Adaptation and the Phenomenology of Perception

Webster, Michael A.; Field, John S. Werner amp; David, James P.

doi:10.1093/acprof:oso/9780198529699.003.0010

Cited by 60 publications

(74 citation statements)

References 68 publications

(67 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…22 Hence, a meaning for a word is not 22 This may be a general cognitive principle: Whether an object is seen as white depends on the object in its environment and the perceiver's state of adaptation. If the perceiver has been in a red environment and visually adapted to red, then an object that looks white to her will look red to someone entering the red environment from a white environment (Webster et al 2005).…”

Section: Then: the Context That R Should Use To Hypothesize A Meaningmentioning

confidence: 99%

Contextual Vocabulary Acquisition: from Algorithm to Curriculum

Rapaport¹,

Kibby²

2014

Castañeda and His Guises

View full text Add to dashboard Cite

Deliberate contextual vocabulary acquisition (CVA) is a reader's ability to figure out a (not the) meaning for an unknown word from its "context", without external sources of help such as dictionaries or people. The appropriate context for such CVA is the "belief-revised integration" of the reader's prior knowledge with the reader's "internalization" of the text. We discuss unwarranted assumptions behind some classic objections to CVA, and present and defend a computational theory of CVA that we have adapted to a new classroom curriculum designed to help students use CVA to improve their reading comprehension. Running head: Contextual Vocabulary Acquisition Keywords:Artificial Intelligence, Education, Linguistics, Philosophy; Instruction, Language acquisition, Language understanding, Learning, Reasoning, Semantics; Knowledge representation, Logic, Symbolic computational modeling.2 From Guise Theory to Contextual Vocabulary Acquisition An Opening AnecdoteWhen Hector-Neri Castañeda was ill, Randall R. Dipert and I (WJR) visited him in Bloomington. He was weak and tired, and his speech was even harder to decipher than it had been when he was healthy. He was happy to see us, however, and, after a few preliminaries, suggested that we have dinner later at his favorite macrobiotic restaurant.The restaurant was exceedingly noisy, rendering dinner conversation virtually impossible. Although he was tired after dinner, he insisted that we come back to his house to talk philosophy. And, as soon as we began our philosophical conversation, his stamina returned, his voice became stronger and clearer, and we were back with the Hector of old. Philosophy gave him strength. Castañeda's Guise Theory.Guise theory (Castañeda 1972(Castañeda , 1999, and many works in between; for a summary, see Rapaport 2005b) has two goals: to serve as a theory of the mechanism of reference and to serve as a theory of the world as it appears to us.And it has two principal sources: Frege's (1892) puzzle of reference and the "univocality" of language. Frege's puzzle concerns the way in which the President of the US is the "same" as the Commander-in-Chief of the US Armed Forces. The univocality of language is the claim that talk and thought about truth and reality behaves exactly the same as talk and thought about falsehood and fiction: Language treats objects of both kinds of talk and thought in the same way. This is associated with a principle of "metaphysical internalism": Talk and thought about the world is internal to experience; the (experienced) world must be understood from the "inside", not "externally"-from a first-person point of view, not from God's point of view or Nagel's (1986) "view from nowhere".Metaphysical internalism has proven useful in artificial intelligence (AI) and cognitive modeling (Shapiro & Rapaport 1987.According to guise theory, there are properties, sets of properties (called "guise cores"), and an "individuating operator" (c) that maps guise cores to guises. E.g., where Round is the property of being round and Squ...

show abstract

Section: Then: the Context That R Should Use To Hypothesize A Meaningmentioning

confidence: 99%

Contextual Vocabulary Acquisition: from Algorithm to Curriculum

Rapaport¹,

Kibby²

2014

Castañeda and His Guises

View full text Add to dashboard Cite

show abstract

“…A number of researchers have proposed models of adaptation based on learning the statistics of recent visual inputs [6,76,79]. Adaptation aftereffects operate on a much shorter time scale than perceptual learning in the environment.…”

Section: A U T H O R ' S P E R S O N a L C O P Ymentioning

confidence: 99%

“…There is also evidence that motion adaptation can improve both speed and direction discrimination [17,19,63]. However, whether similar improvements in pattern discrimination occur following adaptation to patterns remains controversial, and when these are reported, they seem somewhat weak [79]. The constrained optimization model of Fig.…”

