scholarly journals Temporal integration of feature probability distributions in working memory

2020 ◽  
Author(s):  
Sabrina Hansmann-Roth ◽  
Sóley Thorsteinsdóttir ◽  
Joy Geng ◽  
Arni Kristjansson
Keyword(s):  
Visual Search ◽  
Visual System ◽  
Uniform Distribution ◽  
Probability Distributions ◽  
Temporal Integration ◽  
Target Feature ◽  
Target Distribution ◽  
Distribution Shape ◽  
Complex Target ◽  
Feature Probability

Humans are surprisingly good at learning the characteristics of their visual environment. Recent studies have revealed that not only can the visual system learn repeated features of visual search distractors, but their actual probability distributions. Search times were determined by the frequency of distractor features over consecutive search trials. Distractor distributions involve many exemplars on each trial, but whether observers can learn distributions where only a single exemplar from the distribution is presented on each trial is unknown. Here, we investigated potential learning of probability distributions of single targets during visual search. Over blocks of trials observers searched for an oddly-colored target that was drawn from either a Gaussian or uniform distribution. Not only was search influenced by the repetition of a target feature but more interestingly also by the probability of that feature within trial blocks. The same search targets, coming from the extremes of the two distributions were found significantly slower during the blocks where the distractors were drawn from a Gaussian distribution than from a uniform distribution indicating that observers were sensitive to the target probability determined by the distribution shape. In Experiment 2 we replicated the effect using binned distributions and revealed the limitations of target distribution encoding by using a more complex target distribution. Our results demonstrate detailed internal representations of target feature distributions and that the visual system integrates probability distributions of target colors over surprisingly long trial sequences.

scholarly journals Testing temporal integration of feature probability distributions using role-reversal effects in visual search

2020 ◽  
Author(s):  
Ömer Dağlar Tanrıkulu ◽  
Andrey Chetverikov ◽  
Arni Kristjansson
Keyword(s):  
Visual Search ◽  
Visual System ◽  
Response Times ◽  
Probability Distributions ◽  
Temporal Integration ◽  
Internal Representation ◽  
Statistical Properties ◽  
Role Reversal ◽  
Sensory History ◽  
Feature Probability

The visual system is sensitive to statistical properties of complex scenes and can encode feature probability distributions in detail. This encoding could reflect a passive process due to the visual system’s sensitivity to temporal perturbations in the input or a more active process of building probabilistic representations. To investigate this, we examined how observers temporally integrate two different orientation distributions from sequentially presented visual search trials. If the encoded probabilistic information is used in a Bayesian optimal way, observers should weigh more reliable information more strongly, such as feature distributions with low variance. We therefore manipulated the variance of the two feature distributions. Participants performed sequential odd-one-out visual search for an oddly oriented line among distractors. During successive learning trials, the distractor orientations were sampled from two different Gaussian distributions on alternating trials. Then, observers performed a ‘test trial’ where the orientations of the target and distractors were switched, allowing to assess observer’s internal representation of distractor distributions based on changes in response times. In three experiments we observed that observer’s search times on test trials depended mainly on the very last learning trial, indicating little temporal integration. Since temporal integration has been previously observed with this method, we conclude that when the input is unreliable, the visual system relies on the most recent stimulus instead of integrating it with previous ones. This indicates that the visual system prefers to utilize sensory history when the statistical properties of the environment are relatively stable

scholarly journals Testing temporal integration of feature probability distributions using role-reversal effects in visual search

2021 ◽  
Vol 188 ◽  
pp. 211-226
Author(s):  
Ömer Dağlar Tanrıkulu ◽  
Andrey Chetverikov ◽  
Árni Kristjánsson
Keyword(s):  
Visual Search ◽  
Probability Distributions ◽  
Temporal Integration ◽  
Role Reversal ◽  
Feature Probability

scholarly journals Temporal integration of feature probability distributions in visual working memory

2021 ◽  
Vol 21 (9) ◽  
pp. 1969
Author(s):  
Sabrina Hansmann-Roth ◽  
Sóley Thorsteinsdóttir ◽  
Joy Geng ◽  
Árni Kristjánsson
Keyword(s):  
Working Memory ◽  
Visual Working Memory ◽  
Probability Distributions ◽  
Temporal Integration ◽  
Feature Probability

scholarly journals Temporal integration of feature probability distributions

2022 ◽  
Author(s):  
Sabrina Hansmann-Roth ◽  
Sóley Þorsteinsdóttir ◽  
Joy J. Geng ◽  
Árni Kristjánsson
Keyword(s):  
Probability Distributions ◽  
Temporal Integration ◽  
Feature Probability

scholarly journals Feature distribution learning by passive exposure

2021 ◽  
Author(s):  
David Pascucci ◽  
Gizay Ceylan ◽  
Arni Kristjansson
Keyword(s):  
Visual Search ◽  
Response Times ◽  
Search Display ◽  
Perceptual System ◽  
Active Engagement ◽  
Target Feature ◽  
Initial Exposure ◽  
Passive Exposure ◽  
Distribution Shape ◽  
Exposure Stage

