Speed & Intelligence: Correlations and Implications

This manuscript explores the intricate relationship between the speed of information processing and intelligence, a topic that has captivated psychometricians for decades. Utilizing elementary cognitive tasks (ECTs) as a primary measure for processing speed, findings since the 1980s reveal a noteworthy negative correlation between reaction times to ECTs and intelligence levels. Interestingly, the correlations amplify when integrating multiple ECTs or incorporating intricate tasks. However, there's a distinct difference between speed in processing information and test-taking speed, with the latter aligning more with personality attributes than cognitive functions. A deeper dive into the study highlights the integral role of working memory and task intricacy in deciphering the association between processing speed and intelligence. As tasks become more demanding, the significance of processing speed escalates. The study also illuminates that item difficulty and individual capacity can influence this relationship, with challenging tasks exhibiting potential positive correlations with ability. Despite the evident connection between processing speed and intelligence, it remains but a fragment of the multifaceted nature of intelligence. It is reaffirmed that IQ tests consistently serve as a dependable metric for cognitive capability, emphasizing the importance of a comprehensive interpretation of speed and intelligence.

Speed of Information Processing

How many times have we heard references to someone we know, who is labeled as clever because he is fast at grasping a concept or quick on some mental arithmetic and phrases used to describe them like “speed of cop-on”  when we associate speed with intelligence. It seems one could almost say that being fast is synonymous with being smart. Certainly in the field of psychometrics speed of information processing and its correlation with g, the proxy for intelligence is of much interest and the subject of numerous studies. Speed of information processing can easily be measured by elementary cognitive tasks (ECTs). An example of this, would be a task requiring a correct response to a visual stimuli (e.g. whether an arrow that appears on a screen is pointing left or right) of which the response is measured from the time the stimuli appears to the time a subject responds by pressing a button. It almost seems counterintuitive that such simple tasks, reducible to basic fundamental neuro-physiological functions, of which involves no cognitive operations can capture the complex and multi-dimensional quality of the brain i.e. intelligence. However many studies have been carried out since the 1980s, and have shown robust correlations (negative) between the reaction time (RT) of responses to ECTs and intelligence.

Typical correlations between single ECTs (elementary cognitive tasks) and IQ are moderate, ranging from -0.2 to -0.4 (Jensen 1998, Sheppard 2007) but increase dramatically when tests are carried out using a battery of ECTs or if the complexity of the ECTs increases by using competing tasks (Fogarty & Stankov 1995) or dual tasks (Jensen 1998), although the ECTs bear no resemblance to conventional psychometric tests. Jensen explains that the reason that a battery of ECTs correlate higher with g, is due to the fact that the “global” speed component is cumulative with every ECT added to the test battery while the unique non-cognitive speed component for each ECT is only added once. This is analogous to adding various items in a conventional IQ test which increases the test correlations with other tests. With respect to speed of processing between high and low ability groups, the RT difference between High IQ subjects and Low IQ subjects are in ways that are seemingly paradoxical. A small percentage of the fastest RTs of low IQ subjects are almost as fast as the fastest RTs of High IQ subjects and faster that the median RTs of the High IQ subjects (Jensen 1998). But where they differ most is in the longest RTs produced. The High IQ subjects almost never produce any RTs that are as slow as most of the RTs produced by the Low IQ subjects. More importantly, it is the differences in longer RTs that are highly correlated with IQ and not the shortest RTs as observed by Larson et al. (1990) and Krantzler (1992).

Test Taking Speed

A cursory look at a typical speeded IQ test like the Cattell Culture Fair III (CCFT) which is considered a good measure of fluid intelligence and highly speeded (50 items to be completed in a time frame of 12.5 minutes or an average of 15 seconds per item) seemingly indicates that speed is indeed an important component of intelligence or a part of intelligence if you like. Does this mean that a high IQ measured on the CCFT would require a high speed of information processing? Vernon (1989) carried out  five studies of which he extracted a g factor from a battery of tests which included the WAIS + RAPM and the MAB (Multidimensional Aptitude Battery) of which he labeled IQg . He also extracted a general factor from a battery of ECTs from which he labeled RTg. Paradoxically, he found that the correlations between the untimed version of the RAPM and RTg  was the highest and in contrast the digi-symbol, a speeded  subtest of the WAIS battery had the lowest correlation with RTg bearing in mind that the RAPM had no commonality in content with any of the ECTs. Further, Jensen (1998) found that the time taken by university graduates on the RAPM was not significantly correlated to the number of questions they answered correctly. Consequently, he distinguishes test taking speed with speed of information processing and considers these to be completely different abilities, with test taking speed more associated with personality and not cognitive factors.

