Belief loads of assumptions impact brain networks underlying logical reasoning: A machine learning approach

Abstract

AbstractPrior knowledge and beliefs influence our reasoning in daily life and may lead us to draw unwarranted conclusions with undesirable consequences. The underlying neural correlates of the interaction between belief and logic, prior to making logical decisions, are largely unknown. In this study, we aimed to identify brain regions important in distinguishing belief load of assumptions in logical decision making. Thirty-one healthy volunteers (18-29 years old) participated in an fMRI study and were asked to respond to a series of syllogistic arguments in which assumptions were either congruent (believable) or incongruent (unbelievable) with common knowledge. An interpretable machine learning algorithm, an L1 regularized Support Vector Machine, was used to explain the discriminatory pattern of conditions given the brain activation patterns. Behavioral results confirmed that believable premises were incorrectly endorsed more than unbelievable ones. Imaging results revealed that several connectivity patterns anchored around the insula, amygdala, and IFG were important in distinguishing believable from unbelievable assumptions at different time points preceding logical decisions. Our convergent behavioral and imaging results underscore the importance of the belief loads of our assumptions for a logically sound decision. Our results provide new insights into neural and potential cognitive mechanisms underlying the interaction between belief and logic systems, with important practical implications for social, complex decisions.HighlightsBelief load of premises impact logical decisionsRegional importance in distinguishing belief load changes in different TRsBelief load of assumptions elicits emotional and salience responsesRegional connectivity changes as the reasoning process evolves at different TRsInsula, caudate, amygdala, and IFG were among highly connected hubs during the taskSignificanceIt has been experimentally shown that decision-makers often ignore given assumptions in favor of their own beliefs, potentially leading them towards a subjective rather than a logical decision. Consider “Carbon emission tax” given the assumption of “global warming”. If a decision-maker does not believe in “global warming”, the final decision on “Carbon emission tax” is not driven by factual premises but by the personal belief of the decision-maker. Understanding neural mechanisms underlying the interaction between the decision-makers’ beliefs and factual premises sheds light on factors driving belief bias and potential interventions to circumvent it. The main contribution of this study is to investigate neural mechanisms in a logical reasoning task in which the belief load of the assumptions was manipulated.