Time-Aware Multi-Agent Symbiosis.

Contemporary research in human-machine symbiosis has mainly concentrated on enhancing relevant sensory, perceptual, and motor capacities, assuming short-term and nearly momentary interaction sessions. Still, human-machine confluence encompasses an inherent temporal dimension that is typically overlooked. The present work shifts the focus on the temporal and long-lasting aspects of symbiotic human-robot interaction (sHRI).

The explicit judgment of long durations of several minutes in everyday life: Conscious retrospective memory judgment and the role of affects?

In this study, individuals estimated interval times of several minutes (from 2 to 32 minutes) during their everyday lives using a cell phone they kept with them. Their emotional state, the difficulty of the activity performed during this interval, and the attention that it required were also assessed, together with their subjective experience of the passage of time. The results showed that the mean time estimates and their variability increased linearly with increasing interval duration, indicating that the fundamental scalar property of time found for short durations also applies to very long durations of several minutes.

Passage of time judgments in everyday life are not related to duration judgments except for long durations of several minutes.

This study investigated relations between judgments of passage of time and judgments of long durations in everyday life with an experience sampling method. Several times per day, the participants received an alert via mobile phone. On each alert, at the same time as reporting their experience of the passage of time, the participants also estimated durations, between 3 and 33s in Experiment 1, and between 2 and 8min in Experiment 2.

When and How-Long: A Unified Approach for Time Perception.

The representation of the environment assumes the encoding of four basic dimensions in the brain, that is the 3D space and time. The vital role of time for cognition is a topic that recently attracted increasing research interest. Surprisingly, the scientific community investigating mind-time interactions has mainly focused on interval timing, paying less attention on the encoding and processing of distant moments.

Time models and cognitive processes: a review.

The sense of time is an essential capacity of humans, with a major role in many of the cognitive processes expressed in our daily lifes. So far, in cognitive science and robotics research, mental capacities have been investigated in a theoretical and modeling framework that largely neglects the flow of time. Only recently there has been a rather limited, but constantly increasing interest in the temporal aspects of cognition, integrating time into a range of different models of perceptuo-motor capacities.

Self-organizing high-order cognitive functions in artificial agents: implications for possible prefrontal cortex mechanisms.

In our daily life, we often adapt plans and behaviors according to dynamically changing world circumstances, selecting activities that make us feel more confident about the future. In this adaptation, the prefrontal cortex (PFC) is believed to have an important role, applying executive control on other cognitive processes to achieve context switching and confidence monitoring; however, many questions remain open regarding the nature of neural processes supporting executive control. The current work explores possible mechanisms of this high-order cognitive function, transferring executing control in the domain of artificial cognitive systems.

Temporal cognition: a key ingredient of intelligent systems.

Experiencing the flow of time is an important capacity of biological systems that is involved in many ways in the daily activities of humans and animals. However, in the field of robotics, the key role of time in cognition is not adequately considered in contemporary research, with artificial agents focusing mainly on the spatial extent of sensory information, almost always neglecting its temporal dimension. This fact significantly obstructs the development of high-level robotic cognitive skills, as well as the autonomous and seamless operation of artificial agents in human environments.

Explorations on artificial time perception.

In the field of biologically inspired cognitive systems, time perception, a fundamental aspect of natural cognition is not sufficiently explored. The majority of existing works ignore the importance of experiencing the flow of time, and the implemented agents are rarely furnished with time processing capacities. The current work aims at shedding light on this largely unexplored issue, focusing on the perception of temporal duration.

Agent-based brain modeling by means of hierarchical cooperative coevolution.

We address the development of brain-inspired models that will be embedded in robotic systems to support their cognitive abilities. We introduce a novel agent-based coevolutionary computational framework for modeling assemblies of brain areas. Specifically, self-organized agent structures are employed to represent brain areas.

Modelling brain emergent behaviours through coevolution of neural agents.

Recently, many research efforts focus on modelling partial brain areas with the long-term goal to support cognitive abilities of artificial organisms. Existing models usually suffer from heterogeneity, which constitutes their integration very difficult. The present work introduces a computational framework to address brain modelling tasks, emphasizing on the integrative performance of substructures.