It is well established that intelligence testing in its modern form developed and was deployed slightly differently in several countries, most notably France, England and the United States. Less widely recognized is the fact that its originators were all part of a close network of scholars who liaised internationally, exchanged ideas and were thoroughly acquainted with each other’s work. Their exchanges resulted from the transnational drive to develop a new social science of psychology involving a determination to find empirical (...) evidence on which to base understandings of the human mind. This soon led to the development of a means of testing human performance, not least in the field of intelligence. In the process, close contacts between all those who were to be pioneers of intelligence testing around the globe developed, in touch with and feeding off each others’ ideas. This article is an account of the development of that network. (shrink)

The concept of emotional intelligence (EI) has drawn a great amount of scholarly interest in recent years; however, attempts to measure individual differences in this ability remain controversial. Although the Mayer–Salovey–Caruso Emotional Intelligence Test (MSCEIT) remains the flagship test of EI, no study has comprehensively examined the full interpretive argument tying variation in observed test performance to variation in the underlying ability. Employing a modern perspective on validation, this article reviews and synthesizes available evidence and (...) discusses sources of concern at every level of the interpretive argument. It is argued that a focus on causal explanation of observed variation in test performance would significantly improve the validity of the MSCEIT as a measure of EI. (shrink)

The most interesting feature about Mr. Richardson's criticism of my paper is that it reveals the typical attitude of the traditional intelligence tester which I set out to criticize. He accepts the view that intelligence deals mainly with the grasping of relationships, that intelligence thus defined is an innate ability, that it may be relatively isolated by the use of suitably designed tests and treated independently of other abilities.

This article examines the rise and decline of the enthusiasm for intelligence testing in early twentieth-century China, focusing on the appeal, the challenges, and the critiques revolving around this psychological instrument. The introduction of intelligence testing reflected not only China’s urgent needs in modernizing its merit system, but also Chinese psychologists’ aspirations for pursuing exactitude and redefining the racial characteristics of their compatriots against foreign interpretations. But despite psychologists’ endeavors, the political and geographical fragmentation of Republican China troubled (...) the epistemic imperative of uniformity demanded by Euro-American psychometrics and therefore undermined the validity of measurement. Subsequently, the legitimacy of intelligence testing began to be questioned by several influential Chinese psychologists in the late 1920s and 30s. The difficulties in standardization and the hostility within the psychology community formed a vicious cycle, impeding the progress of nationwide testing. Through this history, the article demonstrates not only the elevation of measurement to epistemic authority in modern China, but also how its promise was challenged by a diverse and rapidly changing society. (shrink)

The nature of intelligence and how it can be measured has occupied psychologists, educationalists, biologists and philosophers for hundreds of years. However, there has been little investigation into the rise of the traditional dominant educational ideology that intelligence and IQ have innate limits and are unchanging and unchangeable. This book traces the roots of this mind set back to early puritan communities on both sides of the Atlantic, drawing parallels between puritan dogma and the development of the traditional (...) curricula and selection processes that are still firmly embedded in school practice today. Drawing on the work of Galton, Pearson, Burt, Goddard, Terman and others in his search for the truth about intelligence testing, John White looks at the personal histories and socialised religious backgrounds of these key psychologists and casts an entirely new light on schooling in Britain and the USA in modern times. This work also shows how we can transcend this heritage and base our educational system on values and practices more in tune with the twenty-first century. (shrink)

I DO not have to apologize for entering upon a discussion of intelligence and intelligence tests; it is a field which comes within the purview of philosophy as well as psychology. Any method of testing intelligence is therefore of common interest, especially as the methodology employed is usually based upon some definite theory as to its nature. The very word intelligence covers a wide range of meanings and psychologists seem to select sections of this range at (...) will in accordance with their particular interest. It may include most of the behavioural activities of man, or be narrowed down so that it refers to certain quantitative or relational aspects of experience. To take the case of the factor analyst, after his concept of intelligence has been analysed and classified into g's and s's, it becomes not a description of the mind, but rather a closed cognitive model of it. (shrink)

Emotional intelligence is a psychological skill that aids athletes in the control of emotions and optimization of sports performance. The present study investigated the psychometric properties of the Self-Report Emotional Intelligence Test in 508 Brazilian youth and adult athletes. Data analysis was conducted through Exploratory and Confirmatory Factor Analysis, Cronbach’s alpha, composite reliability and Pearson’s Correlation. EFA revealed the one-factor model with 26 items with the best adjustment. CFA confirmed the one-factor model with 26 items with best (...) greater fit. No evidence of invariance was found, suggesting that the SSEIT must be used with caution when comparing gender and age groups. The external validity was found in the correlation of EI and cognitive anxiety and self-confidence. It is concluded that the Brazilian version of SSEIT for the sports context presented acceptable psychometric properties, however, showed limitations that should be explored in the future. (shrink)

To fit the magnificence of this setting in Beijing’s Great Hall of the People, and the honor of giving the 2007 Sir John Crawford Memorial Lecture, it is well to have a subject of suitable proportions. I have chosen one of global size and urgent time frame: our climate crisis. We only have one future and one global climate–and now it looks as if we only have one chance to rescue our civilization from collapse and prevent a mass extinction of (...) species during the 21st century. Unless you have been keeping up with climate science for the past twenty-five years, you likely do not know how serious the matter has become. The notion that we might slowly get into serious trouble by mid-century has been conveyed by the media and understood by at least some political leaders. But that scenario depends on somehow avoiding sudden shifts in climate in the meantime, instant setbacks at a time when we lack maneuvering room. An abrupt shift in drought area occurred in 1983 and we had a near-miss of a mass extinction of Amazon species in 1999. It is easy to appreciate that one more degree of global warming will seriously reduce crop yields in the tropics, but in the words of climate scientist Claudia Tebaldi 1, “It’s the extremes, not the averages, that cause the most damage to.. (shrink)

We emphasize the feature of Webb's presentation that bears most directly on contemporary research with real animals. Many neuroscience modelers erroneously conclude that a model that performs like an animal must have achieved this goal through processes analogous with those used by the animal. A simulation failure justifies rejecting a model, but success does not justify acceptance. However, an important benefit of models, successful or otherwise, is to stimulate new research.

