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A range of studies published in the last few decades promotes the cognitive aspects of life: all organisms, from bacteria to mammals, are capable of sensing/perception, decision-making, problem-solving, learning, and other cognitive functions, including sentience and consciousness. In this paper I present a scientific and philosophical synthesis of these studies, leading to an integrated view of cognitive biology. This view is expressed through the four principles applicable to all living systems: (1) sentience and consciousness, (2) autopoiesis, (3) free energy principle and relational biology, and (4) cognitive repertoire. The principles are circular, and they reinforce themselves. The circularity is not rigid, meaning that hierarchical and heterarchical shifts are widespread in the biosphere. The above principles emerged at the dawn of life, with the first cells, bacteria and archaea. All biogenic forms and functions that emerged since then can be traced to the first cells – indivisible units of biological agency. Following these principles, I developed the concept of biocivilisations to explain various forms of social intelligence in different kingdoms of life. The term biociviloisations draws on the human interpretation of the concept of civilisation, which searches for non-human equivalents of communication, engineering, science, medicine, art, and agriculture, in all kingdoms of life by applying the principles of cognitive biology. Potential avenues for testing the concept of biocivilisations are highlighted. [ABSTRACT FROM AUTHOR]

Decision making (DM) 2 2 Decision making is abbreviated as DM throughout the text. is a complex process that appears to involve several brain structures. In particular, amygdala, orbitofrontal cortex (OFC) and lateral prefrontal cortex (LPFC) seem to be essential in human decision making, where both emotional and cognitive aspects are taken into account. In this paper, we present a computational network model representing the neural information processing of DM, from perception to behavior. We model the population dynamics of the three neural structures (amygdala, OFC and LPFC), as well as their interaction. In our model, the neurodynamic activity of amygdala and OFC represents the neural correlates of secondary emotion, while the activity of certain neural populations in OFC alone represents the outcome expectancy of different options. The cognitive/rational aspect of DM is associated with LPFC. Our model is intended to give insights on the emotional and cognitive processes involved in DM under various internal and external contexts. Different options for actions are represented by the oscillatory activity of cell assemblies, which may change due to experience and learning. Knowledge and experience of the outcome of our decisions and actions can eventually result in changes in our neural structures, attitudes and behaviors. Simulation results may have implications for how we make decisions for our individual actions, as well as for societal choices, where we take examples from transport and its impact on CO 2 emissions and climate change. [ABSTRACT FROM AUTHOR]

Recently, there have been several attempts to replicate theory of mind, which explains how humans infer the mental states of other people using multiple sensory input, with artificial systems. One example of this is a robot that observes the behavior of other artificial systems and infers their internal models, mapping sensory inputs to the actuator’s control signals. In this paper, we present the internal model as an artificial neural network, similar to biological systems. During inference, an observer can use an active incremental learning algorithm to guess an actor’s internal neural model. This could significantly reduce the effort needed to guess other people’s internal models. We apply an algorithm to the actor–observer robot scenarios with/without prior knowledge of the internal models. To validate our approach, we use a physics-based simulator with virtual robots. A series of experiments reveal that the observer robot can construct an “other’s self-model”, validating the possibility that a neural-based approach can be used as a platform for learning cognitive functions. [ABSTRACT FROM AUTHOR]

Abstract: EEG signals are important to capture brain disorders. They are useful for analyzing the cognitive activity of the brain and diagnosing types of seizure and potential mental health problems. The Event Related Potential can be measured through the EEG signal. However, it is always difficult to interpret due to its low amplitude and sensitivity to changes of the mental activity. In this paper, we propose a novel approach to incrementally detect the pattern of this kind of EEG signal. This approach successfully summarizes the whole stream of the EEG signal by finding the correlations across the electrodes and discriminates the signals corresponding to various tasks into different patterns. It is also able to detect the transition period between different EEG signals and identify the electrodes which contribute the most to these signals. The experimental results show that the proposed method allows the significant meaning of the EEG signal to be obtained from the extracted pattern. [Copyright &y& Elsevier]