An integrated perspective on the relation between response speed and intelligence
Introduction
Sir Francis Galton (1822–1911), one of the founders of differential psychology, believed that “general mental ability” manifests itself by the speed with which people perform elementary cognitive tasks. That is, intelligent people should be faster than less intelligent people at deciding, say, whether a clearly presented arrow points to the left or to the right. Galton’s idea could be taken to imply that individual differences in intelligence are caused by individual differences in fundamental, low-level neurophysiological characteristics (e.g., Anderson & Reid, 2005) such as brain glucose metabolic rate, intracellular pH levels, or the degree of neural myelinization.
Galton’s idea was reductionist to such an extent that it struck many people as counter-intuitive: how can something so complex and multidimensional as human intelligence be captured by something so simple and unidimensional as response speed in elementary cognitive tasks? The initial opposition to Galton’s idea was strong enough to have it be rejected and ignored until the 1980s. Since then, overwhelming empirical evidence has been gathered in support of Galton’s idea (for reviews see Deary, 1994, Jensen, 2006). Indeed, there is now an entire subfield called “differential mental chronometry”, the goal of which is to study the relation between measures of general intelligence (g) and response time (RT) in elementary cognitive tasks.
Over the course of several decades, researchers in the field of differential mental chronometry have discovered various regularities that any theory of the relation between RT and g should try to accommodate (e.g., Jensen, 2006, chap. 11). Here we focus on the following key regularities:
- 1.
Right-skewed RT distributions. RT distributions have a pronounced right skew. In addition, low-g people generate RTs that are more spread-out than those for high-g people (e.g., Baumeister, 1998).
- 2.
The Worst Performance Rule. Slow RTs are more indicative of g than are fast RTs (e.g., Larson and Alderton, 1990, Unsworth et al., 2010; for a review see Coyle, 2003).
- 3.
Stronger correlation between g and the standard deviation of RT (RTSD) than between g and the mean of RT (RTm). It is generally found that RTSD correlates slightly higher with g than does RTm, which – as suggested by Jensen (2006) – in turn correlates slightly higher with g than does the median RT (Baumeister, 1998, Jensen, 1992, Walhovd and Fjell, 2007).
- 4.
Linear relation between RTm and RTSD. As observed by Jensen (2006, p. 202): “(…) there is a near-perfect correlation between individual differences in RTm and RTSD. (… ) Empirically measured diameters and circumferences of different-size circles are no more highly correlated than are RTm and RTSD. The slight deviations of their correlation coefficient from unity are simply measurement errors.”
- 5.
Linear Brinley plots. Across several tasks that vary in difficulty, the RTm of a group of low-g people is a constant multiple of the RTm of a group of high-g people (e.g., Rabbitt, 1996).
- 6.
Stronger correlation between g and inspection time (IT) than between g and RTm. The time needed to obtain a predetermined level of accuracy in a simple visual inspection task is more strongly related to g than is RTm from a response time task (Jensen, 1998, Jensen, 2006).
We demonstrate that all of the above regularities can be viewed as manifestations of a single latent relationship. We make this argument using one of the most popular models for RT tasks: the diffusion model (e.g., Ratcliff, 1978, Ratcliff et al., 2008). This single latent relationship is between individual differences in “drift rate” and individual differences in g. Drift rate is a diffusion model parameter that quantifies the signal-to-noise ratio of the information-accumulation process. Since drift rate represents a signal-to-noise ratio, it can be affected by stimulus manipulations and task demands. However, even in identical decision environments, different people will evince different drift rates, and we assume that these individual differences are associated with intelligence, with high-g people having high drift rates. The primary aim of this article is to demonstrate that, although each of the six benchmark phenomena may appear different, and have inspired different research efforts, they can all be accounted for by this one common assumption. The diffusion model provides a unifying account of these six benchmark phenomena, but also makes testable predictions about different, related, phenomena. The secondary aim of this article is to outline the advantages of a diffusion model analysis as a tool in the study of the relation between response speed and general intelligence.
The outline of this paper is as follows. The first section briefly outlines Ratcliff’s diffusion model. The second section describes how the single assumption that individual differences in the diffusion model’s drift rate parameter correlate with g naturally predicts the six key phenomena in the field of differential mental chronometry. Earlier papers on this relationship laid the groundwork for establishing some results in this section (right-skewed RT distributions, the worst performance rule, the linear relation between RT mean and RT standard deviation, and linear Brinley plots). We add to these results the stronger correlation between g and RT standard deviation than between g and RT mean, the stronger correlation between g and inspection time than between g and RT mean, and a non-trivial prediction of the worst performance rule: that the worst performance rule is not specific to g, but generalizes to other phenomena that affect drift rate, such as stimulus difficulty. The third section lists the conceptual and practical advantages, as well as two drawbacks, of a diffusion model approach to the study of intelligence. The fourth, concluding section discusses what we have learned by attributing g to drift rate.
Section snippets
The diffusion model
In the diffusion model (Ratcliff, 1978, Ratcliff and Rouder, 2000, van Ravenzwaaij and Oberauer, 2009, Wagenmakers, 2009), stimulus processing is conceptualized as the noisy accumulation of evidence over time. A response is initiated when the accumulated evidence reaches a predefined threshold (Fig. 1).
The model applies to tasks in which the participant has to decide quickly between two alternatives. For instance, in a lexical decision task, participants have to decide whether a letter string
Key phenomena in intelligence research captured by drift rate
We discuss six important phenomena in the study of mental chronometry in turn, each with a demonstration that the diffusion model naturally predicts the data, with the common assumption that g manifests itself through drift rate v.
Advantages and limitations of a diffusion model approach to the study of intelligence
We have shown how a simple assumption in a computational model can provide a unifying account of six different phenomena from the intelligence literature. When we assume that differences in drift rate in the diffusion model are associated with differences in intelligence (g), we find that the model predicts: right-skewed RT distributions; the worst performance rule; the fact that g correlates stronger with RTSD then with RTm; the linear relation between RTm and RTSD; linear Brinley plots; and
Concluding comments
The diffusion model provides an elegant, quantitative, and unifying account of previously disparate empirical phenomena. This means that while substantial research efforts have been devoted to each of the individual phenomena, these efforts represent an ill-advised division of labor. We combined results from previous research with new results regarding the stronger correlation between g and RT standard deviation than between g and RT mean, the linear relation between RT mean and RT standard
Acknowledgements
This research was supported by a Vidi grant from the Dutch Organization for Scientific Research (NWO) and a scientific visit grant from the Australian Academy of Sciences. We thank Jeff Rouder for providing data from a stimulus brightness experiment (Ratcliff & Rouder, 1998).
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