We report two experiments attempting to identify the role of semantic relatedness in picture-word interference studies. Previously published data sets have rendered results which directly contradict each other, with one study suggesting that the stronger the relation between picture and distractor, the more semantic interference is obtained, and another study suggesting the opposite pattern. We replicated the two key experiments with only minor procedural modifications, and found semantic interference effects in both. Critically, these were largely independent of the strength of semantic overlap. Additionally, we attempted to predict individual interference effects per target picture, via various measures of semantic overlap, which also failed to account for the effects. From our results it appears that semantic interference effects in picture-word tasks are similarly present for weakly and strongly overlapping combinations. Implications are discussed in the light of the recent debate on the role of competition in lexical selection.

Statistical learning (SL) is a powerful learning mechanism that supports word segmentation and language acquisition in infants and young adults. However, little is known about how this ability changes over the life span and interacts with age-related cognitive decline. The aims of this study were to: (a) examine the effect of aging on speech segmentation by SL, and (b) explore core mechanisms underlying SL. Across four testing sessions, young, middle-aged, and older adults were exposed to continuous speech streams at two different speech rates, both with and without cognitive load. Learning was assessed using a two-alterative forced-choice task in which words from the stream were pitted against either part-words, which occurred across word boundaries in the stream, or nonwords, which never appeared in the stream. Participants also completed a battery of cognitive tests assessing working memory and executive functions. The results showed that speech segmentation by SL was remarkably resilient to aging, although age effects were visible in the more challenging conditions, namely, when words had to be discriminated from part-words, which required the formation of detailed phonological representations, and when SL was performed under cognitive load. Moreover, an analysis of the cognitive test data indicated that performance against part-words was predicted mostly by memory updating, whereas performance against nonwords was predicted mostly by working memory storage capacity. Taken together, the data show that SL relies on a combination of implicit and explicit skills, and that age effects on SL are likely to be linked to an age-related selective decline in memory updating.

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Whereas traditional teaching environments encourage lively and engaged interaction and reward extrovert qualities, introverts, and others with symptoms that make social engagement difficult, such as autism spectrum disorder (ASD), are often disadvantaged. This population is often more engaged in quieter, low-key learning environments and often does not speak up and answer questions in traditional lecture-style classes. These individuals are often passed over in school and later in their careers for not speaking up and are assumed to not be as competent as their gregarious and outgoing colleagues. With the rise of the metaverse and democratization of virtual reality (VR) technology, post-secondary education is especially poised to capitalize on the immersive learning environments social VR provides and prepare students for the future of work, where virtual collaboration will be key. This study seeks to reconsider the role of VR and the metaverse for introverts and those with ASD. The metaverse has the potential to continue the social and workplace changes already accelerated by the pandemic and open new avenues for communication and collaboration for a more inclusive audience and tomorrow.

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