This paper describes work towards an object-oriented language for cognitive modeling. Existing modeling languages (such as C, LISP and Prolog) tend to be far removed from the techniques employed by psychologists in developing their theories. In addition, they encourage the confusion of implementation detail necessary for computational completeness with theoretically motivated aspects. The language described here (OOS) has been designed so as to facilitate this theory/implementation separation, while at the same time simplifying the modeling process for computationally non-sophisticated users by providing a set of classes of basic "cognitive" objects. The object classes are tailored to the implementation of functionally modular cognitive models in the box/arrow style. The language is described (in terms of its execution model and its basic classes) before a sketch is given of a simple production system which has been implemented within the language. W e conclude with a discussion of on-going work aimed at extending the coverage of the language and further simplifying the modeling process.

Visual dominance refers to the tendency for visual stimuli to dominate awareness of stimuli of similar or lesser intensity presented simultaneously in other modalities. The effect may be seen in simple and choice reaction time studies. When visual and auditory stimuli are presented separately, visual reaction time is typically slower than auditory reaction time. However, when visual and auditory stimuli are presented simultaneously the visual stimulus generally provokes the first response. In this paper we provide a computational account of such visual dominance effects. The simulation extends an existing computational model of routine action selection, accounting for the counterintuitive visual dominance findings whilst providing further support for the original action selection model.

Perseverative subgoaling, the repeated successful solution of subgoals, is a common feature of much problem solving, and its pervasive nature suggests that it is an emergent property of a problem solving architecture. This paper presents a set of minimal requirements on a production system architecture for problem solving which will allow perseverative subgoaling whilst guaranteeing the possibility of recovery from such situations. The fundamental claim is that perseverative subgoaling arises during problem solving when the results of subgoals are forgotten before they can be used. This prompts further attempts at the offending subgoals. In order for such attempts to be effective, however, the production system must satisfy three requirements concerning working memory structure, production structure, and memory decay. The minimal requirements are embodied in a model (developed within the COGENT modelling software) which is explored with respect to the task of multicolumn addition. The inter-relationship between memory decay and task difficulty within this task (measured in terms of the number of columns) is discussed.

The Wisconsin Card Sorting Test (WCST) is commonly used to assess executive (dys-)function, particularly in neuropsychological patients. Performance on the test typically yields two types of error: perseverative errors, where participants persist in applying an inferred rule despite negative feedback, and set-loss errors, were participants cease applying an inferred rule despite positive feedback. The two types of error are known to dissociate. In this paper we apply an existing model of the WCST -- the model of Bishara et al. (2010) -- to a novel dataset, focussing specifically on the distribution of the two types of error over the duration of the task. Using Maximum Likelihood Estimation to fit the model to the data, we argue that the model provides a good account of the performance of some participants, but a poor account of individual differences. It is argued that this is because the model is essentially a competence model which fails to incorporate performance factors, and that accounting for the different types of error, and in particular the error distribution during the task, requires incorporating performance factors into the model. Some consequences of this for the broader enterprise of developing normative competence models are discussed.

In this paper we compare the behaviour of three competing accounts of decision making under uncertainty (a Bayesian account, an associationist account, and a hypothesis testing account) with subject performance in a medical diagnosis task. The task requires that subjects first learn a set of symptom/disease associations. Later, subjects are required to form diagnoses based on limited symptom information. The competing theoretical accounts are embodied in three computational models, each with a single parameter governing the learning rate. Subjects' diagnostic accuracy was used to calibrate the learning rates of the models. The resulting parameter-free models were then used to predict subjects' symptom querying behaviour in a subsequent task. The fit between the Associationist model's predictions and subject behaviour was poor. The fit was slightly better in the case of the Bayesian model, but the hypothesis testing account proved to provide the most adequate account of the data.

In this paper we explore the effect of normal aging on executive function and present a computational account of the effectof aging in a standard executive task. We tested 25 younger adults and 25 older adults (both with no known neurologicalcondition) on the Wisconsin Card Sorting Task (WCST), a classic test of executive function. The test produces multiplemeasures related to the types of error made by participants, the rate of learning, and so on. As hypothesised, results showno difference between the groups in the number of perseverative errors (i.e., in continuing with a previously successful rulein the presence of negative feedback), but a significantly increased tendency for older adults relative to younger adults tocommit set loss errors (i.e., to switch away from a rule despite positive feedback). We fit an existing neurocomputationalmodel of the task to the experimental data by searching through the models parameter space in order to find the best set ofparameter values for the two different age groups. This leads to a proposition regarding the effect of aging on the value ofthe epsilon ctx parameter, which we argue elsewhere reflects cortical dopamine concentration. We further reanalyse thedata by clustering participants by performance (rather than by age) and show that there are multiple points in parameterspace that fit each cluster of participants. We argue on the basis of this and the behavioural data, that different parametervalues reflect different solutions to optimizing task performance, and that older participants may compensate for changesin epsilon ctx (reflecting dopamine concentration) by effortful changes in other parameters (specifically, by increasingattentional focus).