Signal Detection Theory: 10 Examples and Definition

signal detection theory definition and outcomes

Signal Detection Theory (SDT) is an informative model for understanding how humans make decisions based on sensory input.

This vital concept helps comprehend why people can distinguish between noisy, signal-filled settings and those with little or no signals present. 

SDT delves into how people can differentiate meaningful sensory data (e.g., “signals”) from excess noise or inconsequential information.

According to the SDT, people determine their decisions by assessing both the strength of a signal they detect and the amount of evidence needed before accepting its existence.

In essence, SDT reveals how people review the pros and cons of their choices in order to identify which outcome is most positive, thereby influencing their ultimate decision.

Thus, SDT explains how people can differentiate between true signals versus false alarms in a signal-rich environment and make genuinely valid decisions.

Signal Detection Theory Definition

Signal Detection Theory is a psychophysical model that explains how humans make decisions based on sensory information.

It is based on the idea that individuals may pick up on meaningful information, known as “signals,” from noisy and ambiguous stimuli. 

This theory looks at how humans assess the strength of a signal and their confidence in making a decision based on what they have detected. 

According to Kelly and Hahn (2019), SDT:

“…is used when psychologists want to measure the way we make decisions under conditions of uncertainty, such as how we would perceive distances in foggy conditions” (p. 159).

Lynn and Barret (2014) believe that “SDT’s power as an analytic tool comes from separating a perceiver’s behavior into two underlying components, sensitivity and bias” (p. 1664). 

The discernment of a perceiver to distinguish between targets and foils, such as an angry person versus one who is not, is known as sensitivity. 

Meanwhile, their inclination or tendency to classify these stimuli into categories like the ones mentioned above has been referred to as bias, which can be either liberal, neutral, or conservative (Lynn & Barret, 2014).

Interestingly, according to Weber’s Law, shifts in signal detection can be more easily concealed the more intense the stimuli are.

Signal Detection Theory Examples

  • Detecting an emergency vehicle’s siren in the background noise of a busy city street. In this case, the signal is the siren, and the noise is the other traffic sounds.
  • When a customer at an electronics store hears the sound of their phone ringing in their pocket amidst the chatter and beeps from various nearby devices. So, the signal is the phone ringtone, and the noise is everything else in the store.
  • Parents monitor their children for signs of distress. The signal is the sound or behavior that indicates the child needs help. In contrast, noise is all other sounds and behaviors.
  • When a person listens to music at a party, they use SDT since they can distinguish the music from all the talking. Here, the signal is the music, and the noise is people’s voices.
  • During a meeting or lecture, individuals use SDT to decide whether somebody is speaking too loudly. In this case, the signal is the person’s voice, and the noise is all other conversations.
  • When a person is looking for their car keys, among other objects in a drawer, they are also using SDT. The signal is the clacking of keys as they collide with other surfaces, while the noise is all extraneous sounds that can be heard in your surroundings.
  • For a hunter tracking their prey, the signal is the sound of movement from the animal, while all other environmental noises are considered noise.
  • If a child is trying to hear something in the distance, they utilize SDT. In this case, the signal is what they are trying to hear, while the noise is all other background noises.
  • A consumer that evaluates the quality of a product based on its packaging and marketing claims is a prime example of SDT. Here, the signal is the product’s value, while the noise comes from external factors like advertising.
  • If a passenger listens to an announcement on a noisy train platform, they use SDT. In this case, the signal is the announcement, while all other environmental noises around them constitute noise. 

Origins of Signal Detection Theory

During World War II, signal detection theory was brilliantly utilized to distinguish radar signals from other haphazard noise. The pioneering process allowed for more efficient radar operations and proved a decisive factor in the war’s eventual outcome (Swets et al., 2001).

Nearly two centuries ago, the signal detection theory was initially crafted by Gustav Fechner in 1860. He had a simple idea: If there is noise in the environment, then we as humans must be able to differentiate between signals and non-meaningful noise. 

This basic understanding of signal detection theory has been further developed and applied in various fields, including psychology (Swets et al., 2001).

In 1966, two well-respected psychophysicists – John Swets and David Green – suggested the signal detection theory as a better option than pre-existing alternatives.

They argued that the standard stimulus detection practices disregarded individual idiosyncrasies, such as motivation and expectations when searching for a signal (Swets et al., 2001).

As a result of this, they suggested that the definitive measure of performance should take into account these discrepancies and accurately identify substantive signals.

Since then, signal detection theory has evolved in many ways and is now used to explain a wide range of human behavior and reactions.

Today, people use it daily, from identifying the faintest sound to telling the difference between a friend’s voice on a windy day.

