Canonical Size in Haptic Drawings

Perception ◽  
2021 ◽  
Vol 50 (1) ◽  
pp. 97-100
Author(s):  
Magdalena Szubielska ◽  
Marcin Wojtasiński
Keyword(s):  
Real World ◽  
Physical Size ◽  
Visual Domain ◽  
Familiar Objects

This study aimed to test differences in drawn size of familiar objects of different physical size in haptic drawings produced by blindfolded sighted participants. Using two sizes of the foil sheets on which they made convex drawings, they drew one object per foil. The results showed that the size of drawings increased linearly with the rising rank of real-world size. Although larger drawings were created on larger foils than on smaller ones, the ratio of the object drawn size within the foil sheet size did not differ across foil sizes. Hence, canonical size—a phenomenon known so far from studies on the visual domain—revealed here in a task performed in the haptic domain.

scholarly journals Canonical Size for Real-Word Objects in Drawings Performed under Haptic Control

2020 ◽  
Vol 23 (2) ◽  
pp. 191-200
Author(s):  
Magdalena Szubielska ◽  
Marcin Wojtasiński ◽  
Katarzyna Biedroń ◽  
Mateusz Bobel ◽  
Natalia Chudziak
Keyword(s):  
Size Effect ◽  
Real World ◽  
Real Word ◽  
Object Processing ◽  
Physical Size ◽  
Drawing Task ◽  
Physical Objects ◽  
Visual Size ◽  
Visual Domain ◽  
Haptic Control

To date canonical size for physical objects has been exclusively investigated in the visual domain and termed canonical visual size. As the visual and haptic modalities are interconnected in object processing, we have investigated if canonical size occurs in the tactile domain, namely, in embossed drawings made by sighted adults when blindfolded. 17 participants were asked to draw 16 objects of 8 different ranks of physical size. In the visual domain, they drew on sheets of paper, and in the tactile domain, they drew (when blindfolded) on special plastic sheets for embossed graphics haptically controlling the performance with hands. In both the visual and the tactile domain the size of drawings increased linearly with the logarithm of the physical size of real-world objects indicating occurrence of canonical size effect in both domains. Our findings demonstrated that canonical size is not only visual in character but that it is also revealed in a haptic drawing task. It suggests that spatial images (at least visual and tactile) are shared instead of being unimodal in nature.

scholarly journals Semantic knowledge influences visual working memory in adults and children

PLoS ONE ◽  
2020 ◽  
Vol 15 (11) ◽  
pp. e0241110
Author(s):  
Ariel Starr ◽  
Mahesh Srinivasan ◽  
Silvia A. Bunge
Keyword(s):  
Working Memory ◽  
Visual Working Memory ◽  
Real World ◽  
Visual Information ◽  
Semantic Knowledge ◽  
Visual Features ◽  
Low Level ◽  
Adults And Children ◽  
Abstract Stimuli ◽  
Familiar Objects

We can retain only a portion of the visual information that we encounter within our visual working memory. Which factors influence how much information we can remember? Recent studies have demonstrated that the capacity of visual working memory is influenced by the type of information to be remembered and is greater for real-world objects than for abstract stimuli. One explanation for this effect is that the semantic knowledge associated with real-world objects makes them easier to maintain in working memory. Previous studies have indirectly tested this proposal and led to inconsistent conclusions. Here, we directly tested whether semantic knowledge confers a benefit for visual working memory by using familiar and unfamiliar real-world objects. We found a mnemonic benefit for familiar objects in adults and children between the ages of 4 and 9 years. Control conditions ruled out alternative explanations, namely the possibility that the familiar objects could be more easily labeled or that there were differences in low-level visual features between the two types of objects. Together, these findings demonstrate that semantic knowledge influences visual working memory, which suggests that the capacity of visual working memory is not fixed but instead fluctuates depending on what has to be remembered.

scholarly journals A Subspace Based Transfer Joint Matching with Laplacian Regularization for Visual Domain Adaptation

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4367
Author(s):  
Rakesh Kumar Sanodiya ◽  
Leehter Yao
Keyword(s):  
Real World ◽  
Nearest Neighbor ◽  
Domain Adaptation ◽  
Clustering Algorithms ◽  
K Nearest Neighbor ◽  
Target Domain ◽  
Illumination Variation ◽  
Machine Learning Classification ◽  
Visual Domain ◽  
Laplacian Regularization

In a real-world application, the images taken by different cameras with different conditions often incur illumination variation, low-resolution, different poses, blur, etc., which leads to a large distribution difference or gap between training (source) and test (target) images. This distribution gap is challenging for many primitive machine learning classification and clustering algorithms such as k-Nearest Neighbor (k-NN) and k-means. In order to minimize this distribution gap, we propose a novel Subspace based Transfer Joint Matching with Laplacian Regularization (STJML) method for visual domain adaptation by jointly matching the features and re-weighting the instances across different domains. Specifically, the proposed STJML-based method includes four key components: (1) considering subspaces of both domains; (2) instance re-weighting; (3) it simultaneously reduces the domain shift in both marginal distribution and conditional distribution between the source domain and the target domain; (4) preserving the original similarity of data points by using Laplacian regularization. Experiments on three popular real-world domain adaptation problem datasets demonstrate a significant performance improvement of our proposed method over published state-of-the-art primitive and domain adaptation methods.