Humans can rapidly estimate the statistical properties of groups of stimuli, including their average and variability. But recent studies of so-called Feature Distribution Learning (FDL) have shown that observers can quickly learn even more complex aspects of feature distributions. In FDL, observers learn the full shape of a distribution of features in a set of distractor stimuli and use this information to improve visual search: response times (RT) are slowed if the target feature lies inside the previous distractor distribution, and the RT patterns closely reflect the distribution shape. FDL requires only a few trials and is markedly sensitive to different distribution types. It is unknown, however, whether our perceptual system encodes feature distributions automatically and by passive exposure, or whether this learning requires active engagement with the stimuli. In two experiments, we sought to answer this question. During an initial exposure stage, participants passively viewed a display of 36 lines that included one orientation singleton or no singletons. In the following search display, they had to find an oddly oriented target. The orientations of the lines were determined either by a Gaussian or a uniform distribution. We found evidence for FDL only when the passive trials contained an orientation singleton. Under these conditions, RT decreased as a function of the orientation distance between the target and the exposed distractor distribution. These results suggest that FDL can occur by passive exposure, but only if an orientation singleton appears during exposure to the distribution.

W-Peakedness: A Simulation based Study

2015 ◽  
Vol 1 (1) ◽  
Author(s):  
Suboohi Safdar ◽  
Dr. Ejaz Ahmed
Keyword(s):  
Probability Distributions ◽  
Extensive Study ◽  
Descriptive Statistics ◽  
Target Point ◽  
Density Functions ◽  
Central Value ◽  
Distribution Shape ◽  
Simulation Based ◽  
Different Shapes ◽  
Kurtosis Coefficient

Kurtosis is a commonly used descriptive statistics. Kurtosis “Coefficient of excess” is critically reviewed in different aspects and is called as, measuring the fatness of the tails of the density functions, concentration towards the central value, scattering away from the target point or degree of peakedness of probability distribution. Kurtosis is referred to the shape of the distribution but many distributions having same kurtosis value may have different shapes while Kurtosis may exist when peak of a distribution is not in existence. Through extensive study of kurtosis on several distributions, Wu (2002) introduced a new measure called “W-Peakedness” that offers a fine capture of distribution shape to provide an intuitive measure of peakedness of the distribution which is inversely proportional to the standard deviation of the distribution. In this paper the work is extended for different others continuous probability distributions. Empirical results through simulation illustrate the proposed method to evaluate kurtosis by W-peakedness

scholarly journals Visual search for object categories is predicted by the representational architecture of high-level visual cortex

2017 ◽  
Vol 117 (1) ◽  
pp. 388-402 ◽  
Author(s):  
Michael A. Cohen ◽  
George A. Alvarez ◽  
Ken Nakayama ◽  
Talia Konkle
Keyword(s):  
Visual Cortex ◽  
Visual Search ◽  
Visual System ◽  
Visual Processing ◽  
Search Task ◽  
Visual Search Task ◽  
Visual Object ◽  
Neural Responses ◽  
Object Categories ◽  
High Level

Visual search is a ubiquitous visual behavior, and efficient search is essential for survival. Different cognitive models have explained the speed and accuracy of search based either on the dynamics of attention or on similarity of item representations. Here, we examined the extent to which performance on a visual search task can be predicted from the stable representational architecture of the visual system, independent of attentional dynamics. Participants performed a visual search task with 28 conditions reflecting different pairs of categories (e.g., searching for a face among cars, body among hammers, etc.). The time it took participants to find the target item varied as a function of category combination. In a separate group of participants, we measured the neural responses to these object categories when items were presented in isolation. Using representational similarity analysis, we then examined whether the similarity of neural responses across different subdivisions of the visual system had the requisite structure needed to predict visual search performance. Overall, we found strong brain/behavior correlations across most of the higher-level visual system, including both the ventral and dorsal pathways when considering both macroscale sectors as well as smaller mesoscale regions. These results suggest that visual search for real-world object categories is well predicted by the stable, task-independent architecture of the visual system. NEW & NOTEWORTHY Here, we ask which neural regions have neural response patterns that correlate with behavioral performance in a visual processing task. We found that the representational structure across all of high-level visual cortex has the requisite structure to predict behavior. Furthermore, when directly comparing different neural regions, we found that they all had highly similar category-level representational structures. These results point to a ubiquitous and uniform representational structure in high-level visual cortex underlying visual object processing.

