Complexity of Geometric Inductive Reasoning Tasks: Contribution to the Understanding of Fluid Intelligence.

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Fluid Intelligence as a Predictor of Learning: A Longitudinal Multilevel Approach Applied to Math. (2010, background): “Novel and complex situations require higher cognitive abilities for the systematic processes of selection,maintenance, updating, and rerouting, which are crucial for dealing with situations of “information overload” (Primi, 2002; Primi et al., 2001).”
Use of Response Time for Measuring Cognitive Ability (2016, methodology): “A number of studies have applied this model to the analysis of items from cognitive tests [111], such as figural matrices [112], and reading comprehension [113].”
When are fluid intelligence and working memory isomorphic and when are they not (2013, background): “…by selective attention, because the need to effectively select a subset of elements from the whole scene (e.g., a figural matrix, a description of a problem) seems to be a key factor influencing the difficulty of both items in reasoning tests (Primi, 2001) and insight problems (Davidson, 1995).”