A large number of studies have demonstrated that semantic richness dimensions [e.g., number of features, semantic neighborhood density, semantic diversity , concreteness, emotional valence] influence word recognition processes. Some of these richness effects appear to be task-general, while others have been found to vary across tasks. Importantly, almost all of these findings have been found in the visual word recognition literature. To address this gap, we examined the extent to which these semantic richness effects are also found in spoken word recognition, using a megastudy approach that allows for an examination of the relative contribution of the various semantic properties to performance in two tasks: lexical decision, and semantic categorization. The results show that concreteness, valence, and number of features accounted for unique variance in latencies across both tasks in a similar direction—faster responses for spoken words that were concrete, emotionally valenced, and with a high number of features—while arousal, semantic neighborhood density, and semantic diversity did not influence latencies. Implications for spoken word recognition processes are discussed.

Psycholinguists have developed a number of measures to tap different aspects of a word's semantic representation. The influence of these measures on lexical processing has collectively been described as semantic richness effects. However, the effects of these word properties on memory are currently not well understood. This study examines the relative contributions of lexical and semantic variables in free recall and recognition memory at the item-level, using a megastudy approach. Hierarchical regression of recall and recognition performance on a number of lexical-semantic variables showed task-general effects where the structural component, frequency, number of senses, and arousal accounted for unique variance in both free recall and recognition memory. Task-specific effects included number of features, imageability, and body-object interaction, which accounted for unique variance in recall, whereas age of acquisition, familiarity, and extremity of valence accounted for unique variance in recognition. Forward selection regression analyses generally converged on these findings. Hierarchical regression also revealed that lexical variables accounted for more variance in recognition compared with recall, whereas semantic variables accounted for more unique variance above and beyond lexical variables in recall compared with recognition. Implications of the findings are discussed.

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Recent studies had demonstrated that specific emotional intelligence (EI) abilities (as measured using the MSCEIT) were related to better performance on cognitive tasks that involved emotional information but not on their non-emotional counterparts. These findings suggest that cognitive control and other executive functioning processes (e.g., working memory) contribute to EI abilities. A well-functioning EI ability is crucial for a number of everyday activities and life outcomes. However, the evidence for training ability EI remains vague as to how these improvements occur. The purpose of this narrative review was to synthesize findings from past EI training research, specifically focusing on their methodology. This was to identify key aspects of the interventions used, to determine the prototypical features between them, as well as to propose a compelling research agenda for future EI training studies. Based on the features found in these studies, we identified two possible approaches in which EI improvements occurred. The first approach was through increasing emotional knowledge and related competencies through teaching and practice. These features were found in the majority of training interventions using a workshop-style training format, reflections, role-plays, and practice with other participants. The second approach used brain-training principles to improve basic cognitive processes, such as executive control or emotional inhibition. Using a cognitive training approach to EI training can provide several advantages, such as allowing researchers to examine EI improvements using the theories of (1) transfer; (2) plasticity; and (3) process-specific changes.

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