scholarly journals A signal detection account of visual short-term memory for orientation and spatial frequency

2004 ◽  
Vol 4 (8) ◽  
pp. 389-389 ◽  
Author(s):  
W. J. Ma ◽  
P. Wilken
Keyword(s):  
Spatial Frequency ◽  
Signal Detection ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Visual Short Term Memory

Short-Term Memory for Speed

Perception ◽  
1996 ◽  
Vol 25 (1_suppl) ◽  
pp. 156-156
Author(s):  
P Thompson ◽  
R Stone ◽  
E Walton
Keyword(s):  
Spatial Frequency ◽  
Short Term Memory ◽  
Temporal Frequency ◽  
Frequency Discrimination ◽  
Sine Wave ◽  
Short Term ◽  
Term Memory ◽  
Visual Short Term Memory ◽  
Speed Discrimination ◽  
Sine Wave Gratings

We have measured the retention of information about stimulus speed in visual short-term memory by measuring speed discrimination in a two-interval forced-choice task. We have also measured such discrimination in conditions where a ‘memory masker’ is presented during the interstimulus interval (ISI) in a fashion analogous to the experiment of Magnussen et al (1991 Vision Research31 1213 – 1219). Magnussen et al found that spatial frequency discrimination was disrupted when the mask had a spatial frequency that differed from the test spatial frequency by an octave or more. We have investigated the speed discrimination of 8 Hz, 1 cycle deg−1 drifting sine-wave gratings with the following drifting masks presented in the ISI: (i) 8 Hz 1 cycle deg−1, same direction as the test; (ii) 8 Hz, 8 cycles deg−1, opposite direction to the test; (iii) 8 Hz, 8 cycles deg−1, same direction as the test; (iv) 24 Hz, 3 cycles deg−1, same direction as the test. These masks were chosen to investigate whether the temporal frequency, the spatial frequency, the speed, or the direction of motion of the mask affected retention. We found that in none of these conditions was the discrimination of the test gratings impaired significantly. This pattern of results is therefore different from that found with spatial frequency discrimination and suggests that, whatever mechanism is responsible for the retention of information about speed, it is different from that responsible for the retention of information about spatial frequency.

A Signal-Detection Model of Visual Short-Term Memory Performance for Random Block Patterns

Perception ◽  
1997 ◽  
Vol 26 (1_suppl) ◽  
pp. 112-112
Author(s):  
F W Cornelissen ◽  
M W Greenlee
Keyword(s):  
Signal Detection ◽  
Short Term Memory ◽  
Memory Performance ◽  
Memory Task ◽  
Time Interval ◽  
Short Term ◽  
Term Memory ◽  
Detection Model ◽  
Visual Short Term Memory ◽  
Random Block

On the basis of signal-detection theory, we have formulated a model that accurately explains performance on a visual short-term memory task involving random block patterns. The model assumes that the internal response of an observer for detecting a change in any given element of the block pattern is noisy and has a Gaussian-shaped distribution. On this basis we can calculate the likelihood that an observer correctly or falsely identifies a change in the pattern after a certain time interval (ISI). Using this likelihood, we can then predict the likelihood that an observer correctly identifies a whole pattern as having changed or not as a function of the number of elements that changed in the pattern. We have previously shown (Cornelissen and Greenlee, 1993 Perception22 Supplement, 46) that memory performance declines when changes occur in pattern elements located on the perimeter of the pattern. Therefore the model also incorporates a circular symmetric ‘memory field’ that shows a Gaussian-shaped decline of memory performance from the point of fixation. The model has three parameters: d' (detectability of a change), lambda (criterion level), and the standard deviation of the Gaussian of the memory field. In the experiments we performed, block patterns made up of 50 light and 50 dark randomly arranged elements (0.5 deg checks) were briefly (200 ms) shown. In a forced-choice task, subjects judged whether two sequentially presented (with ISIs of 1, 3, or 10 s) block patterns were the same or different. Task difficulty was varied by varying the number of elements in the patterns that changed on ‘different’ trials. The model is able to accurately predict memory performance at the three different ISIs for various levels of pattern differences (changes in 1, 2, 4, 8, 16, 20, and 50 out of 100 elements).

Dynamic Visual Noise Affects Visual Short-Term Memory for Surface Color, but not Spatial Location

2010 ◽  
Vol 57 (1) ◽  
pp. 17-26 ◽  
Author(s):  
Kevin Dent
Keyword(s):  
Short Term Memory ◽  
Spatial Location ◽  
Visual Noise ◽  
Dynamic Visual Noise ◽  
Short Term ◽  
Surface Color ◽  
Term Memory ◽  
Visual Short Term Memory ◽  
Experimental Tool ◽  
Spatial Locations

In two experiments participants retained a single color or a set of four spatial locations in memory. During a 5 s retention interval participants viewed either flickering dynamic visual noise or a static matrix pattern. In Experiment 1 memory was assessed using a recognition procedure, in which participants indicated if a particular test stimulus matched the memorized stimulus or not. In Experiment 2 participants attempted to either reproduce the locations or they picked the color from a whole range of possibilities. Both experiments revealed effects of dynamic visual noise (DVN) on memory for colors but not for locations. The implications of the results for theories of working memory and the methodological prospects for DVN as an experimental tool are discussed.

Time Window from Visual Images to Visual Short-Term Memory: Consolidation or Integration?

2004 ◽  
Vol 51 (1) ◽  
pp. 45-51 ◽  
Author(s):  
Yuhong Jiang
Keyword(s):  
Short Term Memory ◽  
Time Window ◽  
Short Interval ◽  
Visual Images ◽  
Short Term ◽  
Temporal Window ◽  
Term Memory ◽  
Visual Short Term Memory ◽  
Probe Task ◽  
Long Time

Abstract. When two dot arrays are briefly presented, separated by a short interval of time, visual short-term memory of the first array is disrupted if the interval between arrays is shorter than 1300-1500 ms ( Brockmole, Wang, & Irwin, 2002 ). Here we investigated whether such a time window was triggered by the necessity to integrate arrays. Using a probe task we removed the need for integration but retained the requirement to represent the images. We found that a long time window was needed for performance to reach asymptote even when integration across images was not required. Furthermore, such window was lengthened if subjects had to remember the locations of the second array, but not if they only conducted a visual search among it. We suggest that a temporal window is required for consolidation of the first array, which is vulnerable to disruption by subsequent images that also need to be memorized.

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