Nonarbitrary phenomena in language, such as systematic association in the form-meaning interface, have been widely reported in the literature. Exploiting such systematic associations previous studies have demonstrated that pseudowords can be indicative of meaning. However, whether semantic activation from words and pseudowords is supported by the very same processes, activating a common semantic memory system, is currently not known. Here, we take advantage of recent progresses from computational linguistics models allowing to induce meaning representations for out-of-vocabulary strings of letters via domain-general associative-learning mechanisms applied to natural language. We combined these models with data from priming tasks, in which participants are showed two strings of letters presented sequentially one after the other and are then asked to indicate if the latter is a word or a pseudoword. In Experiment 1 we reanalyzed the data of the largest behavioral database on semantic priming, while in Experiment 2 we ran an independent replication on a new language, Italian, controlling for a series of possible confounds. Results were consistent across the two experiments and showed that the prime-word meaning interferes with the semantic pattern elicited by the target pseudoword (i.e., at increasing estimated semantic relatedness between prime word and target pseudoword, participants' reaction times increased and accuracy decreased). These findings indicate that the same associative mechanisms governing word meaning also subserve the processing of pseudowords, suggesting in turn that human semantic memory can be conceived as a distributional system that builds upon a general-purpose capacity of extracting knowledge from complex statistical patterns. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Traditionally, the cerebellum has been linked to motor coordination, but growing evidence points to its involvement in a wide range of non-motor functions. Though the number of studies using transcranial magnetic stimulation (TMS) to investigate cerebellar involvement in cognitive processes is growing exponentially, these findings have not yet been synthesized in a meta-analysis. Here, we used meta-analysis to estimate the effects of cerebellar TMS on performance in cognitive tasks for healthy participants. Outcomes included participants’ accuracy and response times (RTs) of several non-motor tasks performed either during or after the administration of TMS. We included overall 41 studies, of which 44 single experiments reported effects on accuracy and 41 on response times (RTs). The meta-analyses showed medium effect sizes (for accuracy: d = 0.61 [95% CI = 0.48, .073]; for RTs: d = 0.40 [95% CI = 0.30, 0.49]), with leave-one-out analyses indicating that cumulative effects were robust, and with moderate heterogeneity. For both accuracy and RTs, the effect of TMS was moderated by the stimulation paradigm adopted but not by the cognitive function investigated, while the timing of the stimulation moderated only the effects on RTs. Further analyses on lateralization revealed no moderation effects of the TMS site. Taken together, these findings indicate that TMS administered over the cerebellum is able to modulate cognitive performance, affecting accuracy or RTs, and suggest that the various stimulation paradigms play a key role in determining the efficacy of cerebellar TMS.

Although the cerebellum has long been believed to be involved uniquely in sensorimotor processes, recent research works pointed to its participation in a wide range of cognitive predictive functions. Here, we review the available evidence supporting a generalized role of the cerebellum in predictive computation. We then discuss the anatomo-physiological properties that make the cerebellum the ideal hub of the predictive brain. We further argue that cerebellar involvement in cognition may follow a continuous gradient, with higher cerebellar activity occurring for tasks relying more on predictive processes, and outline the empirical scenarios to probe this hypothesis.

Cognitive maps are assumed to be fundamentally spatial and grounded only in perceptual processes, as supported by the discovery of functionally dedicated cell types in the human brain, which tile the environment in a maplike fashion. Challenging this view, we demonstrate that spatial representations—such as large-scale geographical maps—can be as well retrieved with high confidence from natural language through cognitively plausible artificial-intelligence models on the basis of nonspatial associative-learning mechanisms. More critically, we show that linguistic information accounts for the specific distortions observed in tasks when college-age adults have to judge the geographical positions of cities, even when these positions are estimated on real maps. These findings indicate that language experience can encode and reproduce cognitive maps without the need for a dedicated spatial-representation system, thus suggesting that the formation of these maps is the result of a strict interplay between spatial- and nonspatial-learning principles.