experiments, the IAT was sensitive to (a) near-universal evaluative differences (e.g., flower vs. insect), (b) expected individual differences in evaluative associations (Japanese + pleasant vs. Korean + pleasant for Japanese vs. Korean subjects), and (c) consciously disavowed evaluative differences (Black

the subjective portions of the document. Extracting these portions can be implemented using efficient techniques for finding minimum cuts in graphs; this greatly facilitates incorporation of cross-sentence contextual constraints. Publication info: Proceedings of the ACL, 2004. 1

Most papers that employ Differences-in-Differences estimation (DD) use many years of data and focus on serially correlated outcomes but ignore that the resulting standard errors are incon-sistent. To illustrate the severity of this issue, we randomly generate placebo laws in state-level data

2.2.2 Start-up of the Experimenter PC............................................................................................... 9 2.2.3 Start-up of the Subject PCs....................................................................................................... 9

This book presents a unified treatment of many different kinds of planning algorithms. The subject lies at the crossroads between robotics, control theory, artificial intelligence, algorithms, and computer graphics. The particular subjects covered include motion planning, discrete planning

welfare—sacrificing to increase the payoffs for all recipients, especially lowpayoff recipients—than with reducing differences in payoffs (as supposed in recent models). Subjects are also motivated by reciprocity: They withdraw willingness to sacrifice to achieve a fair outcome when others are themselves

, etc However, each player has a subjective probability distribution over the alternative possibibties In most of the paper it is assumed that these probability distributions entertained by the different players are mutually "consistent", in the sense that they can be regarded as conditional

A unified quantitative approach to modeling subjects ' identification and categorization of multidimensional perceptual stimuli is proposed and tested. Two subjects identified and categorized the same set of perceptually confusable stimuli varying on separable dimensions. The identification

, which includes level of description, transitions among expression, eliciting conditions, reliability and validity of training and test data, individual differences in subjects, head orientation and scene complexity, image characteristics, and relation to non-verbal behavior. We then present the CMU

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