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In sum, item scoring through weights obtained by rawscores PCA deserves more consideration than it has received so far The standard objection to treating scores on a Likert scale as absolute is that strong assumptions would be imposed on the data I am unable to see the validity of that argument So-called weak models may in fact be very strong: To assume that the midpoint of a personality scale has no meaning and, consequently, that respondents evaluations can be reversed, is about as strong as hypothesizing, for example, that a large proportion of the population cannot be trusted At the very least, the absolute conception of Likert-scale scores is no more indefensible than the interval conception A Review of the Grounds for the Number Five A way to obtain many principal components is to analyze matrices with large numbers of variables, in this case single trait descriptors.



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Words in memory are modeled wide enough to accommodate every possible feature that a human can experience: shape, shade, tone, smell, feel, emotional strength, and so on, for thousands of elemental features Each feature has a given location in a word Features are encoded from the senses by a functional box labeled the Sensory Encoder If a novel combination of features is used repeatedly, a need-to-learn detector signals the sensory encoder to learn new features This detector, used again in 5, may be based on digital lters, also proposed below for memorization enable Looking ahead to learning in the sensory encoder, the color chartreuse might be learned as a digital AND of yellow and green.

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Earlier, limitations on computing capacity virtually prevented the number of trait variables from being much larger than the 35 employed by Tupes and Christal (1961/ 1992) With the expanding power of computers, however, it became feasible to analyze the very large numbers of variables that were needed to justify claims of representativeness if not exhaustiveness However, the sorcerer s apprentice problem then becomes keeping the number of factors from getting out of hand With hundreds of variables, it will take many factors to get down to the time-honored eigenvalue 1 threshold; for example, the 20th factor in Ostendorf s (1990) PCA of 430 traits still has an eigenvalue of 3 Hofstee et al (1998) proposed a more stringent criterion based on the alpha reliability of principal components, which is approximately 1 1/E with large numbers of variables, E being the eigenvalue of that principal component Setting the minimum alpha at .

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Combinational learning like this (no analog parameters) is assumed driven by a need for ef ciency in successful species that cannot afford to waste time rst recalling yellow, then green, then a de nition of chartreuse Another type of learning, state-machine learning, involves development of an unconscious procedure that executes automatically Humans easily execute many such procedures, similar to long poems learned by heart to be performed mindlessly Once initiated, one simply brushes the teeth, drives the car, or operates the keyboard without a lot of pondering, as an aid to survival of the human species Neural state-machine learning is driven by a need for ef ciency, since it avoids passing procedural steps through short-term memory, discussed in detail in 5 The cue editor is responsible for selecting useful subsets of cues from the contents of short-term memory for recalling long-term memories Cues are selected pseudorandomly as described below.

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75, an eigenvalue 4 threshold results ( E = 1 gives = 0) Using this criterion, Ostendorf (1990) should still have set the dimensionality of the personality sphere at about 14 rather than 5; with even larger numbers of traits, the dimensionality would only increase There can be no doubt that the 5-D model discards linear composites of traits that are of suf cient internal consistency, and would add to the number of dimensions It is of interest to note that the most prominent 5-D questionnaire, Costa and McCrae s (1992) NEO-PI-R, in fact postulates 30 dimensions rather than 5, as each of the 30 subscales is deemed to have speci c variance in their hierarchical model (The ve second-order factors do not add to the dimensionality, as they are linear combinations of six subscales at a time.

Cue Editor A cue editor for the random selection of cues appears in Figure 4-2a Cues are taken from short-term memory and masked using AND gates The selection of cues used to recall long-term memories is determined by a shift register counter, an example of which is given in Figure 4-2b The space of N features in shortterm memory is assumed to be organized into m clusters with n features per.

) An entirely valid pragmatic reason to restrict the number of factors is parsimony The rst principal component is the linear combination of traits that explains a maximum of.

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