Thematic object pairs produce stronger and faster perceptual grouping than taxonomic pairs

2021 ◽  
Author(s):  
Joseph Nah ◽  
Joy Geng
Keyword(s):  
Visual Processing ◽  
Perceptual Grouping ◽  
Semantic Information ◽  
Semantic Knowledge ◽  
Perceptual Unit ◽  
Critical Difference ◽  
Common Event ◽  
Taxonomic Relationships ◽  
Different Types ◽  
Fundamental Units

While objects are fundamental units of vision that convey meaning, how different types of semantic knowledge affect perception is not fully understood. In contrast, the concept literature divides semantic information into taxonomic and thematic types. Taxonomic relationships reflect categorization by similarities (e.g., dog – wolf); thematic groups are based on complementary relationships shared within a common event (e.g., swimsuit – goggles; pool). A critical difference between these two information types is that thematic relationships are learned from the experienced co-occurrence of objects whereas taxonomic relationships are learned abstractly. In two studies, we test the hypothesis that visual processing of thematically related objects is more rapid because they serve as mutual visual primes and form a perceptual unit. The results demonstrate that learned co-occurrence not only shapes semantic knowledge, but also affects low level visual processing, revealing a link between how information is acquired (e.g., experienced vs. unobserved) and how it modulates perception.

How Well Do Distributional Models Capture Different Types of Semantic Knowledge?

2015 ◽  
Author(s):  
Dana Rubinstein ◽  
Effi Levi ◽  
Roy Schwartz ◽  
Ari Rappoport
Keyword(s):  
Semantic Knowledge ◽  
Different Types

scholarly journals Perceptual and conceptual processing of visual objects across the adult lifespan

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Rose Bruffaerts ◽  
◽  
Lorraine K. Tyler ◽  
Meredith Shafto ◽  
Kamen A. Tsvetanov ◽  
...  
Keyword(s):  
Visual Processing ◽  
Semantic Information ◽  
Fluid Intelligence ◽  
Naming Task ◽  
Object Processing ◽  
Adult Lifespan ◽  
Conceptual Processing ◽  
Mature Adults ◽  
Visual Objects ◽  
Age Related

Abstract Making sense of the external world is vital for multiple domains of cognition, and so it is crucial that object recognition is maintained across the lifespan. We investigated age differences in perceptual and conceptual processing of visual objects in a population-derived sample of 85 healthy adults (24–87 years old) by relating measures of object processing to cognition across the lifespan. Magnetoencephalography (MEG) was recorded during a picture naming task to provide a direct measure of neural activity, that is not confounded by age-related vascular changes. Multiple linear regression was used to estimate neural responsivity for each individual, namely the capacity to represent visual or semantic information relating to the pictures. We find that the capacity to represent semantic information is linked to higher naming accuracy, a measure of task-specific performance. In mature adults, the capacity to represent semantic information also correlated with higher levels of fluid intelligence, reflecting domain-general performance. In contrast, the latency of visual processing did not relate to measures of cognition. These results indicate that neural responsivity measures relate to naming accuracy and fluid intelligence. We propose that maintaining neural responsivity in older age confers benefits in task-related and domain-general cognitive processes, supporting the brain maintenance view of healthy cognitive ageing.

The Role of Repetition and Associative Interference in New Semantic Learning in Amnesia: A Case Experiment

1993 ◽  
Vol 5 (4) ◽  
pp. 375-389 ◽  
Author(s):  
C. A. Gordon Hayman ◽  
Carol A. Macdonald ◽  
Endel Tulving
Keyword(s):  
Semantic Information ◽  
Semantic Knowledge ◽  
Amnesic Patient ◽  
Factual Information ◽  
Associative Interference ◽  
Extensive Experiment ◽  
Semantic Learning ◽  
Control Subjects ◽  
Target Words

The question of whether globally amnesic subjects can learn new semantic (factual) information is controversial. Some students of amnesia believe that they can, others that they cannot. In this article we report an extensive experiment conducted with the amnesic patient K.C. in which we examined the role of repetition and associative interference in his learning of new semantic information. In the course of 8 study sessions distributed over 4 weeks, we taught K.C. novel, amusing definitions of 96 target words (e.g., “a talkative featherbrain—PARAKEET”). We varied systematically the degree of both pre-experimental and intraexperimental associative interference, as well as the amount of study. The results of the experiment showed that K.C. can learn new semantic knowledge, and retain it over a period as long as 30 months indistinguishably from control subjects. The results further showed that the efficacy of such learning depends critically on both repetition of the material and the absence, or minimization, of pre-experimental and intraexperimental associative interference. These findings suggest that the extent to which at least some amnesic patients can acquire and retain new semantic knowledge depends on the conditions under which learning occurs, and that unqualified statements regarding the deficiency or absence of such learning in amnesia are not justified.

scholarly journals Defining the Units of Competition: Influences of Perceptual Organization on Competitive Interactions in Human Visual Cortex