The article describes the possibilities of using and modifying existing machine learning technologies in the field of natural language processing for the purpose of designing a system for automatically generating control and test tasks (CTT). The reason for such studies was the limitations in generating theminimumrequired amount ofCTtomaintain student engagement in game-based learning formats, such as quizzes, and others. These limitations are associated with the lack of time resources among training professionals for manual generation of tests. The article discusses (...) the applied research of the Large Language Model (LLM) and Generative pre-trained transformer (GPT) technologies for the development of a system for automatic generation of tests for the purpose of its implementation in the BoxBattle gamified learning platform. The result of such applied research can be a system for automatic generation of tests, which will reduce the time for developing tests. As a result, this will allow teachers to free up time to implement a personalized approach to teaching and develop students’soft skills. (shrink)

Alan Turing’s 1950 imitation game has been widely understood as a means for testing if an entity is intelligent. Following a series of papers by Diane Proudfoot, I offer a socio-technological interpretation of Turing’s paper and present an alternative way of understanding both the imitation game and Turing’s concept of intelligence. Turing, I claim, saw intelligence as a social concept, meaning that possession of intelligence is a property determined by society’s attitude toward the entity. He realized that (...) as long as human society held a prejudiced attitude toward machinery—seeing machines a priori as mindless objects—machines could not be said to be intelligent, by definition. He also realized, though, that if humans’ a priori, chauvinistic attitude toward machinery changed, the existence of intelligent machines would become logically possible. Turing thought that such a change would eventually occur: He believed that when scientists overcome the technological challenge of constructing sophisticated machines that could imitate human verbal behavior—i.e., do well in the imitation game—humans’ prejudiced attitude toward machinery will have altered in such a way that machines could be said to be intelligent. The imitation game, for Turing, was not an intelligence test, but a technological aspiration whose realization would likely involve a change in society’s attitude toward machines. (shrink)

The Turing test has a peculiar status in the artificial intelligence (AI) research community. On the one hand, it is presented as an important topic in virtually every AI textbook, and the research direction focused on developing AI systems that behave in human-like fashion is standardly called the “Turing test approach”. On the other hand, reports of computer programs passing the Turing test have had relatively little effect. Does this mean that the Turing test is (...) no longer relevant as a test, doomed to be a theoretical notion with little connection to AI practice? In this paper, I argue that there is one problem in particular with common traditional versions of the Turing test, namely their focus on deception. The criterion for passing the Turing test is standardly connected to an AI system’s ability to deceive the interrogator about its identity. But why should we connect intelligence to the ability deceive? Here I present a revised version of an intelligence test that is not based on deception. In what I call the Community-based intelligence test (CBIT), an AI is introduced to a community of human subjects. If after a sufficient number of interactions within that community the humans are not able to identify the AI system as a computer, it is considered to have passed CBIT. I discuss whether that should be enough to ascribe intelligence to the AI, and if not, what more would be needed? (shrink)

Engagement with artificial intelligence (AI) can be highly beneficial for theology. This article maps the landscape of the various ways such engagement can occur. It begins by outlining the opportunities and limitations of computational theology before diving into speculative territory by imagining how robot theologians might think of divine revelation. The topic of AI and imago Dei is then reviewed, illustrating several ways AI can inform theological anthropology. The article concludes with a more speculative take on the possible implications (...) of AI for divine infinity, incarnation, theodicy, and demonic intelligence. (shrink)

This chapter has two main goals: to update philosophers on the state of the art in the scientific psychology of intelligence, and to explain and evaluate challenges to the measurement invariance of intelligence tests. First, we provide a brief history of the scientific psychology of intelligence. Next, we discuss the metaphysics of intelligence in light of scientific studies in psychology and neuroimaging. Finally, we turn to recent skeptical developments related to measurement invariance. These have largely focused (...) on attributability: Where do the mechanisms and dispositions that explain people’s performance on tests of intelligence inhere – in the agent, in the local testing environment, in the culture, or in the interactions among these? After explaining what measurement invariance is in the context of intelligence testing, we explore the phenomenon of stereotype threat as a challenge to measurement invariance, as well as more recent work on overcoming or buffering against stereotype threat. (shrink)

The article describes the possibilities of using and modifying existing machine learning technologies in the field of natural language processing for the purpose of designing a system for automatically generating control and test tasks (CTT). The reason for such studies was the limitations in generating theminimumrequired amount ofCTtomaintain student engagement in game-based learning formats, such as quizzes, and others. These limitations are associated with the lack of time resources among training professionals for manual generation of tests. The article discusses (...) the applied research of the Large Language Model (LLM) and Generative pre-trained transformer (GPT) technologies for the development of a system for automatic generation of tests for the purpose of its implementation in the BoxBattle gamified learning platform. The result of such applied research can be a system for automatic generation of tests, which will reduce the time for developing tests. As a result, this will allow teachers to free up time to implement a personalized approach to teaching and develop students’soft skills. (shrink)