Working Memory, Complexity & Speed

Theories on the limited capacity of working memory (WM) tend to lend support to the correlation between speed of processing and ability (Nȩcka, 1992). It is theorized that working memory has two properties which make speed of information processing crucial for solving tasks in a given amount of time. WM is limited in its capacity to store information and the information entered into storage decays over time. An example of a WM task is remembering a 10-digit phone number and then adding the digits to produce an answer. The capacity of the WM in retaining the 10 digits is crucial before the individual can start the addition process. The cause of a subject failing to complete the task successfully may be attributed to two reasons; i) if his WM capacity is low, the 10-digit information is lost before the addition can be completed or even take place, or ii) the speed of which he adds the 10 digits is too slow and he fails to execute the adding operation before the information in WM eventually decays and has to be re-entered. Hence, the speed with which the information can be processed is a decisive factor to explain the differences in ability of individuals (Vernon 1983a, 1983b) since the information retained in working memory decays if not processed in time. Since task complexity increases the demand it places on the retention of the information in WM, then it stands to reason that the successful processing of the task would then be mediated by the speed of which it can be processed. In other words the heavier the burden on working memory, the more beneficial speed of processing becomes lending support to a speed dependent WM.

Vernon and Kantor (1986) predicted an increase in correlations between speed of processing and reasoning from timed to untimed conditions. They reasoned that if time constraints are relaxed for reasoning tasks (which correlates highly with WM) the correlations between speed of processing and reasoning should be higher since the measure under untimed conditions will contain more speed variance, due to the fact that under untimed conditions the subjects are able to work through all of the items in a test, and the higher ability subjects will be able to solve the hardest items (not usually possible under timed conditions), thereby introducing more variance in scores. The opposing hypothesis to this is that under timed conditions the more able will be able to go through more items with a higher percentage of correct responses, since they will work faster and more accurately through the items compared to the slower subjects. So placing time constraints will increase the correlations between speed of processing and speeded reasoning. Wilhelm et al. (2001), sets out to test this hypothesis by using 367 high school students. Test instruments using reasoning and processing speed subtests were obtained from the Berlin Model of Intelligence Test (BIS-4) of which  were divided into 2 groups of reasoning tests and 1 group of speed test. The subjects were divided into 6 groups of which all were tested on both groups of reasoning tests under timed and untimed conditions, and 4 groups worked on the speed of processing tests. The correlations between tests taken by all 6 groups are summarized in table 4 reproduced below.

The correlations between speed of processing and speeded reasoning is higher than that between speed of processing and un-speeded reasoning which runs counter to what was hypothesized by Vernon & Kantor based on the concept of a speed dependent WM, although the relatively lower correlations are still significant enough that a speed dependent WM cannot be dismissed outright.

Speed, Item Difficulty & Ability

What would the correlations look like if with increase task difficulty, and if the performance of individuals on the tasks were calibrated according to ability? Would we still get the negative correlations so ubiquitous in the numerous studies carried out on information processing tasks? Danthiir et. al. (2005) carried out studies on 186 undergraduate university students and subjects recruited from, the wider community, with Gs (processing speed tasks or ECTs), Gf and Gc tasks. CFA was carried out on the scores for the 3 types of tasks (4 each for GS, Gf and Gc tasks) to confirm the ability factors labeled as Gs, Gf and Gc. What they found was that as the item difficulty increase, the response times for both Gf and Gc items increased with larger latencies for Gf items compared to Gc items of similar difficulty. The findings for the easy item are as expected where the subjects higher on Gf perform faster for easy items but slower as the item difficulty increases. A similar relationship is found for Gc items. This means that the correlations start from negative, reducing as the level of difficulty increases to a point where positive correlations are obtained for the hardest items. The negative correlations are highest for the easiest items, with negative correlations higher on Gc items compared to Gf items. Processing speed seems to play a more important role for easier tasks (more so for Gc items) compared to more difficult items. Correlations between Mean Correct Response Latencies for the easiest items and Ability Factor Scores, are -0.48 with Gc, -0.38 with Gf and -0.25 with Gs for Gc items, and -0.20 with Gc, -0.26 with Gf and -0.22 with Gs for Gf items. See figure 2 (from the original paper).

