The universal law of generalization holds for naturalistic stimuli.

Shepard's universal law of generalization is a remarkable hypothesis about how intelligent organisms should perceive similarity. In its broadest form, the universal law states that the level of perceived similarity between a pair of stimuli should decay as a concave function of their distance when embedded in an appropriate psychological space. While extensively studied, evidence in support of the universal law has relied on low-dimensional stimuli and small stimulus sets that are very different from their real-world counterparts. This is largely because pairwise comparisons-as required for similarity judgments-scale quadratically in the number of stimuli. We provide strong evidence for the universal law in a naturalistic high-dimensional regime by analyzing an existing data set of 214,200 human similarity judgments and a newly collected data set of 390,819 human generalization judgments (N = 2,406 U.S. participants) across three sets of natural images. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Stress, intertemporal choice, and mitigation behavior during the COVID-19 pandemic.

Delayed gratification is an important focus of research, given its potential relationship to forms of behavior, such as savings, susceptibility to addiction, and pro-social behaviors. The COVID-19 pandemic may be one of the most consequential recent examples of this phenomenon, with people's willingness to delay gratification affecting their willingness to socially distance themselves. COVID-19 also provides a naturalistic context by which to evaluate the ecological validity of delayed gratification. This article outlines four large-scale online experiments (total N = 12, 906) where we ask participants to perform Money Earlier or Later (MEL) decisions (e.g., $5 today vs. $10 tomorrow) and to also report stress measures and pandemic mitigation behaviors. We found that stress increases impulsivity and that less stressed and more patient individuals socially distanced more throughout the pandemic. These results help resolve longstanding theoretical debates in the MEL literature as well as provide policymakers with scientific evidence that can help inform response strategies in the future. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Extracting Low-Dimensional Psychological Representations from Convolutional Neural Networks.

Convolutional neural networks (CNNs) are increasingly widely used in psychology and neuroscience to predict how human minds and brains respond to visual images. Typically, CNNs represent these images using thousands of features that are learned through extensive training on image datasets. This raises a question: How many of these features are really needed to model human behavior? Here, we attempt to estimate the number of dimensions in CNN representations that are required to capture human psychological representations in two ways: (1) directly, using human similarity judgments and (2) indirectly, in the context of categorization. In both cases, we find that low-dimensional projections of CNN representations are sufficient to predict human behavior. We show that these low-dimensional representations can be easily interpreted, providing further insight into how people represent visual information. A series of control studies indicate that these findings are not due to the size of the dataset we used and may be due to a high level of redundancy in the features appearing in CNN representations.

Deep models of superficial face judgments.

The diversity of human faces and the contexts in which they appear gives rise to an expansive stimulus space over which people infer psychological traits (e.g., trustworthiness or alertness) and other attributes (e.g., age or adiposity). Machine learning methods, in particular deep neural networks, provide expressive feature representations of face stimuli, but the correspondence between these representations and various human attribute inferences is difficult to determine because the former are high-dimensional vectors produced via black-box optimization algorithms. Here we combine deep generative image models with over 1 million judgments to model inferences of more than 30 attributes over a comprehensive latent face space. The predictive accuracy of our model approaches human interrater reliability, which simulations suggest would not have been possible with fewer faces, fewer judgments, or lower-dimensional feature representations. Our model can be used to predict and manipulate inferences with respect to arbitrary face photographs or to generate synthetic photorealistic face stimuli that evoke impressions tuned along the modeled attributes.

Using large-scale experiments and machine learning to discover theories of human decision-making.

Predicting and understanding how people make decisions has been a long-standing goal in many fields, with quantitative models of human decision-making informing research in both the social sciences and engineering. We show how progress toward this goal can be accelerated by using large datasets to power machine-learning algorithms that are constrained to produce interpretable psychological theories. Conducting the largest experiment on risky choice to date and analyzing the results using gradient-based optimization of differentiable decision theories implemented through artificial neural networks, we were able to recapitulate historical discoveries, establish that there is room to improve on existing theories, and discover a new, more accurate model of human decision-making in a form that preserves the insights from centuries of research.

The Role of Cell Tracing and Fate Mapping Experiments in Cardiac Outflow Tract Development, New Opportunities through Emerging Technologies.

Whilst knowledge regarding the pathophysiology of congenital heart disease (CHDs) has advanced greatly in recent years, the underlying developmental processes affecting the cardiac outflow tract (OFT) such as bicuspid aortic valve, tetralogy of Fallot and transposition of the great arteries remain poorly understood. Common among CHDs affecting the OFT, is a large variation in disease phenotypes. Even though the different cell lineages contributing to OFT development have been studied for many decades, it remains challenging to relate cell lineage dynamics to the morphologic variation observed in OFT pathologies. We postulate that the variation observed in cellular contribution in these congenital heart diseases might be related to underlying cell lineage dynamics of which little is known. We believe this gap in knowledge is mainly the result of technical limitations in experimental methods used for cell lineage analysis. The aim of this review is to provide an overview of historical fate mapping and cell tracing techniques used to study OFT development and introduce emerging technologies which provide new opportunities that will aid our understanding of the cellular dynamics underlying OFT pathology.