2010 ◽  
Vol 22 (11) ◽  
pp. 2417-2426 ◽  
Author(s):  
Stephanie A. McMains ◽  
Sabine Kastner
Keyword(s):  
Visual Cortex ◽  
Perceptual Organization ◽  
Visual Processing ◽  
Perceptual Grouping ◽  
Neural Representation ◽  
Competitive Interactions ◽  
Illusory Figure ◽  
Processing Stream ◽  
Neural Processes ◽  
Early Visual Cortex

Multiple stimuli that are present simultaneously in the visual field compete for neural representation. At the same time, however, multiple stimuli in cluttered scenes also undergo perceptual organization according to certain rules originally defined by the Gestalt psychologists such as similarity or proximity, thereby segmenting scenes into candidate objects. How can these two seemingly orthogonal neural processes that occur early in the visual processing stream be reconciled? One possibility is that competition occurs among perceptual groups rather than at the level of elements within a group. We probed this idea using fMRI by assessing competitive interactions across visual cortex in displays containing varying degrees of perceptual organization or perceptual grouping (Grp). In strong Grp displays, elements were arranged such that either an illusory figure or a group of collinear elements were present, whereas in weak Grp displays the same elements were arranged randomly. Competitive interactions among stimuli were overcome throughout early visual cortex and V4, when elements were grouped regardless of Grp type. Our findings suggest that context-dependent grouping mechanisms and competitive interactions are linked to provide a bottom–up bias toward candidate objects in cluttered scenes.

scholarly journals Listening to speech and non-speech sounds activates phonological and semantic knowledge differently

2020 ◽  
Vol 73 (8) ◽  
pp. 1135-1149 ◽  
Author(s):  
James Bartolotti ◽  
Scott R Schroeder ◽  
Sayuri Hayakawa ◽  
Sirada Rochanavibhata ◽  
Peiyao Chen ◽  
...  
Keyword(s):  
Auditory Processing ◽  
Semantic Information ◽  
Semantic Knowledge ◽  
Phonological Information ◽  
Opposite Pattern ◽  
Semantic Access ◽  
Word Forms ◽  
Time Courses ◽  
The Mind ◽  
Phonological Activation

How does the mind process linguistic and non-linguistic sounds? The current study assessed the different ways that spoken words (e.g., “dog”) and characteristic sounds (e.g., <barking>) provide access to phonological information (e.g., word-form of “dog”) and semantic information (e.g., knowledge that a dog is associated with a leash). Using an eye-tracking paradigm, we found that listening to words prompted rapid phonological activation, which was then followed by semantic access. The opposite pattern emerged for sounds, with early semantic access followed by later retrieval of phonological information. Despite differences in the time courses of conceptual access, both words and sounds elicited robust activation of phonological and semantic knowledge. These findings inform models of auditory processing by revealing the pathways between speech and non-speech input and their corresponding word forms and concepts, which influence the speed, magnitude, and duration of linguistic and nonlinguistic activation.

Object detection and mapping for service robot tasks

Robotica ◽  
2007 ◽  
Vol 25 (2) ◽  
pp. 175-187 ◽  
Author(s):  
Staffan Ekvall ◽  
Danica Kragic ◽  
Patric Jensfelt
Keyword(s):  
Object Detection ◽  
Recognition System ◽  
Detection Algorithm ◽  
Semantic Knowledge ◽  
Service Robot ◽  
Map Building ◽  
Extensive Evaluation ◽  
Localization And Mapping ◽  
Different Types ◽  
Robot Tasks

SUMMARYThe problem studied in this paper is a mobile robot that autonomously navigates in a domestic environment, builds a map as it moves along and localizes its position in it. In addition, the robot detects predefined objects, estimates their position in the environment and integrates this with the localization module to automatically put the objects in the generated map. Thus, we demonstrate one of the possible strategies for the integration of spatial and semantic knowledge in a service robot scenario where a simultaneous localization and mapping (SLAM) and object detection recognition system work in synergy to provide a richer representation of the environment than it would be possible with either of the methods alone. Most SLAM systems build maps that are only used for localizing the robot. Such maps are typically based on grids or different types of features such as point and lines. The novelty is the augmentation of this process with an object-recognition system that detects objects in the environment and puts them in the map generated by the SLAM system. The metric map is also split into topological entities corresponding to rooms. In this way, the user can command the robot to retrieve a certain object from a certain room. We present the results of map building and an extensive evaluation of the object detection algorithm performed in an indoor setting.

scholarly journals How visual perceptual grouping influences foot placement

2015 ◽  
Vol 2 (7) ◽  
pp. 150151 ◽  
Author(s):  
John Fennell ◽  
Charlotte Goodwin ◽  
Jeremy F. Burn ◽  
Ute Leonards
Keyword(s):  
Perceptual Grouping ◽  
Step Width ◽  
Foot Placement ◽  
Visual Grouping ◽  
Visual Distractor ◽  
Close Proximity ◽  
Different Types ◽  
Regular Path ◽  
Visual Characteristics ◽  
The Impact

