Human observers often experience strongly negative impressions of human-like objects falling within a particular range of visual similarity to real humans (the "uncanny valley" phenomenon). We hypothesized that negative impressions in the uncanny valley phenomenon are related to a difficulty in object categorization. We produced stimulus images by morphing two of each of real, stuffed, and cartoon human face images (Experiment 1). Observers were asked to categorize each of these images as either category and evaluated the likability of the image. The results revealed that the longest latency, the highest ambiguity in categorization, and the lowest likability score co-occurred at consistent morphing percentages. Similar results were obtained even when we employed stimulus images that were created by morphing two of each of real, stuffed, and cartoon dog images (Experiment 2). However, the effect of categorization difficulty on evaluation was weak when two real human faces were morphed (Experiment 3). These results suggest that the difficulty in categorizing an object as either of two dissimilar categories is linked to negative evaluation regardless of whether the object is human-related or not.

This N = 173,426 social science dataset was collected through the collaborative COVIDiSTRESS Global Survey – an open science effort to improve understanding of the human experiences of the 2020 COVID-19 pandemic between 30th March and 30th May, 2020. The dataset allows a cross-cultural study of psychological and behavioural responses to the Coronavirus pandemic and associated government measures like cancellation of public functions and stay at home orders implemented in many countries. The dataset contains demographic background variables as well as measures of Asian Disease Problem, perceived stress (PSS-10), availability of social provisions (SPS-10), trust in various authorities, trust in governmental measures to contain the virus (OECD trust), personality traits (BFF-15), information behaviours, agreement with the level of government intervention, and compliance with preventive measures, along with a rich pool of exploratory variables and written experiences. A global consortium from 39 countries and regions worked together to build and translate a survey with variables of shared interests, and recruited participants in 47 languages and dialects. Raw plus cleaned data and dynamic visualizations are available.

The COVID-19 pandemic has increased negative emotions and decreased positive emotions globally. Left unchecked, these emotional changes might have a wide array of adverse impacts. To reduce negative emotions and increase positive emotions, we tested the effectiveness of reappraisal, an emotion-regulation strategy that modifies how one thinks about a situation. Participants from 87 countries and regions (n = 21,644) were randomly assigned to one of two brief reappraisal interventions (reconstrual or repurposing) or one of two control conditions (active or passive). Results revealed that both reappraisal interventions (vesus both control conditions) consistently reduced negative emotions and increased positive emotions across different measures. Reconstrual and repurposing interventions had similar effects. Importantly, planned exploratory analyses indicated that reappraisal interventions did not reduce intentions to practice preventive health behaviours. The findings demonstrate the viability of creating scalable, low-cost interventions for use around the world.

During the onset of the COVID-19 pandemic, the COVIDiSTRESS Consortium launched an open-access global survey to understand and improve individuals’ experiences related to the crisis. A year later, we extended this line of research by launching a new survey to address the dynamic landscape of the pandemic. This survey was released with the goal of addressing diversity, equity, and inclusion by working with over 150 researchers across the globe who collected data in 48 languages and dialects across 137 countries. The resulting cleaned dataset described here includes 15,740 of over 20,000 responses. The dataset allows cross-cultural study of psychological wellbeing and behaviours a year into the pandemic. It includes measures of stress, resilience, vaccine attitudes, trust in government and scientists, compliance, and information acquisition and misperceptions regarding COVID-19. Open-access raw and cleaned datasets with computed scores are available. Just as our initial COVIDiSTRESS dataset has facilitated government policy decisions regarding health crises, this dataset can be used by researchers and policy makers to inform research, decisions, and policy.

Artificial objects often subjectively look eerie when their appearance to some extent resembles a human, which is known as the uncanny valley phenomenon. From a cognitive psychology perspective, several explanations of the phenomenon have been put forth, two of which are object categorization and realism inconsistency. Recently, MacDorman and Chattopadhyay (2016) reported experimental data as evidence in support of the latter. In our estimation, however, their results are still consistent with categorization-based stranger avoidance. In this Discussions paper, we try to describe why categorization-based stranger avoidance remains a viable explanation, despite the evidence of MacDorman and Chattopadhyay, and how it offers a more inclusive explanation of the impression of eeriness in the uncanny valley phenomenon.