scholarly journals Evidence from contralateral delay activity that proto-objects are a good approximation of real-world set size

2021 ◽  
Vol 21 (9) ◽  
pp. 1880
Author(s):  
Michael Miuccio ◽  
Gregory Zelinsky ◽  
Joseph Schmidt
Keyword(s):  
Real World ◽  
Set Size ◽  
Contralateral Delay Activity

scholarly journals Estimating the statistical power to detect set‐size effects in contralateral delay activity

2021 ◽  
Vol 58 (5) ◽  
Author(s):  
William X. Q. Ngiam ◽  
Kirsten C. S. Adam ◽  
Colin Quirk ◽  
Edward K. Vogel ◽  
Edward Awh
Keyword(s):  
Size Effects ◽  
Statistical Power ◽  
Set Size ◽  
Contralateral Delay Activity ◽  
Set Size Effects

Validation-Based Sparse Gaussian Process Classifier Design

2009 ◽  
Vol 21 (7) ◽  
pp. 2082-2103 ◽  
Author(s):  
Shirish Shevade ◽  
S. Sundararajan
Keyword(s):  
Bayesian Methods ◽  
Real World ◽  
Basis Vector ◽  
Data Sets ◽  
Training Set ◽  
Classifier Design ◽  
Set Size ◽  
Benchmark Data ◽  
Regression Problems ◽  
Classification And Regression

Gaussian processes (GPs) are promising Bayesian methods for classification and regression problems. Design of a GP classifier and making predictions using it is, however, computationally demanding, especially when the training set size is large. Sparse GP classifiers are known to overcome this limitation. In this letter, we propose and study a validation-based method for sparse GP classifier design. The proposed method uses a negative log predictive (NLP) loss measure, which is easy to compute for GP models. We use this measure for both basis vector selection and hyperparameter adaptation. The experimental results on several real-world benchmark data sets show better or comparable generalization performance over existing methods.

scholarly journals Depth and Size Information Reduce Effective Set Size for Visual Search in Real-World Scenes

2011 ◽  
Vol 11 (11) ◽  
pp. 1334-1334 ◽  
Author(s):  
A. M. Sherman ◽  
M. R. Greene ◽  
J. M. Wolfe
Keyword(s):  
Visual Search ◽  
Real World ◽  
Size Information ◽  
Set Size

scholarly journals Predicting Search Performance in Heterogeneous Visual Search Scenes with Real-World Objects

2017 ◽  
Vol 3 (1) ◽  
Author(s):  
Zhiyuan Wang ◽  
Simona Buetti ◽  
Alejandro Lleras
Keyword(s):  
Visual Search ◽  
Real World ◽  
Visual Processing ◽  
Search Performance ◽  
Computational Simulations ◽  
Fixed Target ◽  
Set Size ◽  
Search Data ◽  
Exhaustive Processing ◽  
Processing Architecture

Previous work in our lab has demonstrated that efficient visual search with a fixed target has a reaction time by set size function that is best characterized by logarithmic curves. Further, the steepness of these logarithmic curves is determined by the similarity between target and distractor items (Buetti et al., 2016). A theoretical account of these findings was proposed, namely that a parallel, unlimited capacity, exhaustive processing architecture is underlying such data. Here, we conducted two experiments to expand these findings to a set of real-world stimuli, in both homogeneous and heterogeneous search displays. We used computational simulations of this architecture to identify a way to predict RT performance in heterogeneous search using parameters estimated from homogeneous search data. Further, by examining the systematic deviation from our predictions in the observed data, we found evidence that early visual processing for individual items is not independent. Instead, items in homogeneous displays seemed to facilitate each other’s processing by a multiplicative factor. These results challenge previous accounts of heterogeneity effects in visual search, and demonstrate the explanatory and predictive power of an approach that combines computational simulations and behavioral data to better understand performance in visual search.

scholarly journals Individual differences in visual search performance extend from artificial arrays to naturalistic environments.

