Theory of Mind Might Have Spontaneously Emerged in Large Language Models

We explore the intriguing possibility that theory of mind (ToM), or the uniquely human ability to impute unobservable mental states to others, might have spontaneously emerged in large language models (LLMs). We designed 40 false-belief tasks, considered a gold standard in testing ToM in humans, and administered them to several LLMs. Each task included a false-belief scenario, three closely matched true-belief controls, and the reversed versions of all four. Smaller and older models solved no tasks; GPT-3-davinci-001 (from May 2020) and GPT-3-davinci-002 (from January 2022) solved 10%; and GPT-3-davinci-003 (from November 2022) and ChatGPT-3.5-turbo (from March 2023) solved 35% of the tasks, mirroring the performance of three-year-old children. ChatGPT-4 (from June 2023) solved 90% of the tasks, matching the performance of seven-year-old children. These findings suggest the intriguing possibility that ToM, previously considered exclusive to humans, may have spontaneously emerged as a byproduct of LLMs' improving language skills.Comment: TRY RUNNING ToM EXPERIMENTS ON YOUR OWN: The code and tasks used in this study are available at Colab (https://colab.research.google.com/drive/1ZRtmw87CdA4xp24DNS_Ik_uA2ypaRnoU). Don't worry if you are not an expert coder, you should be able to run this code with no-to-minimum Python skills. Or copy-paste the tasks to ChatGPT's web interfac

Machine intuition: Uncovering human-like intuitive decision-making in GPT-3.5

Artificial intelligence (AI) technologies revolutionize vast fields of society. Humans using these systems are likely to expect them to work in a potentially hyperrational manner. However, in this study, we show that some AI systems, namely large language models (LLMs), exhibit behavior that strikingly resembles human-like intuition - and the many cognitive errors that come with them. We use a state-of-the-art LLM, namely the latest iteration of OpenAI's Generative Pre-trained Transformer (GPT-3.5), and probe it with the Cognitive Reflection Test (CRT) as well as semantic illusions that were originally designed to investigate intuitive decision-making in humans. Our results show that GPT-3.5 systematically exhibits "machine intuition," meaning that it produces incorrect responses that are surprisingly equal to how humans respond to the CRT as well as to semantic illusions. We investigate several approaches to test how sturdy GPT-3.5's inclination for intuitive-like decision-making is. Our study demonstrates that investigating LLMs with methods from cognitive science has the potential to reveal emergent traits and adjust expectations regarding their machine behavior

Facial recognition technology can expose political orientation from facial images even when controlling for demographics and self-presentation

A facial recognition algorithm was used to extract face descriptors from carefully standardized images of 591 neutral faces taken in the laboratory setting. Face descriptors were entered into a cross-validated linear regression to predict participants' scores on a political orientation scale (Cronbach's alpha=.94) while controlling for age, gender, and ethnicity. The model's performance exceeded r=.20: much better than that of human raters and on par with how well job interviews predict job success, alcohol drives aggressiveness, or psychological therapy improves mental health. Moreover, the model derived from standardized images performed well (r=.12) in a sample of naturalistic images of 3,401 politicians from the U.S., UK, and Canada, suggesting that the associations between facial appearance and political orientation generalize beyond our sample. The analysis of facial features associated with political orientation revealed that conservatives had larger lower faces, although political orientation was only weakly associated with body mass index (BMI). The predictability of political orientation from standardized images has critical implications for privacy, regulation of facial recognition technology, as well as the understanding the origins and consequences of political orientation

Rethinking privacy in the age of psychological targeting

"Psychological targeting" is the practice of predicting people's psychological profiles from their digital footprints (e.g. their Facebook profiles, transaction records or Google searches) in order to influence their attitudes, emotions or behaviours with the help of psychologically informed interventions. For example, knowing that a person is extroverted makes it possible to personalise recommendations in a way that aligns with their [...

What your Facebook Profile Picture Reveals about your Personality

People spend considerable effort managing the impressions they give others. Social psychologists have shown that people manage these impressions differently depending upon their personality. Facebook and other social media provide a new forum for this fundamental process; hence, understanding people's behaviour on social media could provide interesting insights on their personality. In this paper we investigate automatic personality recognition from Facebook profile pictures. We analyze the effectiveness of four families of visual features and we discuss some human interpretable patterns that explain the personality traits of the individuals. For example, extroverts and agreeable individuals tend to have warm colored pictures and to exhibit many faces in their portraits, mirroring their inclination to socialize; while neurotic ones have a prevalence of pictures of indoor places. Then, we propose a classification approach to automatically recognize personality traits from these visual features. Finally, we compare the performance of our classification approach to the one obtained by human raters and we show that computer-based classifications are significantly more accurate than averaged human-based classifications for Extraversion and Neuroticism

How are you doing? : emotions and personality in Facebook

User generated content on social media sites is a rich source of information about latent variables of their users. Proper mining of this content provides a shortcut to emotion and personality detection of users without filling out questionnaires. This in turn increases the application potential of personalized services that rely on the knowledge of such latent variables. In this paper we contribute to this emerging domain by studying the relation between emotions expressed in approximately 1 million Facebook (FB) status updates and the users' age, gender and personality. Additionally, we investigate the relations between emotion expression and the time when the status updates were posted. In particular, we find that female users are more emotional in their status posts than male users. In addition, we find a relation between age and sharing of emotions. Older FB users share their feelings more often than young users. In terms of seasons, people post about emotions less frequently in summer. On the other hand, December is a time when people are more likely to share their positive feelings with their friends. We also examine the relation between users' personality and their posts. We find that users who have an open personality express their emotions more frequently, while neurotic users are more reserved to share their feelings

Contemporary management of prenatally diagnosed spina bifida aperta — an update

Spina bifida aperta is a relatively common congenital defect that occurs in the general population. Once the disorder hasbeen diagnosed, a discussion, that can be emotionally-charged, ensues about whether to treat it prenatally or to only offer surgery postnatally. Given that there are good arguments for and against both options, it is of paramount importance to gain a good understanding of the major advantages and disadvantages of the various surgical approaches. The aim of our paper is to summarize current knowledge about spina bifida and the potential benefits of prenatal surgery

Characterizing a psychiatric symptom dimension related to deficits in goal-directed control.

Prominent theories suggest that compulsive behaviors, characteristic of obsessive-compulsive disorder and addiction, are driven by shared deficits in goal-directed control, which confers vulnerability for developing rigid habits. However, recent studies have shown that deficient goal-directed control accompanies several disorders, including those without an obvious compulsive element. Reasoning that this lack of clinical specificity might reflect broader issues with psychiatric diagnostic categories, we investigated whether a dimensional approach would better delineate the clinical manifestations of goal-directed deficits. Using large-scale online assessment of psychiatric symptoms and neurocognitive performance in two independent general-population samples, we found that deficits in goal-directed control were most strongly associated with a symptom dimension comprising compulsive behavior and intrusive thought. This association was highly specific when compared to other non-compulsive aspects of psychopathology. These data showcase a powerful new methodology and highlight the potential of a dimensional, biologically-grounded approach to psychiatry research.Funded by a Sir Henry Wellcome Postdoctoral Fellowship (101521/Z/12/Z) awarded to CM Gillan. Claire M Gillan: Wellcome Trust 101521/Z/12/Z Nathaniel D Daw: National Institute on Drug Abuse 1R01DA038891 Nathaniel D Daw: James S. McDonnell Foundation Scholar AwardThis is the final version of the article. It first appeared from eLife Sciences Publications via http://dx.doi.org/10.7554/eLife.1130