Vertebrate species use geometric information and non-geometric or featural cues to orient. Under some circumstances, when both geometric and non-geometric information are available, the geometric information overwhelms non-geometric cues (geometric primacy). In other cases, we observe the inverse tendency or the successful integration of both cues. In past years, modular explanations have been proposed for the geometric primacy: geometric and non-geometric information are processed separately, with the geometry module playing a dominant role. The modularity issue is related to the recent debate on the encoding of geometric information: is it innate or does it depend on environmental experience? In order to get insight into the mechanisms that cause the wide variety of behaviors observed in nature, we used Artificial Life experiments. We demonstrated that agents trained mainly with a single class of information oriented efficiently when they were exposed to one class of information (geometric or non-geometric). When they were tested in environments that contained both classes of information, they displayed a primacy for the information that they had experienced more during their training phase. Encoding and processing geometric and non-geometric information was run in a single cognitive neuro-representation. These findings represent a theoretical proof that the exposure frequency to different spatial information during a learning/adaptive history could produce agents with no modular neuro-cognitive systems that are able to process different types of spatial information and display various orientation behaviors (geometric primacy, non-geometric primacy, no primacy at all).
Most of the educational materials, such as the exercises contained in activity books or the Montessori-like games that are commonly used in kindergartens and primary schools share a common deep structure and have similar features. Capturing these commonalities in an explanatory model leads to a general framework that may be useful to develop games engine, in particular, digital games for children that enhance the learning and teaching experiences. In this paper, we analyze the structure of above mentioned educational materials and propose to describe it in terms of interacting agents' model. Moreover, we present Block Magic and SNIFF, first two examples of educational Montessori-like games to better explain how this general framework can be employed in educational game developmen
The core principles of the evolutionary theories of emotions declare that affective states represent crucial drives for action selection in the environment and regulated the behavior and adaptation of natural agents in ancestrally recurrent situations. While many different studies used autonomous artificial agents to simulate emotional responses and the way these patterns can affect decision-making, few are the approaches that tried to analyze the evolutionary emergence of affective behaviors directly from the specific adaptive problems posed by the ancestral environment. A model of the evolution of affective behaviors is presented using simulated artificial agents equipped with neural networks and physically inspired on the architecture of the iCub humanoid robot. We use genetic algorithms to train populations of virtual robots across generations, and investigate the spontaneous emergence of basic emotional behaviors in different experimental conditions. In particular, we focus on studying the emotion of fear, therefore the environment explored by the artificial agents can contain stimuli that are safe or dangerous to pick. The simulated task is based on classical conditioning and the agents are asked to learn a strategy to recognize whether the environment is safe or represents a threat to their lives and select the correct action to perform in absence of any visual cues. The simulated agents have special input units in their neural structure whose activation keep track of their actual “sensations” based on the outcome of past behavior. We train five different neural network architectures and then test the best ranked individuals comparing their performances and analyzing the unit activations in each individual’s life cycle. We show that the agents, regardless of the presence of recurrent connections, spontaneously evolved the ability to cope with potentially dangerous environment by collecting information about the environment and then switching their behavior to a genetically selected pattern in order to maximize the possible reward. We also prove the determinant presence of an internal time perception unit for the robots to achieve the highest performance and survivability across all conditions.
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