Learning a face space for experiments on human identity

Generative models of human identity and appearance have broad applicability to behavioral science and technology, but the exquisite sensitivity of human face perception means that their utility hinges on the alignment of the model's representation to human psychological representations and the photorealism of the generated images. Meeting these requirements is an exacting task, and existing models of human identity and appearance are often unworkably abstract, artificial, uncanny, or biased. Here, we use a variational autoencoder with an autoregressive decoder to learn a face space from a uniquely diverse dataset of portraits that control much of the variation irrelevant to human identity and appearance. Our method generates photorealistic portraits of fictive identities with a smooth, navigable latent space. We validate our model's alignment with human sensitivities by introducing a psychophysical Turing test for images, which humans mostly fail. Lastly, we demonstrate an initial application of our model to the problem of fast search in mental space to obtain detailed "police sketches" in a small number of trials.Comment: 10 figures. Accepted as a paper to the 40th Annual Meeting of the Cognitive Science Society (CogSci 2018). *JWS and JCP contributed equally to this submissio

Deciding to Remember:Memory Maintenance as a Markov Decision Process

Working memory is a limited-capacity form of human mem-ory that actively holds information in mind. Which memoriesought to be maintained? We approach this question by showingan equivalence between active maintenance in working mem-ory and a Markov decision process in which, at each moment,a cognitive control mechanism selects a memory as the targetof maintenance. The challenge of remembering is then findinga maintenance policy well-suited to the task at hand. We com-pute the optimal policy under various conditions and defineplausible cognitive mechanisms that can approximate these op-timal policies. Framing the problem of maintenance in thisway makes it possible to capture in a single model many of theessential behavioral phenomena of memory maintenance, in-cluding directed forgetting and self-directed remembering. Fi-nally, we consider the case of imperfect metamemory — wherethe current state of memory is only partially observable — andshow that the fidelity of metamemory determines the effective-ness of maintenance

Capturing human category representations by sampling in deep feature spaces

Understanding how people represent categories is a core problem in cognitive science. Decades of research have yielded a variety of formal theories of categories, but validating them with naturalistic stimuli is difficult. The challenge is that human category representations cannot be directly observed and running informative experiments with naturalistic stimuli such as images requires a workable representation of these stimuli. Deep neural networks have recently been successful in solving a range of computer vision tasks and provide a way to compactly represent image features. Here, we introduce a method to estimate the structure of human categories that combines ideas from cognitive science and machine learning, blending human-based algorithms with state-of-the-art deep image generators. We provide qualitative and quantitative results as a proof-of-concept for the method's feasibility. Samples drawn from human distributions rival those from state-of-the-art generative models in quality and outperform alternative methods for estimating the structure of human categories.Comment: 6 pages, 5 figures, 1 table. Accepted as a paper to the 40th Annual Meeting of the Cognitive Science Society (CogSci 2018

Orthogonal multi-view three-dimensional object representations in memoryrevealed by serial reproduction

The internal representations of three dimensional objectswithin visual memory are only partially understood. Previousresearch suggests that 3D object perception is viewpoint de-pendent, and that the visual system stores viewpoint perspec-tives in a biased manner. The aim of this project was to ob-tain detailed estimates of the distributions of 3D object viewsin shared human memory. We devised a novel experimentalparadigm based on transmission chains to investigate memorybiases for the 3D orientation of objects. We found that memorytends to be biased towards orthogonal diagrammatic perspec-tives aligned with the ends of the standard basis for a set ofcommon 3D objects, and that these biases are strongest for sideviews as well as top or bottom views for a small set of bilater-ally symmetric objects. Finally, we found that views sampledfrom the modes were easier to categorize in a recognition task

Geometrical Magnetic Frustration in Rare Earth Chalcogenide Spinels

We have characterized the magnetic and structural properties of the CdLn2Se4 (Ln = Dy, Ho), and CdLn2S4 (Ln = Ho, Er, Tm, Yb) spinels. We observe all compounds to be normal spinels, possessing a geometrically frustrated sublattice of lanthanide atoms with no observable structural disorder. Fits to the high temperature magnetic susceptibilities indicate these materials to have effective antiferromagnetic interactions, with Curie-Weiss temperatures theta ~ -10 K, except CdYb2S4 for which theta ~ -40 K. The absence of magnetic long range order or glassiness above T = 1.8 K strongly suggests that these materials are a new venue in which to study the effects of strong geometrical frustration, potentially as rich in new physical phenomena as that of the pyrochlore oxides.Comment: 17 pages, 5 figures, submitted to Phys Rev B; added acknowledgement

The psychological science accelerator’s COVID-19 rapid-response dataset

In response to the COVID-19 pandemic, the Psychological Science Accelerator coordinated three large-scale psychological studies to examine the effects of loss-gain framing, cognitive reappraisals, and autonomy framing manipulations on behavioral intentions and affective measures. The data collected (April to October 2020) included specific measures for each experimental study, a general questionnaire examining health prevention behaviors and COVID-19 experience, geographical and cultural context characterization, and demographic information for each participant. Each participant started the study with the same general questions and then was randomized to complete either one longer experiment or two shorter experiments. Data were provided by 73,223 participants with varying completion rates. Participants completed the survey from 111 geopolitical regions in 44 unique languages/dialects. The anonymized dataset described here is provided in both raw and processed formats to facilitate re-use and further analyses. The dataset offers secondary analytic opportunities to explore coping, framing, and self-determination across a diverse, global sample obtained at the onset of the COVID-19 pandemic, which can be merged with other time-sampled or geographic data.info:eu-repo/semantics/publishedVersio

The Psychological Science Accelerator's COVID-19 rapid-response dataset

In response to the COVID-19 pandemic, the Psychological Science Accelerator coordinated three large-scale psychological studies to examine the effects of loss-gain framing, cognitive reappraisals, and autonomy framing manipulations on behavioral intentions and affective measures. The data collected (April to October 2020) included specific measures for each experimental study, a general questionnaire examining health prevention behaviors and COVID-19 experience, geographical and cultural context characterization, and demographic information for each participant. Each participant started the study with the same general questions and then was randomized to complete either one longer experiment or two shorter experiments. Data were provided by 73,223 participants with varying completion rates. Participants completed the survey from 111 geopolitical regions in 44 unique languages/dialects. The anonymized dataset described here is provided in both raw and processed formats to facilitate re-use and further analyses. The dataset offers secondary analytic opportunities to explore coping, framing, and self-determination across a diverse, global sample obtained at the onset of the COVID-19 pandemic, which can be merged with other time-sampled or geographic data

The Psychological Science Accelerator’s COVID-19 rapid-response dataset

In response to the COVID-19 pandemic, the Psychological Science Accelerator coordinated three large-scale psychological studies to examine the effects of loss-gain framing, cognitive reappraisals, and autonomy framing manipulations on behavioral intentions and affective measures. The data collected (April to October 2020) included specific measures for each experimental study, a general questionnaire examining health prevention behaviors and COVID-19 experience, geographical and cultural context characterization, and demographic information for each participant. Each participant started the study with the same general questions and then was randomized to complete either one longer experiment or two shorter experiments. Data were provided by 73,223 participants with varying completion rates. Participants completed the survey from 111 geopolitical regions in 44 unique languages/dialects. The anonymized dataset described here is provided in both raw and processed formats to facilitate re-use and further analyses. The dataset offers secondary analytic opportunities to explore coping, framing, and self-determination across a diverse, global sample obtained at the onset of the COVID-19 pandemic, which can be merged with other time-sampled or geographic data