Probabilistic models of language processing and acquisition

Probabilistic methods are providing new explanatory approaches to fundamental cognitive science questions of how humans structure, process and acquire language. This review examines probabilistic models defined over traditional symbolic structures. Language comprehension and production involve probabilistic inference in such models; and acquisition involves choosing the best model, given innate constraints and linguistic and other input. Probabilistic models can account for the learning and processing of language, while maintaining the sophistication of symbolic models. A recent burgeoning of theoretical developments and online corpus creation has enabled large models to be tested, revealing probabilistic constraints in processing, undermining acquisition arguments based on a perceived poverty of the stimulus, and suggesting fruitful links with probabilistic theories of categorization and ambiguity resolution in perception.     

Does stimulus appearance affect learning?

We examined the learning process with 3 sets of stimuli that have identical symbolic structure but differ in appearance (meaningless letter strings, arrangements of geometric shapes, and sequences of cities). One hypothesis is that the learning process aims to encode symbolic regularity in the same way, largely regardless of appearance. Another is that different types of stimuli bias the learning process to operate in different ways. Using the experimental paradigm of artificial grammar learning, we provided a preliminary test of these hypotheses. In Experiments 1 and 2 we measured performance in terms of grammaticality and found no difference across the 3 sets of stimuli. In Experiment 3 we analyzed performance in terms of both grammaticality and chunk strength. Again we found no differences in performance. Our tentative conclusion is that the learning process aims to encode symbolic regularity independent of stimulus appearance.     

Modeling the antecedents of proactive behavior at work

Using a sample of U.K. wire makers (N = 282), the authors tested a model in which personality and work environment antecedents affect proactive work behavior via cognitive-motivational mechanisms. Self-reported proactive work behaviors (proactive idea implementation and proactive problem solving) were validated against rater assessments for a subsample (n = 60) of wire makers. With the exception of supportive supervision, each antecedent was important, albeit through different processes. Proactive personality was significantly associated with proactive work behavior via role breadth self-efficacy and flexible role orientation, job autonomy was also linked to proactive behavior via these processes, as well as directly; and coworker trust was associated with proactive behavior via flexible role orientation. In further support of the model, the cognitive-motivational processes for proactive work behavior differed from those for the more passive outcome of generalized compliance.     

Two Theories of Just War

As it is traditionally conceived, Just War Theory is not well suited for dealing with nation vs non-nation wars. It thus makes sense to create a second Just War Theory to deal with these wars. This article explores the differences and similarities between the two theories.

The generalized optic acceleration cancellation theory of catching

The generalized optic acceleration cancellation (GOAC) theory of catching proposes that the path of a fielder running to catch a ball is determined by the attempt to satisfy 2 independent constraints. The 1st is to keep the angle of elevation of gaze to the ball increasing at a decreasing rate. The 2nd is to control the rate of horizontal rotation necessary to maintain fixation on the ball. Depending on the lateral velocity of the ball relative to the fielder, this rate may be zero or constant at a negative or positive value. The authors show that a simulated fielder implementing the GOAC strategy follows a path indistinguishable from that of real fielders running to catch balls thrown on the same trajectories.