Modeling parallelization and flexibility improvements in skill acquisition: from dual tasks to complex dynamic skills

Emerging parallel processing and increased flexibility during the acquisition of cognitive skills form a combination that is hard to reconcile with rule-based models that often produce brittle behavior. Rule-based models can exhibit these properties by adhering to 2 principles: that the model gradually learns task-specific rules from instructions and experience, and that bottom-up processing is used whenever possible. In a model of learning perfect time-sharing in dual tasks (Schumacher et al., 2001), speedup learning and bottom-up activation of instructions can explain parallel behavior. In a model of a complex dynamic task (Carnegie Mellon University Aegis Simulation Program [CMU-ASP], Anderson et al., 2004), parallel behavior is explained by the transition from serially organized instructions to rules that are activated by both top-down (goal-driven) and bottom-up (perceptually driven) factors. Parallelism lets the model opportunistically reorder instructions, leading to the gradual emergence of new task strategies.     

An activation-based model of sentence processing as skilled memory retrieval

We present a detailed process theory of the moment-by-moment working-memory retrievals and associated control structure that subserve sentence comprehension. The theory is derived from the application of independently motivated principles of memory and cognitive skill to the specialized task of sentence parsing. The resulting theory construes sentence processing as a series of skilled associative memory retrievals modulated by similarity-based interference and fluctuating activation. The cognitive principles are formalized in computational form in the Adaptive Control of Thought–Rational (ACT–R) architecture, and our process model is realized in ACT–R. We present the results of 6 sets of simulations: 5 simulation sets provide quantitative accounts of the effects of length and structural interference on both unambiguous and garden-path structures. A final simulation set provides a graded taxonomy of double center embeddings ranging from relatively easy to extremely difficult. The explanation of center-embedding difficulty is a novel one that derives from the model' complete reliance on discriminating retrieval cues in the absence of an explicit representation of serial order information. All fits were obtained with only 1 free scaling parameter fixed across the simulations; all other parameters were ACT–R defaults. The modeling results support the hypothesis that fluctuating activation and similarity-based interference are the key factors shaping working memory in sentence processing. We contrast the theory and empirical predictions with several related accounts of sentence-processing complexity.     

The structural relationships among generalized anxiety, obsessions-compulsions, and depression at the syndrome and sub-syndrome level

This study explored anxiety's relationship to depression by assessing a group of 592 undergraduate psychology students at Washington State University. Multiple measures of generalized anxiety (GA), obsessions–compulsions (O–C), and depression were used to conduct several confirmatory factor analyses (CFAs). A three-factor model of GA, O–C, and depression was found superior in fit to a one-factor “negative-affect” model, a two-factor model of anxiety and depression, and a second-order three-factor model. Further CFAs divided GA, O–C, and depression into six independent symptom category factors utilizing instrument subscales (e.g., worry, somatic anxiety, obsessions, compulsions, cognitive depression, and somatic depression). The fit for this model was superior to three alternative measurement models. The correlations among the six symptom category constructs revealed differential patterns among the cognitive and somatic symptoms.     

数学证明:数学真理的来源?

对于一个数学实在论者来说,一个特别急迫的任务是回答数学证明是如何建立起关于数学对象的真理性问题。本文批判性考察由Resnik所提出的认知解答,指出了其中不成立的前提。并且在此基础上提出我们自己的解答,它是基于由Oliver．G所提倡的方面(aspects)概念而来的社会——论辩方案。     

Category-specific visual agnosia: lesion to semantic memory versus extra-lesional variables in a case study and a connectionist model

There is a current debate on the causes of category-specific agnosia. The aim of this study was to examine the effects of lesional and extra-lesional variables on object recognition. Extra-lesional variables, such as visual complexity or familiarity, are factors that influence recognition. Using a connectionist model based on study, we provide evidence that extra-lesional variables can yield dissociations in the recognition rate of different categories. Furthermore, it is shown that lesional and extra-lesional variables can interact (p < .01) when both are simultaneously modeled. Category-specific agnosia might thus result from complex interactions.     

From eye movement to cognition: Toward a general framework of inference comment on Liechty et al., 2003

Liechty, Pieters & Wedel (2003) developed a hidden Markov Model (HMM) to identify the states of an attentional process in an advertisement viewing task. This work is significant because it demonstrates the benefits of stochastic modeling and Bayesian estimation in making inferences about cognitive processes based on eye movement data. One limitation of the proposed approach is that attention is conceptualized as an autonomous random process that is affected neither by the overall layout of the stimulus nor by the visual information perceived during the current fixation. An alternative model based on the input-output hidden Markov model (IOHMM; Bengio, 1999) is suggested as an extension of the HMM. The need for further studies that validate the HMM classification results is also discussed.     

How pigeons discriminate the relative frequency of events.