From convolutional neural networks to models of higher-level cognition (and back again).

The remarkable successes of convolutional neural networks (CNNs) in modern computer vision are by now well known, and they are increasingly being explored as computational models of the human visual system. In this paper, we ask whether CNNs might also provide a basis for modeling higher-level cognition, focusing on the core phenomena of similarity and categorization. The most important advance comes from the ability of CNNs to learn high-dimensional representations of complex naturalistic images, substantially extending the scope of traditional cognitive models that were previously only evaluated with simple artificial stimuli. In all cases, the most successful combinations arise when CNN representations are used with cognitive models that have the capacity to transform them to better fit human behavior. One consequence of these insights is a toolkit for the integration of cognitively motivated constraints back into CNN training paradigms in computer vision and machine learning, and we review cases where this leads to improved performance. A second consequence is a roadmap for how CNNs and cognitive models can be more fully integrated in the future, allowing for flexible end-to-end algorithms that can learn representations from data while still retaining the structured behavior characteristic of human cognition.

Capturing human categorization of natural images by combining deep networks and cognitive models.

Human categorization is one of the most important and successful targets of cognitive modeling, with decades of model development and assessment using simple, low-dimensional artificial stimuli. However, it remains unclear how these findings relate to categorization in more natural settings, involving complex, high-dimensional stimuli. Here, we take a step towards addressing this question by modeling human categorization over a large behavioral dataset, comprising more than 500,000 judgments over 10,000 natural images from ten object categories. We apply a range of machine learning methods to generate candidate representations for these images, and show that combining rich image representations with flexible cognitive models captures human decisions best. We also find that in the high-dimensional representational spaces these methods generate, simple prototype models can perform comparably to the more complex memory-based exemplar models dominant in laboratory settings.

Parallelograms revisited: Exploring the limitations of vector space models for simple analogies.

Classic psychological theories have demonstrated the power and limitations of spatial representations, providing geometric tools for reasoning about the similarity of objects and showing that human intuitions sometimes violate the constraints of geometric spaces. Recent machine learning methods for deriving vector-space embeddings of words have begun to garner attention for their surprising capacity to capture simple analogies consistently across large corpora, giving new life to a classic model of analogies as parallelograms that was first proposed and briefly explored by psychologists. We evaluate the parallelogram model of analogy as applied to modern data-driven word embeddings, providing a detailed analysis of the extent to which this approach captures human behavior in the domain of word pairs. Using a large novel benchmark dataset of human analogy completions, we show that word similarity alone surprisingly captures some aspects of human responses better than the parallelogram model. To gain a fine-grained picture of how well these models predict relational similarity, we also collect a large dataset of human relational similarity judgments and find that the parallelogram model captures some semantic relationships better than others. Finally, we provide evidence for deeper limitations of the parallelogram model of analogy based on the intrinsic geometric constraints of vector spaces, paralleling classic results for item similarity. Taken together, these results show that while modern word embeddings do an impressive job of capturing semantic similarity at scale, the parallelogram model alone is insufficient to account for how people form even the simplest analogies.

Disturbed nitric oxide signalling gives rise to congenital bicuspid aortic valve and aortopathy.

Patients with a congenital bicuspid aortic valve (BAV), a valve with two instead of three aortic leaflets, have an increased risk of developing thoracic aneurysms and aortic dissection. The mechanisms underlying BAV-associated aortopathy are poorly understood. This study examined BAV-associated aortopathy in Nos3-/- mice, a model with congenital BAV formation. A combination of histological examination and in vivo ultrasound imaging was used to investigate aortic dilation and dissections in Nos3-/- mice. Moreover, cell lineage analysis and single-cell RNA sequencing were used to observe the molecular anomalies within vascular smooth muscle cells (VSMCs) of Nos3-/- mice. Spontaneous aortic dissections were found in ascending aortas located at the sinotubular junction in ∼13% of Nos3-/- mice. Moreover, Nos3-/- mice were prone to developing aortic dilations in the proximal and distal ascending aorta during early adulthood. Lower volumes of elastic fibres were found within vessel walls of the ascending aortas of Nos3-/- mice, as well as incomplete coverage of the aortic inner media by neural crest cell (NCC)-derived VSMCs. VSMCs of Nos3-/- mice showed downregulation of 15 genes, of which seven were associated with aortic aneurysms and dissections in the human population. Elastin mRNA was most markedly downregulated, followed by fibulin-5 expression, both primary components of elastic fibres. This study demonstrates that, in addition to congenital BAV formation, disrupted endothelial-mediated nitric oxide (NO) signalling in Nos3-/- mice also causes aortic dilation and dissection, as a consequence of inhibited elastic fibre formation in VSMCs within the ascending aorta.

