scholarly journals On the noisy spatiotopic encoding of word positions during reading: Evidence from the change-detection task

2020 ◽  
Author(s):  
Felipe Pegado ◽  
Jonathan Grainger
Keyword(s):  
Change Detection ◽  
Short Term Memory ◽  
Detection Task ◽  
Matching Task ◽  
Change Detection Task ◽  
Visual Short Term Memory ◽  
Word Position ◽  
Standard Response ◽  
Position Coding ◽  
The Difference

Abstract The present study builds on our prior work showing evidence for noisy word-position coding in an immediate same-different matching task. In that research, participants found it harder to judge that two successive brief presentations of five-word sequences were different when the difference was caused by transposing two adjacent words compared with different word replacements – a transposition effect. Here we used the change-detection task with a 1-s delay introduced between sequences – a task thought to tap into visual short-term memory. Concurrent articulation was used to limit the contribution of active rehearsal. We used standard response-time (RT) and error-rate analyses plus signal detection theory (SDT) measures of discriminability (d’) and bias (c). We compared the transposition effects for ungrammatical word sequences and nonword sequences observed with these different measures. Although there was some evidence for transposition effects with nonwords, the effects were much larger with word sequences. These findings provide further support for the hypothesized noisy assignment of word identities to spatiotopic locations along a line of text during reading.

Testing visual short-term memory of pigeons (Columba livia) and a rhesus monkey (Macaca mulatta) with a location change detection task

2013 ◽  
Vol 16 (5) ◽  
pp. 839-844 ◽  
Author(s):  
Kenneth J. Leising ◽  
L. Caitlin Elmore ◽  
Jacquelyne J. Rivera ◽  
John F. Magnotti ◽  
Jeffrey S. Katz ◽  
...  
Keyword(s):  
Rhesus Monkey ◽  
Macaca Mulatta ◽  
Short Term Memory ◽  
Columba Livia ◽  
Detection Task ◽  
Change Detection Task ◽  
Short Term ◽  
Term Memory ◽  
Visual Short Term Memory ◽  
Location Change

scholarly journals Adaptation supports short-term memory in a visual change detection task

2020 ◽  
Author(s):  
Brian Hu ◽  
Marina E. Garrett ◽  
Peter A. Groblewski ◽  
Douglas R. Ollerenshaw ◽  
Jiaqi Shang ◽  
...  
Keyword(s):  
Neural Network ◽  
Change Detection ◽  
Neural Activity ◽  
Short Term Memory ◽  
Detection Task ◽  
Change Detection Task ◽  
Short Term ◽  
Term Memory ◽  
Visual Change ◽  
Visual Change Detection

AbstractThe maintenance of short-term memories is critical for survival in a dynamically changing world. Previous studies suggest that this memory can be stored in the form of persistent neural activity or using a synaptic mechanism, such as with short-term plasticity. Here, we compare the predictions of these two mechanisms to neural and behavioral measurements in a visual change detection task. Mice were trained to respond to changes in a repeated sequence of natural images while neural activity was recorded using two-photon calcium imaging. We also trained two types of artificial neural networks on the same change detection task as the mice. Following fixed pre-processing using a pretrained convolutional neural network, either a recurrent neural network (RNN) or a feedforward neural network with short-term synaptic depression (STPNet) was trained to the same level of performance as the mice. While both networks are able to learn the task, the STPNet model contains units whose activity are more similar to the in vivo data and produces errors which are more similar to the mice. When images are omitted, an unexpected perturbation which was absent during training, mice often do not respond to the omission but are more likely to respond to the subsequent image. Unlike the RNN model, STPNet also produces a similar pattern of behavior. These results suggest that simple neural adaptation mechanisms may serve as an important bottom-up memory signal in this task, which can be used by downstream areas in the decision-making process.Author SummaryAnimals have to adapt to environments with rich dynamics and maintain multiple types of memories. In this study, we focus on a visual change detection task in mice which requires short-term memory. Learning which features need to be maintained in short-term memory can be realized in a recurrent neural network by changing connections in the network, resulting in memory maintenance through persistent activity. However, in biological networks, a large diversity of time-dependent intrinsic mechanisms are also available. As an alternative to persistent neural activity, we find that learning to make use of internal adapting dynamics better matches both the observed neural activity and behavior of animals in this simple task. The presence of a large diversity of temporal traces could be one of the reasons for the diversity of cells observed. We believe that both learning to keep representations of relevant stimuli in persistent activity and learning to make use of intrinsic time-dependent mechanisms exist, and their relative use will be dependent on the exact task.

scholarly journals Adaptation supports short-term memory in a visual change detection task

2021 ◽  
Vol 17 (9) ◽  
pp. e1009246
Author(s):  
Brian Hu ◽  
Marina E. Garrett ◽  
Peter A. Groblewski ◽  
Douglas R. Ollerenshaw ◽  
Jiaqi Shang ◽  
...  
Keyword(s):  
Neural Network ◽  
Change Detection ◽  
Neural Activity ◽  
Short Term Memory ◽  
Detection Task ◽  
Change Detection Task ◽  
Short Term ◽  
Visual Change ◽  
Visual Change Detection

