Dunst et al’s Neural Efficiency

Dunst et al, Neural efficiency as a function of task demands Intelligence
Volume 42, January–February 2014, Pages 22-30

Abstract

The neural efficiency hypothesis describes the phenomenon that brighter individuals show lower brain activation than less bright individuals when working on the same cognitive tasks. … Less bright individuals received sample-based easy and medium tasks, whereas bright subjects received sample-based medium and difficult tasks. … These results suggest that neural efficiency reflects an (ability-dependent) adaption of brain activation to the respective task demands.

1. Introduction

Fluid intelligence (gf) tests measure the ability to solve novel problems that depend relatively little on stored knowledge or the ability to learn. In order to correctly answer fluid intelligence tasks it is necessary to actively maintain domain-specific information and domain-general attention or “executive” control of ongoing processes.

The focus of the current study is on the neurobiological underpinnings of fluid intelligence.

[Jung and Haier]  found that mostly frontal and parietal areas of the cortex are related to intelligence. After a first sensory analysis of incoming information in the occipital cortex the sensory information will be abstracted and elaborated in parietal areas. Testing hypotheses concerning a problem and finding the best solution are conducted in the frontal cortex, which interacts with the parietal cortex. The anterior cingulate cortex accounts for the response selection and inhibits competing ones. … Haier introduced the neural efficiency hypothesis that claims that brighter individuals show more efficient brain functioning than less intelligent individuals.

[But] With increasing task difficulty stronger activation for participants with high intelligence was found.: … brighter individuals increased their metabolic rates during the difficult task whereas less intelligent individuals decreased their metabolic rates. … However, effort investment is only beneficial up to a certain point (of moderate intensity of motivation), above that point performance should again decline.

1.1. Aim of this study

The current study aims at testing whether the person-specific level of task difficulty accounts for individual differences in neural efficiency. … According to the person-specific approach the task difficulty is defined relative to an individual’s intellectual ability. Using this approach ensures that the perceived level of task difficulty is the same for individuals with varying intellectual ability.

The task employed in the current study is the number series completion task which is used to assess numerical inductive reasoning.

2. Method

2.2.1. Intelligence screening

In order to obtain a screening measure of psychometric g the following four subtests were completed: figural-inductive reasoning (FID), arithmetic flexibility (NF), verbal short-term memory (VEK) and word meaning (WB).

2.2. Measures

2.2.2. Number series task

The task of the participants was to discover the rules, which govern the number series item and to complete the number series by selecting the number, that completes the series (e.g., 4 8 16 32 64 128 ? correct response: 256).

3. Results

3.1. Behavioral results

[Although] a self-paced design was employed, intelligence groups did not differ in the average time-on-task, which is most likely due to the adaption of task-difficulty to the ability level.

3.3. Intelligence effects on tasks with the same person-specific task difficulty

[The] two intelligence groups did not differ in their brain activation when compared at a person-specific equal level of task difficulty.

3.4. Intelligence effects on tasks with the same sample-based task difficulty

The T-contrast revealed that the right insula is more strongly activated in the lower intelligence group as compared to the higher intelligence group when working on tasks with same sample-based difficulty.

4. Discussion

[Neural efficiency] reflects some kind of compensatory effort, presumably due to the fact that less bright individuals perceive the same tasks as more difficult.

5. fMRI results

The insula, together with a fronto-parietal network has been repeatedly related to intelligence. These are brain regions that are assumed to constitute an important network involved in complex information processing. The insula is involved in high-level cognitive control and attentional processes, such as engaging task-relevant cognitive processes while disengaging task-irrelevant systems. The insula is also involved in the bottom-up detection of salient events, the switching between large-scale networks (default-mode network and central-executive network), autonomic reactivity to salient stimuli and the access to the motor system. The insula as part of the salience network is important for monitoring the salience of external inputs and internal brain events. If relevant stimuli from the vast and continuous stream of sensory stimuli are identified, then the insula is thought to dynamically switch between default-mode network and central-executive network. The right insula plays a crucial role in activating the central-executive network (mediating attentional, working memory and higher order cognitive processes) and deactivating the default-mode network.

