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Widely recognized in differential psychology, but less so in cognitive science, the positive manifold is the phenomena of all cognitive tests inter-correlating positively. Frequently demonstrated in people, it can also be observed in non-human species. With 217 Ecuadorian adult participants, who performed 11 cognitive tests, we show that all 55 pairwise inter-correlations are positive, and of large magnitude. Additionally, factor analysis revealed a single underlying general, or g factor, often identified as general intelligence. This robustly replicates the positive manifold in a non- WEIRD (Western, Educated, Industrialized, Rich, Democratic) context. We further demonstrate that tests of lexical knowledge, such as word pronunciation, have particularly high loadings on g. We explore explanations for the positive manifold, and the implications for understanding the mind as being composed of independent cognitive processing modules. We propose that the positive manifold reveals a neglected but important role of lexical-conceptual knowledge in high-level, top-down, domain-general cognitive processing.

Free\Libre Open Source Software (FLOSS) environments are increasingly dubbed as learning environments where practical software engineering skills can be acquired. Numerous studies have extensively investigated how knowledge is acquired in these environments through a collaborative learning model that define a learning process. Such a learning process, identified either as a result of surveys or by means of questionnaires, can be depicted through a series of graphical representations indicating the steps FLOSS community members go through as they acquire and exchange skills. These representations are referred to as a-priori learning models. They are Petri net-like workflow nets (WF-net) that provide a visual representation of the learning process as it is expected to occur. These models are representations of a learning framework or paradigm in FLOSS communities. As such, the credibility of any models is estimated through a process of model verification and validation. Therefore in this paper, we analyze these models in comparison with the real behavior captured in FLOSS repositories by means of conformance verification in process mining. The purpose of our study is twofold. Firstly, the results of our analysis provide insights on the possible discrepancies that are observed between the initial theoretical representations of learning processes and the real behavior captured in FLOSS event logs, constructed from mailing archives. Secondly, this comparison helps foster the understanding on how learning actually takes place in FLOSS environments based on empirical evidence directly from the data.

Despite its huge success and increasing incorporation in complex, industrial- strength applications, open source software, by the very nature of its open, unconventional, distributed development model, is hard to assess and certify in an effective, sound and independent way. This makes its use and integration within safely for security-critical systems, a risk. And, simultaneously an opportunity and a challenge for rigourous, mathematically based, methods which aim at pushing software analysis and development to the level of a mature engineering discipline. This paper discusses such a challenge and proposes a number of ways in which open source development may benefit from the whole, patrimony of formal methods. [ABSTRACT FROM AUTHOR]