You reach the counter and the menu seems to go on forever. Cappuccino or flat white? Decaf or an extra shot? Almond milk or soy? Meanwhile, the line behind you grows while you hesitate.

Most people would call this Hick’s Law in action, but the truth is more interesting. What you are feeling is really two separate effects: a small, well-measured slowdown in how quickly we respond when there are more alternatives (Hick’s Law), and a larger, more contested effect on how we feel and act when faced with too many options (choice overload). This updated guide separates them, shows what the research says today, and turns it into practical advice.

Key Takeaways:

• Hick’s Law describes how choice reaction time grows with the number of equally likely alternatives, and the growth is logarithmic, not linear.
• The time cost of an extra option is small, but the larger business cost of oversized choice sets comes from choice overload, which depends heavily on context.
• Research reviews show choice overload is real in some conditions (complex options, unclear preferences, a goal of minimizing effort) and absent in others. “Fewer is always better” is not supported.
• Recent field experiments (2025) confirm that shrinking a recommendation set or assortment can help, mainly because fewer people start browsing at all when the set is large.
• The strongest design moves are curation, clear categories, relevant personalization, simple forms, and removing visual clutter, not blindly deleting options.

What is Hick’s Law?

Hick’s Law, also called the Hick–Hyman Law, comes from the work of British psychologist William Edmund Hick (1952) and American psychologist Ray Hyman (1953). They studied choice reaction time: how long it takes to respond correctly when one of several possible signals appears, such as pressing the matching key when one of n lights comes on.

The law is usually written as:

RT = a + b · log₂(n)

• RT is reaction time.
• a is the time not linked to the decision (perceiving the signal, moving your hand).
• b is a constant reflecting how much extra processing time each doubling of options adds.
• n is the number of equally likely alternatives. (Hick’s original formulation used n + 1 to account for uncertainty about whether to respond at all.)

A Worked Example

Using an illustrative b of 0.155 seconds, eight equally likely options give 0.155 × log₂(8) = 0.155 × 3 = 0.465 seconds above the baseline. Four options give 0.155 × 2 = 0.31 seconds. Halving the menu saves about 0.155 seconds.

But two conditions follow from this: First, b is not a universal human constant, as it varies with the task, the person and how well the responses fit the signals, so 0.155 is a teaching figure, not a law of nature. Second, a saving of a fraction of a second is tiny, as Hick’s Law matters in design mainly because those fractions repeat across many micro-decisions, and because the same logic helps explain larger frustrations such as abandoned tasks.

What Hick’s Law is, and is not

A 2018 review by Robert Proctor and Darryl Schneider in the Quarterly Journal of Experimental Psychology traces the law’s history and lists known limits: the effect shrinks or changes with stimulus–response compatibility and practice, behaves differently for very large sets, and is affected by sequential dependencies. The review also notes that the law remains influential in interface design.

But, that influence has drawn scrutiny as in a paper at the ACM CHI 2020 conference, Wanyu Liu and colleagues argue that Hick’s Law is often misapplied in human–computer interaction. Their points include that many logarithmic patterns in interface studies come from other processes, such as visual search, that the classic stimulus-response paradigm rarely matches real interface tasks, and that the law does not simply justify the popular principle that “less is better”. They actually recommend studying visual search and decision making directly.

Why Large Choice Sets Can Hurt

Cognitive Load

When we choose, we hold options and criteria in working memory, which is limited. Nelson Cowan’s influential 2001 review in Behavioral and Brain Sciences argued that the real capacity is about four chunks of information, well below the older “seven plus or minus two” estimate. Each extra option competes for that limited space, especially when options are complex or hard to compare.

Choice Overload

The best-known demonstration is the 2000 “jam study” by Sheena Iyengar and Mark Lepper, which found that shoppers were more likely to buy from a smaller display than a larger one. Later research complicated the story:

• A 2010 meta-analysis by Benjamin Scheibehenne, Rainer Greifeneder and Peter Todd covered 50 experiments (N = 5,036) and found an average effect of virtually zero, with large differences between studies.
• A 2015 meta-analysis by Alexander Chernev, Ulf Böckenholt and Joseph Goodman (99 observations, N = 7,202) found that four factors predict when overload appears: choice set complexity, decision task difficulty, preference uncertainty, and the decision goal. Overload is more likely when options are complex, the task is hard, people are unsure what they want, and they are trying to minimize effort.

