People | Istituto di Scienze e Tecnologie della Cognizione

Giovanni Granato

Email:

giovanni.granato@istc.cnr.it

Telephone:

3336269749

Address:

Rome, G. Romagnosi 18A street, 4G room

Resume:

granatogiovanni_cv_072025.pdf

Group membership:

Laboratory of Computational Embodied Neuroscience

Laboratory of Embodied Natural and Artificial Intelligence

Additional details

Profile

Position:

Researcher (Fixed Term)

Giovanni Granato is researcher at the Laboratory of Embodied Natural and Artificial Intelligence (LENAI), belonging to the Institute of Cognitive Sciences and Technologies (ISTC) at the National Research Council (CNR). At the LENAI, Giovanni is coordinator of a research line in "Computational Neuropsychology/Psychiatry", with a focus on "Neurocognitive processes and computational principles underlying human flexible cognition and behaviour".

Training: Giovanni is a Computational Neuropsychologist trained in Cognitive Sciences, Neuroscience, Machine Learning, Computational Modeling, Cognitive Robotics. In particular, he obtained a BA degree in "Cognitive Sciences" at the "University of Florence'' (thesis in Theoretical Neuroscience) and a MA degree in "Cognitive Neuroscience" at the “University of Rome La sapienza" (thesis in Theoretical and Computational Neuroscience). He also obtained a certificate from the “Advanced School in Artificial Intelligence” (AS-AI) with a project in Computational Neuropsychology. At last, he obtained an international PhD in "Computer Sciences" (thesis in Computational Neuroscience/Neuropsychology) at the University of Plymouth (UK) and the National Research Council of Rome.

Professional activities: Giovanni has published in peer-reviewed journals and international conferences, for which he is also a reviewer for several topics (neuro-inspired Machine Learning, Computational Modeling, Neuro-robotics, Experimental Psychology, Psychiatry, etc). He also supervised master's thesis, internships and students of the Advance School of Artificial Intelligence (AS-AI). Finally, he works as a “autistic scientific consultant” and “teacher specialized in Educational Robotics” for for external research laboratories and companies.

Key publications:

Granato, G., Costanzo, R., Borghi, A., Mattera, A., Carruthers, S., Rossell, S., & Baldassarre, G. (2025). An experimental and computational investigation of executive functions and inner speech in schizophrenia spectrum disorders. Scientific Reports, 15(1), 5185.
Granato, G., & Baldassarre, G. (2024). Bridging flexible goal-directed cognition and consciousness: The Goal-Aligning Representation Internal Manipulation theory. Neural Networks, 106292.
Granato, G., Borghi, A. M., Mattera, A., & Baldassarre, G. (2022). A computational model of inner speech supporting flexible goal-directed behaviour in Autism. Scientific reports, 12(1), 1-15.
Granato, G., & Baldassarre, G. (2021). Internal manipulation of perceptual representations in human flexible cognition: A computational model. Neural Networks, 143, 572-594.
Granato, G., Borghi, A. M., & Baldassarre, G. (2020). A computational model of language functions in flexible goal-directed behaviour. Scientific reports, 10(1), 1-13.

Research

Overview

(LAST WEBPAGE UPDATE: 05/2025)

Topics: My research focuses on the neurocognitive basis of flexible cognition (e.g., executive functions, metacognition, consciousness) and its alteration in clinical populations. I proposed neuro-computational theories such the “Motivated Categorical Perception theory” (Granato et al., 2022; Granato, 2022), the "Three-components theory of flexible cognition" (Granato and Baldassarre, 2021, Granato et al., 2020, Granato et al, 2025), the "Goal-Aligning Representation Internal Manipulation theory" (Granato and Baldassarre, 2024). At last, my investigationsextend to AI-based profiling systems (e.g, digital-twin tools for healtcare and human resources), Machine Learning (e.g., generative models, etc), Robotics (e.g., Machine Consciousness).

