Computational models of the development of perceptual expertise

In a recent article, Palmeri, Wong and Gauthier have argued that computational models may help direct hypotheses about the development of perceptual expertise. They support their claim by an analysis of models from the object-recognition and perceptual-categorization literatures. Surprisingly, however, they do not consider any computational models from traditional research into expertise, essentially the research deriving from Chase and Simon’s chunking theory, which itself was influenced by De Groot’s study of chessplayers. This is unfortunate, as a series of computational models based on perceptual chunking have explained a substantial number of phenomena related to expert behaviour and provide mechanisms that directly address the question of perceptual expertise

Building models of learning and expertise with CHREST

CHREST (Chunk Hierarchy and REtrieval STructures) is a complete computational architecture implementing processes of learning and perception. CHREST models have successfully simulated human data in a variety of domains, such as the acquisition of syntactic categories, expertise in programming and in chess, concept formation, implicit learning, and the acquisition of multiple representations in physics for problem solving. In this tutorial, we describe the learning, perception and attention mechanisms within CHREST as well as key empirical data captured by CHREST models. Apart from the theoretical material, this tutorial also introduces participants to an implementation of CHREST and its use in a variety of domains. Material and examples are provided so participants can adapt and extend the CHREST architecture

Broad expertise retrieval in sparse data environments

Expertise retrieval has been largely unexplored on data other than the W3C collection. At the same time, many intranets of universities and other knowledge-intensive organisations offer examples of relatively small but clean multilingual expertise data, covering broad ranges of expertise areas. We first present two main expertise retrieval tasks, along with a set of baseline approaches based on generative language modeling, aimed at finding expertise relations between topics and people. For our experimental evaluation, we introduce (and release) a new test set based on a crawl of a university site. Using this test set, we conduct two series of experiments. The first is aimed at determining the effectiveness of baseline expertise retrieval methods applied to the new test set. The second is aimed at assessing refined models that exploit characteristic features of the new test set, such as the organizational structure of the university, and the hierarchical structure of the topics in the test set. Expertise retrieval models are shown to be robust with respect to environments smaller than the W3C collection, and current techniques appear to be generalizable to other settings

Experts and Decision Making: First Steps Towards a Unifying Theory of Decision Making in Novices, Intermediates and Experts

Expertise research shows quite ambiguous results on the abilities of experts in judgment and decision making (JDM) classic models cannot account for. This problem becomes even more accentuated if different levels of expertise are considered. We argue that parallel constraint satisfaction models (PCS) might be a useful base to understand the processes underlying expert JDM and the hitherto existing, differentiated results from expertise research. It is outlined how expertise might influence model parameters and mental representations according to PCS. It is discussed how this differential impact of expertise on model parameters relates to empirical results showing quite different courses in the development of expertise; allowing, for example, to predict under which conditions intermediates might outperform experts. Methodological requirements for testing the proposed unifying theory under complex real-world conditions are discussed.Judgment and Decision Making, Expertise, Intermediate Effects, Parallel Constraint Satisfaction, Mental Representation

Three Models of Democratic Expertise

How can expertise best be integrated within democratic systems? And how can such systems best enable lay judgment of expert claims? These questions are obscured by the common framing of democratic politics against an imagined system of pure and unmixed expert rule or ‘epistocracy’. Drawing on emerging research that attempts to think critically and institutionally about expertise, this reflections essay distinguishes three ways of democratically organising relations between experts and non-experts: representative expertise, in which experts are taken to exercise limited and delegated power under the supervision of political representatives; participatory expertise, in which expertise is integrated with publics by means of directly participatory processes; and associative expertise, in which civil society groups, advocacy organisations, and social movements organise expert knowledge around the objectives of a self-organised association. Comparing these models according to the cognitive demands they make on lay citizens, the epistemic value of citizen contributions, and the ways in which they enable public scrutiny and contestation, the essay goes on to explore how they can support and undermine one another, and how they can open up new questions about democracy, trust and expertise in political science and political theory

Voting for candidates: adapting data fusion techniques for an expert search task

In an expert search task, the users' need is to identify people who have relevant expertise to a topic of interest. An expert search system predicts and ranks the expertise of a set of candidate persons with respect to the users' query. In this paper, we propose a novel approach for predicting and ranking candidate expertise with respect to a query. We see the problem of ranking experts as a voting problem, which we model by adapting eleven data fusion techniques.We investigate the effectiveness of the voting approach and the associated data fusion techniques across a range of document weighting models, in the context of the TREC 2005 Enterprise track. The evaluation results show that the voting paradigm is very effective, without using any collection specific heuristics. Moreover, we show that improving the quality of the underlying document representation can significantly improve the retrieval performance of the data fusion techniques on an expert search task. In particular, we demonstrate that applying field-based weighting models improves the ranking of candidates. Finally, we demonstrate that the relative performance of the adapted data fusion techniques for the proposed approach is stable regardless of the used weighting models

CML: the commonKADS conceptual modelling language

We present a structured language for the specification of knowledge models according to the CommonKADS methodology. This language is called CML (Conceptual Modelling Language) and provides both a structured textual notation and a diagrammatic notation for expertise models. The use of our CML is illustrated by a variety of examples taken from the VT elevator design system

Financial disclosure and the Board: A case for non-independent directors

In listed companies, the Board of directors has ultimate responsibility for information disclosure. The conventional wisdom is that director independence is an essential factor in improving the quality of that disclosure. In a sense, this approach subordinates expertise to independence. We argue that effective certification may require firm-specific expertise, in particular for intangible-intensive business models. However, this latter form of expertise is negatively related to independence as it is commonly measured and evaluated. Accordingly, there exists an optimal share of independent directors for each company, related to the level of intangible resources.

Financial Disclosure and the Board: Is Independence of Directors Always Efficient

In listed companies, the Board of directors is the ultimate responsible of information disclosure. The "conventional wisdom" considers independence of directors as the essential attribute to improve the quality of that disclosure. In a sense, this approach subordinates expertise to independence. However, effective certification may require finn-specific expertise, in particular for intangible-intensive business models. However, this latter form of expertise is negatively related to independence as it is commonly measured and evaluated. We show that there exists an optimal share of independent directors for each company, related to the magnitude of intangible resources.Board of directors; information disclosure; accounting; intangible resources