A re-analysis of the data was carried out, this time with the response latencies calibrated according to ability of the subjects. The scores were divided into 3 ability groups for each of the Gf and Gc items with the results on table 4 re-tabulated below.

For the Gf items, the latencies increase from easy to difficult items across ability groups with the highest latencies for the highest ability group. The latencies are highest for the high ability group even for the easiest items. For the Gc items the latencies increase from easy to the hardest items with the highest latencies demonstrated among the average group. The latencies are much higher in general for the Gf items compared to the Gc items. What is obvious from the results are i) the data does not suggest that the subjects higher on Gf and Gc are faster on the easy items nor does data suggest that ii) the difference in latencies reduces the more difficult the items become. This pattern or rather the rank order of latencies with respect to item difficulty  still holds, although to a much lesser extent for the Gc items. An important point to note is that the difficulty of the items used for the higher ability group are harder than that for the lower ability group, and hence what can be correctly concluded is that the high ability group uses more time for each increment in difficulty of the items, compared to the low ability group. The findings are different from that showed in table 2, where the high ability subjects (on both Gf and Gc items) are faster on the easy items. It would seem evident from this study, that the speed of information processing or the reciprocal measure i.e. the magnitude of the latencies are mediated by item difficulty and when calibrated for ability, are dependent on the ability level of the subjects. Also the results of this study, seemingly provides conclusions that run counter to what other studies have indicated with faster processing speed for higher ability subjects. Interestingly, in testing how mental speed relates to real-world criteria such as school performance, Rindermann & Neubauer (2002) with their structural equation models obtained correlations of school performance (on 271 students between years 9 to 11) with IQ of 0.5, processing speed and school performance of 0.35 (0.39 for high ability subjects compared to 0.26 for average ability subjects). They concluded that although processing speed can “index” performance such as school grades and cognitive abilities, it cannot substitute psychometric intelligence or g since the correlations between school performance and speed of processing cannot be as high as that between school performance and IQ.

It seems then that the differences in general ability is more pronounced when measured using more complex cognitive operations and tasks and that IQ tests are still a more reliable measure of cognitive ability compared to applying a common index graded along a single continuum of measurement, speed.

Conclusion

This article underscores the complex relationship between speed and intelligence. Primarily, there exists a moderate negative correlation between information processing speed, gauged through elementary cognitive tasks, and intelligence. The observations indicate that those with higher intelligence quotients (IQs) not only outpace others in task completion but more crucially, are adept at averting the slowest response times. Factors such as working memory's temporal decay and limited capacity emphasize the significance of processing speed, especially within time-bound scenarios. Furthermore, as tasks vary in complexity, the relevance of speed does too, becoming more pivotal for simpler tasks and less essential for intricate ones.

Interestingly, as task items amplify in difficulty, the once negative correlation between speed and intelligence lessens, at times even becoming positive for the most challenging tasks. This implies an sophisticated interplay between task difficulty and individual capacity in determining the speed-intelligence dynamics. Real-world implications reveal that while processing speed holds value, it doesn't overshadow the correlation between IQ and outcomes like academic performance.

The findings of this article underline the multifaceted nature of the relationship between speed and intelligence, molded by elements like working memory, task intricacy, and personal capability. While speed is undeniably influential in cognitive tasks, it's but a fragment of the intelligence spectrum, with IQ assessments retaining their place as trustworthy indicators of cognitive prowess. Grasping this nuanced relationship not only deepens our comprehension of human intelligence but also paves the way for enriching future inquiries into cognitive studies.