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This study investigates two questions: first, how individuals with high-intelligence allocate cognitive resources while solving linguistic, mathematical and visuo-spatial tasks with varying degree of difficulty as compared to individuals with low intelligence? Second, how to distinguish between high and low intelligent individuals by analyzing pupil dilation and eye blink together? We measured the response time, error rates along with pupil dilation and eye blink rate that indicate resource allocation. We divided the whole processing into three stages namely: pre-stimuli (5s prior to stimuli onset), during stimuli and post stimuli (until 5s after the response) for better assessment of preparation and resource allocation strategies. Individuals with high intelligence showed greater task evoked pupil dilation, decreased eye blink with less response time and error rates during-stimuli stage (processing) of tough linguistic and visuo-spatial tasks but not during mathematical tasks. The finding suggests that individuals with high intelligence allocate more resources if the task demands are high else they allocate less resources. Greater pre-stimuli pupil dilation and increased eye blink of high intelligent individuals in all tasks indicated their attentiveness and preparedness. The result of our study shows that individuals with high intelligence are more attentive and flexible in terms of altering the resource allocation strategy according to task demand. Eye-blinks along with pupil dilation and other behavioral parameters can be reliably used to assess the intelligence of an individual and the analysis of pupil dilation and blink rate at pre-stimuli stage can be crucial in distinguishing individuals with varying intelligence.

In this chapter we present three studies that were conducted to investigate the role of perceptual features in metaphor comprehension. Results suggest that priming verbal metaphors with explicit perceptual images facilitates comprehension. Moreover, stimulating the source concept facilitates metaphor comprehension more effectively than stimulating the target concept. Finally, on analysing the semantic associations between the source, target, and metaphor features, we found that when both the source and the target are primed with their respective images, relatively more perceptual features of the source are related to the metaphor's features; as opposed to the no-priming condition when relatively more conceptual features of the source are related to the metaphor's features. Based on the results of these studies, a perception-based model of metaphor processing is proposed.

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