In the context of e-learning during the COVID-19 pandemic, medical education has suffered a significant impact on its training process due to various factors, such as the suspension of access to university and/or clinical fields, therefore, it is essential to know the perception of medical students about teaching methods during the pandemic period and its impact on their careers. In this way, this study aimed to investigate, in medical students from the University of Atacama (UDA), Chile, about their perception and factor related to the digital teaching methods adopted by UDA-Faculty of Medicine courses. As a preliminary study, we studied a survey through a sample of 51 students and containing 32 questions that involved 3 theoretical dimensions (sociodemographic background, information preferences, and learning perception styles). We aimed to investigate elements, using a causal inference method (Bayesian Network), that unravels the relationship across these theoretical dimensions that correspond to meaningful learning. Based on these analyzes, we were able to find the information process flow related to 20 questions towards the significant learning, and characteristics associated with the learning process. This work sought to develop a better understanding of alternative active methodologies and their effectiveness for the medical career.

Information theory is concerned with the study of transmission, processing, extraction, and utilization of information. In its most abstract form, information is conceived as a means of resolving uncertainty. Shannon and Weaver (1949) were among the first to develop a conceptual framework for information theory. One of the key assumptions of the model is that uncertainty increases linearly with the amount of complexity (in bit units) of information transmitted or generated (C.E. Shannon, W. Weaver. The mathematical Theory of communication, University of Illinois, Urbana III, 1949). A whole body of data from the cognitive neurosciences has shown since that the time of human response or action increases in a similar fashion as a function of information complexity in various different situations and contexts. In this paper, I will discuss what is currently known about the limitations of human information processing. The implications for the development of parsimonious computational models in science and the idea of reliable Artificial Intelligence for science and society will be made clear under the light of arguments from the cognitive neurosciences and computational philosophy. The goal of the presentation is to carve out a conceptual framework that is to inspire future studies on the problems identified.

Previous Information Behaviour (IB) models were originally designed for the traditional environment and are not fit to explain human IB in the digital era. Also, the continuous and elastic revolutions of ICTs have questioned previous IB models, leaving a continuous need to review and rework previous models thereby subjecting them to scrutiny in the changing globalization and digital environment. Therefore, there is need to provide an up-to-date IB model. This study presents a digital information behaviour (DIB) model that is relevant in the changing digital environment. It adopts a correlational survey design; and a multi-stage technique was used to select 400 respondents but 233 questionnaires were retrieved from the field giving a total of approximately 58% retrieval rate. Questionnaires were used to obtain information and the psychometric property was presented. The descriptive (frequency and percentage) and inferential (ANOVA and Regression Analysis) statistics was adopted to analyse information obtained. This study revealed that there are significant relationships among IB components such as information needs, search and use but none exists between users’ information use and archival/disposal. In addition, ICT literacy of users does not give a significant impetus to users’ IB, which could affect the output quality of IB. Furthermore, among the four cognitive abilities, only verbal comprehension influenced users’ IB. The study recommends the need to revisit curriculums in all fields of study, to expose students to the necessary ICT literacy levels and cognitive abilities necessary for enhancing IB quality in the digital environment.

Effort-to-Compress (ETC) is a measure of complexity based on a lossless data-compression algorithm that has been used extensively in characterization and analysis of time-series. ETC has been shown to give good performance for short and noisy time series data and has found applications in the study of cardiovascular dynamics, cognitive research and regulating the feedback of musical instruments. It has also been used to develop causal inference methods for time series data. In this work, a theoretical analysis helps us to demonstrate the links of ETC measure to the total self-information contained in the joint occurrence of most dominant (shortest) patterns occurring at different scales (of time) in a time-series. This formulation helps us to visualize ETC as a dimension like quantity that computes the effective dimension at which patterns in a time-series (translated to a symbolic sequence) appear. We also show that the algorithm that computes ETC can be used as a means for an analysis akin to ‘multifractal analysis’ using which the power contained in patterns appearing at different scales of the sequence/ series can be estimated. Multifractal analysis has been used widely in analysis of biomedical signals, financial and geophysical data. Our work provides a theoretical understanding of the ETC complexity measure that links it to information theory and opens up more avenues for its meaningful usage and application.

In this talk I will present a framework of natural cognition and intelligence based on info-computation in living agents. The underlying assumption is that cognition in nature is a manifestation of biological processes, that subsume chemical and physical processes (Maturana and Varela 1992; Stewart 1996; Dodig-Crnkovic 2007; Lyon 2005; Lyon and Kuchling 2021), from single cells to humans.

Cognitive science with roots in psychology and philosophy of mind, historically focused on the human as cognizing agent. Recently (Piccinini 2020) presented cognition as result of neurocomputation in organisms with nervous systems. Piccinini goes a step beyond anthropocentric understanding of cognition, but he retains neurocentrism. However, “cognitive operations we usually ascribe to brains—sensing, information processing, memory, valence, decision making, learning, anticipation, problem solving, generalization and goal directedness—are all observed in living forms that don’t have brains or even neurons.” (Levin et al. 2021). Thus, we generalize cognition a step further, to include all living forms, not only those with nervous systems.

I will argue that new insights about cognition and its evolution and development in nature (Walker, Davies, and Ellis 2017) (Dodig-Crnkovic 2017), from cellular to human cognition (Manicka and Levin 2019; Levin et al. 2021; Lyon et al. 2021; Stewart 1996; Dodig-Crnkovic 2014) can be modelled as natural information processing - natural computation – morphological computation.

In the info-computational approach, evolution in the sense of extended evolutionary synthesis (Laland et al. 2015; Ginsburg and Jablonka 2019; Jablonka and Lamb 2014) is a result of interactions between natural agents, cells and their groups.