The contribution of object size, manipulability, and stability on neural responses to inanimate objects

AbstractIn human occipitotemporal cortex, brain responses to depicted inanimate objects have a large-scale organization by real-world object size. Critically, the size of objects in the world is systematically related to behaviorally-relevant properties: small objects are often grasped and manipulated (e.g., forks), while large objects tend to be less motor-relevant (e.g., tables), though this relationship does not always have to be true (e.g., picture frames and wheelbarrows). To determine how these two dimensions interact, we measured brain activity with functional magnetic resonance imaging while participants viewed a stimulus set of small and large objects with either low or high motor-relevance. The results revealed that the size organization was evident for objects with both low and high motor-relevance; further, a motor-relevance map was also evident across both large and small objects. Targeted contrasts revealed that typical combinations (small motor-relevant vs. large non-motor-relevant) yielded more robust topographies than the atypical covariance contrast (small non-motor-relevant vs. large motor-relevant). In subsequent exploratory analyses, a factor analysis revealed that the construct of motor-relevance was better explained by two underlying factors: one more related to manipulability, and the other to whether an object moves or is stable. The factor related to manipulability better explained responses in lateral small-object preferring regions, while the factor related to object stability (lack of movement) better explained responses in ventromedial large-object preferring regions. Taken together, these results reveal that the structure of neural responses to objects of different sizes further reflect behavior-relevant properties of manipulability and stability, and contribute to a deeper understanding of some of the factors that help the large-scale organization of object representation in high-level visual cortex.Highlights-Examined the relationship between real-world size and motor-relevant properties in the structure of responses to inanimate objects.-Large scale topography was more robust for contrast that followed natural covariance of small motor-relevant vs. large non-motor-relevant, over contrast that went against natural covariance.-Factor analysis revealed that manipulability and stability were, respectively, better explanatory predictors of responses in small- and large-object regions.

Download Full-text

Mid-level feature differences underlie early animacy and object size distinctions: Evidence from EEG decoding

10.1101/2022.01.12.475180 ◽

2022 ◽

Author(s):

Ruosi Wang ◽

Daniel Janini ◽

Talia Konkle

Keyword(s):

Temporal Processing ◽

Large Scale ◽

Object Size ◽

Cortical Surface ◽

Neural Processing ◽

Neural Responses ◽

Neural Basis ◽

Topographical Organization ◽

Explicit Recognition ◽

Feature Information

Responses to visually-presented objects along the cortical surface of the human brain have a large-scale organization reflecting the broad categorical divisions of animacy and object size. Mounting evidence indicates that this topographical organization is driven by differences between objects in mid-level perceptual features. With regard to the timing of neural responses, images of objects quickly evoke neural responses with decodable information about animacy and object size, but are mid-level features sufficient to evoke these rapid neural responses? Or is slower iterative neural processing required to untangle information about animacy and object size from mid-level features? To answer this question, we used electroencephalography(EEG) to measure human neural responses to images of objects and their texform counterparts - unrecognizable images which preserve some mid-level feature information about texture and coarse form. We found that texform images evoked neural responses with early decodable information about both animacy and real-world size, as early as responses evoked by original images. Further, successful cross-decoding indicates that both texform and original images evoke information about animacy and size through a common underlying neural basis. Broadly, these results indicate that the visual system contains a mid-level feature bank carrying linearly decodable information on animacy and size, which can be rapidly activated without requiring explicit recognition or protracted temporal processing.

Download Full-text

Animacy and object size are reflected in perceptual similarity computations by the preschool years

10.31234/osf.io/gu76h ◽

2019 ◽

Author(s):

Bria Long ◽

Mariko Moher ◽

Susan Carey ◽

Talia Konkle

Keyword(s):

Visual Cortex ◽

Visual Search ◽

Real World ◽

Cognitive Architecture ◽

Object Size ◽

Search Performance ◽

Perceptual Similarity ◽

Neural Responses ◽

Neural Organization ◽

Perceptual Representations

By adulthood, animacy and object size jointly structure neural responses in visual cortex and influence perceptual similarity computations. Here, we take a first step in asking about the development of these aspects of cognitive architecture by probing whether animacy and object size are reflected in perceptual similarity computations by the preschool years. We used visual search performance as an index of perceptual similarity, as research with adults suggests search is slower when distractors are perceptually similar to the target. Preschoolers found target pictures more quickly when targets differed from distractor pictures in either animacy (Experiment 1) or in real-world size (Experiment 2; the pictures themselves were all the same size), versus when they do not. Taken together, these results suggest that the visual system has abstracted perceptual features for animates vs. inanimates and big vs. small objects as classes by the preschool years and call for further research exploring the development of these perceptual representations and their consequences for neural organization in childhood.