2021 ◽  
Author(s):  
Thomas L. Botch ◽  
Brenda D. Garcia ◽  
Yeo Bi Choi ◽  
Caroline E. Robertson
Keyword(s):  
Individual Differences ◽  
Visual Search ◽  
Eye Movements ◽  
Real World ◽  
Search Task ◽  
Visual Search Task ◽  
Search Behavior ◽  
Search Performance ◽  
Set Size ◽  
Visual Clutter

Visual search is a universal human activity in naturalistic environments. Traditionally, visual search is investigated under tightly controlled conditions, where head-restricted participants locate a minimalistic target in a cluttered array presented on a computer screen. Do classic findings of visual search extend to naturalistic settings, where participants actively explore complex, real-world scenes? Here, we leverage advances in virtual reality (VR) technology to relate individual differences in classic visual search paradigms to naturalistic search behavior. In a naturalistic visual search task, participants looked for an object within their environment via a combination of head-turns and eye-movements using a head-mounted display. Then, in a classic visual search task, participants searched for a target within a simple array of colored letters using only eye-movements. We tested how set size, a property known to limit visual search within computer displays, predicts the efficiency of search behavior inside immersive, real-world scenes that vary in levels of visual clutter. We found that participants' search performance was impacted by the level of visual clutter within real-world scenes. Critically, we also observed that individual differences in visual search efficiency in classic search predicted efficiency in real-world search, but only when the comparison was limited to the forward-facing field of view for real-world search. These results demonstrate that set size is a reliable predictor of individual performance across computer-based and active, real-world visual search behavior.

scholarly journals Estimating the statistical power to detect set size effects in contralateral delay activity

2020 ◽  
Author(s):  
William Xiang Quan Ngiam ◽  
Kirsten C. S. Adam ◽  
Colin Quirk ◽  
Edward K. Vogel ◽  
Ed Awh
Keyword(s):  
Effect Size ◽  
Statistical Power ◽  
Simulated Data ◽  
Multilevel Modelling ◽  
Event Related Potential ◽  
Set Size ◽  
Contralateral Delay Activity ◽  
Potential Component ◽  
Robust Tests ◽  
Set Size Effects

The contralateral delay activity (CDA) is an event-related potential component commonly used to examine the online processes of visual working memory. Here, we provide a robust analysis of the statistical power that is needed to achieve reliable and reproducible results with the CDA. Using two very large EEG datasets that examined the contrast between CDA amplitude with set sizes 2 and 6 items (Unsworth et al., 2015) and set sizes 2 and 4 items (Hakim et al., 2019), we present a subsampling analysis that estimates the statistical power achieved with varying numbers of subjects and trials based on the proportion of significant tests in 10,000 iterations. We also generated simulated data using Bayesian multilevel modelling to estimate power beyond the bounds of the original datasets. The number of trials and subjects required depends critically on the effect size. Detecting the presence of the CDA – a reliable difference between contralateral and ipsilateral electrodes during the memory period – required only 30-50 clean trials with a sample of 25 subjects to achieve approximately 80% statistical power. However, for detecting a difference in CDA amplitude between two set sizes, a substantially larger number of trials and subjects was required; approximately 400 clean trials with 25 subjects to achieve 80% power. Thus, to achieve robust tests of how CDA activity differs across conditions, it is essential to be mindful of the estimated effect size. We recommend researchers designing experiments to detect set size differences in the CDA collect substantially more trials per subject.

Replications can cause distorted belief in scientific progress

2018 ◽  
Vol 41 ◽  
Author(s):  
Michał Białek
Keyword(s):  
Real World ◽  
Scientific Progress ◽  
The Public ◽  
Psychological Science

AbstractIf we want psychological science to have a meaningful real-world impact, it has to be trusted by the public. Scientific progress is noisy; accordingly, replications sometimes fail even for true findings. We need to communicate the acceptability of uncertainty to the public and our peers, to prevent psychology from being perceived as having nothing to say about reality.

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