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Perception :: The science of perception :: Construction and inference in perception

• Jüri Allik & Kenn Konstabel (2005). G. F. Parrot and the theory of unconscious inferences.   (More)
  Additional links for this entry:
  http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=16196051&dopt=Citation
  http://doi.wiley.com/10.1002/jhbs.20114
  
• Richard Gregory (1974). Perceptions as hypotheses.   (More)
  Additional links for this entry:
  http://www.journals.royalsoc.ac.uk/index/226W686672226774.pdf
  http://philosophy.arts.unsw.edu.au/students/resources/lecture_notes/PHIL2207/PHIL2207-Week 8.pdf
  http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=6106237&dopt=Citation
  http://links.jstor.org/sici?sici=0080-4622(19800708)290:1038<181:PAH>2.0.CO;2-B
  
• Gary Hatfield (2002). Perception As Unconscious Inference.   (Abstract & more)
  Abstract: Consider for a moment the spatial and chromatic dimensions of your visual expe- rience. Suppose that as you gaze about the room you see a table, some books, and papers. Ignore for now the fact that you immediately recognize these objects to be a table with books and papers on it. Concentrate on how the table looks to you: its top spreads out in front of you, stopping at edges beyond which lies unfilled space, leading to more or less distant chairs, shelves, or expanses of floor. The books and paper on the table top create shaped visual boundaries between areas of different color, within which there may be further variation of color or visual texture. Propelled by a slight breeze, a sheet of paper slides across the table, and you experience its smooth motion before it floats out of sight
  Additional links for this entry:
  http://repository.upenn.edu/ircs_reports/9
  http://www.cis.upenn.edu/~hatfield/uncinf.pdf
  http://repository.upenn.edu/cgi/viewcontent.cgi?article=1021&context=ircs_reports
  
• Kirk Ludwig (1996). Explaining why things look the way they do.   (Abstract & more)
  Abstract: How are we able to perceive the world veridically? If we ask this question as a part of the scientific investigation of perception, then we are not asking for a transcendental guarantee that our perceptions are by and large veridical; we presuppose that they are. Unless we assumed that we perceived the world for the most part veridically, we would not be in a position to investigate our perceptual abilities empirically. We are interested, then, not in how it is possible in general for us to perceive the world veridically, but instead in what the relation is between our environment and its properties, of which we have knowledge, on the one hand, and our perceptual mechanisms, on the other, that results in very many, even most of our perceptions being veridical in everyday life
• Dhanraj Vishwanath (2005). The epistemological status of vision and its implications for design.   (Abstract & more)
  Abstract: Computational theories of vision typically rely on the analysis of two aspects of human visual function: (1) object and shape recognition (2) co-calibration of sensory measurements. Both these approaches are usually based on an inverse-optics model, where visual perception is viewed as a process of inference from a 2D retinal projection to a 3D percept within a Euclidean space schema. This paradigm has had great success in certain areas of vision science, but has been relatively less successful in understanding perceptual representation, namely, the nature of the perceptual encoding. One of the drawbacks of inverse-optics approaches has been the difficulty in defining the constraints needed to make the inference computationally tractable (e.g. regularity assumptions, Bayesian priors, etc.). These constraints, thought to be learned assumptions about the nature of the physical and optical structures of the external world, have to be incorporated into any workable computational model in the inverse-optics paradigm. But inference models that employ an inverse optics plus structural assumptions approach inevitably result in a naïve realist theory of perceptual representation. Another drawback of inference models for theories of perceptual representation is their inability to explain central features of the visual experience. The one most evident in the process and visual understanding of design is the fact that some visual configurations appear, often spontaneously, as perceptually more coherent than others. The epistemological consequences of inferential approaches to vision indicate that they fail to capture enduring aspects of our visual experience. Therefore they may not be suited to a theory of perceptual representation, or useful for an understanding of the role of perception in the design process and product
  Additional links for this entry:
  http://www.ingentaconnect.com/content/klu/axio/2005/00000015/00000003/00005445
  http://www.springerlink.com/content/k78g2806773198v6/fulltext.pdf
  http://www.springerlink.com/index/K78G2806773198V6.pdf
  

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