Outcomes of Signal Detection Theory

Signal detection theory (SDT) predicts four possible outcomes that can occur in a detection task, depending on whether the observer responds “yes” or “no” to the presence of a signal – hit, miss, false alarm, and correct rejection.

These four outcomes are important for understanding the sensitivity and decision criteria of the observer in a detection task (Lerman et al., 2010).

The four outcomes are:

  1. Hit: A hit occurs when the observer correctly detects the presence of a signal and responds, “yes.” It is considered a correct response.
  1. Miss: A miss occurs when the observer fails to detect the presence of a signal and responds “no” when the signal is actually present. It is considered an incorrect response.
  1. False alarm: A false alarm occurs when the observer reports the presence of a signal when there is none. It can happen if the observer is too liberal in their response criterion and responds “yes” too frequently. It is also considered an incorrect response.
  1. Correct rejection: A correct rejection occurs when the observer correctly detects the absence of a signal and responds, “no.” It is considered a correct response.

The hit and false alarm rates can be used to calculate measures of sensitivity (such as d’ or A’) and decision criteria (such as c or beta), which are key parameters in SDT (Lerman et al., 2010).

Understanding these outcomes can help researchers and practitioners improve the accuracy and reliability of detection tasks in various contexts, from medical diagnosis to criminal investigations to user interface design.

Strengths of Signal Detection Theory

Signal detection theory has several strengths that have enabled it to become one of the most widely used models of human perceptual and cognitive processes, including ease of use, flexibility, and generality.

First, the theory is easy to understand and apply. SDT doesn’t mandate complicated mathematical or statistical comprehension, unlike certain models of human behavior.

It uses simple terms and equations that anyone can understand, making it accessible to many different fields and research areas. 

Secondly, the theory is incredibly adaptive and can be implemented in various scenarios. As an example, it can calculate reactions toward visual, auditory, or tactile stimuli.

It can also be applied to various tasks, from simple discrimination tasks to more complicated decision-making tasks.

SDT is remarkably flexible and can be adapted to a variety of contexts. Moreover, its predictions have been experimentally verified in multiple scenarios, only further strengthening the evidence for its accuracy.

Weaknesses of Signal Detection Theory

While SDT unquestionably provides many benefits, there are some prominent drawbacks too. For instance, it does not contemplate emotional factors such as fear or anxiety that can influence how a person reacts to an alert.

SDT does not account for the impact of cognitive or emotional factors when making decisions. It is a purely statistical model, meaning it doesn’t account for things like fatigue, stress, or other mental states affecting decision-making.

In addition, SDT is based on the assumption of a single-channel system, which assumes all aspects of a stimulus are processed through one channel. 

However, this is not always the case – sometimes, multiple channels are involved in a decision-making task, which SDT cannot account for.

Finally, SDT relies on the accuracy of participant responses when constructing models and calculating statistics. However, human responses are often inaccurate, which can skew the results of SDT-based studies.

Summary of Strengths and Weaknesses

Strengths of Signal Detection TheoryWeaknesses of Signal Detection Theory
Easy to understand and applyDoes not account for emotional factors
Uses simple terms and equationsDoes not account for cognitive or emotional factors
Can be implemented in various scenariosBased on the assumption of a single-channel system
Remarkably flexible and adaptableRelies on the accuracy of participant responses
Predictions experimentally verified in multiple scenariosHuman responses are often inaccurate


Signal detection theory is a critical model for understanding how humans make decisions based on sensory information. It helps people distinguish between significant signals and noise in a signal-rich environment. 

The theory originated in Gustav Fechner’s work and was later developed by John Swets and David Green, who proposed SDT as a superior alternative to existing options.

The model’s evolution and application have allowed people to identify faint sounds and differentiate between stimuli in various settings, from emergency vehicle sirens to friend’s voices on a windy day. 

Understanding SDT helps us appreciate how humans can differentiate between true signals and false alarms and make genuinely valid decisions.


Kelly, C., & Hahn, C. (2019). Clinical psychology. Scientific e-Resources.

Lerman, D. C., Tetreault, A., Hovanetz, A., Bellaci, E., Miller, J., Karp, H., Mahmood, A., Strobel, M., Mullen, S., Keyl, A., & Toupard, A. (2010). Applying signal-detection theory to the study of observer accuracy and bias in behavioral assessment. Journal of Applied Behavior Analysis43(2), 195–213.

Lynn, S. K., & Barrett, L. F. (2014). “Utilizing” signal detection theory. Psychological Science25(9), 1663–1673.

Swets, J. A. (2001, January 1). Signal detection theory, history of (N. J. Smelser & P. B. Baltes, Eds.). ScienceDirect; Pergamon.

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Dr. Chris Drew is the founder of the Helpful Professor. He holds a PhD in education and has published over 20 articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education. [Image Descriptor: Photo of Chris]

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