Distinct From IQ Or Working Memory, “Implicit Learning Ability” Is An Important, Stable Trait That Varies Between Individuals

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The striking findings have implications for our understanding of intelligence

By Emma Young

There’s a huge amount of research into how people differ in their ability to learn things deliberately and “explicitly”, such as memorising a list of words or instructions, for example. Far less studied is “implicit learning”. Ask a five-year-old to explain the grammatical rules of their language and they’ll likely have no clue where to start. And yet, they do know them – or at least, well enough to form coherent sentences. This kind of unconscious acquisition of abstract knowledge is an example of “implicit” learning. 

Implicit learning may be especially important for young children, but adults depend on it, too. It “is recognised as a core system that underlies learning in multiple domains, including language, music and even learning about the statistical structure of our environments,” note the authors of a new paper, published in Cognition. 

It’s been assumed that, as an evolutionarily primitive strategy for learning, there’s probably little difference between people in implicit learning ability. But, given that this is such a core system, with wide-ranging implications for our functioning in everyday life, there’s been astoundingly little research into whether this is in fact the case, or into how stable it might be across different kinds of relevant task. The new study fills some of these gaps, with a series of striking findings that have implications for our understanding of intelligence, as well as for who might be best-matched to what type of job. 

Priya Kalra at the University of Wisconsin-Madison and her colleagues gave 64 healthy young adults four types of tasks that required implicit learning. One involved detecting an artificial grammar (after studying a series of letter strings that all adhered to undisclosed grammatical rules, the participants had to judge which strings among a new set were “grammatical” and which were not.) The second task required them to learn whether a particular group of images was going to trigger one outcome, or another (and they were given feedback to help them to learn). For the third task, they had to predict where a circle was going to appear on a screen, based on prior experience, during which the circle sometimes appeared in a predictable sequence of positions and sometimes did not. Finally, they had to learn visual categories implicitly: with the help of feedback, they had to classify abstract visual stimuli into one of two categories. (Explicit learning could have fed into some of these tasks, but the researchers made efforts to investigate, and take into account, its contribution for each individual.) 

One week later, the participants returned to complete different versions of all these tasks, as well as tests of working memory, explicit learning (they had to deliberately learn a list of words) and IQ.

For three of the four implicit learning tasks, the researchers found a “medium” level relationship between a participant’s initial performance and how well they did a week later. This suggests stability in implicit learning ability. The exception was the artificial grammar task; the researchers think it’s possible that explicit learning “contaminated” implicit learning in this task at the second time-point.

The team also found that how good a participant was at implicit learning bore no relation to their IQ or working memory results. It seems, then, to be driven by independent neural processes to those that underpin explicit learning, which is linked to IQ.

This finding fits with earlier work that has tied explicit and implicit learning to different brain regions and networks. (The hippocampus is important for explicit but not implicit earning, for example, whereas damage to the basal ganglia and cerebellum impair implicit, but not explicit, learning.) 

The new results have implications for theories that intelligence depends upon a single fundamental factor, such as processing speed, the researchers write. “These data … provide evidence for the existence of a completely uncorrelated cognitive ability,” they added. 

The findings also imply that someone might feasibly be smart, as measured by an IQ test, but poorer at implicit learning than someone else with a significantly lower IQ score. For some real-world tasks, such as identifying only barely discernible hints of a tumour in a medical scan, a strength at implicit learning could be a big advantage, the researchers note. 

Their findings also prompt a host of other important questions: Will the findings from this study also show up across a wider variety of tasks, and over longer time gaps between testing? Can implicit learning mechanisms be trained? How might explicit learning help or hinder implicit learning and subsequent performance? Earlier work led by Amy Finn, the senior author of this new study, has certainly suggested that a focus on explicit learning processes can “get in the way” of implicit learning. It’ll be fascinating to see the results of follow-up research in this field. 

Evidence of stable individual differences in implicit learning

Image: a 3-d rendered image of a synapse, not from the current research (Getty images/Koto_feja)

Emma Young (@EmmaELYoung) is Staff Writer at BPS Research Digest

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