What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Optics
- Statistics
The scientist’s investigation covers issues in Artificial intelligence, Perception, Spatial frequency, Optics and Visual perception.
His Artificial intelligence research is multidisciplinary, relying on both Numerosity adaptation effect, Computer vision and Pattern recognition.
The concepts of his Computer vision study are interwoven with issues in Measure, Crowding, Psychophysics and Perceptual Masking.
His research integrates issues of Cognitive psychology, Social psychology and Foveal in his study of Perception.
His research in Optics intersects with topics in Orientation and Algorithm.
The Visual perception study combines topics in areas such as Peripheral vision, Autism, Asperger syndrome and Superior temporal sulcus.
His most cited work include:
- Theories of developmental dyslexia: insights from a multiple case study of dyslexic adults (1096 citations)
- Vagaries of Visual Perception in Autism (555 citations)
- Visual perception and its impairment in schizophrenia. (307 citations)
What are the main themes of his work throughout his whole career to date?
Steven C. Dakin mainly investigates Artificial intelligence, Computer vision, Optics, Crowding and Pattern recognition.
His work carried out in the field of Artificial intelligence brings together such families of science as Stimulus, Psychophysics, Perception and Communication.
His Computer vision research incorporates elements of Methods of contour integration and Fixation.
His Pattern recognition research is multidisciplinary, incorporating perspectives in Texture, Noise and Numerosity adaptation effect.
His Spatial frequency research includes elements of Visual acuity and Masking.
His research investigates the connection between Visual processing and topics such as Visual perception that intersect with problems in Cognitive psychology and Visual cortex.
He most often published in these fields:
- Artificial intelligence (38.04%)
- Computer vision (18.84%)
- Optics (19.57%)
What were the highlights of his more recent work (between 2015-2021)?
- Optometry (16.30%)
- Visual acuity (19.93%)
- Eye movement (8.33%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Optometry, Visual acuity, Eye movement, Visual perception and Cognitive psychology.
His study looks at the intersection of Visual acuity and topics like Monocular with Set and Pattern recognition.
His Visual perception study integrates concerns from other disciplines, such as Vision test and Visual cortex.
His biological study spans a wide range of topics, including Visual attention and Fixation.
His studies deal with areas such as Perception and Artificial intelligence as well as Stimulus.
His Artificial intelligence study incorporates themes from Social cue, Computer vision and Pattern recognition.
Between 2015 and 2021, his most popular works were:
- Adaptable history biases in human perceptual decisions. (101 citations)
- Binocular therapy for childhood amblyopia improves vision without breaking interocular suppression (35 citations)
- Visual Population Receptive Fields in People with Schizophrenia Have Reduced Inhibitory Surrounds (33 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Statistics
Steven C. Dakin focuses on Visual perception, Visual acuity, Vision test, Cognitive psychology and Stimulus.
He has researched Visual perception in several fields, including Visual processing, Visual cortex and Gaze.
His Visual acuity research incorporates themes from Optometry, Monocular and Chart.
His research in the fields of Visual search overlaps with other disciplines such as Age groups.
His studies in Stimulus integrate themes in fields like Artificial intelligence, Perception, Audiology and Computer vision.
Steven C. Dakin has included themes like Sensory stimulation therapy and Operations research in his Artificial intelligence study.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