Methods: I adopt a “Computational Neuropsychology” approach combining neuropsychological testing, AI methods, and simulations of neuro-inspired agents. A key aspect is the development of digital-twin pipelines, implemented on the EBRAINS-Italy platform (https://www.ebrains-italy.eu/resources/analysistools/9). These tools simulate and profile higher-order cognition (e.g. metacognition, flexibility) in humans, supporting both experimental and clinical research. The models are validated through standard neuropsychological tests commonly used in clinics and research.

Structure: The research line is articulated into basic (theoretical/experimental), applied (e.g., model usability in research and clinical contexts), and experimental development (AI-based diagnostic tools). The pipelines aim to become reference tools for clinical profiling and personalized intervention planning, thanks to their
integration in FAIR-compliant infrastructures (e.g., EBRAINS).

Basic research activities are divided into “theoretical-computational” and “experimental”. In the first I develop theories regarding higher-order cognition in physiological and clinical conditions. Formalizations guide the development of computational models - validated on human experimental data obtained with standard neuropsychological tests - that explain/predict data useful for theoretical, experimental and clinical research. Experimental activities include the administration of neuropsychological tests and the cognitive assessment - based on models - of participants. Therefore, the two research sections interact strongly, supporting highly interdisciplinary and translational projects (from clinical data to therapeutic predictions). Applied research activities aim to develop methodologies that can improve basic research and stimulate its application to social issues (e.g. “digital-twin” technologies). In particular, these researches aim to create infrastructures that can improve the usability of computational models in the research and clinical community. Experimental development activities aim to develop AI-based software that can support neuropsychological profiling, thus falling within the emerging field of “digital-twins” health technologies.

I carry out my research activity as research coordinator at the Laboratory of Embodied Natural and Artificial Intelligence (LENAI). For an overview of my research line in the context of my laboratory, see my lab's webpage: https://www.istc.cnr.it/en/group/lenai.

Theoretical and computational contributions

A high interdisciplinarity (integration of fields and directions) leaded to the development of three theories of cognition and related validated computational models.

Motivated Categorical Perception hypothesis (MCPH; Granato et al., 2022; Granato 2022): the MCP hypothesis formalises the early motivational and sensory-motor processes leading to the emergence of adaptive perceptual representations (categorical perception). It proposes that the brain representation learning processes are based on the interactions between cortical and sub-cortical macro-systems (e.g. striate/exstrastriate cortices, basal ganglia, motor cortices), supporting specific computational functions (e.g. perceptual abstraction, motivational bias, motor selection). In particular, it proposes that (a) these macro-systems show different contributions of unsupervised learning (UL) and reinforcement learning (RL) along the sensory-motor hierarchy (UL in Striate cortex, UL + RL in Extrastriate cortices, RL in motor cortices) and (b) the emergences of a motivated categorical perception needs of motivated/goal-directed agent-environment continuous interactions (embodied perception).

Computational model and experimental validation: this hypothesis led to the development of a theory-based computational model, supported by a system-level neuro-inspired architecture, that performs a sorting task. We experimentally corroborated the hypothesis in Granato et al., 2022, where we compared different perceptual profiles of the model and perceptual functioning of autistic persons, thus explaining the altered categorical perception in Autism.
References:
- Granato G., Cartoni E, Da Rold F, Mattera A, Baldassarre G (2022) Integrating unsupervised and reinforcement learning in human categorical perception: A computational model. PLoS ONE 17(5): e0267838
- Granato, G. (2022). Flexible goal-directed manipulation of representations: computational models of healthy and pathological human cognition (Doctoral dissertation, University of Plymouth).

mcp_hypothesis_website.png

Three-component theory (3CT; Granato and Baldassarre, 2021; Granato et al., 2020): the 3C theory formalises the neuro-cognitive processes that boost cognitive flexibility during goal-directed behaviours. Overall, it proposes that “flexible cognition depends on the integration between a top-down goal-directed manipulation of representations and sensory–motor interactions with the environment”. In particular, the theory proposes that three main elements are at the basis of flexible goal-directed behaviour: (a) Three key architectural components (executive Working Memory, Hierarchical Perceptual systems, Top-down manipulators), (b) First-order representations/manipulations (e.g., percepts - selective attention) and second-order representations/manipulations (e.g., goals - inner-speech) , (c) embodied sensory-motor loops.

