Boundary extension: Insights from signal detection theory
Journal of Vision
|June 10, 2016
Summary
Boundary extension, a visual memory error, was analyzed using signal detection theory (SDT). Results show that both sensitivity and response bias contribute to this phenomenon, indicating false memories of wider scenes.
Area of Science:
- Cognitive Psychology
- Visual Perception
- Memory Studies
Background:
- Boundary extension is a common visual memory error where people recall seeing more than was originally visible.
- Previous research has not extensively applied signal detection theory (SDT) to understand this phenomenon.
Purpose of the Study:
- To investigate boundary extension using signal detection theory (SDT).
- To determine the contributions of discrimination sensitivity and response bias to boundary extension.
Main Methods:
- Two experiments were conducted where participants studied images presented in close-up or wide-angle views.
- Participants rated the similarity of test images (identical or altered angle) to the studied images on a 6-point scale.
Main Results:
- Both discrimination sensitivity and response bias were found to contribute to the boundary extension effect.
- A significant difference in discrimination sensitivity (at least 28%) was observed, refuting explanations based solely on response bias.
- Findings suggest participants experience false memories extending beyond the original visual field.
Conclusions:
- Boundary extension is not solely a result of response bias but involves genuine differences in perceptual discrimination.
- The results support the interpretation of boundary extension as a form of false memory, where individuals perceive a wider scene than was actually presented.
Related Concept Videos
Difference from Background: Limit of Detection
8.8K
The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
The LOD indicates the presence or absence...
8.8K
Classification of Signals
1.5K
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
1.5K
Signal and System
1.7K
A signal x(t) is a set of data or a time function representing a variable of interest. Signals typically convey information about a phenomenon, such as atmospheric temperature, humidity, human voice, television images, a dog's bark, or birdsongs. More generally, a signal can be a function of more than one independent variable. For instance, images depend on horizontal and vertical positions and can be regarded as two-dimensional signals. However, this text will focus on one-dimensional...
1.7K
Region of Convergence of Laplace Tarnsform
1.4K
The Region of Convergence (ROC) is a fundamental concept in signal processing and system analysis, particularly associated with the Laplace transform. The ROC represents an area in the complex plane where the Laplace transform of a given signal converges, determining the transform's applicability and utility.
Consider a decaying exponential signal that begins at a specific time. When deriving its Laplace transform, the time-domain variable is replaced with a complex variable. This...
Consider a decaying exponential signal that begins at a specific time. When deriving its Laplace transform, the time-domain variable is replaced with a complex variable. This...
1.4K
Sensation
1.8K
Sensory receptors are specialized neurons that respond to specific types of external stimuli, initiating the process known as sensation. This occurs when sensory input, such as light entering the eye, is detected by these receptors, causing chemical changes in the cells of the retina. These cells then convert the sensory stimulus into action potentials that are transmitted to the central nervous system, a process termed transduction.
Absolute thresholds can quantify the sensitivity of sensory...
Absolute thresholds can quantify the sensitivity of sensory...
1.8K
Bandpass Sampling
607
In signal processing, bandpass sampling is an effective technique for sampling signals that have most of their energy concentrated within a narrow frequency band. This type of signal is known as a bandpass signal. The key principle of bandpass sampling involves sampling the signal at a rate that is greater than twice the signal's bandwidth to prevent aliasing.
A bandpass signal has a spectrum with a lower frequency limit, denoted as ω1, and an upper frequency limit, denoted as ω2....
A bandpass signal has a spectrum with a lower frequency limit, denoted as ω1, and an upper frequency limit, denoted as ω2....
607


