Behavioral scientists routinely publish broad claims about human psychology and behavior in the world's top journals based on samples drawn entirely from Western, Educated, Industrialized, Rich, and Democratic (WEIRD) societies. Researchers - often implicitly - assume that either there is little variation across human populations, or that these "standard subjects" are as representative of the species as any other population. Are these assumptions justified? Here, our review of the comparative database from across the behavioral sciences suggests both that there is substantial variability in experimental results across populations and that WEIRD subjects are particularly unusual compared with the rest of the species - frequent outliers. The domains reviewed include visual perception, fairness, cooperation, spatial reasoning, categorization and inferential induction, moral reasoning, reasoning styles, self-concepts and related motivations, and the heritability of IQ. The findings suggest that members of WEIRD societies, including young children, are among the least representative populations one could find for generalizing about humans. Many of these findings involve domains that are associated with fundamental aspects of psychology, motivation, and behavior - hence, there are no obvious a priori grounds for claiming that a particular behavioral phenomenon is universal based on sampling from a single subpopulation. Overall, these empirical patterns suggests that we need to be less cavalier in addressing questions of human nature on the basis of data drawn from this particularly thin, and rather unusual, slice of humanity. We close by proposing ways to structurally re-organize the behavioral sciences to best tackle these challenges.

/pdf/the-weirdest-people-in-the-world-480wr4m3kb.pdf

The WEIRDest People in the World

While it is nearly effortless for humans to quickly assess the perceptual similarity between two images, the underlying processes are thought to be quite complex. Despite this, the most widely used perceptual metrics today, such as PSNR and SSIM, are simple, shallow functions, and fail to account for many nuances of human perception. Recently, the deep learning community has found that features of the VGG network trained on ImageNet classification has been remarkably useful as a training loss for image synthesis. But how perceptual are these so-called "perceptual losses"? What elements are critical for their success? To answer these questions, we introduce a new dataset of human perceptual similarity judgments. We systematically evaluate deep features across different architectures and tasks and compare them with classic metrics. We find that deep features outperform all previous metrics by large margins on our dataset. More surprisingly, this result is not restricted to ImageNet-trained VGG features, but holds across different deep architectures and levels of supervision (supervised, self-supervised, or even unsupervised). Our results suggest that perceptual similarity is an emergent property shared across deep visual representations.

/pdf/the-unreasonable-effectiveness-of-deep-features-as-a-10k66u5qas.pdf

The Unreasonable Effectiveness of Deep Features as a Perceptual Metric

Introduction 1. Woman's Place in Man's Life Cycle 2. Images of Relationship 3. Concepts of Self and Morality 4. Crisis and Transition 5. Women's Rights and Women's Judgment 6. Visions of Maturity References Index of Study Participants General Index

In a Different Voice. Psychological Theory and Women’s Development. Cambridge, MA (Harvard University Press) 1982.

How do people know as much as they do with as little information as they get? The problem takes many forms; learning vocabulary from text is an especially dramatic and convenient case for research. A new general theory of acquired similarity and knowledge representation, latent semantic analysis (LSA), is presented and used to successfully simulate such learning and several other psycholinguistic phenomena. By inducing global knowledge indirectly from local co-occurrence data in a large body of representative text, LSA acquired knowledge about the full vocabulary of English at a comparable rate to schoolchildren. LSA uses no prior linguistic or perceptual similarity knowledge; it is based solely on a general mathematical learning method that achieves powerful inductive effects by extracting the right number of dimensions (e.g., 300) to represent objects and contexts. Relations to other theories, phenomena, and problems are sketched.

/pdf/a-solution-to-plato-s-problem-the-latent-semantic-analysis-4jfk7ozfms.pdf

A Solution to Plato's Problem: The Latent Semantic Analysis Theory of Acquisition, Induction, and Representation of Knowledge.

Most theories dealing with ill-defined concepts assume that performance is based on category level information or a mixture of category level and specific item information. A context theory of classificatio n is described in which judgments are assumed to derive exclusively from stored exemplar information. The main idea is that a probe item acts as a retrieval cue to access information associated with stimuli similar to the probe. The predictions of the context theory are contrasted with those of a class of theories (including prototype theory) that assume that the information entering into judgments can be derived from an additive combination of information from component cue dimensions. Across four experiments using both geometric forms and schematic faces as stimuli, the context theory consistently gave a better account of the data. The relation of the context theory to other theories and phenomena associated with ill-defined concepts is discussed in detail. One of the major components of cognitive behavior concerns abstracting rules and forming concepts. Our entire system of naming objects and events, talking about them, and interacting with them presupposes the ability to group experiences into appropriate classes. Young children learn to tell the difference between dogs and cats, between clocks and fans, and between stars and street lights. Since few concepts are formally taught, the evolution of concepts from experience with exemplars must be a fundamental learning phenomenon. The focus of the present article is to explore how such conceptual achievements emerge from individual instances.

/pdf/context-theory-of-classification-learning-zof1911sr6.pdf

Context theory of classification learning.

The question of what makes a concept coherent (what makes its members form a comprehensible class) has received a variety of answers. In this article we review accounts based on similarity, feature correlations, and various theories of categorization. We find that each theory provides an inadequate account of conceptual coherence (or no account at all) because none provides enough constraints on possible concepts. We propose that concepts are coherent to the extent that they fit people's background knowledge or naive theories about the world. These theories help to relate the concepts in a domain and to structure the attributes that are internal to a concept. Evidence of the influence of theories on various conceptual tasks is presented, and the possible importance of theories in cognitive development is discussed.

/pdf/the-role-of-theories-in-conceptual-coherence-456zncufwj.pdf

The role of theories in conceptual coherence.

Categories and concepts

This article reviews the status of similarity as an explanatory construct with a focus on similarity judgments. For similarity to be a useful construct, one must be able to specify the ways or respects in which two things are similar. One solution to this problem is to restrict the notion of similarity to hard-wired perceptual processes. It is argued that this view is too narrow and limiting. Instead, it is proposed that an important source of constraints derives from the similarity comparison process itself. Both new experiments and other evidence are described that support the idea that respects are determined by processes internal to comparisons

https://www.cogsci.ucsd.edu/%7Ecoulson/203/medin.pdf

Respects for similarity

Research and theory on categorization and conceptual structure have recently undergone two major shifts. The first shift is from the assumption that concepts have defining properties (the classical view) to the idea that concept representations may be based on properties that are only characteristic or typical of category examples (the probabilistic view). Both the probabilistic view and the classical view assume that categorization is driven by similarity relations. A major problem with describing cat- egory structure in terms of similarity is that the notion of similarity is too unconstrained to give an account of con- ceptual coherence. The second major shift is from the idea that concepts are organized by similarity to the idea that concepts are organized around theories. In this article, the evidence and rationale associated with these shifts are described, and one means of integrating similarity-based and theory-driven categorization is outlined. What good are categories? Categorization involves treat- ing two or more distinct entities as in some way equivalent

/pdf/concepts-and-conceptual-structure-589yyqnu4n.pdf

Concepts and conceptual structure.

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