Computational model and experimental validation: this theory led to the development of two theory-based embodied computational models, supported by a system-level neuro-inspired architecture, that perform a rule-based sorting task (Wisconsin Card Sorting Test; Heaton, 2000). The second model expects the addition of an “inner-speech component”, that emulates a self-directed high-order representation manipulation. We experimentally corroborated the theory and models - thus reproducing the task performance - in Granato and Baldassarre (2021; young adults, old adults, frontal lesions, Parkinson), Granato et al (2020; young adults in three different experimental conditions), Granato et al. (2022; autistic and neurotypical children, teenagers, young adults, middle adults), Granato et al. (2023; young adults, Schizophrenia). Overall, the model reproduced the cognitive and behavioural features of 17 human groups in healthy, clinical and neuro-divergent conditions.
References:
- Granato, G., & Baldassarre, G. (2021). Internal manipulation of perceptual representations in human flexible cognition: A computational model. Neural Networks, 143, 572-594.
- Granato, G., Borghi, A. M., & Baldassarre, G. (2020). A computational model of language functions in flexible goal-directed behaviour. Scientific reports, 10(1), 1-13.
- Granato, G., Borghi, A. M., Mattera, A., & Baldassarre, G. (2022). A computational model of inner speech supporting flexible goal-directed behaviour in Autism. Scientific reports, 12(1), 1-15.
- Granato, G., Costanzo, R., Borghi, A., Mattera, A., Carruthers, S., Rossell, S., & Baldassarre, G. (2025). An experimental and computational investigation of executive functions and inner speech in schizophrenia spectrum disorders. Scientific Reports, 15(1), 5185.

3ch_website_1.png

Goal-Aligning Representation Internal Manipulation (GARIM) theory (Granato and Baldassarre, 2024): the GARIM theory extended the three-component theory, thus formalising neuro-cognitive processes at the basis of high-order cognition and Consciousness. The central idea of the GARIM theory is that “conscious states support the active manipulation of internal representations, making them more aligned with the goals pursued. This goal-oriented alignment leads to the generation of the necessary knowledge to face novel situations and goals and to make goal-directed behaviour more flexible and effective”.

Computational model and experimental validation: although the theory has not received any computational/experimental validation, it is compatible with the main theories of Consciousness and provides an architectural blueprint for future Computational Models and ML/Robotics systems.
References:
- Granato, G., & Baldassarre, G. (2024). Bridging flexible goal-directed cognition and consciousness: The Goal-Aligning Representation Internal Manipulation theory. Neural Networks, 106292.

garim_website_1.png

Ongoing activities and future directions

I am working to extend my theoretical proposals and related computational models towards higher order cognition, including for example the fundamental concepts of Metacognition (Monitoring and Control). In accordance with this direction, I will extend my theories to include "Visual Planning", thus further investigating the relationship between "representation manipulation" and higher order cognition (Planning/Problem Solving).

I'm starting an experimental project that will integrate neuropsychology profiling and computational modelling to describe human metacognition and executive functioning

I'm developing a machine learning pipeline (theory-based and data-driven algorythms) for supporting the neuropsychological profiling for basic and clinical research.

I will extend my investigations on Inner-Speech, thus comparing its different forms and alterations.

I expect to extend my theories and models to generate theoretical proposals and "proofs-of-concept" in Machine Learning and Robotics.

Resources

I work at "National Research Council - Institute of Cognitive Sciences and Technologies" (second floor, right corridor), located in Via G. Romagnosi 18A, 00196, Roma, Italy. If you want to contact me, please send a mail to my main address (giovanni.granato@istc.cnr.it).