Download Full-text

Diversity associations as stochastic variables

Paleobiology ◽

10.1017/s0094837300005091 ◽

1977 ◽

Vol 3 (1) ◽

pp. 41-48 ◽

Cited By ~ 5

Author(s):

Charles A. F. Smith

Keyword(s):

Factor Analysis ◽

Real World ◽

Fossil Record ◽

Large Scale ◽

Environmental Changes ◽

Original Data ◽

Turnover Rates ◽

Gas Laws ◽

Number Of Factors ◽

Independent Variable

Diversity data from stochastic phylogenies and from uniformly spaced Gaussian curves were subjected to Q-mode factor analysis in order to determine whether a few factors would account for a large percentage of the original variance. In both analyses, a small number of factors show systematic variations in time and account for more than 90% of original data variance. To further study the question of evolutionary pulsations, turnover rates were calculated between successive samples. These turnover rates indicate that stochastic phylogenies have pulses similar to those recorded in the fossil record. Large scale environmental changes are not required to explain such pulses. Therefore the observed existence in the real world of biologic diversity associations and evolutionary pulsations can as equally well be accounted for in a stochastic world (in which each species is an independent variable) as in a deterministic world. This supports the notion that there may be stochastic laws in paleontology akin to the gas laws of chemistry.

Download Full-text

A mid-level organization of the ventral stream

10.1101/213934 ◽

2017 ◽

Cited By ~ 3

Author(s):

Bria Long ◽

Chen-Ping Yu ◽

Talia Konkle

Keyword(s):

Large Scale ◽

Texture Synthesis ◽

Ventral Stream ◽

Object Size ◽

Neural Responses ◽

Visual Stream ◽

Visual Appearance ◽

Explicit Recognition ◽

High Level ◽

Form Information

ABSTRACTHuman object-selective cortex shows a large-scale organization characterized by the high-level properties of both animacy and object-size. To what extent are these neural responses explained by primitive perceptual features that distinguish animals from objects and big objects from small objects? To address this question, we used a texture synthesis algorithm to create a novel class of stimuli—texforms—which preserve some mid-level texture and form information from objects while rendering them unrecognizable. We found that unrecognizable texforms were sufficient to elicit the large-scale organizations of object-selective cortex along the entire ventral pathway. Further, the structure in the neural patterns elicited by texforms was well predicted by curvature features and by intermediate layers of a deep convolutional neural network, supporting the mid-level nature of the representations. These results provide clear evidence that a substantial portion of ventral stream organization can be accounted for by coarse texture and form information, without requiring explicit recognition of intact objects.SIGNIFICANCE STATEMENTWhile neural responses to object categories are remarkably systematic across human visual cortex, the nature of these responses been hotly debated for the past 20 years. In this paper, a new class of stimuli (“texforms”) is used to examine how mid-level features contribute to the large-scale organization of the ventral visual stream. Despite their relatively primitive visual appearance, these unrecognizable texforms elicited the entire large-scale organizations of the ventral stream by animacy and object size. This work demonstrates that much of ventral stream organization can be explained by relatively primitive mid-level features, without requiring explicit recognition of the objects themselves.

Download Full-text

1588-P: Therapy Trends in Initial 6 Months of the First Large-Scale Longitudinal Nationwide Study on Management and Real-World Outcomes of Diabetes in India (LANDMARC)

Diabetes ◽

10.2337/db20-1588-p ◽

2020 ◽

Vol 69 (Supplement 1) ◽

pp. 1588-P ◽

Cited By ~ 1

Author(s):

ROMIK GHOSH ◽

ASHOK K. DAS ◽

AMBRISH MITHAL ◽

SHASHANK JOSHI ◽

K.M. PRASANNA KUMAR ◽

...

Keyword(s):

Real World ◽

Large Scale ◽

Nationwide Study

Download Full-text

2258-PUB: Impact of Cardiovascular (CV) Risk Factors during the Initial 6 Months of the First Large-Scale Longitudinal Nationwide Study on Management and Real-World Outcomes of Diabetes in India (LANDMARC)

Diabetes ◽

10.2337/db20-2258-pub ◽

2020 ◽

Vol 69 (Supplement 1) ◽

pp. 2258-PUB

Author(s):

ROMIK GHOSH ◽

ASHOK K. DAS ◽

SHASHANK JOSHI ◽

AMBRISH MITHAL ◽

K.M. PRASANNA KUMAR ◽

...

Keyword(s):

Risk Factors ◽

Real World ◽

Large Scale ◽

Nationwide Study

Download Full-text

The graph neural networking challenge

ACM SIGCOMM Computer Communication Review ◽

10.1145/3477482.3477485 ◽

2021 ◽

Vol 51 (3) ◽

pp. 9-16

Author(s):

José Suárez-Varela ◽

Miquel Ferriol-Galmés ◽

Albert López ◽

Paul Almasan ◽

Guillermo Bernárdez ◽

...

