Distributed representations of behaviour-derived object dimensions in the human visual system.

Object vision is commonly thought to involve a hierarchy of brain regions processing increasingly complex image features, with high-level visual cortex supporting object recognition and categorization. However, object vision supports diverse behavioural goals, suggesting basic limitations of this category-centric framework. To address these limitations, we mapped a series of dimensions derived from a large-scale analysis of human similarity judgements directly onto the brain.

Heritability of sudden cardiac death in Thoroughbred racehorses.

Background: Sudden cardiac death (SCD) is an important cause of exercise-associated fatalities in Thoroughbred racehorses. Equine deaths share similarities with fatalities in human athletes that result from inherited cardiac disease. Whilst genetic causes have been postulated in horses, these have not been confirmed and heritability of SCD has not previously been estimated in Thoroughbred racehorses.

Genetic parameters for yearling male reproduction traits in tropical composite cattle population.

Fertility is economically important but is hard to quantify and measure in breeding programs which has led extensive breeding programs to ignore fertility in their selection criteria. While female fertility traits have been extensively researched, male fertility traits have been largely ignored. It is estimated that 20% to 40% of bulls have sub-fertility, reducing the number of calves born and profits, highlighting the importance of investigating bull fertility.

Parallel cognitive maps for multiple knowledge structures in the hippocampal formation.

The hippocampal-entorhinal system uses cognitive maps to represent spatial knowledge and other types of relational information. However, objects can often be characterized by different types of relations simultaneously. How does the hippocampal formation handle the embedding of stimuli in multiple relational structures that differ vastly in their mode and timescale of acquisition? Does the hippocampal formation integrate different stimulus dimensions into one conjunctive map or is each dimension represented in a parallel map? Here, we reanalyzed human functional magnetic resonance imaging data from Garvert et al.

Dynamic representation of multidimensional object properties in the human brain.

Our visual world consists of an immense number of unique objects and yet, we are easily able to identify, distinguish, interact, and reason about the things we see within a few hundred milliseconds. This requires that we integrate and focus on a wide array of object properties to support specific behavioral goals. In the current study, we examined how these rich object representations unfold in the human brain by modelling time-resolved MEG signals evoked by viewing single presentations of tens of thousands of object images.

Distributed representations of behavior-derived object dimensions in the human visual system.

Object vision is commonly thought to involve a hierarchy of brain regions processing increasingly complex image features, with high-level visual cortex supporting object recognition and categorization. However, object vision supports diverse behavioral goals, suggesting basic limitations of this category-centric framework. To address these limitations, we mapped a series of dimensions derived from a large-scale analysis of human similarity judgments directly onto the brain.

The features underlying the memorability of objects.

What makes certain images more memorable than others? While much of memory research has focused on participant effects, recent studies using a stimulus-centric perspective have sparked debate on the determinants of memory, including the roles of semantic and visual features and whether the most prototypical or atypical items are best remembered. Prior studies have typically relied on constrained stimulus sets, limiting a generalized view of the features underlying what we remember. Here, we collected more than 1 million memory ratings for a naturalistic dataset of 26,107 object images designed to comprehensively sample concrete objects.

THINGSplus: New norms and metadata for the THINGS database of 1854 object concepts and 26,107 natural object images.

To study visual and semantic object representations, the need for well-curated object concepts and images has grown significantly over the past years. To address this, we have previously developed THINGS, a large-scale database of 1854 systematically sampled object concepts with 26,107 high-quality naturalistic images of these concepts. With THINGSplus, we significantly extend THINGS by adding concept- and image-specific norms and metadata for all 1854 concepts and one copyright-free image example per concept.

A data-driven investigation of human action representations.

Understanding actions performed by others requires us to integrate different types of information about people, scenes, objects, and their interactions. What organizing dimensions does the mind use to make sense of this complex action space? To address this question, we collected intuitive similarity judgments across two large-scale sets of naturalistic videos depicting everyday actions. We used cross-validated sparse non-negative matrix factorization to identify the structure underlying action similarity judgments.

Assessment of P Wave Indices in Healthy Standardbred Horses.

P wave indices are used as non-invasive electrocardiographic markers of atrial remodelling in humans. Few studies have investigated their use in animals. The aim of this study was to measure P wave duration and P wave dispersion (Pd) in healthy standardbred horses and investigate variables that might influence these measurements.

Ovarian follicle dynamics in ewes treated with intra-vaginal progesterone pessaries. 2. Factors affecting timing of estrus and reproductive outcomes following artificial insemination.

This study extends observations on the effects of intra-vaginal progesterone treatment on the relationships between the time of luteolysis, emergence of the ovulatory follicle, timing of estrus and ewe fertility. Observations were made in progesterone - treated ewes in autumn, the spring equinox and late spring (Experiment 1, Data set 1) and in progesterone - treated ewes and naturally cycling ewes in autumn and the spring equinox (Experiment 1, Data set 2). In Data set 1, the day of emergence of both the first and second ovulatory follicle was positively related to the day luteal regression within each season.

The Three Terms Task - an open benchmark to compare human and artificial semantic representations.

Word processing entails retrieval of a unitary yet multidimensional semantic representation (e.g., a lemon's colour, flavour, possible use) and has been investigated in both cognitive neuroscience and artificial intelligence.

THINGS-data, a multimodal collection of large-scale datasets for investigating object representations in human brain and behavior.

