Publications by authors named "Giovanni Granato"

3 Publications

  • Page 1 of 1

Internal manipulation of perceptual representations in human flexible cognition: A computational model.

Neural Netw 2021 Jul 15;143:572-594. Epub 2021 Jul 15.

Laboratory of Computational Embodied Neuroscience, Institute of Cognitive Sciences and Technologies, National Research Council of Italy, Rome, Italy. Electronic address:

Executive functions represent a set of processes in goal-directed cognition that depend on integrated cortical-basal ganglia brain systems and form the basis of flexible human behaviour. Several computational models have been proposed for studying cognitive flexibility as a key executive function and the Wisconsin card sorting test (WCST) that represents an important neuropsychological tool to investigate it. These models clarify important aspects that underlie cognitive flexibility, particularly decision-making, motor response, and feedback-dependent learning processes. However, several studies suggest that the categorisation processes involved in the solution of the WCST include an additional computational stage of category representation that supports the other processes. Surprisingly, all models of the WCST ignore this fundamental stage and they assume that decision making directly triggers actions. Thus, we propose a novel hypothesis where the key mechanisms of cognitive flexibility and goal-directed behaviour rely on the acquisition of suitable representations of percepts and their top-down internal manipulation. Moreover, we propose a neuro-inspired computational model to operationalise this hypothesis. The capacity of the model to support cognitive flexibility was validated by systematically reproducing and interpreting the behaviour exhibited in the WCST by young and old healthy adults, and by frontal and Parkinson patients. The results corroborate and further articulate the hypothesis that the internal manipulation of representations is a core process in goal-directed flexible cognition.
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http://dx.doi.org/10.1016/j.neunet.2021.07.013DOI Listing
July 2021

A computational model of language functions in flexible goal-directed behaviour.

Sci Rep 2020 12 10;10(1):21623. Epub 2020 Dec 10.

Laboratory of Computational Embodied Neuroscience, Institute of Cognitive Sciences and Technologies, National Research Council of Italy, Rome, Italy.

The function of language in high-order goal-directed human cognition is an important topic at the centre of current debates. Experimental evidence shows that inner speech, representing a self-directed form of language, empowers cognitive processes such as working memory, perception, categorization, and executive functions. Here we study the relations between inner speech and processes like feedback processing and cognitive flexibility. To this aim we propose a computational model that controls an artificial agent who uses inner speech to internally manipulate its representations. The agent is able to reproduce human behavioural data collected during the solution of the Wisconsin Card Sorting test, a neuropsychological test measuring cognitive flexibility, both in the basic condition and when a verbal shadowing protocol is used. The components of the model were systematically lesioned to clarify the specific impact of inner speech on the agent's behaviour. The results indicate that inner speech improves the efficiency of internal representation manipulation. Specifically, it makes the representations linked to specific visual features more disentangled, thus improving the agent's capacity to engage/disengage attention on stimulus features after positive/negative action outcomes. Overall, the model shows how inner speech could improve goal-directed internal manipulation of representations and enhance behavioural flexibility.
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http://dx.doi.org/10.1038/s41598-020-78252-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7729881PMC
December 2020

An Embodied Agent Learning Affordances With Intrinsic Motivations and Solving Extrinsic Tasks With Attention and One-Step Planning.

Front Neurorobot 2019 26;13:45. Epub 2019 Jul 26.

Laboratory of Computational Embodied Neuroscience, Institute of Cognitive Sciences and Technologies, National Research Council of Italy, Rome, Italy.

We propose an architecture for the open-ended learning and control of embodied agents. The architecture learns action affordances and forward models based on intrinsic motivations and can later use the acquired knowledge to solve extrinsic tasks by decomposing them into sub-tasks, each solved with one-step planning. An affordance is here operationalized as the agent's estimate of the probability of success of an action performed on a given object. The focus of the work is on the overall architecture while single sensorimotor components are simplified. A key element of the architecture is the use of "active vision" that plays two functions, namely to focus on single objects and to factorize visual information into the object appearance and object position. These processes serve both the acquisition and use of object-related affordances, and the decomposition of extrinsic goals (tasks) into multiple sub-goals (sub-tasks). The architecture gives novel contributions on three problems: (a) the learning of affordances based on intrinsic motivations; (b) the use of active vision to decompose complex extrinsic tasks; (c) the possible role of affordances within planning systems endowed with models of the world. The architecture is tested in a simulated stylized 2D scenario in which objects need to be moved or "manipulated" in order to accomplish new desired overall configurations of the objects (extrinsic goals). The results show the utility of using intrinsic motivations to support affordance learning; the utility of active vision to solve composite tasks; and the possible utility of affordances for solving utility-based planning problems.
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http://dx.doi.org/10.3389/fnbot.2019.00045DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6676802PMC
July 2019
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