scholarly journals How big should this object be? Perceptual influences on viewing-size preferences

2021 ◽  
Author(s):  
Yi-Chia Chen ◽  
Arturo Deza ◽  
Talia Konkle
Keyword(s):  
Real World ◽  
Visual Processing ◽  
Explicit Knowledge ◽  
Semantic Knowledge ◽  
Choice Task ◽  
Visual Features ◽  
Physical Size ◽  
Forced Choice Task ◽  
Visual Size ◽  
Form Information

When viewing objects depicted in a frame, observers prefer to view large objects like cars in larger sizes and smaller objects like cups in smaller sizes. That is, the visual size of an object that "looks best" is linked to its typical physical size in the world. Why is this the case? One intuitive possibility is that these preferences are driven by semantic knowledge: For example, when we recognize a sofa, we access our knowledge about its real-world size, and this influences what size we prefer to view the sofa within a frame. However, might visual processing play a role in this phenomenon--that is, do visual features that are related to big and small objects look better at big and small visual sizes, respectively, even when observers do not have explicit access to semantic knowledge about the objects? To test this possibility, we used "texform" images, which are synthesized versions of recognizable objects, which critically retain local perceptual texture and coarse form information, but are no longer explicitly recognizable. To test for visual size preferences, we used a two-interval forced choice task, in which each texform was presented at the preferred visual size of its corresponding original image, and a visual size slightly bigger or smaller. Observers consistently selected the texform presented at the canonical visual size as the more aesthetically pleasing one. These results suggest that the preferred visual size of an object depends not only on explicit knowledge of its real-world size, but also can be evoked by mid-level visual features that systematically covary with an object's real-world size.

scholarly journals Visual Domain Adaptation by Consensus-Based Transfer to Intermediate Domain

2020 ◽  
Vol 34 (07) ◽  
pp. 10655-10662
Author(s):  
Jongwon Choi ◽  
Youngjoon Choi ◽  
Jihoon Kim ◽  
Jinyeop Chang ◽  
Ilhwan Kwon ◽  
...  
Keyword(s):  
Real World ◽  
Domain Adaptation ◽  
Ensemble Classifiers ◽  
Training Algorithm ◽  
Target Domain ◽  
Intermediate Domain ◽  
Previous State ◽  
Real World Datasets ◽  
Visual Domain ◽  
Adaptation Scenarios

We describe an unsupervised domain adaptation framework for images by a transform to an abstract intermediate domain and ensemble classifiers seeking a consensus. The intermediate domain can be thought as a latent domain where both the source and target domains can be transferred easily. The proposed framework aligns both domains to the intermediate domain, which greatly improves the adaptation performance when the source and target domains are notably dissimilar. In addition, we propose an ensemble model trained by confusing multiple classifiers and letting them make a consensus alternately to enhance the adaptation performance for ambiguous samples. To estimate the hidden intermediate domain and the unknown labels of the target domain simultaneously, we develop a training algorithm using a double-structured architecture. We validate the proposed framework in hard adaptation scenarios with real-world datasets from simple synthetic domains to complex real-world domains. The proposed algorithm outperforms the previous state-of-the-art algorithms on various environments.

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.

Current Issues: Evidence-Based Practice for the Real World: Thoughts on Clinical Education in Communication Disorders

2010 ◽  
Vol 20 (3) ◽  
pp. 100-105 ◽  
Author(s):  
Anne K. Bothe
Keyword(s):  
Clinical Education ◽  
Real World ◽  
Evidence Based Practice ◽  
Communication Disorders ◽  
University Faculty ◽  
Evidence Based ◽  
Speech Language Pathology ◽  
Basic Principles ◽  
The Real ◽  
Language Pathology

This article presents some streamlined and intentionally oversimplified ideas about educating future communication disorders professionals to use some of the most basic principles of evidence-based practice. Working from a popular five-step approach, modifications are suggested that may make the ideas more accessible, and therefore more useful, for university faculty, other supervisors, and future professionals in speech-language pathology, audiology, and related fields.

Qualitative Case Study Research Design

2015 ◽  
Vol 25 (1) ◽  
pp. 39-45 ◽  
Author(s):  
Jennifer Tetnowski
Keyword(s):  
Research Design ◽  
Real World ◽  
Qualitative Case Study ◽  
Case Study Research ◽  
Study Research

Qualitative case study research can be a valuable tool for answering complex, real-world questions. This method is often misunderstood or neglected due to a lack of understanding by researchers and reviewers. This tutorial defines the characteristics of qualitative case study research and its application to a broader understanding of stuttering that cannot be defined through other methodologies. This article will describe ways that data can be collected and analyzed.

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