The Paradox of Human Expertise: Why Experts Can Get It Wrong

Expertise is correctly, but one-sidedly, associated with special abilities and enhanced performance. The other side of expertise, however, is surreptitiously hidden. Along with expertise, performance may also be degraded, culminating in a lack of flexibility and error. Expertise is demystified by explaining the brain functions and cognitive architecture involved in being an expert. These information processing mechanisms, the very making of expertise, entail computational trade-offs that sometimes result in paradoxical functional degradation. For example, being an expert entails using schemas, selective attention, chunking information, automaticity, and more reliance on top-down information, all of which allow experts to perform quickly and efficiently; however, these very mechanisms restrict flexibility and control, may cause the experts to miss and ignore important information, introduce tunnel vision and bias, and can cause other effects that degrade performance. Such phenomena are apparent in a wide range of expert domains, from medical professionals and forensic examiners, to military fighter pilots and financial traders

An overview of the question-response system in American English conversation

This article, part of a 10 language comparative project on question–response sequences, discusses these sequences in American English conversation. The data are video-taped spontaneous naturally occurring conversations involving two to five adults. Relying on these data I document the basic distributional patterns of types of questions asked (polar, Q-word or alternative as well as sub-types), types of social actions implemented by these questions (e.g., repair initiations, requests for confirmation, offers or requests for information), and types of responses (e.g., repetitional answers or yes/no tokens). I show that declarative questions are used more commonly in conversation than would be suspected by traditional grammars of English and questions are used for a wider range of functions than grammars would suggest. Finally, this article offers distributional support for the idea that responses that are better “fitted” with the question are preferred

Consciosusness in Cognitive Architectures. A Principled Analysis of RCS, Soar and ACT-R

This report analyses the aplicability of the principles of consciousness developed in the ASys project to three of the most relevant cognitive architectures. This is done in relation to their aplicability to build integrated control systems and studying their support for general mechanisms of real-time consciousness.\ud To analyse these architectures the ASys Framework is employed. This is a conceptual framework based on an extension for cognitive autonomous systems of the General Systems Theory (GST).\ud A general qualitative evaluation criteria for cognitive architectures is established based upon: a) requirements for a cognitive architecture, b) the theoretical framework based on the GST and c) core design principles for integrated cognitive conscious control systems

Structure Selection from Streaming Relational Data

Statistical relational learning techniques have been successfully applied in a wide range of relational domains. In most of these applications, the human designers capitalized on their background knowledge by following a trial-and-error trajectory, where relational features are manually defined by a human engineer, parameters are learned for those features on the training data, the resulting model is validated, and the cycle repeats as the engineer adjusts the set of features. This paper seeks to streamline application development in large relational domains by introducing a light-weight approach that efficiently evaluates relational features on pieces of the relational graph that are streamed to it one at a time. We evaluate our approach on two social media tasks and demonstrate that it leads to more accurate models that are learned faster

Flexible constrained sampling with guarantees for pattern mining

Pattern sampling has been proposed as a potential solution to the infamous pattern explosion. Instead of enumerating all patterns that satisfy the constraints, individual patterns are sampled proportional to a given quality measure. Several sampling algorithms have been proposed, but each of them has its limitations when it comes to 1) flexibility in terms of quality measures and constraints that can be used, and/or 2) guarantees with respect to sampling accuracy. We therefore present Flexics, the first flexible pattern sampler that supports a broad class of quality measures and constraints, while providing strong guarantees regarding sampling accuracy. To achieve this, we leverage the perspective on pattern mining as a constraint satisfaction problem and build upon the latest advances in sampling solutions in SAT as well as existing pattern mining algorithms. Furthermore, the proposed algorithm is applicable to a variety of pattern languages, which allows us to introduce and tackle the novel task of sampling sets of patterns. We introduce and empirically evaluate two variants of Flexics: 1) a generic variant that addresses the well-known itemset sampling task and the novel pattern set sampling task as well as a wide range of expressive constraints within these tasks, and 2) a specialized variant that exploits existing frequent itemset techniques to achieve substantial speed-ups. Experiments show that Flexics is both accurate and efficient, making it a useful tool for pattern-based data exploration.Comment: Accepted for publication in Data Mining & Knowledge Discovery journal (ECML/PKDD 2017 journal track

Bidirectional syntactic priming across cognitive domains: from arithmetic to language and back

Scheepers et al. (2011) showed that the structure of a correctly solved mathematical equation affects how people subsequently complete sentences containing high vs. low relative-clause attachment ambiguities. Here we investigated whether such effects generalise to different structures and tasks, and importantly, whether they also hold in the reverse direction (i.e., from linguistic to mathematical processing). In a questionnaire-based experiment, participants had to solve structurally left- or right-branching equations (e.g., 5 × 2 + 7 versus 5 + 2 × 7) and to provide sensicality ratings for structurally left- or right-branching adjective-noun-noun compounds (e.g., alien monster movie versus lengthy monster movie). In the first version of the experiment, the equations were used as primes and the linguistic expressions as targets (investigating structural priming from maths to language). In the second version, the order was reversed (language-to-maths priming). Both versions of the experiment showed clear structural priming effects, conceptually replicating and extending the findings from Scheepers et al. (2011). Most crucially, the observed bi-directionality of cross-domain structural priming strongly supports the notion of shared syntactic representations (or recursive procedures to generate and parse them) between arithmetic and language