Attention resources refer to the amount of available activation for information storing and processing. Previous studies have found that the allocation of attention resources is affected by individual cognitive differences such as fluid intelligence. The efficiency hypothesis suggests that, compared to average intelligent individuals, high intelligent individuals tend to allocate fewer attention resources to accomplish the same task. Although this hypothesis had been supported by many empirical studies, some studies found that this result may be modulated by other factors such as task type and task difficulty. However, previous theoretical and empirical studies did not take both factors into consideration, which leads to the inexplicable contradiction of existing results.
In this regard, the present study proposed a new integrative control hypothesis to further explain the relationship between fluid intelligence and attention resource allocation by including both task type and difficulty into the theoretical model. This hypothesis suggests that both task type and task difficulty can influence the relationship between intelligence and attention resource allocation. Specifically, in the exploration tasks, high intelligent individuals tend to exert more resources to solve problems at high difficulty levels but not at low difficulty levels. In contrast, in the exploitation task, individuals with high intelligence tend to use fewer resources to solve the problems than individuals with average intelligence regardless of the task difficulty.
This study aims to make use of multimodal psychophysiological evidence to verify the integrated control hypothesis through two sub-studies. Study 1 takes task-evoked pupillary response (TEPR) as the index of attention resource allocation to explore the attention resource allocation of individuals with different intelligence. The results of Study 1 showed that individuals with higher fluid intelligence were able to use fewer resources (represented by smaller TEPR) to solve the problems in the exploitation task with all difficulty levels. However, in the exploration task, only male participants with higher intelligence exerted more resources to solve the problem at the high difficulty level. In Study 2, electroencephalogram (EEG) indices including parietal-occipital alpha event-related desynchronization (ERD) and frontal theta event-related synchronization (ERS) were adopted to measure an individual’s attention resource allocation in the same experimental design. Similarly, the results of Study 2 showed that individuals with higher intelligence tended to use fewer resources (smaller theta-ERS) to solve the problem in the exploitation task with all difficulty levels. However, in the exploration task, only male individuals with high intelligence allocated more resources (larger alpha-ERD) in the medium to high difficulty condition.
The results of both sub-studies preliminarily support the integrative control hypothesis: individuals with higher intelligence can solve problems with fewer attention resources in the exploration task. In contrast, in the exploration task, individuals with high intelligence would allocate more attention resources in high difficulty conditions instead of low difficulty conditions. These results indicated that the characteristics of highly intelligent individuals are probably the flexible and adaptive attention control ability rather than simply neural efficiency or having more resources. In addition, it is important to note that the hypothesis of the exploration task in the integration control hypothesis was only supported by male participants in this study. On the one hand, this may result from the present experimental tasks, which were visuospatial and more conducive to male subjects. On the other hand, this may result from the personality factors that females tend to be more cautious in cognitive tasks.
In summary, this study puts forward the integrated control hypothesis about fluid intelligence and attention resource allocation. The verification of this hypothesis will help us to further explore the relationship between intelligence and attention resource allocation and clarify the role of task type and task difficulty in it. In addition, this study will examine the allocation of attention resources from two relatively independent perspectives of arousal level and cortical activation using pupillometry and EEG recording, which is helpful to provide multimodal evidence for the cognitive neural basis of individual differences in intelligence.
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