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Spurious, Frivolous, or Necessary

Spurious, Frivolous, or Necessary - Spurious Frivolous or...

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Spurious, Frivolous, or Necessary? Is the Category-Specific Deficit for Living Things Spurious? Said Saillant December 2, 2008
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Summary Hypothesis: The article tests the claim that the category-specific deficit for living things is due to noise in the predictor variable (normal patient accuracy [NORMACC]), which rotates the regression line for living and nonliving things and factitiously creates two separate lines. Gaffan and Heywood (1993) use this claim to explain the disproportionate impairment of two patients in recognizing living or nonliving things and thus avoid rejecting their hypothesis that the category-specific deficit is the result of a modality-specific impairment—the differential difficulty account. But Kurbat and Farah (1998) claim that the recognition of living things depends on specialized mechanisms that are not necessary (or less necessary) for the recognition of lifeless things—the specialized systems account. The hypothesis is that the “predictor’s noise” account is inadequate and that the specialized systems account is a more consistent explanation. Methodology: Monte Carlo strategies are used to simulate original data provided by Dr. D. Gaffan and Farah et. al. (1991). To carry out the simulation, the authors use Gaffan and Heywood’s (1993) assumption that patient accuracy is a linear function of NORMACC, measured with error. The linear functions are estimated via regression. The regression equations’ bias is corrected using Snedecor and Cochran’s (1967) regression model, measurement theory ( L = 1- R ) and the fact that the regression lines must be “flatten” by rotating around the mean of the predictor variable. Using the corrected regression equations data is simulated. Snodgrass and Vanderwart (1980) pictures have 94 living things and 166 nonliving things depicted, so each simulation had many of each type. Each simulated score was estimated from a simulated normal distribution with the same mean and variance as the actual NORMACC scores. Simulated normal distributions were created via the Box-Mueller Method, which took as input uniformly distributed random numbers created by a congruential generator. The noise 2
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distribution had mean 0 and L times the variance of the observed distribution (with L defined separately for living and nonliving things); the “true” distribution had the mean of the observed distribution and (1 – L ) times the variance of that distribution. Simulated accuracy scores that exceeded 0 or 100% were truncated, which altered the means and variances of the resulting distributions. Thus means and variances were incrementally adjusted to match those in the actual NORMACC data (within three decimal places of accuracy, averaging over 50 replications of the simulation). Since there was no reliable between them, the simulated distribution of NORMACC scores for living and nonliving things match the actual distributions. Later, 50 simulated versions of actual NORMACC data and 50 simulated versions of each patient’s data were generated in order to compare the two types.
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