title: An algebra for the analysis of object encoding.
abstract: The encoding of the objects from the world around us is one of the major topics of cognitive psychology, yet the principles of object coding in the human brain remain unresolved. Beyond referring to the particular features commonly associated with objects, our ability to categorize and discuss objects in detailed linguistic propositions implies that we have access to generic concepts of each object category with well-specified boundaries between them. Consideration of the nature of generic object concepts reveals that they must have the structure of a probabilistic list array specifying the Bayesian prior on all possible features that the object can possess, together with mutual covariance matrices among the features. Generic object concepts must also be largely context independent for propositions to have communicable meaning. Although, there is good evidence for local feature processing in the occipital lobe and specific responses for a few basic object categories in the posterior temporal lobe, the encoding of the generic object concepts remains obscure. We analyze the conceptual underpinnings of the study of object encoding, draw some necessary clarifications in relation to its modality-specific and amodal aspects, and propose an analytic algebra with specific reference to functional Magnetic Resonance Imaging approaches to the issue of how generic (amodal) object concepts are encoded in the human brain.
authors.affiliation: Smith-Kettlewell Eye Research Institute, USA. cwt@ski.org
authors.forename: Christopher W
authors.initials: CW
authors.lastname: Tyler
authors.forename: Lora T
authors.initials: LT
authors.lastname: Likova
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D001921:Brain / Q000502:physiology*; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008606:Mental Processes / Q000502:physiology*; D008959:Models, Neurological; D009434:Neural Pathways / Q000502:physiology; D011336:Probability