Lying appears in everyday oral and written communication. As a consequence, detecting it on the basis of linguistic analysis is particularly important. Our study aimed to verify whether the differences between true and false statements in terms of complexity and sentiment that were reported in previous studies can be confirmed using tools dedicated to measuring those factors. Further, we investigated whether linguistic features that differentiate true and false utterances in English—namely utterance length, concreteness, and particular parts-of-speech—are also present in the Polish language. We analyzed nearly 1,500 true and false statements, half of which were transcripts while the other half were written statements. Our results show that false statements are less complex in terms of vocabulary, are more concise and concrete, and have more positive words and fewer negative words. We found no significant differences between spoken and written lies. Using this data, we built classifiers to automatically distinguish true from false utterances, achieving an accuracy of 60%. Our results provide a significant contribution to previous conclusions regarding linguistic deception indicators.

Detection of mental disorders from textual input is an emerging field for applied machine and deep learning methods. Here, we explore the limits of automated detection of autism spectrum disorder (ASD) and schizophrenia (SCZ). We compared the performance of: (1) dedicated diagnostic tools that involve collecting textual data, (2) automated methods applied to the data gathered by these tools, and (3) psychiatrists. Our article tests the effectiveness of several baseline approaches, such as bag of words and dictionary-based vectors, followed by a machine learning model. We employed two more refined Sentic text representations using affective features and concept-level analysis on texts. Further, we applied selected state-of-the-art deep learning methods for text representation and inference, as well as experimented with transfer and zero-shot learning. Finally, we also explored few-shot methods dedicated to low data size scenarios, which is a typical problem for the clinical setting. The best breed of automated methods outperformed human raters (psychiatrists). Cross-dataset approaches turned out to be useful (only from SCZ to ASD) despite different data types. The few-shot learning methods revealed promising results on the SCZ dataset. However, more effort is needed to explore the approaches to efficiently training models, given the very limited amounts of labeled clinical data. Psychiatry is one of the few medical fields in which the diagnosis of most disorders is based on the subjective assessment of a psychiatrist. Therefore, the introduction of objective tools supporting diagnostics seems to be pivotal. This paper is a step in this direction.

This study aimed to investigate if two weeks of working memory (WM) training on a progressive N-back task can generate changes in the activity of the underlying WM neural network. Forty-six healthy volunteers (23 training and 23 controls) were asked to perform the N-back task during three fMRI scanning sessions: (1) before training, (2) after the half of training sessions, and (3) at the end. Between the scanning sessions, the experimental group underwent a 10-session training of working memory with the use of an adaptive version of the N-back task, while the control group did not train anything. The N-back task in the scanning sessions was relatively easy (n = 2) in order to ensure high accuracy and a lack of between-group differences at the behavioral level. Such training-induced differences in neural efficiency were expected. Behavioral analyses revealed improved performance of both groups on the N-back task. However, these improvements resulted from the test-retest effect, not the training outside scanner. Performance on the non-trained stop-signal task did not demonstrate any transfer effect. Imaging analysis showed changes in activation in several significant clusters, with overlapping regions of interest in the frontal and parietal lobes. However, patterns of between-session changes of activation did not show any effect of training. The only finding that can be linked with training consists in strengthening the correlation between task performance accuracy and activation of the parietal regions of the neural network subserving working memory (left superior parietal lobule and right supramarginal gyrus posterior). These results suggest that the effects of WM training consist in learning that, in order to ensure high accuracy in the criterion task, activation of the parietal regions implicated in working memory updating must rise.

Lying is an integral part of everyday communication in both written and oral forms. Detecting lies is therefore essential and has many possible applications. Our study aims to investigate the performance of automated lie detection methods, namely the most recent breed of pre-trained transformer neural networks capable of processing the Polish language. We used a dataset of nearly 1500 true and false statements, half of which were transcripts and the other half written statements, originating from possibly the largest study of deception in the Polish language. Technically, the problem was posed as text classification. We found that models perform better on typed than spoken utterances. The best-performing model achieved an accuracy of 0.69, which is much higher than the human performance average of 0.56. For transcribed utterances, human performance was at 0.58 and the models reached 0.62. We also explored model interpretability based on integrated gradient to shed light on classifier decisions. Our observations highlight the role of first words and phrases in model decisions, but more work is needed to systematically explore the observed patterns.

Lying is essential to social communication. Despite years of research, its detection still poses many challenges. This is partly because some individuals are perceived as truthful and reliable, even when lying. However, relatively little is known about these effective liars. In our study, we focused on the cognitive functioning of effective liars. We tested 400 participants who completed tasks measuring executive functions, verbal fluency, and fluid intelligence, and also made four statements (two true and two false, half of them written and half oral). The reliability of the statements was then assessed. Only fluid intelligence was found to be relevant for reliable lying. This relationship was only evident for oral statements, suggesting that the importance of intelligence is highlighted when statements are made spontaneously without prior preparation.