The maintenance of short-term memories is critical for survival in a dynamically changing world. Previous studies suggest that this memory can be stored in the form of persistent neural activity or using a synaptic mechanism, such as with short-term plasticity. Here, we compare the predictions of these two mechanisms to neural and behavioral measurements in a visual change detection task. Mice were trained to respond to changes in a repeated sequence of natural images while neural activity was recorded using two-photon calcium imaging. We also trained two types of artificial neural networks on the same change detection task as the mice. Following fixed pre-processing using a pretrained convolutional neural network, either a recurrent neural network (RNN) or a feedforward neural network with short-term synaptic depression (STPNet) was trained to the same level of performance as the mice. While both networks are able to learn the task, the STPNet model contains units whose activity are more similar to the in vivo data and produces errors which are more similar to the mice. When images are omitted, an unexpected perturbation which was absent during training, mice often do not respond to the omission but are more likely to respond to the subsequent image. Unlike the RNN model, STPNet produces a similar pattern of behavior. These results suggest that simple neural adaptation mechanisms may serve as an important bottom-up memory signal in this task, which can be used by downstream areas in the decision-making process.

Vivid Imagers Are Better at Detecting Salient Changes

2006 ◽  
Vol 27 (4) ◽  
pp. 218-228 ◽  
Author(s):  
Paul Rodway ◽  
Karen Gillies ◽  
Astrid Schepman
Keyword(s):  
Individual Differences ◽  
Change Detection ◽  
Visual Imagery ◽  
Detection Task ◽  
Level Of Detail ◽  
Detection Accuracy ◽  
Change Detection Task ◽  
Term Change ◽  
High Level

This study examined whether individual differences in the vividness of visual imagery influenced performance on a novel long-term change detection task. Participants were presented with a sequence of pictures, with each picture and its title displayed for 17  s, and then presented with changed or unchanged versions of those pictures and asked to detect whether the picture had been changed. Cuing the retrieval of the picture's image, by presenting the picture's title before the arrival of the changed picture, facilitated change detection accuracy. This suggests that the retrieval of the picture's representation immunizes it against overwriting by the arrival of the changed picture. The high and low vividness participants did not differ in overall levels of change detection accuracy. However, in replication of Gur and Hilgard (1975) , high vividness participants were significantly more accurate at detecting salient changes to pictures compared to low vividness participants. The results suggest that vivid images are not characterised by a high level of detail and that vivid imagery enhances memory for the salient aspects of a scene but not all of the details of a scene. Possible causes of this difference, and how they may lead to an understanding of individual differences in change detection, are considered.

An attentional bias for LEGO® people using a change detection task: Are LEGO® people animate?

2016 ◽  
Vol 70 (3) ◽  
pp. 219-231 ◽  
Author(s):  
Mitchell R. P. LaPointe ◽  
Rachael Cullen ◽  
Bianca Baltaretu ◽  
Melissa Campos ◽  
Natalie Michalski ◽  
...  
Keyword(s):  
Change Detection ◽  
Attentional Bias ◽  
Detection Task ◽  
Change Detection Task

scholarly journals How Do Subway Signs Affect Pedestrians’ Wayfinding Behavior through Visual Short-Term Memory?

2021 ◽  
Vol 13 (12) ◽  
pp. 6866
Author(s):  
Haoru Li ◽  
Jinliang Xu ◽  
Xiaodong Zhang ◽  
Fangchen Ma
Keyword(s):  
Public Transportation ◽  
Short Term Memory ◽  
Memory Capacity ◽  
Cognitive Test ◽  
Subway Station ◽  
Short Term ◽  
Term Memory ◽  
Visual Short Term Memory ◽  
The Difference ◽  
Types Of Information

Recently, subways have become an important part of public transportation and have developed rapidly in China. In the subway station setting, pedestrians mainly rely on visual short-term memory to obtain information on how to travel. This research aimed to explore the short-term memory capacities and the difference in short-term memory for different information for Chinese passengers regarding subway signs. Previous research has shown that people’s general short-term memory capacity is approximately four objects and that, the more complex the information, the lower people’s memory capacity. However, research on the short-term memory characteristics of pedestrians for subway signs is scarce. Hence, based on the STM theory and using 32 subway signs as stimuli, we recruited 120 subjects to conduct a cognitive test. The results showed that passengers had a different memory accuracy for different types of information in the signs. They were more accurate regarding line number and arrow, followed by location/text information, logos, and orientation. Meanwhile, information type, quantity, and complexity had significant effects on pedestrians’ short-term memory capacity. Finally, according to our results that outline the characteristics of short-term memory for subway signs, we put forward some suggestions for subway signs. The findings will be effective in helping designers and managers improve the quality of subway station services as well as promoting the development of pedestrian traffic in such a setting.

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