Heart rate variability biofeedback (HRVB) has been used for a number of years to treat depressive symptoms, a common mental health issue, which is often comorbid with other psychopathological and medical conditions. The aim of the present meta-analysis is to test whether and to what extent HRVB is effective in reducing depressive symptoms in adult patients. We conducted a literature search on Pubmed, ProQuest, Ovid PsycInfo, and Embase up to October 2020, and identified 721 studies. Fourteen studies were included in the meta-analysis. Three meta-regressions were also performed to further test whether publication year, the questionnaire used to assess depressive symptoms, or the interval of time between T0 and T1 moderated the effect of HRVB. Overall, we analysed 14 RCTs with a total of 794 participants. The random effect analysis yielded a medium mean effect size g = 0.38 [95% CI = 0.16, 0.60; 95% PI =  − 0.19, 0.96], z = 3.44, p = 0.0006. The total heterogeneity was significant, QT = 23.49, p = 0.03, I2 = 45%, which suggested a moderate variance among the included studies. The year of publication (χ2(1) = 4.08, p = 0.04) and the questionnaire used to assess symptoms (χ2(4) = 12.65, p = 0.01) significantly moderated the effect of the interventions and reduced heterogeneity. Overall, results showed that HRVB improves depressive symptoms in several psychophysiological conditions in adult samples and should be considered as a valid technique to increase psychological well-being.

In recent years, a growing body of literature has shown that being in a foreign language (FL) context affects the way in which people make choices. This phenomenon is known as the foreign language effect (FLE). The FLE affects both moral decisionmaking and risk-aversion tendencies, but no cumulative evidence is available. Herein, we aimed to estimate, through a metaanalytical approach, the effect of being in an FL context as compared with that of a native language (NL). We found 17 studies matching our criteria and, in total, 47 experiments were included (N = 38 investigated the FLE in the moral decision-making domain; N = 9 investigated the FLE in the risk-aversion domain). Results showed that FL affects participants' decisions as compared with NL in both the moral decision-making and risk-aversion domains, inducing participants to be more willing to accept harm in order to maximize outcomes in the former and reducing risk aversion in the latter. In addition, two metaregressions were performed on the studies that investigated the moral decision-making domain in order to assess whether participants' proficiency in the FL, or NL-FL similarity, moderated the observed effect. Our findings indicate that proficiency in the FL does not moderate the observed effect, while NL-FL similarity does. Our results support previous findings on the FLE and provide suggestions for future research.

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The exact semantic processes subserving the formation of false memories are still poorly understood. Here, we directly probed the semantic origins of false memories in a typical Deese-Roediger-McDermott (DRM) task, by predicting participants' performance in this task through data-driven distributional semantic models. Participants were required to study lists of words and then to perform a recognition task. Our findings indicate that the participants' performance is better accounted for by a local rather than a global strategy on the task at hand: the single lists composing the task activate specific semantic clusters that are responsible for the occurrence of false memories. In particular, memory performance followed a continuous semantic gradient, with higher false recognitions occurring for higher sematic similarity between the lures (i.e., the false memory items) and the words in the relative lists. Crucially, our findings also show that semantic memory is differently involved in veridical and false memories, with this pattern being consistent across two reanalyses of data from previous studies and being replicated in an independent experiment. We thus outline an empirically-driven theoretical framework to account for the semantic processes supporting veridical and false memories formation.

Mounting evidence suggests that the cerebellum, a structure previously linked to motor function, is also involved in a wide range of non-motor processes. It has been proposed that the cerebellum performs the same computational processes in both motor and non-motor domains. Within motor functions, the cerebellum is involved in the integration of signals from multiple systems. Here we hypothesized that cerebellum may be involved in integration within semantic memory as well. Specifically, understanding a noun-adjective combination (e.g. red apple) requires combining the meaning of the adjective (red) with the meaning of the noun (apple). In two experiments, participants were asked to judge whether noun-adjective word-pairs were semantically related (e.g., red apple) or not (e.g., lucky milk) while online transcranial magnetic stimulation (TMS) was administered over the right cerebellum or over a control site (vertex in Experiment 1 and visual cortex in Experiment 2). Cerebellar TMS caused a decrease in participants’ accuracy for related word-pairs while accuracy for unrelated stimuli was not affected. A third experiment using a control task where subjects compared pairs of random letters showed no effect of TMS. Taken together these results indicate that the right cerebellum is involved specifically in the processing of semantically related stimuli. These results are consistent with theories that proposed the existence of a unified cerebellar function within motor and non-motor domains, as well with recent perspectives about cerebellar involvement in semantic memory and predictive cognition.

Despite mouse-tracking has been taken as a real-time window on different aspects of human decision-making processes, whether purely semantic information affects response conflict at the level of motor output is still unknown. Here, we investigated the possible effects of semantic knowledge on hand movements by predicting participants’ performance in a mouse-tracking task through distributional semantics, a usage-based modelling approach to meaning. Participants were shown word pairs and were required to perform a two-alternative forced choice task by moving the computer mouse and selecting either the more abstract or the more concrete word, depending on the experimental condition. Results showed that mouse trajectories were affected by the semantic relatedness of the word-pairs as indexed by the distributional semantic model, despite this dimension was not relevant for the task at hand. In particular, mouse trajectories reflected the response conflict and its temporal evolution, with larger deviation and movement uncertainty for increasing word semantic relatedness. These findings testify the effects of word meaning on decision-making processes as indexed by a mouse-tracking conflict. They also document a strict interplay between sensory-motor processes and the structure of human semantic memory.

Theoretical reflections on memory and prediction, linking these concepts to the role of the cerebellum in higher cognition. What is memory? What is memory for? Where is memory in the brain? Although memory is probably the most studied function in cognition, these fundamental questions remain challenging. We can try to answer the question of memory's purpose by defining the function of memory as remembering the past. And yet this definition is not consistent with the many errors that characterize our memory, or with the phylogenetic and ontogenetic origin of memory. In this book, Tomaso Vecchi and Daniele Gatti argue that the purpose of memory is not to remember the past but to predict the future. Vecchi and Gatti link memory and prediction to the role of the cerebellum in higher cognition, relying on recent empirical data to support theoretical reflections. They propose a new model of memory functions that comprises a system devoted to prediction, based in the cerebellum and mediated by the hippocampus, and a parallel system with a major role for cortical structures and mediated by the amygdala. Although memory is often conceived as a kind of storehouse, this storehouse is constantly changing, integrating new information in a continual process of modification. In order to explain these characteristics, Vecchi and Gatti argue, we must change our interpretation of the nature and functions of the memory system.

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.