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Individuation of objects and object parts rely on the same neuronal mechanism.

ABSTRACT: Recent results have shown that participants can enumerate multiple parts of a single object as efficiently as multiple distinct objects, suggesting a shared mechanism for individuation of objects and object parts. Here we used the subitizing phenomenon to investigate the neural mechanism underlying the individuation of object parts. In two experiments, we measured a lateralized EEG response (N2pc) previously associated with individuation of multiple objects. In line with the subitizing effect, participants' error rate was low (less than 10%) when enumerating up to approximately three parts of an object but increased for larger numerosities. The N2pc amplitude increased as a function of the number of object parts, and reached an asymptote corresponding to the subitizing limit, replicating previous reports for separate objects. These results invite the inference that the same neural mechanism underlies individuation of multiple distinct objects and multiple parts of a single object.

SUBMITTER: Poncet M

PROVIDER: S-EPMC5141436 | biostudies-literature | 2016 Dec

REPOSITORIES: biostudies-literature

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Individuation of objects and object parts rely on the same neuronal mechanism.

Poncet Marlene M Caramazza Alfonso A Mazza Veronica V

Scientific reports 20161207

Recent results have shown that participants can enumerate multiple parts of a single object as efficiently as multiple distinct objects, suggesting a shared mechanism for individuation of objects and object parts. Here we used the subitizing phenomenon to investigate the neural mechanism underlying the individuation of object parts. In two experiments, we measured a lateralized EEG response (N2pc) previously associated with individuation of multiple objects. In line with the subitizing effect, p ...[more]

PMID: 27924910

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Project description:BackgroundSeveral new programming languages and technologies have emerged in the past few decades in order to ease the task of modelling complex systems. Modelling the dynamics of complex systems requires various levels of abstractions and reductive measures in representing the underlying behaviour. This also often requires making a trade-off between how realistic a model should be in order to address the scientific questions of interest and the computational tractability of the model.MethodsIn this paper, we propose a novel programming paradigm, called temporal constrained objects, which facilitates a principled approach to modelling complex dynamical systems. Temporal constrained objects are an extension of constrained objects with a focus on the analysis and prediction of the dynamic behaviour of a system. The structural aspects of a neuronal system are represented using objects, as in object-oriented languages, while the dynamic behaviour of neurons and synapses are modelled using declarative temporal constraints. Computation in this paradigm is a process of constraint satisfaction within a time-based simulation.ResultsWe identified the feasibility and practicality in automatically mapping different kinds of neuron and synapse models to the constraints of temporal constrained objects. Simple neuronal networks were modelled by composing circuit components, implicitly satisfying the internal constraints of each component and interface constraints of the composition. Simulations show that temporal constrained objects provide significant conciseness in the formulation of these models. The underlying computational engine employed here automatically finds the solutions to the problems stated, reducing the code for modelling and simulation control. All examples reported in this paper have been programmed and successfully tested using the prototype language called TCOB. The code along with the programming environment are available at http://github.com/compneuro/TCOB_Neuron.DiscussionTemporal constrained objects provide powerful capabilities for modelling the structural and dynamic aspects of neural systems. Capabilities of the constraint programming paradigm, such as declarative specification, the ability to express partial information and non-directionality, and capabilities of the object-oriented paradigm especially aggregation and inheritance, make this paradigm the right candidate for complex systems and computational modelling studies. With the advent of multi-core parallel computer architectures and techniques or parallel constraint-solving, the paradigm of temporal constrained objects lends itself to highly efficient execution which is necessary for modelling and simulation of large brain circuits.

Dataset Information

Individuation of objects and object parts rely on the same neuronal mechanism.

Publications

Individuation of objects and object parts rely on the same neuronal mechanism.

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