About this Research Topic
This Research Topic will focus on how the visual system recognizes objects regardless of variations in the viewpoint, illumination, retinal size, background, etc.
Contributors are encouraged to submit articles describing novel results, models, viewpoints, perspectives and/or methodological innovations relevant to this topic.
The issues we wish to cover include, but are not limited to, perceptual invariance under one or more of the following types of image variation:
• Object shape
• Task
• Viewpoint (from the translation and rotation of the object relative to the viewer)
• Illumination, shading, and shadows
• Degree of occlusion
• Retinal size
• Color
• Surface texture
• Visual context, including background clutter and crowding
• Object motion (including biological motion).
Examples of questions that are particularly interesting in this context include, but are not limited to:
• Empirical characterizations of properties of invariance: does invariance always exist? How wide is its range and how strong is the tolerance to viewing conditions within this range?
• Invariance in naïve vs. experienced subjects: Is invariance built-in or learned? How can it be learned, under which conditions and how effectively? Is it learned incidentally, or are specific task and reward structures necessary for learning? How is generalizability and transfer of learning related to the generalizability/invariance of perception?
• Invariance during inference: Are there conditions (e.g. fast presentation time or otherwise resource-constrained recognition) when invariance breaks?
• What are some plausible computational or neural mechanisms by which invariance could be achieved?
Contributors are encouraged to submit articles describing novel results, models, viewpoints, perspectives and/or methodological innovations relevant to this topic.
The issues we wish to cover include, but are not limited to, perceptual invariance under one or more of the following types of image variation:
• Object shape
• Task
• Viewpoint (from the translation and rotation of the object relative to the viewer)
• Illumination, shading, and shadows
• Degree of occlusion
• Retinal size
• Color
• Surface texture
• Visual context, including background clutter and crowding
• Object motion (including biological motion).
Examples of questions that are particularly interesting in this context include, but are not limited to:
• Empirical characterizations of properties of invariance: does invariance always exist? How wide is its range and how strong is the tolerance to viewing conditions within this range?
• Invariance in naïve vs. experienced subjects: Is invariance built-in or learned? How can it be learned, under which conditions and how effectively? Is it learned incidentally, or are specific task and reward structures necessary for learning? How is generalizability and transfer of learning related to the generalizability/invariance of perception?
• Invariance during inference: Are there conditions (e.g. fast presentation time or otherwise resource-constrained recognition) when invariance breaks?
• What are some plausible computational or neural mechanisms by which invariance could be achieved?
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
