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| '''Cue validity''' is the [[conditional probability]] that an object falls in a particular category given a particular feature or ''cue''. The term was popularized by {{Harvtxt|Beach|1964}}, {{Harvtxt|Reed|1972}} and especially by [[Eleanor Rosch]] in her investigations of the acquisition of so-called [[Prototype_theory#Basic_level_categories | ''basic categories'']] ({{Harvnb|Rosch|Mervis|1975}};{{Harvnb|Rosch|1978}}).
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| ==Definition of cue validity==
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| Formally, the cue validity of a feature <math>f_i\ </math> with respect to category <math>c_j\ </math> has been defined in the following ways:
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| * As the conditional probability <math>p(c_j|f_i)\ </math>; see {{Harvtxt|Reed|1972}}, {{Harvtxt|Rosch|Mervis|1975}}, {{Harvtxt|Rosch|1978}}.
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| * As the deviation of the conditional probability from the category base rate, <math>p(c_j|f_i)-p(c_j)\ </math>; see {{Harvtxt|Edgell|1993}}, {{Harvtxt|Kruschke|Johansen|1999}}.
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| * As a function of the linear correlation; see {{Harvtxt|Smedslund|1955}}, {{Harvtxt|Castellan|1973}}, {{Harvtxt|Sawyer|1991}}, {{Harvtxt|Busemeyer|Myung|McDaniel|1993}}.
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| * Other definitions; see {{Harvtxt|Restle|1957}}, {{Harvtxt|Martignon|Vitouch|Takezawa|Forster|2003}}.
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| For the definitions based on probability, a high cue validity for a given feature means that the feature or attribute is more diagnostic of the class membership than a feature with low cue validity. Thus, a high-cue validity feature is one which conveys more information about the category or class variable, and may thus be considered as more useful for identifying objects as belonging to that category. Thus, high cue validity expresses high feature ''informativeness''. For the definitions based on linear correlation, the expression of "informativeness" captured by the cue validity measure is not the full expression of the feature's informativeness (as in [[mutual information]], for example), but only that portion of its informativeness that is expressed in a linear relationship. For some purposes, a bilateral measure such as the [[mutual information]] or [[category utility]] is more appropriate than the cue validity.
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| ==Examples==
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| As an example, let us consider the domain of "numbers" and allow that every number has an attribute (i.e., a ''cue'') named "<code>is_positive_integer</code>", which we call <math>f_{p\mbox{-}int}\ </math>, and which adopts the value 1 if the number is actually a positive [[integer]]. Then we can inquire what the validity of this cue is with regard to the following classes: {<code>rational number</code>, <code>irrational number</code>, <code>even integer</code>}:
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| * If we know that a number is a positive integer we know that it is a [[rational number]]. Thus, <math>p(c_{rational}|f_{p\mbox{-}int}) = 1\ </math>, the cue validity for <code>is_positive_integer</code> as a cue for the category <code>rational number</code> is 1.
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| * If we know that a number is a positive integer then we know that it is ''not'' an [[irrational number]]. Thus, <math>p(c_{irrational}|f_{p\mbox{-}int}) = 0\ </math>, the cue validity for <code>is_positive_integer</code> as a cue for the category <code>irrational number</code> is 0.
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| * If we know only that a number is a positive integer, then its chances of being even or odd are 50-50 (there being the same number of even and odd integers). Thus, <math>p(c_{even}|f_{p\mbox{-}int}) = 0.5\ </math>, the cue validity for <code>is_positive_integer</code> as a cue for the category <code>even integer</code> is 0.5, meaning that the attribute <code>is_positive_integer</code> is entirely uninformative about the number's membership in the class <code>even integer</code>.
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| In [[perception]], "cue validity" is often short for ''[[ecological validity (perception)|ecological validity]]'' of a perceptual cue, and is defined as a correlation rather than a probability (see above). In this definition, an uninformative perceptual cue has an ecological validity of 0 rather than 0.5.
