A diffusion model analysis of feature-based access to internal representations in visual short-term memory

Author(s):  
Qi Li ◽  
Jun SAIKI
2016 ◽  
Vol 16 (12) ◽  
pp. 1070
Author(s):  
Zampeta Kalogeropoulou ◽  
Akshay Jagadeesh ◽  
Sven Ohl ◽  
Martin Rolfs

Neuron ◽  
2018 ◽  
Vol 99 (1) ◽  
pp. 215-226.e4 ◽  
Author(s):  
Nicholas M. Dotson ◽  
Steven J. Hoffman ◽  
Baldwin Goodell ◽  
Charles M. Gray

2017 ◽  
Author(s):  
Kristjan Kalm ◽  
Dennis Norris

AbstractHuman bias towards more recent events is a common and well-studied phenomenon. Recent studies in visual perception have shown that this recency bias persists even when past events contain no information about the future. Reasons for this suboptimal behaviour are not well understood and the internal model that leads people to exhibit recency bias is unknown. Here we use a well-known orientation estimation task to frame the human recency bias in terms of incremental Bayesian inference. We show that the only Bayesian model capable of explaining the recency bias relies on a weighted mixture of past states. Furthermore, we suggest that this mixture model is a consequence of participants’ failure to infer a model for data in visual short term memory, and reflects the nature of the internal representations used in the task.


Author(s):  
Kevin Dent

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.


Author(s):  
Yuhong Jiang

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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