scholarly journals Distraction biases working memory for faces

2019 ◽  
Author(s):  
Remington Mallett ◽  
Anurima Mummaneni ◽  
Jarrod Lewis-Peacock
Keyword(s):  
Working Memory ◽  
Irrelevant Information ◽  
Visual Features ◽  
Maintenance Period ◽  
Stimulus Space ◽  
Estimation Task ◽  
Low Level ◽  
The Face ◽  
High Level ◽  
Task Irrelevant

Working memory persists in the face of distraction, yet not without consequence. Previous research has shown that memory for low-level visual features is systematically influenced by the maintenance or presentation of a similar distractor stimulus. Responses are frequently biased in stimulus space towards a perceptual distractor, though this has yet to be determined for high-level stimuli. We investigated whether these influences are shared for complex visual stimuli such as faces. To quantify response accuracies for these stimuli, we used a delayed-estimation task with a computer-generated “face space” consisting of eighty faces that varied continuously as a function of age and sex. In a set of three experiments, we found that responses for a target face held in working memory were biased towards a distractor face presented during the maintenance period. The amount of response bias did not vary as a function of distance between target and distractor. Our data suggest that, similar to low-level visual features, high-level face representations in working memory are biased by the processing of related but task-irrelevant information.

scholarly journals View-tuned and view-invariant face encoding in IT cortex is explained by selected natural image fragments

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yunjun Nam ◽  
Takayuki Sato ◽  
Go Uchida ◽  
Ekaterina Malakhova ◽  
Shimon Ullman ◽  
...  
Keyword(s):  
Face Recognition ◽  
Visual Pathway ◽  
Visual Features ◽  
Natural Image ◽  
Face Representation ◽  
Low Level ◽  
Ventral Visual Pathway ◽  
Neural Substrate ◽  
The Face

AbstractHumans recognize individual faces regardless of variation in the facial view. The view-tuned face neurons in the inferior temporal (IT) cortex are regarded as the neural substrate for view-invariant face recognition. This study approximated visual features encoded by these neurons as combinations of local orientations and colors, originated from natural image fragments. The resultant features reproduced the preference of these neurons to particular facial views. We also found that faces of one identity were separable from the faces of other identities in a space where each axis represented one of these features. These results suggested that view-invariant face representation was established by combining view sensitive visual features. The face representation with these features suggested that, with respect to view-invariant face representation, the seemingly complex and deeply layered ventral visual pathway can be approximated via a shallow network, comprised of layers of low-level processing for local orientations and colors (V1/V2-level) and the layers which detect particular sets of low-level elements derived from natural image fragments (IT-level).

scholarly journals Robust Image Labeling Using Conditional Random Fields

2021 ◽  
Author(s):  
Maryam Nematollahi Arani
Keyword(s):  
Object Recognition ◽  
Mixture Models ◽  
Random Fields ◽  
Conditional Random Fields ◽  
Semantic Gap ◽  
Visual Features ◽  
Image Labeling ◽  
Low Level ◽  
Robust Image ◽  
High Level

Object recognition has become a central topic in computer vision applications such as image search, robotics and vehicle safety systems. However, it is a challenging task due to the limited discriminative power of low-level visual features in describing the considerably diverse range of high-level visual semantics of objects. Semantic gap between low-level visual features and high-level concepts are a bottleneck in most systems. New content analysis models need to be developed to bridge the semantic gap. In this thesis, algorithms based on conditional random fields (CRF) from the class of probabilistic graphical models are developed to tackle the problem of multiclass image labeling for object recognition. Image labeling assigns a specific semantic category from a predefined set of object classes to each pixel in the image. By well capturing spatial interactions of visual concepts, CRF modeling has proved to be a successful tool for image labeling. This thesis proposes novel approaches to empowering the CRF modeling for robust image labeling. Our primary contributions are twofold. To better represent feature distributions of CRF potentials, new feature functions based on generalized Gaussian mixture models (GGMM) are designed and their efficacy is investigated. Due to its shape parameter, GGMM can provide a proper fit to multi-modal and skewed distribution of data in nature images. The new model proves more successful than Gaussian and Laplacian mixture models. It also outperforms a deep neural network model on Corel imageset by 1% accuracy. Further in this thesis, we apply scene level contextual information to integrate global visual semantics of the image with pixel-wise dense inference of fully-connected CRF to preserve small objects of foreground classes and to make dense inference robust to initial misclassifications of the unary classifier. Proposed inference algorithm factorizes the joint probability of labeling configuration and image scene type to obtain prediction update equations for labeling individual image pixels and also the overall scene type of the image. The proposed context-based dense CRF model outperforms conventional dense CRF model by about 2% in terms of labeling accuracy on MSRC imageset and by 4% on SIFT Flow imageset. Also, the proposed model obtains the highest scene classification rate of 86% on MSRC dataset.