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| ==Use of the cue validity==
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| In much of the work on modeling human category learning, there has been the assumption made (and sometimes validated) that attentional weighting tracks the cue validity, or tracks some related measure of feature informativeness. This would imply that attributes are differently weighted by the perceptual system; informative or high-cue validity attributes being weighted more heavily, while uninformative or low-cue validity attributes are weighted more lightly or ignored altogether (see, e.g., Navarro 1998).
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| ==References==
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| * {{Citation |last=Beach |first=Lee Roy |title=Cue probabilism and inference behavior |journal=Psychological Monographs: General and Applied |volume=78 |issue=5 |year=1964 |pages=1–20}}
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| * {{Citation |last1=Busemeyer |first1=Jerome R. |last2=Myung |first2=In Jae |last3=McDaniel |first3=Mark A. |title=Cue competition effects: Empirical tests of adaptive network learning models |journal= Psychological Science |volume=4 |issue=3 |year=1993 |pages=190–195}}
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| * {{Citation |last=Castellan |first=N. John |title=Multiple-cue probability learning with irrelevant cues |journal=Organizational Behavior and Human Performance |volume=9 |issue=1 |year=1973 |pages=16–29}}
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| * {{Citation |last=Edgell |first=Stephen E. |year=1993 |chapter=Using configural and dimensional information |editor=N. John Castellan |title=Individual and Group Decision Making: Current Issues |publisher= Lawrence Erlbaum |location=[[Hillsdale, New Jersey]] |pages=43–64}}
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| * {{Citation |last1=Kruschke |first1=John K. |last2=Johansen |first2=Mark K. |title=A model of probabilistic category learning |journal=Journal of Experimental Psychology: Learning, Memory, and Cognition |volume=25 |issue=5 |year=1999 |pages=1083–1119}}
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| * {{Citation |last1=Martignon |first1=Laura |last2=Vitouch |first2=Oliver |last3=Takezawa |first3=Masanori |last4=Forster |first4=Malcolm R. |year=2003 |chapter=Naive and yet enlightened: From natural frequencies to fast and frugal decision trees |editor=David Hardman & Laura Macchi |title=Thinking: Psychological Perspectives on Reasoning, Judgment and Decision Making |publisher=John Wiley & Sons |location= [[New York]] |pages=190–211}}
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| * {{Citation |last=Reed |first=Stephen K. |title=Pattern recognition and categorization |journal=Cognitive Psychology |volume=3 |issue=3 |year=1972 |pages=382–407}}
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| * {{Citation |last=Restle |first=Frank |title=Theory of selective learning with probable reinforcements |journal=Psychological Review |volume=64 |issue=3 |year=1957 |pages=182–191}}
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| * {{Citation |last=Rosch |first=Eleanor |year=1978 |chapter=Principles of categorization |editor=Eleanor Rosch & Barbara B. Lloyd |title=Cognition and Categorization |publisher=Lawrence Erlbaum |location=[[Hillsdale, New Jersey]] |pages=27–48}}
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| * {{Citation |last1=Rosch |first1=Eleanor |last2=Mervis |first2=Carolyn B. |title=Family Resemblances: Studies in the Internal Structure of Categories |journal=Cognitive Psychology |volume=7 |issue=4 |year=1975 |pages=573–605}}
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| * {{Citation |last=Sawyer |first=John E. |title=Hypothesis sampling, construction, or adjustment: How are inferences about nonlinear monotonic contingencies developed |journal=Organizational Behavior and Human Decision Processes |volume=49 |year=1991 |pages=124–150}}
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| * {{Citation |last=Smedslund |first=Jan |title=Multiple-Probability Learning: An Inquiry into the Origins of Perception |publisher=Akademisk Forlag |location=[[Oslo]] |year=1955}}
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| [[Category:Probability theory]]
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| [[Category:Cognitive science]]
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| [[Category:Cognition]]
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