Everybody would agree that vision guides locomotion; but how does vision influence choice when there are different solutions for possible foot placement? We addressed this question by investigating the impact of perceptual grouping on foot placement in humans. Participants performed a stepping stone task in which pathways consisted of target stones in a spatially regular path of foot falls and visual distractor stones in their proximity. Target and distractor stones differed in shape and colour so that each subset of stones could be easily grouped perceptually. In half of the trials, one target stone swapped shape and colour with a distractor in its close proximity. We show that in these ‘swapped’ conditions, participants chose the perceptually groupable, instead of the spatially regular, stepping location in over 40% of trials, even if the distance between perceptually groupable steps was substantially larger than normal step width/length. This reveals that the existence of a pathway that could be traversed without spatial disruption to periodic stepping is not sufficient to guarantee participants will select it and suggests competition between different types of visual input when choosing foot placement. We propose that a bias in foot placement choice in favour of visual grouping exists as, in nature, sudden changes in visual characteristics of the ground increase the uncertainty for stability.

scholarly journals Analysis of visual processing capabilities and neural coding strategies of a detailed model for laminar cortical microcircuits in mouse V1

2021 ◽  
Author(s):  
Guozhang Chen ◽  
Franz Scherr ◽  
Wolfgang Maass
Keyword(s):  
Visual Processing ◽  
Neural Coding ◽  
Noise Model ◽  
Projection Neurons ◽  
Detailed Model ◽  
Power Law Decay ◽  
Different Types ◽  
Cortical Microcircuits ◽  
Explained Variance ◽  
Spatial Locations

AbstractThe neocortex is a network of rather stereotypical cortical microcircuits that share an exquisite genetically encoded architecture: Neurons of a fairly large number of different types are distributed over several layers (laminae), with specific probabilities of synaptic connections that depend on the neuron types involved and their spatial locations. Most available knowledge about this structure has been compiled into a detailed model [Billeh et al., 2020] for a generic cortical microcircuit in the primary visual cortex, consisting of 51,978 neurons of 111 different types. We add a noise model to the network that is based on experimental data, and analyze the results of network computations that can be extracted by projection neurons on layer 5. We show that the resulting model acquires through alignment of its synaptic weights via gradient descent training the capability to carry out a number of demanding visual processing tasks. Furthermore, this weight-alignment induces specific neural coding features in the microcircuit model that match those found in the living brain: High dimensional neural codes with an arguably close to optimal power-law decay of explained variance of PCA components, specific relations between signal- and noise-coding dimensions, and network dynamics in a critical regime. Hence these important features of neural coding and dynamics of cortical microcircuits in the brain are likely to emerge from aspects of their genetically encoded architecture that are captured by this data-based model in combination with learning processes. In addition, the model throws new light on the relation between visual processing capabilities and details of neural coding.

scholarly journals Text Matching and Categorization: Mining Implicit Semantic Knowledge from Tree-Shape Structures

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Lin Guo ◽  
Wanli Zuo ◽  
Tao Peng ◽  
Lin Yue
Keyword(s):  
Text Categorization ◽  
Large Scale ◽  
Semantic Information ◽  
Semantic Knowledge ◽  
Semistructured Data ◽  
Experimental Results ◽  
Semantic Relations ◽  
Lexical Analysis ◽  
Tree Shape ◽  
Implicit Information

The diversities of large-scale semistructured data make the extraction of implicit semantic information have enormous difficulties. This paper proposes an automatic and unsupervised method of text categorization, in which tree-shape structures are used to represent semantic knowledge and to explore implicit information by mining hidden structures without cumbersome lexical analysis. Mining implicit frequent structures in trees can discover both direct and indirect semantic relations, which largely enhances the accuracy of matching and classifying texts. The experimental results show that the proposed algorithm remarkably reduces the time and effort spent in training and classifying, which outperforms established competitors in correctness and effectiveness.

Design and Implementation of Mongolian Wordnet Management Platform

2013 ◽  
Vol 756-759 ◽  
pp. 1344-1348
Author(s):  
Hasi ◽  
En Bo Tang
Keyword(s):  
Natural Language Processing ◽  
Language Processing ◽  
Semantic Processing ◽  
Semantic Information ◽  
Semantic Knowledge ◽  
Main Function ◽  
Knowledge Database ◽  
Management Platform ◽  
Design And Implementation ◽  
Function Modules

With the development of natural language processing technology, a powerful tool containing semantic information is in great need in lexical semantic processing. Aiming at automatic processing of words in machine translation and automatic proofreading, Wordnet mainly provides semantic information in the form of a semantic knowledge database. The Mongolian Wordnet management and application platform includes two parts----the user searching function and the administrator maintaining function. Users can search semantic knowledge online and the administrator can maintain the adding, deleting, revising and searching functions of the database online as well. This article mainly introduces the construction theory of Mongolian Wordnet, the designing frame of the management and application platform, and the designing methods of the main function modules.

Sign in / Sign up

Export Citation Format

Share Document