Includes established theories and cutting-edge developments. Presents the work of an international group of experts. Presents the nature, origin, implications, and future course of major unresolved issues in the area.

Three experiments explored the effects of word problem cueing on debiasing versions of the bat-and-ball problem. In the experimental condition order, participants solved a simpler isomorphic version of the problem prior to solving a standard version that, critically, had the same item-and-dollar amounts. Conversely, in the control condition order, participants solved the standard version prior to solving the isomorph. Across the first 2 experiments, participants cued with the isomorph were more likely to correctly solve the standard version of the problem. (...) In the third experiment, this cueing effect was not replicated, but importantly cued participants were more likely to correctly solve the standard version of the problem with other item-and-dollar amounts, indicating near transfer. Far transfer was evident in faster response times to other problems in the Cognitive Reflection Test following practice. Along with providing converging evidence for the cueing account, these results are consistent with recent attentional debiasing findings. (shrink)

This Handbook documents the main trends in current research between logic and language, including its broader influence in computer science, linguistic theory and cognitive science. The history of the combined study of Logic and Linguistics goes back a long way, at least to the work of the scholastic philosophers in the Middle Ages. At the beginning of this century, the subject was revitalized through the pioneering efforts of Gottlob Frege, Bertrand Russell, and Polish philosophical logicians such as Kazimierz Ajdukiewicz. Around (...) 1970, the landmark achievements of Richard Montague established a junction between state-of-the-art mathematical logic and generative linguistic theory. Over the subsequent decades, this enterprise of Montague Grammar has flourished and diversified into a number of research programs with empirical and theoretical substance. This appears to be the first Handbook to bring logic-language interface to the fore. Both aspects of the interaction between logic and language are demonstrated in the book i.e. firstly, how logical systems are designed and modified in response to linguistic needs and secondly, how mathematical theory arises in this process and how it affects subsequent linguistic theory. The Handbook presents concise, impartial accounts of the topics covered. Where possible, an author and a commentator have cooperated to ensure the proper breadth and technical content of the papers. The Handbook is self-contained, and individual articles are of the highest quality. (shrink)

Electron redistribution is the cornerstone of our understanding of chemical reactivity. For the vast majority of organic reactions electrons are assumed to move in pairs providing explanatory mechanisms through the generation of intermediate structures. But for many transformations these discrete steps are idealized constructs, involving intermediates assumed but not empirically justified. This unitary perspective predicated on the curved arrow formalism has resulted in the scenario where for many organic transformations our supposed understanding far surpasses our growing knowledge. Reformulating organic mechanisms (...) to include single electron transfer as a component of, or an alternative to, the prevailing iconic descriptions can provide for a more empirically adequate mechanistic description. In addition using the language of SET presents an opportunity to unify mechanistic concepts under a common donor/acceptor framework. (shrink)

Cognitive neuroscience has inspired a number of methodological advances to extract the highest signal-to-noise ratio from neuroimaging data. Popular techniques used to summarize behavioral data include sum-scores and item response theory. While these techniques can be useful when applied appropriately, item dimensionality and the quality of information are often left unexplored allowing poor performing items to be included in an itemset. The purpose of this study is to highlight how the application of two-stage approaches introduces parameter bias, differential item functioning (...) can manifest in cognitive neuroscience data and how techniques such as the multiple indicator multiple cause model can identify and remove items with DIF and model these data with greater sensitivity for brain–behavior relationships. This was performed using a simulation and an empirical study. The simulation explores parameter bias across two separate techniques used to summarize behavioral data: sum-scores and IRT and formative relationships with those estimated from a MIMIC model. In an empirical study participants performed an emotional identification task while concurrent electroencephalogram data were acquired across 384 trials. Participants were asked to identify the emotion presented by a static face of a child across four categories: happy, neutral, discomfort, and distress. The primary outcomes of interest were P200 event-related potential amplitude and latency within each emotion category. Instances of DIF related to correct emotion identification were explored with respect to an individual’s neurophysiology; specifically an item’s difficulty and discrimination were explored with respect to an individual’s average P200 amplitude and latency using a MIMIC model. The MIMIC model’s sensitivity was then compared to popular two-stage approaches for cognitive performance summary scores, including sum-scores and an IRT model framework and then regressing these onto the ERP characteristics. Here sensitivity refers to the magnitude and significance of coefficients relating the brain to these behavioral outcomes. The first set of analyses displayed instances of DIF within all four emotions which were then removed from all further models. The next set of analyses compared the two-stage approaches with the MIMIC model. Only the MIMIC model identified any significant brain–behavior relationships. Taken together, these results indicate that item performance can be gleaned from subject-specific biomarkers, and that techniques such as the MIMIC model may be useful tools to derive complex item-level brain–behavior relationships. (shrink)

Monetary intelligence theory asserts that individuals apply their money attitude to frame critical concerns in the context and strategically select certain options to achieve financial goals and ultimate happiness. This study explores the dark side of monetary Intelligence and behavioral economics—dishonesty. Dishonesty, a risky prospect, involves cost–benefit analysis of self-interest. We frame good or bad barrels in the environmental context as a proxy of high or low probability of getting caught for dishonesty, respectively. We theorize: The magnitude and intensity of (...) the relationship between love of money and dishonest prospect may reveal how individuals frame dishonesty in the context of two levels of subjective norm—perceived corporate ethical values at the micro-level and Corruption Perceptions Index at the macro-level, collected from multiple sources. Based on 6382 managers in 31 geopolitical entities across six continents, our cross-level three-way interaction effect illustrates: As expected, managers in good barrels, mixed barrels, and bad barrels display low, medium, and high magnitude of dishonesty, respectively. With high CEV, the intensity is the same across cultures. With low CEV, the intensity of dishonesty is the highest in high CPI entities —the Enron Effect, but the lowest in low CPI entities. CPI has a strong impact on the magnitude of dishonesty, whereas CEV has a strong impact on the intensity of dishonesty. We demonstrate dishonesty in light of monetary values and two frames of social norm, revealing critical implications to the field of behavioral economics and business ethics. (shrink)

Heisenberg’s explanation of how two coupled oscillators exchange energy represented a dramatic success for his new matrix mechanics. As matrix mechanics transmuted into wave mechanics, resulting in what Heisenberg himself described as …an extraordinary broadening and enrichment of the formalism of the quantum theory , the term resonance also experienced a corresponding evolution. Heitler and London’s seminal application of wave mechanics to explain the quantum origins of the covalent bond, combined with Pauling’s characterization of the effect, introduced resonance into the (...) chemical lexicon. As the Valence Bond approach gave way to a soon-to-be dominant Molecular Orbital method, our understanding of the term resonance, as it might apply to our understanding the chemical bond, has also changed. (shrink)