Pulmonary ductal coarctation and left pulmonary artery interruption; pathology and role of neural crest and second heart field during development.

OBJECTIVES: In congenital heart malformations with pulmonary stenosis to atresia an abnormal lateral ductus arteriosus to left pulmonary artery connection can lead to a localised narrowing (pulmonary ductal coarctation) or even interruption We investigated embryonic remodelling and pathogenesis of this area. MATERIAL AND METHODS: Normal development was studied in WntCre reporter mice (E10.0-12.5) for neural crest cells and Nkx2.5 immunostaining for second heart field cells. Data were compared to stage matched human embryos and a VEGF120/120 mutant mouse strain developing pulmonary atresia. RESULTS: Normal mouse and human embryos showed that the mid-pharyngeal endothelial plexus, connected side-ways to the 6th pharyngeal arch artery. The ventral segment formed the proximal pulmonary artery. The dorsal segment (future DA) was solely surrounded by neural crest cells. The ventral segment had a dual outer lining with neural crest and second heart field cells, while the distal pulmonary artery was covered by none of these cells. The asymmetric contribution of second heart field to the future pulmonary trunk on the left side of the aortic sac (so-called pulmonary push) was evident. The ventral segment became incorporated into the pulmonary trunk leading to a separate connection of the left and right pulmonary arteries. The VEGF120/120 embryos showed a stunted pulmonary push and a variety of vascular anomalies. SUMMARY: Side-way connection of the DA to the left pulmonary artery is a congenital anomaly. The primary problem is a stunted development of the pulmonary push leading to pulmonary stenosis/atresia and a subsequent lack of proper incorporation of the ventral segment into the aortic sac. Clinically, the aberrant smooth muscle tissue of the ductus arteriosus should be addressed to prohibit development of severe pulmonary ductal coarctation or even interruption of the left pulmonary artery.

Scaling up psychology via Scientific Regret Minimization.

Do large datasets provide value to psychologists? Without a systematic methodology for working with such datasets, there is a valid concern that analyses will produce noise artifacts rather than true effects. In this paper, we offer a way to enable researchers to systematically build models and identify novel phenomena in large datasets. One traditional approach is to analyze the residuals of models-the biggest errors they make in predicting the data-to discover what might be missing from those models. However, once a dataset is sufficiently large, machine learning algorithms approximate the true underlying function better than the data, suggesting, instead, that the predictions of these data-driven models should be used to guide model building. We call this approach "Scientific Regret Minimization" (SRM), as it focuses on minimizing errors for cases that we know should have been predictable. We apply this exploratory method on a subset of the Moral Machine dataset, a public collection of roughly 40 million moral decisions. Using SRM, we find that incorporating a set of deontological principles that capture dimensions along which groups of agents can vary (e.g., sex and age) improves a computational model of human moral judgment. Furthermore, we are able to identify and independently validate three interesting moral phenomena: criminal dehumanization, age of responsibility, and asymmetric notions of responsibility.

Bicuspid aortic valve formation: Nos3 mutation leads to abnormal lineage patterning of neural crest cells and the second heart field.

The bicuspid aortic valve (BAV), a valve with two instead of three aortic leaflets, belongs to the most prevalent congenital heart diseases in the world, occurring in 0.5-2% of the general population. We aimed to understand how changes in early cellular contributions result in BAV formation and impact cardiovascular outflow tract development. Detailed 3D reconstructions, immunohistochemistry and morphometrics determined that, during valvulogenesis, the non-coronary leaflet separates from the parietal outflow tract cushion instead of originating from an intercalated cushion. Nos3-/- mice develop a BAV without a raphe as a result of incomplete separation of the parietal outflow tract cushion into the right and non-coronary leaflet. Genetic lineage tracing of endothelial, second heart field and neural crest cells revealed altered deposition of neural crest cells and second heart field cells within the parietal outflow tract cushion of Nos3-/- embryos. The abnormal cell lineage distributions also affected the positioning of the aortic and pulmonary valves at the orifice level. The results demonstrate that the development of the right and non-coronary leaflets are closely related. A small deviation in the distribution of neural crest and second heart field populations affects normal valve formation and results in the predominant right-non-type BAV in Nos3-/- mice.

Evaluating (and Improving) the Correspondence Between Deep Neural Networks and Human Representations.

Decades of psychological research have been aimed at modeling how people learn features and categories. The empirical validation of these theories is often based on artificial stimuli with simple representations. Recently, deep neural networks have reached or surpassed human accuracy on tasks such as identifying objects in natural images. These networks learn representations of real-world stimuli that can potentially be leveraged to capture psychological representations. We find that state-of-the-art object classification networks provide surprisingly accurate predictions of human similarity judgments for natural images, but they fail to capture some of the structure represented by people. We show that a simple transformation that corrects these discrepancies can be obtained through convex optimization. We use the resulting representations to predict the difficulty of learning novel categories of natural images. Our results extend the scope of psychological experiments and computational modeling by enabling tractable use of large natural stimulus sets.