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

How producer biases can favor the evolution of communication: an analysis of evolutionary dynamics

As any other biological trait, communication can be studied under at least four perspectives: mechanistic, ontogenetic, functional, and phylogenetic (Tinbergen, 1963). Here we focus on the following phylogenetic question: how can communication emerge given that both signal-producing and signal-responding abilities seem to be adaptively neutral until the complementary ability is present in the population? We explore the problem of co-evolution of speakers and hearers with artificial life simulations: a population of artificial neural networks evolving a food call system. The core of the paper is devoted to the careful analysis of the complex evolutionary dynamics demonstrated by our simple simulation. Our analyzes reveal an important factor which might solve the phylogenetic problem: the spontaneous production of good (meaningful) signals by speakers due to the need for organisms to categorize their experience in adaptively relevant ways. We discuss our results with respect both to previous simulative work and to the biological literature on the evolution of communication

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

Evolution, complexity and artificial life

Evolution and complexity characterize both biological and artificial life – by direct modeling of biological processes and the creation of populations of interacting entities from which complex behaviors can emerge and evolve. This edited book includes invited chapters from leading scientists in the fields of artificial life, complex systems, and evolutionary computing. The contributions identify both fundamental theoretical issues and state-of-the-art real-world applications. The book is intended for researchers and graduate students in the related domains

Functions and mechanisms of intrinsic motivations: the knowledge versus competence distinction

Mammals, and humans in particular, are endowed with an exceptional capacity for cumulative learning. This capacity crucially de- pends on the presence of intrinsic motivations, i.e. motivations that are not directly related to an organism\u27s survival and reproduction but rather to its ability to learn. Recently, there have been a number of attempts to model and reproduce intrinsic motivations in artificial systems. Different kinds of intrinsic motivations have been proposed both in psychology and in machine learning and robotics: some are based on the knowl- edge of the learning system, while others are based on its competence. In this contribution we discuss the distinction between knowledge-based and competence-based intrinsic motivations with respect to both the functional roles that motivations play in learning and the mechanisms by which those functions are implemented. In particular, after arguing that the principal function of intrinsic motivations consists in allowing the development of a repertoire of skills (rather than of knowledge), we suggest that at least two different sub-functions can be identified: (a) discovering which skills might be acquired and (b) deciding which skill to train when. We propose that in biological organisms knowledge-based intrinsic motivation mechanisms might implement the former function, whereas competence-based mechanisms might underly the latter one

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