Supplemental Material for Statistical Regularities Induce Spatial as well as Feature-Specific Suppression

2019 ◽  
Keyword(s):  
Specific Suppression ◽  
Statistical Regularities

scholarly journals Statistical regularities induce spatial as well as feature-specific suppression

2019 ◽  
Author(s):  
Michel Failing ◽  
Tobias Feldmann-Wüstefeld ◽  
Benchi Wang ◽  
Christian Nicolas Leon Olivers ◽  
Jan Theeuwes
Keyword(s):  
High Probability ◽  
Search Task ◽  
Visual Search Task ◽  
Visual Features ◽  
Visual Environment ◽  
Attentional Orienting ◽  
Specific Suppression ◽  
Joint Influence ◽  
Statistical Regularities ◽  
Feature Dimension

We are constantly extracting regularities from the visual environment to optimize attentional orienting. Here we examine the phenomenon that recurrent presentation of distractors in a specific location leads to its attentional suppression. Specifically, we address the question whether suppression is specific to the spatial regularities of distractors or also extends to visual features bearing statistical regularities. To that end, we used a visual search task with two high probability locations, each showing one of two distractor types more often than the other. At these high probability locations, target processing was impaired and attentional capture by either distractor was reduced, consistent with feature-unspecific spatial suppression. However, suppression was more facilitated when the distractor feature was presented at the high probability location that matched its features, suggesting feature-specific suppression. Interestingly, feature-unspecific spatial suppression only spread between locations when distractors varied within a feature dimension (e.g. red and green) but not when they varied across feature dimensions (e.g., red and square). Our findings thus demonstrate a joint influence of implicitly learned spatial and feature regularities on attention and reveal how the visual system can benefit from complex statistical regularities.

scholarly journals Statistical regularities induce spatial as well as feature-specific suppression.

2019 ◽  
Vol 45 (10) ◽  
pp. 1291-1303 ◽  
Author(s):  
Michel Failing ◽  
Tobias Feldmann-Wüstefeld ◽  
Benchi Wang ◽  
Christian Olivers ◽  
Jan Theeuwes
Keyword(s):  
Specific Suppression ◽  
Statistical Regularities

The Timecourse of Attention to Statistical Regularities in New Visual Environments

2014 ◽  
Author(s):  
Maria Giammarco ◽  
Brendon Samuels ◽  
Mark J. Fenske ◽  
Naseem Al-Aidroos
Keyword(s):  
Statistical Regularities

Prior real-world experience influences non-linguistic statistical learning

2020 ◽  
Author(s):  
Stephen Charles Van Hedger ◽  
Ingrid Johnsrude ◽  
Laura Batterink
Keyword(s):  
Statistical Learning ◽  
Real World ◽  
Language Experience ◽  
Pitch Range ◽  
Test Items ◽  
Instrument Measuring ◽  
Statistical Regularities ◽  
Context Free ◽  
Specific Learning

Listeners are adept at extracting regularities from the environment, a process known as statistical learning (SL). SL has been generally assumed to be a form of “context-free” learning that occurs independently of prior knowledge, and SL experiments typically involve exposing participants to presumed novel regularities, such as repeating nonsense words. However, recent work has called this assumption into question, demonstrating that learners’ previous language experience can considerably influence SL performance. In the present experiment, we tested whether previous knowledge also shapes SL in a non-linguistic domain, using a paradigm that involves extracting regularities over tone sequences. Participants learned novel tone sequences, which consisted of pitch intervals not typically found in Western music. For one group of participants, the tone sequences used artificial, computerized instrument sounds. For the other group, the same tone sequences used familiar instrument sounds (piano or violin). Knowledge of the statistical regularities was assessed using both trained sounds (measuring specific learning) and sounds that differed in pitch range and/or instrument (measuring transfer learning). In a follow-up experiment, two additional testing sessions were administered to gauge retention of learning (one day and approximately one-week post-training). Compared to artificial instruments, training on sequences played by familiar instruments resulted in reduced correlations among test items, reflecting more idiosyncratic performance. Across all three testing sessions, learning of novel regularities presented with familiar instruments was worse compared to unfamiliar instruments, suggesting that prior exposure to music produced by familiar instruments interfered with new sequence learning. Overall, these results demonstrate that real-world experience influences SL in a non-linguistic domain, supporting the view that SL involves the continuous updating of existing representations, rather than the establishment of entirely novel ones.

