What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Cognition
Communication, Cognitive neuroscience of visual object recognition, Cognitive psychology, Form perception and Perception are his primary areas of study.
The various areas that Michael J. Tarr examines in his Communication study include Mental representation, Object, Artificial intelligence, Set and Psychophysics.
His Artificial intelligence research integrates issues from Visual perception, Computer vision and Pattern recognition.
His work deals with themes such as Depth perception, Cognitive science and Categorization, which intersect with Cognitive neuroscience of visual object recognition.
His Cognitive psychology research is multidisciplinary, incorporating perspectives in Fusiform gyrus, Face, Fusiform face area, Object and Occipital lobe.
His Form perception study combines topics from a wide range of disciplines, such as Stimulus and Lateralization of brain function.
His most cited work include:
- Activation of the middle fusiform 'face area' increases with expertise in recognizing novel objects. (1015 citations)
- Becoming a “Greeble” Expert: Exploring Mechanisms for Face Recognition (794 citations)
- The Fusiform Face Area is Part of a Network that Processes Faces at the Individual Level (699 citations)
What are the main themes of his work throughout his whole career to date?
Michael J. Tarr mainly investigates Artificial intelligence, Cognitive neuroscience of visual object recognition, Cognitive psychology, Perception and Communication.
His research integrates issues of Visual perception, Computer vision and Pattern recognition in his study of Artificial intelligence.
Michael J. Tarr combines subjects such as Pattern recognition, Form perception, Visual cortex and Fusiform gyrus with his study of Cognitive neuroscience of visual object recognition.
His studies deal with areas such as Visual agnosia, Social psychology, Perceptual learning, Fusiform face area and Facial recognition system as well as Cognitive psychology.
His Perception research incorporates elements of Stimulus, Cognitive science, Cognition and Categorization.
The Communication study combines topics in areas such as Depth perception, Object, Psychophysics and Mental representation.
He most often published in these fields:
- Artificial intelligence (46.61%)
- Cognitive neuroscience of visual object recognition (25.34%)
- Cognitive psychology (23.08%)
What were the highlights of his more recent work (between 2013-2021)?
- Artificial intelligence (46.61%)
- Pattern recognition (21.27%)
- Visual perception (12.67%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Artificial intelligence, Pattern recognition, Visual perception, Convolutional neural network and Visual cortex.
His Artificial intelligence research includes elements of Machine learning and Computer vision.
His Pattern recognition study combines topics in areas such as Correlation, Feature, Scaling and Magnetoencephalography.
His Visual perception study integrates concerns from other disciplines, such as Stimulus, Context and Neuroimaging.
His biological study spans a wide range of topics, including Object, Representation and Affordance.
His studies examine the connections between Cognitive neuroscience of visual object recognition and genetics, as well as such issues in Form perception, with regards to Communication.
Between 2013 and 2021, his most popular works were:
- Very high density EEG elucidates spatiotemporal aspects of early visual processing (88 citations)
- Visual Object Recognition: Do We (Finally) Know More Now Than We Did? (32 citations)
- Size precedes view: Developmental emergence of invariant object representations in lateral occipital complex (26 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Cognition
His scientific interests lie mostly in Artificial intelligence, Pattern recognition, Visual perception, Cognitive neuroscience of visual object recognition and Form perception.
Computational neuroscience is closely connected to Computer vision in his research, which is encompassed under the umbrella topic of Artificial intelligence.
His Pattern recognition study incorporates themes from Machine learning, Neuroimaging, Visual cortex and Electroencephalography.
His Visual perception research is multidisciplinary, incorporating elements of Context, Stimulus, Vision science, Machine vision and Semantics.
His Cognitive neuroscience of visual object recognition research includes themes of Cognitive psychology, Cognitive science and Communication.
Michael J. Tarr interconnects Object model, Invariant and Object constancy in the investigation of issues within Communication.
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