High level estradiol impairs and low level estradiol facilitates non-spatial working memory

2004 ◽  
Vol 155 (1) ◽  
pp. 45-53 ◽  
Author(s):  
Jennifer K. Wide ◽  
Katherine Hanratty ◽  
Julia Ting ◽  
Liisa A.M. Galea
Keyword(s):  
Working Memory ◽  
Spatial Working Memory ◽  
Low Level ◽  
High Level

scholarly journals Perturbing Neural Representations of Working Memory with Task-irrelevant Interruption

2020 ◽  
Vol 32 (3) ◽  
pp. 558-569 ◽  
Author(s):  
Nicole Hakim ◽  
Tobias Feldmann-Wüstefeld ◽  
Edward Awh ◽  
Edward K. Vogel
Keyword(s):  
Working Memory ◽  
Memory Task ◽  
Relevant Information ◽  
Alpha Power ◽  
Neural Signals ◽  
Neural Representations ◽  
Contralateral Delay Activity ◽  
The Face ◽  
Task Irrelevant ◽  
The Impact

Working memory maintains information so that it can be used in complex cognitive tasks. A key challenge for this system is to maintain relevant information in the face of task-irrelevant perturbations. Across two experiments, we investigated the impact of task-irrelevant interruptions on neural representations of working memory. We recorded EEG activity in humans while they performed a working memory task. On a subset of trials, we interrupted participants with salient but task-irrelevant objects. To track the impact of these task-irrelevant interruptions on neural representations of working memory, we measured two well-characterized, temporally sensitive EEG markers that reflect active, prioritized working memory representations: the contralateral delay activity and lateralized alpha power (8–12 Hz). After interruption, we found that contralateral delay activity amplitude momentarily sustained but was gone by the end of the trial. Lateralized alpha power was immediately influenced by the interrupters but recovered by the end of the trial. This suggests that dissociable neural processes contribute to the maintenance of working memory information and that brief irrelevant onsets disrupt two distinct online aspects of working memory. In addition, we found that task expectancy modulated the timing and magnitude of how these two neural signals responded to task-irrelevant interruptions, suggesting that the brain's response to task-irrelevant interruption is shaped by task context.

Counteracting Implicit Conflicts by Electrical Inhibition of the Prefrontal Cortex

2016 ◽  
Vol 28 (11) ◽  
pp. 1737-1748 ◽  
Author(s):  
Philipp Alexander Schroeder ◽  
Roland Pfister ◽  
Wilfried Kunde ◽  
Hans-Christoph Nuerk ◽  
Christian Plewnia
Keyword(s):  
Working Memory ◽  
Direct Current ◽  
Transcranial Direct Current Stimulation ◽  
Number Line ◽  
Irrelevant Information ◽  
Snarc Effect ◽  
Direct Current Stimulation ◽  
Magnitude Processing ◽  
Automatic Activation ◽  
Task Irrelevant

Cognitive conflicts and distractions by task-irrelevant information often counteract effective and goal-directed behaviors. In some cases, conflicting information can even emerge implicitly, without an overt distractor, by the automatic activation of mental representations. For instance, during number processing, magnitude information automatically elicits spatial associations resembling a mental number line. This spatial–numerical association of response codes (SNARC) effect can modulate cognitive-behavioral performance but is also highly flexible and context-dependent, which points toward a critical involvement of working memory functions. Transcranial direct current stimulation to the PFC, in turn, has been effective in modulating working memory-related cognitive performance. In a series of experiments, we here demonstrate that decreasing activity of the left PFC by cathodal transcranial direct current stimulation consistently and specifically eliminates implicit cognitive conflicts based on the SNARC effect, but explicit conflicts based on visuospatial distraction remain unaffected. This dissociation is polarity-specific and appears unrelated to functional magnitude processing as classified by regular numerical distance effects. These data demonstrate a causal involvement of the left PFC in implicit cognitive conflicts based on the automatic activation of spatial–numerical processing. Corroborating the critical interaction of brain stimulation and neurocognitive functions, our findings suggest that distraction from goal-directed behavior by automatic activation of implicit, task-irrelevant information can be blocked by the inhibition of prefrontal activity.

scholarly journals Feature-Specific Neural Reactivation during Episodic Memory

2019 ◽  
Author(s):  
Michael B. Bone ◽  
Fahad Ahmad ◽  
Bradley R. Buchsbaum
Keyword(s):  
Frontal Cortex ◽  
Visual Features ◽  
Semantic Features ◽  
Low Level ◽  
Visual Hierarchy ◽  
Strict Interpretation ◽  
Novel Approach ◽  
High Level ◽  
Hierarchical Representations

AbstractWhen recalling an experience of the past, many of the component features of the original episode may be, to a greater or lesser extent, reconstructed in the mind’s eye. There is strong evidence that the pattern of neural activity that occurred during an initial perceptual experience is recreated during episodic recall (neural reactivation), and that the degree of reactivation is correlated with the subjective vividness of the memory. However, while we know that reactivation occurs during episodic recall, we have lacked a way of precisely characterizing the contents—in terms of its featural constituents—of a reactivated memory. Here we present a novel approach, feature-specific informational connectivity (FSIC), that leverages hierarchical representations of image stimuli derived from a deep convolutional neural network to decode neural reactivation in fMRI data collected while participants performed an episodic recall task. We show that neural reactivation associated with low-level visual features (e.g. edges), high-level visual features (e.g. facial features), and semantic features (e.g. “terrier”) occur throughout the dorsal and ventral visual streams and extend into the frontal cortex. Moreover, we show that reactivation of both low- and high-level visual features correlate with the vividness of the memory, whereas only reactivation of low-level features correlates with recognition accuracy when the lure and target images are semantically similar. In addition to demonstrating the utility of FSIC for mapping feature-specific reactivation, these findings resolve the relative contributions of low- and high-level features to the vividness of visual memories, clarify the role of the frontal cortex during episodic recall, and challenge a strict interpretation the posterior-to-anterior visual hierarchy.

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