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There is a rich tradition of building computational models in cognitive science, but modeling, theoretical, and experimental research are not as tightly integrated as they could be. In this paper, we show that computational techniques-even simple ones that are straightforward to use-can greatly facilitate designing, implementing, and analyzing experiments, and generally help lift research to a new level. We focus on the domain of artificial grammar learning, and we give five concrete examples in this domain for (a) formalizing and clarifying theories, (b) generating stimuli, (c) visualization, (d) model selection, and (e) exploring the hypothesis space., (© 2019 The Authors. Topics in Cognitive Science published by Wiley Periodicals, Inc. on behalf of Cognitive Science Society.)

Núñez and colleagues (2019) chronicle in extraordinary detail the "demise" of cognitive science, as it was first defined in the late 1970s. The problem is that their account, however accurate, misses the forest for the trees. Cognitive science circa 2019 is alive and well; it just has not followed the path anticipated by its founders over 40 years ago., (© 2019 Cognitive Science Society, Inc.)

David Marr's (1982) three-level analysis of computational cognition argues for three distinct levels of cognitive information processing-namely, the computational, representational, and implementational levels. But Marr's levels are-and were meant to be-descriptive, rather than interactive and dynamic. For this reason, we suggest that, had Marr been writing today, he might well have gone even farther in his analysis, including the emergence of structure-in particular, explicit structure at the conceptual level-from lower levels, and the effect of explicit emergent structures on the level (or levels) that gave rise to them. The message is that today's cognitive scientists need not only to understand how emergent structures-in particular, explicit emergent structures at the cognitive level-develop but also to understand how they feed back on the sub-structures from which they emerged., (Copyright © 2015 Cognitive Science Society, Inc.)

Computational modeling has long been one of the traditional pillars of cognitive science. Unfortunately, the computer models of cognition being developed today have not kept up with the enormous changes that have taken place in computer technology and, especially, in human-computer interfaces.  For all intents and purposes, modeling is still done today as it was 25, or even 35, years ago. Everyone still programs in his or her own favorite programming language, source code is rarely made available, accessibility of models to non-programming researchers is essentially non-existent, and even for other modelers, the profusion of source code in a multitude of programming languages, written without programming guidelines, makes it almost impossible to access, check, explore, re-use, or continue to develop. It is high time to change this situation, especially since the tools are now readily available to do so. We propose that the modeling community adopt three simple guidelines that would ensure that computational models would be accessible to the broad range of researchers in cognitive science. We further emphasize the pivotal role that journal editors must play in making computational models accessible to readers of their journals., (Copyright © 2012 Cognitive Science Society, Inc.)