Introduction to Hick’s Law

In the realm of psychology and decision science, few principles hold as much sway as Hick’s Law. Named after psychologist William Edmund Hick, this law delves deep into the intricacies of human decision-making processes, shedding light on how we navigate choices amidst a sea of options. Understanding Hick’s Law is not merely an academic pursuit; it has profound implications across diverse fields, from user interface design to sports performance and beyond.

Hick’s Law is elegantly simple yet profoundly impactful. At its core, it postulates a direct relationship between the number of stimuli or options presented to an individual and the time it takes for that individual to make a decision. In essence, the more choices we face, the longer it takes us to decide. This fundamental principle underpins countless aspects of our daily lives, shaping everything from the layout of digital interfaces to the strategies employed by athletes on the field.

In this article, we embark on a journey to explore the depths of Hick’s Law. We’ll delve into its origins, dissect its underlying mechanisms, examine its practical applications, and ponder its implications for the future of decision science. So, buckle up as we navigate the fascinating world of Hick’s Law and unlock the secrets of human decision-making.

Origins and Development of Hick’s Law

To truly grasp the significance of Hick’s Law, it’s essential to trace its roots back to its origins and understand how it evolved over time. The story of Hick’s Law begins in the mid-20th century when psychologist William Edmund Hick, along with his colleague Ray Hyman, embarked on a series of groundbreaking experiments aimed at unraveling the mysteries of decision-making.

The seminal experiments conducted by Hick and Hyman involved presenting participants with various sets of stimuli and measuring their reaction times. The stimuli ranged from simple visual cues to more complex arrays of choices. Through meticulous experimentation and data analysis, Hick and Hyman observed a consistent pattern: as the number of stimuli increased, so did the time it took participants to make a decision.

Building upon these findings, Hick formulated his eponymous law, which succinctly encapsulated this relationship between stimulus complexity and decision time. The law was later refined and mathematically formalized, paving the way for its widespread adoption and application in diverse fields.

One of the key strengths of Hick’s Law lies in its simplicity and universality. It offers a straightforward yet powerful framework for understanding how the human mind processes information and makes decisions in the face of complexity. From its humble beginnings in the confines of the laboratory, Hick’s Law has transcended disciplinary boundaries to become a cornerstone of decision science.

The Mathematical Formulation of Hick’s Law

At the heart of Hick’s Law lies a simple yet elegant mathematical formulation that captures the essence of its predictive power. This formulation provides a quantitative framework for understanding how decision time varies with the number of stimuli presented to an individual.

Hick’s Law is commonly expressed mathematically as:

RT=a+b×log2​(n)

Where:

• $RT$ represents the reaction time, or the time it takes to make a decision.
• $a$ and $b$ are constants that depend on various factors such as individual differences and task complexity.
• $n$ denotes the number of stimuli or options presented.

The logarithmic function log2​(n) reflects the fundamental principle that the relationship between stimulus complexity and decision time is not linear but rather logarithmic in nature. This means that as the number of stimuli doubles, the increase in decision time is relatively constant, regardless of the starting point.

The beauty of this mathematical formulation lies in its simplicity and predictive power. By plugging in different values for $n$ and adjusting the constants $a$ and $b$ to fit specific contexts, researchers and practitioners can accurately predict how changes in stimulus complexity will impact decision time.

Moreover, the logarithmic nature of Hick’s Law highlights an important insight into human cognition: our brains are remarkably adept at processing information within certain bounds. While we may struggle to make decisions when faced with an overwhelming number of choices, we exhibit a remarkable capacity to navigate complex decision spaces with relative ease within a certain range.

By understanding the mathematical underpinnings of Hick’s Law, we gain valuable insights into the cognitive mechanisms that govern human decision-making. Armed with this knowledge, designers, educators, and policymakers can develop strategies to streamline decision processes, optimize user experiences, and enhance performance outcomes.

How Hick’s Law Works

When faced with multiple choices, individuals must process each option, evaluate its merits, and compare it with other alternatives. This cognitive process requires time and mental effort. As the number of choices increases, so does the complexity of decision-making, leading to longer response times.

Applications of Hick’s Law

Hick’s Law has widespread applications across various domains. In user experience design, minimizing the number of options presented to users can improve navigation and decision-making on websites and mobile applications. Marketers can use Hick’s Law to streamline product offerings and enhance customer satisfaction. Additionally, understanding Hick’s Law can optimize organizational decision-making processes, leading to more efficient operations.

The concept of Cognitive Load

When using a digital product or service, users first have to understand how the product works and then figure out how to find the information they need. Tasks such as navigating (or even finding) the navigation bar, understanding website layout, interacting with interface components, or filling out a form all require significant mental effort. During this learning process, users must also stay focused to accomplish the task at hand. Task completion depends on how user-friendly the interface is. The mental resources needed to understand and interact with the interface are referred to as cognitive load.

