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We simulated organisms with an arm terminating with a hand composed by two fingers, a thumb and an index, each composed by two segments, whose behavior was guided by a nervous system simulated through an artificial network. The organisms, which evolved through a genetic algorithm, lived in a bidimensional environment containing four objects, either large or small, either grey or black. In a baseline simulation the organisms had to learn to grasp small objects with a precision grip and large objects with a power grip. In Simulation 1 the organisms learned to perform two tasks: in Task 1 they continued to grasp objects according to their size, in Task 2 they had to decide the objects' color by using a precision or a power grip. Learning occured earlier when the grip required to respond to the object and to decide the color was the same than when it was not, even if object size was irrelevant for the task. The simulation replicates the result of an experiment by Tucker & Ellis (2001) suggesting that seeing objects automatically activates motor information on how to grasp them.

An influential hypothesis of developmental psychology states that, in the first months of their life, infants perform exploratory/random movements ("motor babbling") in order to create associations between such movements and the resulting perceived effects. These associations are later used as building blocks to tackle more complex sensorimotor behaviours. Due to its underlying simplicity, motor babbling might be a learning strategy widely used in the early phases of child development. Various models of this process have been proposed that focus on the acquisition of reaching skills based on the synchronous association between the positions of the postures that cause them. This research tries to understand, on a computational basis, if the principles underlying motor babbling can be extended to the acquisition of behaviours more complex than reaching, such as the execution of non-linear movement trajectories for avoiding obstacles or the acquisition of movements directed to grasp objects. These behaviours are challenging for motor babbling as they involve the execution of movements, or sequences of movements, in time, and so they cannot be learned on the basis of simple synchronous associations between their neural representations and perceptive neural representations. The paper aims to show that infants might still use motor babbling for the development of these behaviours by overcoming its time-limits on the basis of complementary mechanisms such as Pattern Generators and innate reflexes. The computational viability of this hypothesis is demonstrated by testing the proposed models with a 3D simulated dynamic eye-arm-hand robot working on a plane.

What genotypic features explain the evolvability of organisms that have to accomplish many different tasks? The genotype of behaviorally complex organisms may be more likely to encode modular neural architectures because neural modules dedicated to distinct tasks avoid neural interference, i.e., the arrival of conflicting messages for changing the value of connection weights during learning. However, if the connection weights for the various modules are genetically inherited, this raises the problem of genetic linkage: favorable mutations may fall on one portion of the genotype encoding one neural module and unfavorable mutations on another portion encoding another module. We show that this can prevent the genotype from reaching an adaptive optimum. This effect is different from other linkage effects described in the literature and we argue that it represents a new class of genetic constraints. Using simulations we show that sexual reproduction can alleviate the problem of genetic linkage by recombining separate modules all of which incorporate either favorable or unfavorable mutations. We speculate that this effect may contribute to the taxonomic prevalence of sexual reproduction among higher organisms. In addition to sexual recombination, the problem of genetic linkage for behaviorally complex organisms may be mitigated by entrusting evolution with the task of finding appropriate modular architectures and learning with the task of finding the appropriate connection weights for these architectures.

Recent views of categorization suggest that categories are action-based rather than arbitrary symbols. Three connectionist simulations explore the hierarchical organization of categories in the framework of an action-based theory of categorization. In the simulations an organism with a visual system and a two-segment arm has to reach different points in space depending on the object seen and on context. The context indicates whether to put the object in a superordinate or in a basic category. The results show that: (a) superordinate categories are easier to learn than basic ones; (b) the more similar the actions to perform with basic and superordinate categories, the easier to learn the task; (c) violation of category boundaries leads to less good performance.

In two Artificial Life simulations we evolved artificial organisms possessing a visual and a motor system, and whose nervous system was simulated with a neural network. Each organism could see four objects, either upright or reversed, with a left or a right handle. In Task 1 they learned to reach the object handle independently of the handle‟s position. I, in Task 2 they learned to reach one of two buttons located below the handle either to decide either where the handle was (Simulation 1) or whether the object was upright or reversed (Simulation 2). Task 1 simulated real life experience, Task 2 replicated either a classic spatial compatibility task (Simulation 1) or an experiment by Tucker & Ellis (1998) (Simulation 2). In both simulations learning occurred earlier in the Compatible condition, when the button to reach and the handle were on the same side, than in the Incompatible condition.

Behavioural and brain imaging evidence has shown that seeing objects automatically evokes “affordances”, for example it tends to activate internal representations related to the execution of precision or power grips. In line with this evidence, Tucker and Ellis [1] found a compatibility effect between object size (small and large) and the kind of grip (precision and power) used to respond whether seen objects were artefacts or natural objects. This work presents a neural-network model that suggests an interpretation of these experiments in agreement with a recent theory on the general functions of prefrontal cortex. Prefrontal cortex is seen as a source of top-down bias in the competition for behavioural expression of multiple neural pathways carrying different information. The model successfully reproduces the experimental results on compatibility effects and shows how, although such a bias allows organisms to perform actions which differ from those suggested by objects’ affordances, these still exert their influence on behaviour as reflected by longer reaction times.