Identifying the relevant aspects of a problem text

Forty-nine subjects judged the relevancy of sentence parts of a word problem (the Allsports problem). Patterns of subjects' judgments suggest three problem-solving heuristics: a SETS heuristic, a TIME heuristic, and a QUESTION heuristic. Presentation of the question before the problem tends to suppress SETS and TIME heuristics. A computer program (ATTEND) is presented to simulate subjects' behavior on the Allsports problem. The program is context-sensitive in that it can change a relevance judgment upon the acquisition of further information. Averaged subject judgments and ATTEND judgments agree for 87% of the items.     

A new goodness-of-fit measure for probit models: Surrogate R2

Probit models are used extensively for inferential purposes in the social sciences as discrete data are prevalent in a vast body of social studies. Among many accompanying model inference problems, a critical question remains unsettled: how to develop a goodness-of-fit measure that resembles the ordinary least square (OLS) R² used for linear models. Such a measure has long been sought to achieve ‘comparability’ of different empirical models across multiple samples addressing similar social questions. To this end, we propose a novel R² measure for probit models using the notion of surrogacy – simulating a continuous variable $S$ as a surrogate of the original discrete response (Liu & Zhang, Journal of the American Statistical Association, 113, 845 and 2018). The proposed R² is the proportion of the variance of the surrogate response explained by explanatory variables through a linear model, and we call it a surrogate R². This paper shows both theoretically and numerically that the surrogate R² approximates the OLS R² based on the latent continuous variable, preserves the interpretation of explained variation, and maintains monotonicity between nested models. As no other pseudo R², McKelvey and Zavoina's and McFadden's included, can meet all the three criteria simultaneously, our measure fills this crucial void in probit model inference.     

One version of direct response priming requires automatization of the relevant associations but not awareness of the prime

Priming is the influence of one event on performance during a second event. One type of priming is known as semantic priming because it biases interpretation of the subsequent stimulus. Another type, direct response priming, biases responding directly without semantic mediation. Research reviewed in this article indicates that two versions of the second type, direct response priming, can be distinguished. One version, explicit priming, requires awareness of the prime. The other version, associative response priming, occurs even if the prime is masked and not phenomenally visible. This version, which is attributed to associations relating specific sensory events to movements of particular muscles, is enabled only if the association has previously been automatized by brief practice in which the to-be-primed response is made to the stimulus that subsequently appears as the prime. Associative response priming can be explained by a simple stimulus–response interpretation; other varieties of priming are more theoretically challenging.