Measures of Intelligence

There is a widely held view on measurement inferences, that goes back to Stevens’s ([1946]) theory of measurement scales and ‘permissible statistics’. This view defends the following prohibition: you should not make inferences from averages taken with ordinal scales (versus quantitative scales: interval or ratio). This prohibition is general—it applies to all ordinal scales—and it is sometimes endorsed without qualification. Adhering to it dramatically limits the research that the social and biomedical sciences can conduct. I provide a Bayesian analysis of (...) this inferential problem, determining when measurements from ordinal scales can be used to confirm hypotheses about relative group averages. The prohibition, I conclude, cannot be upheld, even in a qualified sense. The beliefs needed to make average comparisons are less demanding than those appropriate for quantitative scales. I illustrate with the alleged paradigm ordinal scale, Mohs’ scale of mineral hardness, arguing that the literature has mischaracterized it. (shrink)

Gignac and Zajenkowski (2020) find that “the degree to which people mispredicted their objectively measured intelligence was equal across the whole spectrum of objectively measured intelligence”. This Comment shows that Gignac and Zajenkowski’s (2020) finding of homoscedasticity is likely the result of a recoding choice by the experimenters and does not in fact indicate that the Dunning-Kruger Effect is a mere statistical artifact. Specifically, Gignac and Zajenkowski (2020) recoded test subjects’ responses to a question regarding self-assessed comparative IQ onto a (...) linear IQ scale when a normal IQ scale would likely have been more appropriate. More generally, researchers studying self-assessed intelligence should be aware of potential measurement problems that may arise when transforming an ordinal scale onto an interval scale. (shrink)

Knowledge of human uses of animals is an important, but understudied, aspect of how humans treat animals. We developed a measure of one kind of knowledge of human uses of animals – knowledge of factory farming. Studies 1 (N = 270) and 2 (N = 270) tested an initial battery of objective, true or false statements about factory farming using Item Response Theory. Studies 3 (N = 241) and 4 (N = 278) provided evidence that responses to a 10-item Knowledge (...) of Factory Farming Scale predicted a reduction in consumption of animal products (rs = −.17- −.27) and approval of political actions aimed at factory farming (rs = .2 – .24). Path models from Studies 3 and 4 suggested that different kinds of knowledge uniquely predicted different outcomes. The Knowledge of Factory Farming scale was a unique predictor of approval of political actions concerning factory farmed animals but not animal consumption. Knowledge of Animals Used as Food predicted animal consumption but not political actions concerning farmed animals. These results highlight that different kinds of knowledge can be relevant for different animal related outcomes. (shrink)

ENG: We all have our own ideas about what it is like to be intelligent. Indeed, even the experts disagree on this topic. This has generated diverse theories on the nature of intelligence and its genetic and environmental bases. Many scientific and philosophical questions thus remain unaddressed: is it possible to characterize intelligence in scientific terms? What do IQ tests measure? How is intelligence influenced by genetics, epigenetics, and the environment? What are the ethical and social implications of the research (...) on this topic? This book aims to provide the readers with the conceptual resources to critically analyze scientific findings and orientate themselves in this multi-faceted debate across biology, psychology, neuroscience, philosophy, and anthropology. -/- ITA: Ognuno di noi ha una propria idea di cosa significhi essere intelligenti, tanto che anche gli scienziati esperti in materia hanno opinioni differenti al riguardo. Ciò ha contribuito a generare teorie contrastanti sulla natura dell’intelligenza e su quali siano le sue basi genetiche e ambientali. Non è un caso, dunque, che diverse domande scientifiche e filosofiche restino ancora aperte: è possibile caratterizzare scientificamente l’intelligenza? Cosa misurano i test del QI? In che modo genetica, epigenetica e ambiente influenzano l’intelligenza? Quali sono le implicazioni etiche e sociali della ricerca sull’argomento? Oltre ad analizzare questi problemi, il testo intende fornire gli strumenti concettuali necessari per leggere criticamente i dati scientifici e orientarsi in uno sfaccettato e affascinante dibattito a cavallo tra biologia, psicologia, neuroscienze, filosofia e antropologia. (shrink)

Psychometric g—a statistical factor capturing intercorrelations between scores on different IQ tests—is of theoretical interest despite being a low-fidelity model of both folk psychological intelligence and its cognitive/neural underpinnings. Psychometric g idealizes away from those aspects of cognitive/neural mechanisms that are not explanatory of the relevant variety of folk psychological intelligence, and it idealizes away from those varieties of folk psychological intelligence that are not generated by the relevant cognitive/neural substrate. In this manner, g constitutes a high-fidelity bridge model of (...) the relationship between its two targets and, thereby, helps demystify the relationship between folk and scientific psychology. (shrink)

In 1976, the Genetics Society of America published a document entitled “Resolution of Genetics, Race, and Intelligence.” This document laid out the Society’s position in the IQ controversy, particularly that on scientific and ethical questions involving the genetics of intellectual differences between human populations. Since the GSA was the largest scientific society of geneticists in the world, many expected the document to be of central importance in settling the controversy. Unfortunately, the Resolution had surprisingly little influence on the discussion. In (...) 1979, William Provine analyzed the possible factors that decreased the impact of the Resolution, among them scientists’ limited understanding of the relationship between science and ethics. Through the analysis of unpublished versions of the Resolution and exchanges between GSA members, I will suggest that the limited impact of the statement likely depended on a shift in the aims of the GSA due to the controversies that surrounded the preparation of the document. Indeed, the demands of the membership made it progressively more impartial in both scientific and political terms, decreasing its potential significance for a wider audience. Notably, the troubled history of the Resolution raises the question of what can make effective or ineffective the communication between scientists and the public—a question with resonance in past and present discussions on topics of social importance. (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 ML community recognizes the importance of anticipating and mitigating the potential negative impacts of benchmark research. In this position paper, we argue that more attention needs to be paid to areas of ethical risk that lie at the technical and scientific core of ML benchmarks. We identify overlooked structural similarities between human IQ and ML benchmarks. Human intelligence and ML benchmarks share similarities in setting standards for describing, evaluating and comparing performance on tasks relevant to intelligence. This enables us (...) to unlock lessons from feminist philosophy of science scholarship that need to be considered by the ML benchmark community. Finally, we outline practical recommendations for benchmark research ethics and ethics review. (shrink)