Section: A U T H O R ' S P E R S O N a L C O P Ymentioning

confidence: 99%

Information maximization in face processing

Bartlett¹

2007

Neurocomputing

View full text Add to dashboard Cite

This perspective paper explores principles of unsupervised learning and how they relate to face recognition. Dependency coding and information maximization appear to be central principles in neural coding early in the visual system. These principles may be relevant to how we think about higher visual processes such as face recognition as well. The paper first reviews examples of dependency learning in biological vision, along with principles of optimal information transfer and information maximization. Next, we examine algorithms for face recognition by computer from a perspective of information maximization. The eigenface approach can be considered as an unsupervised system that learns the first-and second-order dependencies among face image pixels. Eigenfaces maximize information transfer only in the case where the input distributions are Gaussian. Independent component analysis (ICA) learns high-order dependencies in addition to first-and second-order relations, and maximizes information transfer for a more general set of input distributions. Face representations based on ICA gave better recognition performance than eigenfaces, supporting the theory that information maximization is a good strategy for high level visual functions such as face recognition. Finally, we review perceptual studies suggesting that dependency learning is relevant to human face perception as well, and present an information maximization account of perceptual effects such as the atypicality bias, and face adaptation aftereffects. r

show abstract

“…21 Hence, a meaning for a word is not 21 This may be a general cognitive principle: Whether an object is seen as white depends on the object in its environment and the perceiver's state of adaptation. If the perceiver has been in a red environment and visually adapted to red, then an object that looks white to her will look red to someone entering the red environment from a white environment (Webster et al 2005).…”

Section: Then: the Context That R Should Use To Hypothesize A Meaningmentioning

confidence: 99%

Contextual vocabulary acquisition: from algorithm to curriculum

Rapaport

Kibby

IEMC '03 Proceedings. Managing Technologically Driven Organizations: The Human Side of Innovation and Change (IEEE Cat. No.03CH

View full text Add to dashboard Cite

Deliberate contextual vocabulary acquisition (CVA) is a reader's ability to figure out a (not the) meaning for an unknown word from its "context", without external sources of help such as dictionaries or people. The appropriate context for such CVA is the "belief-revised integration" of the reader's prior knowledge with the reader's "internalization" of the text. We discuss unwarranted assumptions behind some classic objections to CVA, and present and defend a computational theory of CVA that we have adapted to a new classroom curriculum designed to help students use CVA to improve their reading comprehension. Running head: Contextual Vocabulary Acquisition Keywords:Artificial Intelligence, Education, Linguistics, Philosophy; Instruction, Language acquisition, Language understanding, Learning, Reasoning, Semantics; Knowledge representation, Logic, Symbolic computational modeling. 2 A Computational Theory of Contextual Vocabulary AcquisitionReaders often don't-or don't want to-look up unfamiliar words "in the dictionary". Sometimes, they don't realize that a word is unfamiliar, reading past it as if it weren't there: Even "skilled readers" skip "about one-third of the words" (Brysbaert et al. 2005), and words that are highly likely to be chosen in a cloze task are skipped more than others (Rayner & Well 1996); paradoxically, "children may misread or ignore unfamiliar words without jeopardizing comprehension" (Bowey & Muller 2005, citing Share 1999. Other times, readers realize that they don't understand a word but are too lazy (or embarrassed) to do anything about it: Perhaps-discouraged by previous, unsuccessful attempts to look words up-they hope that the word isn't important and won't be used again.Or they might be curious as to what the unfamiliar word might mean, i.e., what the author had in mind when using it. If no one is around to ask, or if the only people around don't know the word, then the reader can look it up in "the" dictionary. But what if no dictionary is handy? Or the only one doesn't contain the word? Or it does, but the definition seems inappropriate for the context, or is unhelpful? With the exception of learner's dictionaries designed primarily for ESL readers, dictionaries are often difficult to use and their definitions difficult to interpret (Miller 1985(Miller , 1986Miller & Gildea 1985;Rapaport & Kibby 2007).Alternatively, readers can "figure it out" from the "context". We call this "contextual vocabulary acquisition" (CVA). Just as giving a person a fish feeds them for a day, but teaching them to fish feeds them for a lifetime, so giving a reader a definition tells them one word's meaning, but teaching them CVA enables them to become better readers. We make four principal claims: (1) If "[t]exts ... are ... the soil in which word-meaning understandings are grown" (Wieland 2008), then the reader's prior knowledge is the water enabling that growth. (2) A procedure for successful CVA can be expressed in terms so precise that it can be programmed into a computer and (3) taught to h...

show abstract

Adaptation and the Phenomenology of Perception

Cited by 60 publications

References 68 publications

Contextual Vocabulary Acquisition: from Algorithm to Curriculum

Contextual Vocabulary Acquisition: from Algorithm to Curriculum

Information maximization in face processing

Contextual vocabulary acquisition: from algorithm to curriculum

Contact Info

Product

Resources

About