Abstract
Explanation is an integral component of philosophical, scientific, and everyday thinking. Consequently, modelling explanation and understanding the sources of its variability hold significant importance in philosophy, the behavioural and natural sciences, as well as in the narrativisation of societal events through mass media. To explore one of the sources of explanatory variability, this study examined the modelling of explanation within the space of multiple cognitive representations of the flow of time. Formal modelling was used as a method based on existing theories of explanation, and the role of the cognitive representation of the explanans (the explaining factors) and the explanandum (the thing being explained) was made clear in the dimension of time. This approach was combined with an analysis of experimental studies on the influence of cognitive representations of the flow of time on object evaluation, followed by the integration of theoretical models and experimental findings. Based on theories of explanation, it was concluded that despite their diversity, they explicitly or implicitly rely on the unfolding of events over time, with the flow of time playing a crucial role. Given the cognitive system’s capacity to generate multiple representations of the flow of time and the fact that shifts in these representations determine variability in the perception of the surrounding world – and consequently in the explanans and explanandum – the transition from the conventional singular flow of time in explanatory theories to its representation as a set of distinct, independent cognitive representations with specific properties was substantiated. For various explanatory theories, the significance of this transition from the conventional model of the singular flow of time to the conceptualisation of time as a multiplicity of cognitive representations was explored. The proposed introduction of multiple representations of the flow of time opens new avenues for further theoretical inquiry. In practical terms, it brings explanatory models closer to actual human thought and behaviour, thereby enhancing their reliability and predictive value