think about.

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So let's recap what we've covered. Hicks law tells us that decision time increases

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as the number of options grows.

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More choices equals more time and by extension effort for a decision.

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So Hicks law reminds us that the number of choices punishes decision making time.

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This implies that too many options and too much complexity hurts us.

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This is true and that's why we want to reduce complexity whenever possible.

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But Hicks law also reveals something unexpected.

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Humans are surprisingly efficient decision makers.

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We don't evaluate every option equally. Instead, we narrow the choices quickly,

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cutting through the complexity faster than a simple algorithm would.

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Even so, too many choices slows us down. So what can we do about it?

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Yes, removing options helps, but good design is more than just removing choices.

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It's about making those choices easier to navigate.

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So when we can't reduce options, we can reduce the effort it takes to decide.

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Here's a quick summary of the strategies designers can use to reduce the time

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it takes people to make decisions.

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One strategy is chunking, breaking choices down into categories.

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An example of this is grouping dishes in a menu. This helps the user focus and decide faster.

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Another strategy is progressive disclosure, showing only what's needed step-by-step

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prevents decision overload.

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It's also useful to have good defaults. In other words, you can pre-fill forms

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with common choices or the user's own previous choices. Another strategy to

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use is recommendations.

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Highlight popular or expert picks. You can also leverage visual hierarchy,

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organize options with clear patterns and guide users' attention,

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and simplify comparisons.

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The last strategy we'll mention here is to use guided flows,

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break decisions down into step-by-step choices.

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In the end, Hicks Law isn't just a caution, it's an insight.

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Decision-making isn't about avoiding complexity, but rather moving through it with clarity.

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The best designs don't overwhelm, they don't oversimplify, they guide and they empower.

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As design psychologists, we hold the ability and even the responsibility to

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shape experiences that honor how people think, not just how systems work.

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Every choice we design is an opportunity to reduce friction,

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build confidence, and create moments of clarity in an otherwise noisy world.

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Because when we get it right, we don't just speed up decisions, we make them better.