This study examined how pigeons discriminate the relative frequencies of events when the events occur serially. In a discrete-trials procedure, 6 pigeons were shown one light nf times and then another nl times. Next, they received food for choosing the light that had occurred the least number of times during the sample. At issue were (a) how the discrimination was related to two variables, the difference between the frequencies of the two lights, D = nf - nl, and the total number of lights in the sample, T = nf + nl; and (b) whether a simple mathematical model of the discrimination process could account for the data. In contrast with models that assume that pigeons count the stimulus lights, engage in mental arithmetic on numerons, or remember the number of stimuli, the present model assumed only that the influence of a sample stimulus on choice increases linearly when the stimulus is presented, but decays exponentially when the stimulus is absent. The results showed that, overall, the pigeons discriminated the relative frequencies well. Their accuracy always increased with the absolute value of the difference D and, for D > 0, it decreased with T. Performance also showed clear recency, primacy, and contextual effects. The model accounted well for the major trends in the data.     

Data, model, conclusion, doing it again

This paper explores the robustness of conclusions from a statistical model against variations in model choice (rather than variations in random sampling and random assignment to treatments, which are the usual variations covered by inferential statistics). After the problem formulation in section 1, section 2 presents an example from Box and Tiao in which variation in parent distribution is modeled for a one sample location problem. An adaptive Bayesian procedure permits to use a weighted mixture of parent distributions rather than choosing just one, such as a normal or a uniform distribution.In section 3 similar considerations are applied to an event history model for the influence of education and gender on age at first marriage, but here the conclusions are hardly influenced by the choice of the duration distribution. In section 4 a brief discussion of model choice in factor analysis and structural equation models is followed by a more elaborate treatment of the choice of integer valued slopes for item response functions in the OPLM model which is an extension of the Rasch model. A modest simulation study suggests that Adaptive Bayesian Modeling with a mixture of sets of slopes works better than fixing one set of postulated slopes.The paper concludes with some remarks on the roles and sources of prior distributions followed by a short epilogue which argues that simultaneous consideration of a class of models for the same data is sometimes superior to exclusively analyzing the data under one specific model chosen from such a class.This article is based on the Presidential Address Ivo W. Molenaar gave on June 20, 1998 at the 1998 Annual Meeting of the Psychometric Society held at the University of Illinois in Champaign, Illinois. Thanks is given to John Wiley & Sons and George C. Tiao for granting permission to reprint three figures from the book George C. Tiao wrote with George E. P. Box titled Bayesian Inference in Statistical Analysis.—EditorThanks are due to Anne Boomsma, Jeffrey Hoogland, Mark Huisman, Tom Snijders, Marijtje Van Duijn, and Norman Verhelst for suggesting improvements and/or assisting with data analyses.     

INTERVENTIONS TO REDUCE HIGH-VOLUME PORTABLE HEADSETS: “TURN DOWN THE SOUND!”

Two studies examined effects of interventions to reduce noise levels from portable stereo headphones. Study 1 examined the effectiveness of warning signs posted in and nearby public elevators with 567 passengers possessing a portable headphone (total N = 7,811). During a 9-day baseline, the mean percentage of headphones played at an observer-audible level was 85%. During a subsequent 6-day warning sign phase, the mean percentage of audible headphones declined to 59%, which increased to a mean of 76% during a second baseline phase (5 days). Study 2 assessed the impact of a student confederate who lowered his or her observer-audible headphone volume at the polite request of a second student confederate. Of the 4,069 elevator passengers, 433 possessed a portable headset. The mean percentage of observer-audible headphones during a 4-day baseline was 85%. Subsequently, a 5-day modeling intervention reduced audible volumes to a mean of 46%. During a second baseline phase of 4 days, the mean level was 77%, and during reintroduction of the modeling phase (9 days) the mean level was 42%. The modeling intervention was significantly more effective with women (53% compliance) than men (29% compliance).     

An extension of the rating scale model with an application to the measurement of change

The polytomous unidimensional Rasch model with equidistant scoring, also known as the rating scale model, is extended in such a way that the item parameters are linearly decomposed into certain basic parameters. The extended model is denoted as the linear rating scale model (LRSM). A conditional maximum likelihood estimation procedure and a likelihood-ratio test of hypotheses within the framework of the LRSM are presented. Since the LRSM is a generalization of both the dichotomous Rasch model and the rating scale model, the present algorithm is suited for conditional maximum likelihood estimation in these submodels as well. The practicality of the conditional method is demonstrated by means of a dichotomous Rasch example with 100 items, of a rating scale example with 30 items and 5 categories, and in the light of an empirical application to the measurement of treatment effects in a clinical study.Work supported in part by the Fonds zur Förderung der Wissenschaftlichen Forschung under Grant No. P6414.