How could the insula finding be related to intelligence and efficiency? It can be assumed that the employed reasoning task strongly taxes the central-executive network for identifying and testing rules underlying the number series, but does not require much sensory salience detection. … It hence could be concluded that higher intelligent brains are more efficient in terms of more selectively activating task-relevant brain regions.

Interestingly, our findings are not in line with previous findings by Larson et al. (1995). [But] the task used by Larson was a backwards digit span task commonly used to assess working memory.

6. Conclusion

The results provide evidence that neural efficiency is a function of both intelligence and task demands. Results indicate that the neural efficiency hypothesis needs to be refined. According to the refined definition, neural efficiency describes the phenomenon that more intelligent individuals show lower brain activity than less intelligent ones only when working on cognitive tasks with a comparable sample-based difficulty. We hypothesize that this reflects a more efficient adaption of brain activation due to lower person-specific challenge. However, when comparable person-specific challenge is established lower versus higher IQ brains show similar brain activity levels. These results suggest that the neural efficiency phenomenon may actually be explained by the adaption of brain activation to the person-specific task demands.

My comments

Is fluid intelligence innate?

As someone who has scored highly in tests of fluid intelligence and been considered ‘bright’, this caught my eye. I wonder what those who might score less highly make of it?

It seems to me that scoring in such tests is dependent on two things: a natural aptitude and habit formation. I assume that people who scored highly had both a natural aptitude and had learnt from previous experience how to do such tests. Thus I distinguish between two aspects of ‘adaptation’. I assume that the ability to adapt to a particular test can be primed by previous adaptation to similar tests. (Or else why did my primary school  teach us how to do IQ tests?)

If so, then apparent lack of ‘brightness’ or ‘fluid intelligence’ might be due either to lack of natural ability or to lack of opportunity to pre-adapt. This seems to me an important distinction.

How important is fluid intelligence?

Apparently ‘Fluid intelligence tests measure the ability to solve novel problems that depend relatively little on stored knowledge or the ability to learn.’ It is not obvious to me a priori that a given fluid intelligence test problem has a unique solution, or what makes a ‘good’ solution. Even if one takes a Platonic view of these things, some sort of cultural conditioning would seem to be required before one habitually regarded these tests as straightforward puzzles with set ‘rules’ for judging responses. Hence my own hypothesis:

People are more efficient at solving puzzles when they recognize them as a familiar type of problem with known ‘rules of the game’.

Hence my (tentative) revised concepts of ‘fluid intelligence’:

Narrow ‘fluid intelligence’ is the ability to solve novel variants of familiar types of problem that depend …..

Broad ‘fluid intelligence’ is the ability to make progress in resolving unfamiliar types of problem, or ‘wicked’ problems, or of finding new approaches or (re)solutions to what had been regarded as practically solved.

Hence some conjectures:

  1. Performance on tests of ‘fluid intelligence’ may correlate with familiarity with such tests.
  2. Some (many?) people could be helped to improve their narrow fluid intelligence (and it might be a ‘good thing’ to try.
  3. Broad fluid intelligence may be rarer among adults but is associated with people who make novel contributions to science and society. (And maybe some kinds of art.)

My conclusion

We need to appreciate that things like the neural efficiency hypothesis use a specialist jargon and don’t necessarily imply what they may seem to, to the lay reader.

See Also

Wikepedia  summarises the hypothesis in terms of ‘smartness’. Perhaps in some case people who ‘pragmatically’ treat every real-world problem ‘pragmatically’ as an occasion to deploy their expertise are too ‘smart’?

Elsewhere the hypotheis has been described as:

Greater task proficiency tends to be associated with reduced brain activation.

This formulation avoids any suggestion of ‘general iuntelligence’ or ‘brightness’: proficiency is generally learnt.

Dave Marsay

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