New evidence from the field

Recent studies use real platforms rather than lab menus:

• Recommendation sets (2025): A field experiment on a large online retail platform, published in Manufacturing & Service Operations Management by Xiaoyang Long and colleagues, varied how many products a personalized recommender showed. Larger sets reduced purchase probability, and as much as 64% of that decrease came from fewer shoppers clicking on anything at all. In other words, big sets can put people off before they begin to compare.
• Growing assortments (2025): In Marketing Science, Oded Natan studied an online food delivery platform and found that expanding the assortment attracted new customers but reduced how often returning customers ordered.
• Earlier groundwork: Bollen and colleagues (2010) found that users of a movie recommender experienced more choice difficulty with a high-quality list of 20 titles than with a list of 5.

Together these suggest that the size of a set matters most at the moment of engagement, where people decide whether to dive in based on how daunting the set looks.

How to Apply it, with Evidence

1. Curate instead of dumping the catalog

Streaming is a classic case, as Netflix researchers Carlos Gomez-Uribe and Neil Hunt reported in 2015 that a typical member loses interest after roughly 60 to 90 seconds of choosing, having looked at about 10 to 20 titles, and that the homepage is where two of every three hours streamed are discovered. So here, a short, relevant set is the product.

2. Use clear categories

Grouping options into labeled rows or sections helps people skip what is irrelevant and scan what matters. The goal is a structure the user can predict, so they rarely need to compare everything at once.

3. Personalize where relevance is real

Recommendations help when they reduce the effective choice set to items that fit the person. The field evidence above also shows the limit: even personalized lists get worse when they get too long.

4. Simplify processes, not only screens

Baymard Institute’s long-running research puts the average online cart abandonment rate at about 70%, and reports that 17% of US online shoppers said they abandoned an order in the past quarter because checkout was too long or complicated. Baymard also finds that most checkouts could cut 20% to 60% of the form elements they show by default. Fewer fields, clear progress and sensible defaults matter more than the number of screens.

5. Measure clutter and attention

Visual clutter makes searching slower. Ruth Rosenholtz and colleagues (2007) built clutter measures that correlated with how long people took to find a symbol on a map. Attention prediction tools such as expoze.io generate heatmaps of where viewers are likely to look on a page, shelf or creative. Heatmaps show attention, not decisions, so use them to spot elements that compete for attention or draw none, then simplify. Confirm the finding with an A/B test before changing a high-stakes page.

6. Remember that more can be better

Experts and people with clear preferences often welcome large assortments, and filters, search and sorting let them narrow quickly. Match the number of options to the user’s expertise and the difficulty of the task.

Conclusion

Hick’s Law is a real, well-studied finding about response speed, and it points to a useful design instinct: make choosing easier by structuring and curating. But it is only one piece of the picture. The evidence on choice overload shows that complexity, uncertainty, and goals determine when options hurt, and recent field experiments show that large sets can discourage people from engaging at all. Test, measure, and design for the people actually making the choice.

References

• Bollen, D., Knijnenburg, B. P., Willemsen, M. C., & Graus, M. (2010). Understanding choice overload in recommender systems. Proceedings of the ACM Conference on Recommender Systems.
• Chernev, A., Böckenholt, U., & Goodman, J. (2015). Choice overload: A conceptual review and meta-analysis. Journal of Consumer Psychology, 25(2), 333–358.
• Cowan, N. (2001). The magical number 4 in short-term memory: A reconsideration of mental storage capacity. Behavioral and Brain Sciences, 24(1), 87–114.
• Gomez-Uribe, C. A., & Hunt, N. (2015). The Netflix recommender system: Algorithms, business value, and innovation. ACM Transactions on Management Information Systems, 6(4), Article 13.
• Hagger, M. S., Chatzisarantis, N. L. D., et al. (2016). A multilab preregistered replication of the ego-depletion effect. Perspectives on Psychological Science, 11(4), 546–573.
• Hick, W. E. (1952). On the rate of gain of information. Quarterly Journal of Experimental Psychology, 4(1), 11–26.
• Hyman, R. (1953). Stimulus information as a determinant of reaction time. Journal of Experimental Psychology, 45(3), 188–196.
• 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.
• Liu, W., Gori, J., Rioul, O., Beaudouin-Lafon, M., & Guiard, Y. (2020). How relevant is Hick’s Law for HCI? Proceedings of the ACM CHI Conference on Human Factors in Computing Systems.
• Long, X., Sun, J., Dai, H., Zhang, D., Zhang, J., Chen, Y., Hu, H., & Zhao, B. (2025). The choice overload effect in online recommender systems. Manufacturing & Service Operations Management, 27(1), 249–268.
• Natan, O. R. (2025). Choice frictions in large assortments. Marketing Science, 44(3), 593–625.
• 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.
• Rosenholtz, R., Li, Y., & Nakano, L. (2007). Measuring visual clutter. Journal of Vision, 7(2), 17.
• 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.
• Baymard Institute. Cart abandonment rate statistics and checkout usability research (baymard.com).