Spatial suppression due to statistical regularities is driven by distractor suppression not by target activation

2018 ◽  
Author(s):  
Michel Failing ◽  
Benchi Wang ◽  
Jan Theeuwes
Keyword(s):  
High Probability ◽  
Target Location ◽  
Target Selection ◽  
Visual Space ◽  
Alternative Interpretation ◽  
Distractor Location ◽  
Target Presentation ◽  
Statistical Regularities ◽  
Low Probability ◽  
Presentation Experiment

Where and what we attend to is not only determined by what we are currently looking for but also by what we have encountered in the past. Recent studies suggest that biasing the probability by which distractors appear at locations in visual space may lead to attentional suppression of high probability distractor locations which effectively reduces capture by a distractor but also impairs target selection at this location. However, in many of these studies introducing a high probability distractor location was tantamount to increasing the probability of the target appearing in any of the other locations (i.e. the low probability distractor locations). Here, we investigate an alternative interpretation of previous findings according to which attentional selection at high probability distractor locations is not suppressed. Instead, selection at low probability distractor locations is facilitated. In two visual search tasks, we found no evidence for this hypothesis: neither when there was only a bias in target presentation but no bias in distractor presentation (Experiment 1), nor when there was only a bias in distractor presentation but no bias in target presentation (Experiment 2). We conclude that recurrent presentation of a distractor in a specific location leads to attentional suppression of that location through a mechanism that is unaffected by any regularities regarding the target location.

POLAR LOW DETECTION USING SOLAB SIOWS ARCTIC PORTAL

2017 ◽  
Author(s):  
Olga Mashtaler ◽  
Olga Mashtaler ◽  
Alexander Myasoedov ◽  
Alexander Myasoedov ◽  
Elizaveta Zabolotskikh ◽  
...  
Keyword(s):  
Model Development ◽  
Sharp Decrease ◽  
Open Water ◽  
The Arctic ◽  
Wind Fields ◽  
Polar Lows ◽  
Statistical Regularities ◽  
Meteorological Observations ◽  
Strong Winds ◽  
Synthetic Aperture Radars

The relevance of the polar lows (PLs) research is justified by their great destructive power and creation of threat to the safety of navigation in the high latitudes and along the Northern Sea Route. The most dangerous effects on maritime activities are strong winds, waves and icing. In addition, the study of the PLs acquires relevance due to the sharp decrease of the sea ice area in the Arctic in recent years and the emergence of areas of open water, suitable for the appearance and development of PLs. However, despite the importance of PLs, they are apparently not sufficiently studied. As there are no meteorological observations in the areas of their appearance, the main source of information about them are satellite observations. By using images on the SOLab SIOWS Arctic Portal from multiple satellites operating in the IR and visible ranges (e.g., MODIS and AVHRR), and using near-water wind fields from high resolution synthetic aperture radars (Sentine-1, ASAR) and low resolution scatterometers (ASCAT), we identify polar lows in various parts of the Arctic, revealing statistical regularities in the appearance of PLs, their distribution and intensity. Collected database of Pls and their characteristics will be used for further PLs forecasting model development.

STRUCTURAL CONNECTIONS AND INTERCHANGEABILITY OF ASYMMETRIC TYPES OF THEORETICAL DISTRIBUTIONS

2020 ◽  
Vol 6 (8(77)) ◽  
pp. 13-17
Author(s):  
Azimkhan Kurmankozhayev ◽  
Elmira Seilbekovna Yesbergenova
Keyword(s):  
Weibull Distribution ◽  
Empirical Analysis ◽  
Gamma Distribution ◽  
Statistical Data ◽  
Distribution Patterns ◽  
Structural Relationships ◽  
Statistical Regularities ◽  
Structural Connections ◽  
Characteristic Features ◽  
Structural Connection

Presented the results of evaluation of structural connection, identity and interchangeability of main asymmetric types of theoretical distributions most often acceptable for assessing the distributions of various indicators in geology and technology. The method of empirical analysis and statistical inference was used with the involvement of nonparametric facts according to the distribution patterns. The analysis of the empirical results of the application of the lognormal, gamma distribution and the Weibull distribution with the involvement of extensive statistical data from literary and research sources is carried out. The characteristic features and statistical regularities of distributions inherent to them are revealed, estimated statistical conclusions are obtained, according to which structural relationships between the functions of the lognormal, gamma and Weibull distributions are revealed. The identity and authenticity of the development of probabilistic frequencies in their application have been established, the complex geometric "image" of asymmetry inherent to these types of distributions is generalized. Structural relationships and interchangeability of asymmetric types of distributions are recommended to increase the reliability and credibility of the estimated choice of distribution in conditions of uncertainty and insignificance of statistical data when solving problems associated with forecasts, technological and computer developments.

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