scholarly journals An explicit investigation of the roles that feature distributions play in rapid visual categorization

2020 ◽  
Author(s):  
Hee Yeon Im ◽  
Natalia Tiurina ◽  
Igor Utochkin
Keyword(s):  
Visual System ◽  
Size Distribution ◽  
Size Distributions ◽  
Visual Array ◽  
Visual Categorization ◽  
Range Distribution ◽  
Distribution Shape ◽  
Ensemble Statistics ◽  
Distributional Information ◽  
Ensemble Representations

Ensemble representations are often described as efficient tools when summarizing features of multiple similar objects as a group. However, it can sometimes be more useful not to compute a single summary description for all of the objects if they are substantially different, for example, when they belong to entirely different categories. It was proposed that the visual system can efficiently use the distributional information of ensembles to decide whether simultaneously displayed items belong to single or several different categories. Here we directly tested how the feature distribution of items in a visual array affects an ability to discriminate individual items (Experiment 1) and sets (Experiments 2-3) when participants were instructed explicitly to categorize individual objects based on the median of size distribution. We varied the width (narrow or fat) as well as the shape (smooth or two-peaked) of distributions in order to manipulate the ease of ensemble extraction from the items. We found that observers unintentionally relied on the grand mean as a natural categorical boundary and that their categorization accuracy increased as a function of the size differences among individual items and a function of their separation from the grand mean. For ensembles drawn from two-peaked size distributions, participants showed better categorization performance. They were more accurate at judging within-category ensemble properties in other dimensions (centroid and orientation) and less biased by superset statistics. This finding corroborates the idea that the two-peaked feature distributions support the “segmentability” of spatially intermixed sets of objects. Our results emphasize important roles of ensemble statistics (mean, range, distribution shape) in explicit visual categorization.

scholarly journals Spatial Auditory Cueing for a Dynamic Three-Dimensional Virtual Reality Visual Search Task

2019 ◽  
Vol 63 (1) ◽  
pp. 1766-1770
Author(s):  
Rachel J. Cunio ◽  
David Dommett ◽  
Joseph Houpt
Keyword(s):  
Virtual Reality ◽  
Visual Search ◽  
Visual System ◽  
Spatial Information ◽  
Visual Awareness ◽  
Three Dimensional ◽  
Search Time ◽  
Primary Concern ◽  
Search Performance ◽  
Auditory Cues

Maintaining spatial awareness is a primary concern for operators, but relying only on visual displays can cause visual system overload and lead to performance decrements. Our study examined the benefits of providing spatialized auditory cues for maintaining visual awareness as a method of combating visual system overload. We examined visual search performance of seven participants in an immersive, dynamic (moving), three-dimensional, virtual reality environment both with no cues, non-masked, spatialized auditory cues, and masked, spatialized auditory cues. Results indicated a significant reduction in visual search time from the no-cue condition when either auditory cue type was presented, with the masked auditory condition slower. The results of this study can inform attempts to improve visual search performance in operational environments, such as determining appropriate display types for providing spatial information.

A New Mobile Visual Search System Based on the Human Visual System

2013 ◽  
Vol 461 ◽  
pp. 792-800
Author(s):  
Bo Zhao ◽  
Hong Wei Zhao ◽  
Ping Ping Liu ◽  
Gui He Qin
Keyword(s):  
Visual Search ◽  
Visual System ◽  
Human Visual System ◽  
Large Scale ◽  
Scale Invariant ◽  
Search System ◽  
Mobile Visual Search ◽  
Extraction Algorithm ◽  
Retrieval Scheme ◽  
Scale Invariant Feature

We describe a novel mobile visual search system based on the saliencymechanism and sparse coding principle of the human visual system (HVS). In the featureextraction step, we first divide an image into different regions using thesaliency extraction algorithm. Then scale-invariant feature transform (SIFT)descriptors in all regions are extracted while regional identities arepreserved based on their various saliency levels. According to the sparsecoding principle in the HVS, we adopt a local neighbor preserving Hash functionto establish the binary sparse expression of the SIFT features. In the searchingstep, the nearest neighbors matched to the hashing codes are processed accordingto different saliency levels. Matching scores of images in the database arederived from the matching of hashing codes. Subsequently, the matching scoresof all levels are weighed by degrees of saliency to obtain the initial set of results. In order to further ensure matching accuracy, we propose an optimized retrieval scheme based on global texture information. We conduct extensive experiments on an actual mobile platform in large-scale datasets by using Corel-1000. The resultsshow that the proposed method outperforms the state-of-the-art algorithms on accuracyrate, and no significant increase in the running time of the feature extractionand retrieval can be observed.

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