Understanding object representations requires a broad, comprehensive sampling of the objects in our visual world with dense measurements of brain activity and behavior. Here, we present THINGS-data, a multimodal collection of large-scale neuroimaging and behavioral datasets in humans, comprising densely sampled functional MRI and magnetoencephalographic recordings, as well as 4.70 million similarity judgments in response to thousands of photographic images for up to 1,854 object concepts.

Dimensions underlying human understanding of the reachable world.

Near-scale environments, like work desks, restaurant place settings or lab benches, are the interface of our hand-based interactions with the world. How are our conceptual representations of these environments organized? What properties distinguish among reachspaces, and why? We obtained 1.25 million similarity judgments on 990 reachspace images, and generated a 30-dimensional embedding which accurately predicts these judgments.

The Spatiotemporal Neural Dynamics of Object Recognition for Natural Images and Line Drawings.

Drawings offer a simple and efficient way to communicate meaning. While line drawings capture only coarsely how objects look in reality, we still perceive them as resembling real-world objects. Previous work has shown that this perceived similarity is mirrored by shared neural representations for drawings and natural images, which suggests that similar mechanisms underlie the recognition of both.

Portraying Animal Cruelty: A Thematic Analysis of Australian News Media Reports on Penalties for Animal Cruelty.

Media portrayals of animal cruelty can shape public understanding and perception of animal welfare law. Given that animal welfare law in Australia is guided partially by 'community expectations', the media might indirectly be influencing recent reform efforts to amend maximum penalties in Australia, through guiding and shaping public opinion. This paper reports on Australian news articles which refer to penalties for animal cruelty published between 1 June 2019 and 1 December 2019.

Feature-reweighted representational similarity analysis: A method for improving the fit between computational models, brains, and behavior.

Representational Similarity Analysis (RSA) has emerged as a popular method for relating representational spaces from human brain activity, behavioral data, and computational models. RSA is based on the comparison of representational (dis-)similarity matrices (RDMs or RSMs), which characterize the pairwise (dis-)similarities of all conditions across all features (e.g.

From photos to sketches - how humans and deep neural networks process objects across different levels of visual abstraction.

Line drawings convey meaning with just a few strokes. Despite strong simplifications, humans can recognize objects depicted in such abstracted images without effort. To what degree do deep convolutional neural networks (CNNs) mirror this human ability to generalize to abstracted object images? While CNNs trained on natural images have been shown to exhibit poor classification performance on drawings, other work has demonstrated highly similar latent representations in the networks for abstracted and natural images.

Human EEG recordings for 1,854 concepts presented in rapid serial visual presentation streams.

The neural basis of object recognition and semantic knowledge has been extensively studied but the high dimensionality of object space makes it challenging to develop overarching theories on how the brain organises object knowledge. To help understand how the brain allows us to recognise, categorise, and represent objects and object categories, there is a growing interest in using large-scale image databases for neuroimaging experiments. In the current paper, we present THINGS-EEG, a dataset containing human electroencephalography responses from 50 subjects to 1,854 object concepts and 22,248 images in the THINGS stimulus set, a manually curated and high-quality image database that was specifically designed for studying human vision.

Genomic Analysis of Purebred and Crossbred Angus Cows Quantifies Heterozygosity, Breed, and Additive Effects on Components of Reproduction.

Multiple studies have quantified the production differences of Hereford Angus crossbreds compared to purebred Angus for a range of traits including growth, carcass, and reproductive traits. This study aims to quantify breed and heterosis effects on maternal performance using genomics. Thirty Hereford and thirty Angus sires were mated to 1100 Angus heifers and cows in a large commercial herd run on pasture at Musselroe Bay, Tasmania, Australia.

THINGSvision: A Python Toolbox for Streamlining the Extraction of Activations From Deep Neural Networks.

Over the past decade, deep neural network (DNN) models have received a lot of attention due to their near-human object classification performance and their excellent prediction of signals recorded from biological visual systems. To better understand the function of these networks and relate them to hypotheses about brain activity and behavior, researchers need to extract the activations to images across different DNN layers. The abundance of different DNN variants, however, can often be unwieldy, and the task of extracting DNN activations from different layers may be non-trivial and error-prone for someone without a strong computational background.

Risk factors associated with veterinary attrition from clinical practice: a descriptive study.

A sustainable workforce is important for the veterinary profession to meet the demands of society. To maintain a sustainable workforce, it has been recommended to retain veterinary clinicians. However, there seems to be an increasing issue with retention of veterinarians in clinical practice.

Evaluation of miRNA as Biomarkers of Emotional Valence in Pigs.

The ability to assess the welfare of animals is dependent on our ability to accurately determine their emotional (affective) state, with particular emphasis being placed on the identification of positive emotions. The challenge remains that current physiological and behavioral indices are either unable to distinguish between positive and negative emotional states, or they are simply not suitable for a production environment. Therefore, the development of novel measures of animal emotion is a necessity.

Heritability estimates of atrial fibrillation in Thoroughbred racehorses in Hong Kong and Australia.

Introduction: Atrial fibrillation (AF) is the most common performance-limiting arrhythmia of racehorses. A genetic contribution has been suggested in Standardbred racehorses but has not been investigated in Thoroughbreds. The objective of this study was to determine the heritability of AF in Thoroughbred racehorses.