Related Experiment Video
Updated: Feb 6, 2026

11:01
Examining the Characteristics of Episodic Memory using Event-related Potentials in Patients with Alzheimer's Disease
Published on: August 30, 2011
14.1K
Multiple Time Intervals of Visual Events Are Represented as Discrete Items in Working Memory
1Department of Life Sciences, The University of Tokyo, Tokyo, Japan.
Frontiers in Psychology
|August 18, 2018
Summary
Working memory stores multiple time intervals like visual textures, but time intervals may decay faster. Sub-second intervals are more readily recalled than visual textures.
Area of Science:
- Cognitive Psychology
- Neuroscience
- Human Perception
Background:
- Limited understanding of how the brain processes multiple time intervals in working memory.
- Previous research focused on single time interval perception, leaving multi-interval processing unclear.
Purpose of the Study:
- To investigate the characteristics of working memory for multiple time intervals and visual textures.
- To compare temporal and visual working memory using signal detection theory.
- To explore differences in sub- and supra-second interval processing.
Main Methods:
- Utilized Sternberg's item recognition task with sequential presentation of gratings (varying spatial frequencies, time intervals, and visual textures).
- Participants performed recognition tasks for temporal and visual dimensions.
- Analyzed data using signal detection theory to assess working memory representations.
Main Results:
- Working memory exhibited typical characteristics (load, serial position, similarity effects) for both time intervals and visual textures.
- Time intervals showed a smaller or absent recency effect compared to visual textures.
- Sub-second intervals were more frequently judged as remembered than visual textures.
- Interactions between visual and temporal memory were observed, differing between sub- and supra-second ranges.
Conclusions:
- Multiple time intervals are stored discretely in working memory, akin to visual textures, but may be more prone to decay.
- Distinct processing mechanisms for sub- and supra-second intervals likely exist, potentially involving different decision stages in working memory.
Related Concept Videos
Discrete-time Fourier transform
1.1K
The Discrete-Time Fourier Transform (DTFT) is an essential mathematical tool for analyzing discrete-time signals, converting them from the time domain to the frequency domain. This transformation allows for examining the frequency components of discrete signals, providing insights into their spectral characteristics. In the DTFT, the continuous integral used in the continuous-time Fourier transform is replaced by a summation to accommodate the discrete nature of the signal.
One of the notable...
One of the notable...
1.1K
Basic Discrete Time Signals
731
The unit step sequence is defined as 1 for zero and positive values of the integer n. This sequence can be graphically displayed using a set of eight sample points, showing a step function starting from n=0 and remaining constant thereafter.
The unit impulse or sample sequence is mathematically expressed as zero for all n values except at n=0, where it is one. The unit impulse sequence, denoted by δ(n), is the first difference of the unit step sequence, while the unit step sequence u(n) is the...
The unit impulse or sample sequence is mathematically expressed as zero for all n values except at n=0, where it is one. The unit impulse sequence, denoted by δ(n), is the first difference of the unit step sequence, while the unit step sequence u(n) is the...
731
Discrete-Time Fourier Series
711
The Discrete-Time Fourier Series (DTFS) is a fundamental concept in signal processing, serving as the discrete-time counterpart to the continuous-time Fourier series. It allows for the representation and analysis of discrete-time periodic signals in terms of their frequency components. Unlike its continuous counterpart, which utilizes integrals, the calculation of DTFS expansion coefficients involves summations due to the discrete nature of the signal.
For a discrete-time periodic signal x[n]...
For a discrete-time periodic signal x[n]...
711
The Representativeness Heuristic
16.8K
The representative heuristic describes a biased way of thinking, in which you unintentionally stereotype someone or something. For example, you may assume that your professors spend their free time reading books and engaging in intellectual conversation, because the idea of them spending their time playing volleyball or visiting an amusement park does not fit in with your stereotypes of professors.
16.8K
BIBO stability of continuous and discrete -time systems
941
System stability is a fundamental concept in signal processing, often assessed using convolution. For a system to be considered bounded-input bounded-output (BIBO) stable, any bounded input signal must produce a bounded output signal. A bounded input signal is one where the modulus does not exceed a certain constant at any point in time.
To determine the BIBO stability, the convolution integral is utilized when a bounded continuous-time input is applied to a Linear Time-Invariant (LTI) system....
To determine the BIBO stability, the convolution integral is utilized when a bounded continuous-time input is applied to a Linear Time-Invariant (LTI) system....
941
Prediction Intervals
3.4K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
3.4K

