A system-level neural model of the brain mechanisms underlying instrumental devaluation in rats

Goal-directed behaviours are defined by the presence of two kinds of effect on instrumental learning. First, degrading the contingencies between produced actions and desired outcomes diminishes the number of instrumental responses; second, devaluing a reward results in a lower production of instrumental actions to obtain it. We present a computational model of the neural processes underlying instrumental devaluation in rats. The model reproduces the interaction between the basolateral complex of the amygdala (BLA) and the limbic, associative and somatosensory striato-cortical loops. Firing-rate units are used to abstract the activity features of neural populations. Learning is reproduced through the use of dopamine-dependent simple and differential hebbian rules. Constraints from anatomy of the projections between neural systems are taken into account. The central hypothesis implemented in the model is that pavlovian associations learned within the BLA between manipulanda and rewards modulate goal selection through the activation of the nucleus accumbens core (NaccCo). Selection processes happening in the limbic basal ganglia with the activation of the NaccCo decide which outcome is choosen as a goal within the prelimbic cortex (PL). Connections between the BLA and the NaccCo are learned through hebbian associations mediated by feedbacks from the PL to the NaccCo. Information about selected goals from the limbic striato-cortical loop influences action selection in the sensorimotor loop both through cortico-cortical projections and through a striato-nigro-striatal dopaminergic pathway passing through the associative striato-cortical loop. The model is tested as part of the control system of a simulated rat. Instrumental devaluation tasks are reproduced. Simulated lesions of the BLA, the NaccCo, the PL and the dorsomedial striatum (DMS) both before and after training reproduce the behavioural effect of lesions in real rats. The model provides predictions about the effects of still undocumented lesions

A naturalist perspective on intentionality: interview with Daniel Dennett

No abstract availabl

Language as an aid to categorization: A neural network model of early language acquisition.

The paper describes a neural network model of early language acquisition with an emphasis on how language positively influences the categories with which the child categorizes reality. Language begins when the two separate networks that are responsible for nonlinguistic sensory-motor mappings and for recognizing and repeating linguistic sounds become connected together at 1 year of age. Language makes more similar the internal representations of different inputs that must be responded to with the same action and more different the internal representations of inputs that must be responded to with different actions

Towards a vygotskyan cognitive robotics: the role of language as a cognitive tool

Cognitive Robotics can be defined as the study of cognitive phenomena by their modeling in physical artifacts such as robots. This is a very lively and fascinating field which has already given fundamental contributions to our understanding of natural cognition. Nonetheless, robotics has to date addressed mainly very basic, low-level cognitive phenomena like sensory-motor coordination, perception, and navigation, and it is not clear how the current approach might scale up to explain high-level human cognition. In this paper we argue that a promising way to do that is to merge current ideas and methods of \u27embodied cognition\u27 with the Russian tradition of theoretical psychology which views language not only as a communication system but also as a cognitive tool, that is by developing a Vygotskyan Cognitive Robotics. We substantiate this idea by discussing several domains in which language can improve basic cognitive abilities and permit the development of high-level cognition: learning, categorization, abstraction, memory, voluntary control, and mental life

Talking to oneself as a selective pressure for the emergence of language

Selective pressures for the evolutionary emergence of human language tend to be interpreted as social in nature, i.e., for better social communication and coordination. Using a simple neural network model of language acquisition we demonstrate that even using language for oneself, i.e., as private or inner speech, improves an individual\u27s categorization of the world and, therefore, makes the individual\u27s behavior more adaptive. We conclude that language may have first emerged due to the advantages it confers on individual cognition, and not only for its social advantages

Linguaggio e categorizzazione: un modello neurale dell\u27apprendimento del lessico

No abstract availabl

How can we explain the emergence of a language which benefits the hearer but not the speaker?

No abstract availabl

Producer biases and kin selection in the evolution of communication

The evolution of communication requires the co-evolution of two abilities: the ability of sending useful signals and the ability of reacting appropriately to perceived signals. This fact poses two related but distinct problems, which are often confused the one with the other: (1) the phylogenetic problem regarding how can communication evolve if the two traits that are necessary for its emergence are complementary and seem to require each other for providing reproductive advantages; (2) the adaptive problem regarding how can communication systems that do not advantage both signallers and receivers in the same way emerge, given their altruistic character. Here we clarify the distinction, and provide some insights on how these problems can be solved in both real and artificial systems by reporting experiments on the evolution of artificial agents that have to evolve a simple food-call communication system. Our experiments show that (1) the phylogenetic problem can be solved thanks to the presence of producer biases that make agents spontaneously produce useful signals, an idea that is complementary to the well-known ?receiver bias? hyopthesis found in the biological literature, and (2) the adaptive problem can be solved by having agents communicate preferentially among kin, as predicted by kin selection theory. We discuss these results with respect both to the scientific understanding of the evolution of communication and to the design of embodied and communicating artificial agents