Neural correlates of postformal stages of reasoning: Biological determinants of developmental stage.

Abstract

The latest research on developmental stage, according to the Model of Hierarchical Complexity (MHC), shows that there is only 1 domain, that stage develops as log 2 (age) and that the number of neurons of a species can predict the mean stage attained by that species. This can be interpreted as saying that biology controls stage. However, humans attain different stages and the biological mechanism that limits stage is still unknown. Based on these findings, we argue that cognitive neuroscience studies of human intelligence should shift from the general laws that govern development and brain maturation to focusing on interindividual differences across development, so as to complete the picture of human cognition beyond statistical norms. We here propose a study that looks for differences in patterns of the brain activation between subjects performing below and above formal stages. What differentiates this study from others that have been conducted in the field of developmental psychology and cognitive neuroscience is that this will explain for the first time not how, but why, some individuals are hardwired to perform at higher stages than others. We intend to analyze the data across different hierarchical complexity tasks and extract a saturation index (SI) that informs about the processing load of problem solving. Second, we compare the SI across subjects who attained different stages. This knowledge will provide for understanding the biological basis of cognition, for improving the behavioral predictive MHC, and for developing a connectionist model of cognition that emulates development throughout life.

Hierarchical growth in neural networks structure: Organizing inputs by Order of Hierarchical Complexity

“…At the end, three network structural progressions could be identified across orders of hierarchical complexity problems. The fact that three different structural progressions were found that performed with similar accuracy is aligned with the idea of self-organization [ 5 , 7 , 10 , 11 , 65 , 66 ], where a system accommodates more elements to respond to a more complex situation by reorganizing its internal structure as a whole [ 67 ]. In terms of cognitive performance and brain organization, the idea of self-organization is also associated with the idea of inter-variability [ 15 , 16 ], suggested by the results of this study considering the three ways of internal organization and optimization for achieving the same outputs.…”

“…For this, the input set was segregated into subtasks of increasing complexity, measured according to their Order of Hierarchical Complexity (OHC). There is evidence that the OHC is a reliable indicator of task complexity and a reliable indicator of the underlying problem-solving structure necessary to solve the tasks [ 11 , 19 ].…”

“…The best curve fitting found to describe this developmental trajectory is a logarithmic curve that varies with age [ 28 ]. Following this discovery, the MHC provides evidence that stage of development is imprinted in the neural structure since birth and hypothesizes that each stage has a neural signature [ 11 , 28 ]. This means that perceiving a task of a given OHC triggers the activation of a given neural structure.…”

“…Currently, neural network models designed to solve complex problems are usually programmed with a complex structure built from scratch, which is usually employed for solving the complex problem from scratch [ 29 – 32 ] using enormous datasets [ 33 ], and tending to overfit [ 32 ]. They do not develop from simple to more complex algorithmic structures, or from simpler to more complex artificial reasoning abilities [ 34 ], as we observe in the human brain [ 1 , 7 , 9 – 11 , 35 , 36 ]. Generative architectures are an exception to fixed structures [ 37 , 38 ], but newly generated structures overwrite the existing ones.…”

Psychometric Properties of the Spanish Version of the Complex Postformal Thought Questionnaire: Developmental Pattern and Significance and Its Relationship With Cognitive and Personality Measures

“…The importance of PFT is also understood insofar as it can be used in a wide variety of contexts (Pearson et al, 2018;Sinnott et al, 2017), with obvious implications for adaptation to real-life dynamics (Chiou, 2008) within oneself, in terms of social interactions and even those with a transcendent figure (Sinnott, 2009). Furthermore, PFT offers multiple benefits for science, technology, mathematics, humanities, successful management, and innovation , and its growing importance is also reflected in the interest in finding the neural correlates underlying such thought (Leite et al, 2016). In line with the above, much research has been conducted to both empirically validate the existence of postformal operations (see reviews such as Gurba, 2005;Kallio, 2011;Marchand, 2002), and to analyze the relationship of PFT to important and diverse areas of daily functioning (see Cartwright, 2001;Chang & Chiou, 2014;Day, 2010Day, , 2017Galupo et al, 2010;Griffin et al, 2009;Han & Bai, 2020;Jennings et al, 2009;Jory et al, 2018;Morton et al, 2000;Sinnott, 2001;Sinnott & Johnson, 1997;Weitzman & Weitzman, 2006).…”