You can think of this like a computer’s memory having to handle many applications, resulting in decreased battery life and slower performance, or worse, crashing. The remaining energy to process these tasks will determine performance and depends on memory—a finite resource.

Our brains work similarly: when the amount of information we take in exceeds our capacity, keeping up becomes challenging. Tasks become more difficult, details are missed, and we start to feel overwhelmed. Our working memory, the available buffer to hold information related to the current task, has limited capacity. If immediate tasks demand more space than is available, we start forgetting old information to make room for the new.

This becomes problematic when crucial information is lost related to the current task or what users are searching for. Tasks become harder, and users may feel overloaded, becoming frustrated or even abandoning the task—both symptoms of a poor user experience.

For example

Now that we understand Hick’s Law and cognitive load, let’s look at some examples of applying these principles. There are numerous examples of Hick’s Law applied everywhere, but let’s start with a common one: remote controls.

Today, our TVs come with more features, resulting in remotes with an increasing number of buttons. Eventually, we end up with highly complex TV remotes, requiring users to memorize a plethora of functions or have extraordinary memory skills. This leads to the phenomenon of “grandparent-friendly remotes.” By labeling important buttons, grandchildren can help grandparents use the TV more easily. These remotes have been widely shared online.

Conversely, modern smart TV remotes, the successors of their predecessors, have been simplified, leaving only the most basic buttons. As a result, these remotes require less mental effort and consume less cognitive load. Complexity is shifted to the TV screen, where information is organized logically and displayed progressively when navigating menus.

We’ve explored Hick’s Law in the physical world; now let’s continue with digital examples. As we’ve seen, the number of available choices can directly impact the time needed to make a decision. We can enhance user experience by providing options only when necessary, rather than displaying all choices at once. An excellent example is Google search, which allows filtering results by different formats (all, images, videos, news, etc.) only after you begin typing keywords. This helps people focus on more important tasks rather than being overwhelmed by too many choices.

Let’s look at another example of Hick’s Law. Onboarding is a crucial but risky process for new users, and few apps do it as well as Slack. Instead of introducing basic features to users and then directing them to a page with full features, a bot (Slackbot) is used to greet users and prompt them to learn about messaging features. To prevent new users from feeling overwhelmed, Slack hides all features except for the messaging feature. Once users have learned how to message via Slackbot, they gradually become familiar with additional features.

This is an effective approach for new users because it mimics how we actually learn: we build our understanding based on previous steps and incorporate new things. By revealing features at the right time, we can help users adapt to complex workflows and feature sets without feeling overwhelmed.

Card Sorting

As we’ve seen in previous examples, the number of choices can impact decision time, especially when users are seeking information. Too many choices can lead to a high cognitive load, particularly when the options aren’t clear. Conversely, having too few choices can make it harder for users to identify the most likely option they’re looking for. A particularly useful method for understanding user expectations regarding information architecture is card sorting. This research method is highly effective in understanding how items should be organized based on users’ mental models: simply ask participants to group topics they perceive to be related.

The steps in this method are quite simple. Although there are various approaches to card sorting (open and closed, adjusted and unadjusted), they all follow the same general process. Here are the steps of the most common approach, open adjusted card sorting:

1. Identify topics: The first step is to identify the topics that participants need to sort. These topics should be the main content in your information architecture, with each item written on a separate card (this method can also be performed digitally). Avoid labeling topics with similar words, as this may confuse participants and lead them to group these topics together.
2. Sort topics: The next step is to ask participants to sort the topics, one by one, into groups they find logical. Participants will verbalize their thoughts during this stage, which can provide valuable insights into their thought process.
3. Name groups: After the topics have been grouped, participants are asked to name each group they created based on a word they believe best reflects it. This step is particularly useful as it illuminates each participant’s mental model and helps identify labels for categories in your information architecture.
4. Feedback from participants (optional): The final recommended step in card sorting exercises is to ask participants to explain why they grouped topics in a certain way. This allows you to explore why participants made such decisions, identify difficulties they encountered, and gather their opinions on topics that were not categorized.

Card sorting is a valuable method for understanding users’ mental models and preferences when it comes to organizing information. By following these steps, you can gain valuable insights to inform the structure of your information architecture and improve user experience.

Conclusion

Hick’s Law offers a powerful framework for understanding and optimizing decision-making processes in various domains. By addressing individual differences, balancing simplicity with complexity, adapting to evolving technologies, fostering ethical practices, and measuring impact, organizations can overcome challenges and harness the full potential of Hick’s Law to drive innovation, improve user experiences, and achieve strategic objectives.