What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Cognition
- Computer network
The scientist’s investigation covers issues in Cognitive psychology, Cognition, Reading, Dyslexia and Hemispatial neglect.
His work deals with themes such as Number line, Spatial ability, Perception and Numerical cognition, which intersect with Cognitive psychology.
His research integrates issues of Developmental psychology, Visual perception and Task analysis in his study of Cognition.
His Reading study integrates concerns from other disciplines, such as Connectionism, Artificial intelligence and Natural language processing.
The various areas that he examines in his Dyslexia study include Pronunciation, Word recognition, Phonological awareness, Phonetics and Phonology.
He applies his multidisciplinary studies on Hemispatial neglect and Bisection in his research.
His most cited work include:
- Brain damage: neglect disrupts the mental number line. (571 citations)
- Brain damage: neglect disrupts the mental number line. (571 citations)
- Nested incremental modeling in the development of computational theories: The CDP+ model of reading aloud. (507 citations)
What are the main themes of his work throughout his whole career to date?
Marco Zorzi focuses on Cognitive psychology, Artificial intelligence, Computer network, Cognition and Reading.
His work carried out in the field of Cognitive psychology brings together such families of science as Visual perception, Perception, Numerosity adaptation effect, Developmental psychology and Numerical cognition.
His studies deal with areas such as Numeracy, Approximate number system, Hemispatial neglect and Audiology as well as Developmental psychology.
His Artificial intelligence research includes themes of Machine learning and Natural language processing.
He interconnects Distributed computing, Wireless ad hoc network, Throughput and Wireless network in the investigation of issues within Computer network.
His Cognition study is related to the wider topic of Neuroscience.
He most often published in these fields:
- Cognitive psychology (25.52%)
- Artificial intelligence (20.50%)
- Computer network (15.48%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (20.50%)
- Cognitive psychology (25.52%)
- Cognition (15.90%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Artificial intelligence, Cognitive psychology, Cognition, Arithmetic and Reading.
His Artificial intelligence research is multidisciplinary, relying on both Numerosity adaptation effect, Machine learning and Pattern recognition.
Marco Zorzi has researched Cognitive psychology in several fields, including Mathematical reasoning, Saccadic masking, Eye movement, Gaze and Range.
Marco Zorzi has included themes like Functional magnetic resonance imaging, Human multitasking and Electrophysiology in his Cognition study.
His Arithmetic study incorporates themes from Number line, Representation and Mental representation.
His Reading study combines topics from a wide range of disciplines, such as Developmental psychology, Mathematical learning and Computational model.
Between 2018 and 2021, his most popular works were:
- Post-stroke deficit prediction from lesion and indirect structural and functional disconnection (28 citations)
- Understanding Dyslexia Through Personalized Large-Scale Computational Models. (24 citations)
- A Comparison of Shallow and Deep Learning Methods for Predicting Cognitive Performance of Stroke Patients From MRI Lesion Images. (16 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer network
- Cognition
Marco Zorzi mainly focuses on Artificial intelligence, Cognitive psychology, Deep learning, Arithmetic and Representation.
The study incorporates disciplines such as Resting state fMRI and Neuroimaging in addition to Artificial intelligence.
His Cognitive psychology research incorporates elements of Scale, Dyslexia, Literacy development, Computational model and Learning to read.
His Deep learning research is multidisciplinary, incorporating perspectives in Numerosity adaptation effect, Psychophysics and Convolutional neural network, Pattern recognition.
His work carried out in the field of Numerosity adaptation effect brings together such families of science as Stimulus, Salient, Artificial neural network and Visual perception.
His Arithmetic research integrates issues from Number line and Numerical cognition.
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