Keyword(s):

Machine Learning ◽

Computer Networks ◽

Real World ◽

Large Scale ◽

Lessons Learned ◽

Educational Resources ◽

Global Competition ◽

International Telecommunication Union ◽

International Telecommunication ◽

Broad Audience

During the last decade, Machine Learning (ML) has increasingly become a hot topic in the field of Computer Networks and is expected to be gradually adopted for a plethora of control, monitoring and management tasks in real-world deployments. This poses the need to count on new generations of students, researchers and practitioners with a solid background in ML applied to networks. During 2020, the International Telecommunication Union (ITU) has organized the "ITU AI/ML in 5G challenge", an open global competition that has introduced to a broad audience some of the current main challenges in ML for networks. This large-scale initiative has gathered 23 different challenges proposed by network operators, equipment manufacturers and academia, and has attracted a total of 1300+ participants from 60+ countries. This paper narrates our experience organizing one of the proposed challenges: the "Graph Neural Networking Challenge 2020". We describe the problem presented to participants, the tools and resources provided, some organization aspects and participation statistics, an outline of the top-3 awarded solutions, and a summary with some lessons learned during all this journey. As a result, this challenge leaves a curated set of educational resources openly available to anyone interested in the topic.

Download Full-text

Urdu Translation and Validation of 11-Item Measure to Assess Borderline Personality Features in Pakistani Adolescents

SAGE Open ◽

10.1177/2158244020986157 ◽

2021 ◽

Vol 11 (1) ◽

pp. 215824402098615

Author(s):

Humaira Bibi ◽

Syeda Farhana Kazmi

Keyword(s):

Factor Analysis ◽

Psychometric Properties ◽

Convergent Validity ◽

Goodness Of Fit ◽

Borderline Personality ◽

Two Dimensions ◽

Personality Features ◽

Fit Index ◽

Borderline Personality Features ◽

Translation And Validation

The current study includes Urdu translation and validation of Borderline Personality Features Scale–11 (BPFS-11) in two phases. Phase 1 included forward and back translation of BPFS-11, and Phase 2 included establishment of psychometric properties for BPFS-11. For this purpose, 930 adolescents were selected from different hospitals, schools, and colleges. The reliability value of the scale was .72. Exploratory factor analysis revealed factor structure with four principal dimensions; besides confirmatory factor analysis, goodness-of-fit indices indicated good fit of model to data, and two dimensions of scale and factors showed good values of internal consistency. The obtained value for goodness-of-fit index was .995, for adjusted goodness-of-fit index was .989, for comparative fit index was .998, for incremental fit index was .998, and for root mean square error of approximation (RMSEA) value was .019. Good values of composite reliability and convergent validity were measured for both dimensions of the scale. The analysis of criterion-related validity showed significant positive correlation of BPFS-11 with Affective Lability Scale, Deliberate Self-Harm Inventory, and neuroticism scale of Big Five Inventory. Significant differences were found between scores of individuals having borderline personality disorder and scores of normal individuals. The results of the current study indicated that BPFS-11 is short and easily administered diagnostic tool that has good psychometric properties and can be helpful for diagnosis of borderline personality features in adolescents. It can enhance the understanding of the participants regarding the statements of the scale for Urdu natives.

Download Full-text

A Heuristic Algorithm for Solving a Large-scale Real-world Territory Design Problem

Omega ◽

10.1016/j.omega.2021.102442 ◽

2021 ◽

pp. 102442

Author(s):

Lin Zhou ◽

Lu Zhen ◽

Roberto Baldacci ◽

Marco Boschetti ◽

Ying Dai ◽

...

Keyword(s):

Heuristic Algorithm ◽

Real World ◽

Design Problem ◽

Large Scale ◽

Territory Design

Download Full-text

An information theoretic approach to link prediction in multiplex networks

Scientific Reports ◽

10.1038/s41598-021-92427-1 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Seyed Hossein Jafari ◽

Amir Mahdi Abdolhosseini-Qomi ◽

Masoud Asadpour ◽

Maseud Rahgozar ◽

Naser Yazdani

Keyword(s):

Real World ◽

Link Prediction ◽

Large Scale ◽

Similarity Measures ◽

Prediction Method ◽

General Purpose ◽

Fast Method ◽

Theoretic Approach ◽

Multiplex Networks ◽

Wide Range

AbstractThe entities of real-world networks are connected via different types of connections (i.e., layers). The task of link prediction in multiplex networks is about finding missing connections based on both intra-layer and inter-layer correlations. Our observations confirm that in a wide range of real-world multiplex networks, from social to biological and technological, a positive correlation exists between connection probability in one layer and similarity in other layers. Accordingly, a similarity-based automatic general-purpose multiplex link prediction method—SimBins—is devised that quantifies the amount of connection uncertainty based on observed inter-layer correlations in a multiplex network. Moreover, SimBins enhances the prediction quality in the target layer by incorporating the effect of link overlap across layers. Applying SimBins to various datasets from diverse domains, our findings indicate that SimBins outperforms the compared methods (both baseline and state-of-the-art methods) in most instances when predicting links. Furthermore, it is discussed that SimBins imposes minor computational overhead to the base similarity measures making it a potentially fast method, suitable for large-scale multiplex networks.

Download Full-text