An Event-Related Potential Study of Onset Primacy in Visual Change Detection

AbstractOnset primacy is a behavioural phenomenon whereby humans identify the appearance of an object (onset) with greater efficiency than other kinds of visual change, such as the disappearance of an object (offset). The default mode hypothesis explains this phenomenon by postulating that the attentional system is optimised for onset detection in its initial state. The present study extended this hypothesis by combining a change detection task and measurement of the P300 event-related potential (ERP), which was thought to index the amount of processing resources available to detecting onsets and offsets. In an experiment, participants indicated the locations of onsets and offsets under the condition in which they occurred equally often in the same locations across trials. Although there was no reason to prioritise detecting one type of change over the other, onsets were detected more quickly and they evoked a larger P300 than offsets. These results suggest that processing resources are preferentially allocated to onset detection. This biased allocation may be a basis on which the attentional system defaults to the ‘onset detection’ mode. Possible contributions of other ERP components to onset primacy are also discussed in the article.

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Implicit learning for probable changes in a visual change detection task

Consciousness and Cognition ◽

10.1016/j.concog.2008.06.011 ◽

2008 ◽

Vol 17 (4) ◽

pp. 1192-1208 ◽

Cited By ~ 6

Author(s):

Melissa R. Beck ◽

Bonnie L. Angelone ◽

Daniel T. Levin ◽

Matthew S. Peterson ◽

D. Alexander Varakin

Keyword(s):

Change Detection ◽

Implicit Learning ◽

Detection Task ◽

Change Detection Task ◽

Visual Change ◽

Visual Change Detection

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The Allocation of Attention in Change Detection and Change Blindness

Journal of Psychophysiology ◽

10.1027/0269-8803/a000172 ◽

2017 ◽

Vol 31 (3) ◽

pp. 94-106

Author(s):

Andrea Schankin ◽

Katharina Bergmann ◽

Anna-Lena Schubert ◽

Dirk Hagemann

Keyword(s):

Change Detection ◽

Change Blindness ◽

Lower Number ◽

Event Related Potential ◽

Attention Allocation ◽

Potential Change ◽

Current Experiment ◽

Visual Change ◽

Visual Change Detection ◽

Successful Change

Abstract. Visual change detection often fails when observers’ attention is distracted by some other visual disruptions in the environment that occur simultaneously with the change. This phenomenon is called change blindness. It has been claimed that selective attention is necessary for successful change detection. In the current experiment, two mechanisms of attention allocation in such a task were investigated. First, the number of distracting stimuli was varied to distract observers’ attention and, second, possible change positions were highlighted to allow observers to better focus on potential change locations. The N2pc component of the event-related potential was measured as an indicator of attentional selection. The results show that the sensitivity for changes increased either when observers were less distracted or when they were able to better focus their attention. However, these two mechanisms were reflected differently by the N2pc component. When observers’ attention was less distracted by a lower number of mudsplashes, the N2pc component occurred earlier. In contrast, when observers were able to better focus their attention on potential change locations, the N2pc component not only occurred earlier but also showed an additional enhancement in amplitude. That is, successful change detection depends on both, the properties of distracting and of changing objects. They determine the speed and intensity of the allocation of attention toward a change.

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

10.1101/2020.03.06.977512 ◽

2020 ◽

Cited By ~ 2

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.

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

PLoS Computational Biology ◽

10.1371/journal.pcbi.1009246 ◽

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.

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The effect of practice on visual change detection in computer displays

PsycEXTRA Dataset ◽

10.1037/e577082012-026 ◽

2004 ◽

Author(s):

John L. Neumann ◽

Paula J. Durlach

Keyword(s):

Change Detection ◽

Visual Change ◽

Visual Change Detection

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Probability-independent and -dependent ERPs reflecting visual change detection

Psychophysiology ◽

10.1111/j.1469-8986.2006.00388.x ◽

2006 ◽

Vol 43 (2) ◽

pp. 180-189 ◽

Cited By ~ 44

Author(s):

Motohiro Kimura ◽

Jun'ichi Katayama ◽

Harumitsu Murohashi

Keyword(s):

Change Detection ◽

Visual Change ◽

Visual Change Detection

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Pip and Pop

Proceedings of the Human Factors and Ergonomics Society Annual Meeting ◽

10.1177/1541931213601065 ◽

2016 ◽

Vol 60 (1) ◽

pp. 284-288

Author(s):

Cindy Chamberland ◽

Helen M. Hodgetts ◽

Benoît R. Vallières ◽

François Vachon ◽

Sébastien Tremblay

Keyword(s):

Change Detection ◽

Information Overload ◽

Primary Task ◽

Auditory Modality ◽

Visual Change ◽

Mental Demand ◽

High Workload ◽

Visual Change Detection ◽

Multiple Resource ◽

Visual Changes

Dynamic and complex command and control situations often require the timely recognition of changes in the environment in order to detect potentially malicious actions. Change detection can be challenging within a continually evolving scene, and particularly under multitasking conditions whereby attention is necessarily divided between several subtasks. On-screen tools can assist with detection (e.g., providing a visual record of changes, ensuring that none are overlooked), however, in a high workload environment, this may result in information overload to the detriment of the primary task. One alternative is to exploit the auditory modality as a means to support visual change detection. In the current study, we use a naval air-warfare simulation, and introduce an auditory alarm to coincide with critical visual changes (in aircraft speed/direction) on the radar. We found that participants detected a greater percentage of visual changes and were significantly quicker to detect these changes when they were accompanied by an auditory alarm than when they were not. Furthermore, participants reported that mental demand was lower in the auditory alarm condition, and this was reflected in reduced classification omissions on the primary task. Results are discussed in relation to Wickens’ multiple resource theory of attention and indicate the potential for using the auditory modality to facilitate visual change detection.

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