Modulation of resource allocation by intelligent individuals in linguistic, mathematical and visuo-spatial tasks

Abstract

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.

Attentional resource allocation among individuals with different fluid intelligence: The integrated control hypothesis and its evidence from pupillometry

“…Ahern and Beatty's study (1979) found that high Gf individuals had a smaller TEPR (i.e., allocated fewer resources) when they completed mental arithmetic problems of all difficulty levels, supporting the efficiency hypothesis, as the highly intelligent people required fewer attention resources to solve problems due to their higher efficiency. Furthermore, a recent pupillometry study (Lee et al, 2015) found similar results, supporting the efficiency hypothesis in arithmetic problems.…”

“…This new hypothesis suggests that the interaction between task type and task difficulty can influence the relationship between Gf and resource allocation. In particular, in exploitation tasks (such as arithmetic problems), task difficulty may not influence this relationship, as high Gf people use fewer resources to efficiently resolve tasks in tasks of all difficulties, supporting the efficiency hypothesis (Ahern & Beatty, 1979;Lee et al, 2015). In contrast, in tasks mainly involving exploration (such as graphic symmetry tasks and mental folding tasks requiring constant exploration of the problem space), task difficulty may influence the relationship such that compared with average Gf individuals, high Gf individuals allocate more resources for difficult tasks and fewer or nearly equal resources for easy tasks, supporting the resource hypothesis.…”

“…Previous studies have found that the exploration-exploitation trade-off may be regulated by the LC-NE system (Aston-Jones & Cohen, 2005;Jepma & Nieuwenhuis, 2011). graphic symmetry tasks (Bornemann et al, 2010;van der Meer et al, 2010) or mental folding tasks (Lee et al, 2015), those with higher intelligence exert more attention resources than average Gf individuals because they tend to shift into a higher-gain state.…”

“…In contrast, in tasks mainly requiring the exploitation state (referred to as exploitation tasks), such as arithmetic problems (Ahern & Beatty, 1979;Lee et al, 2015), high Gf people use fewer resources due to their higher efficiency.…”

Spontaneous eyeblinks during breaking continuous flash suppression are associated with increased detection times

“…Eyeblinks tend to happen at implicit breakpoints in information processing, e.g., when listening to a speaker who pauses during a speech (Nakano & Kitazawa, 2010), at the end of a sentence, or when turning over a page while reading (Hall, 1945;Orchard & Stern, 1991). The observation of such temporal relationships between blinking and information processing has led to the suggestion that spontaneous eyeblinks reflect cognitive processing (Lee, Ojha, Kang, & Lee, 2015;Nakano, Kato, Morito, Itoi, & Kitazawa, 2013;Pivik & Dykman, 2004;Siegle, Ichikawa, & Steinhauer, 2008;Sirevaag et al, 1999;Van Bochove, Van Der Haegen, Notebaert, & Verguts, 2013).…”