subs2vec: Word embeddings from subtitles in 55 languages

Balancing novelty and appropriateness leads to creative associations in children

Creative problem solving is a fundamental skill of human cognition and is conceived as a search process whereby a novel and appropriate solution is generated. However, it is unclear whether children are able to balance novelty and appropriateness to generate creative solutions and what are the underlying computational mechanisms. Here, we asked children, ranging from 10 to 11 years old, to perform a word association task according to three instructions, which triggered a more appropriate (ordinary), novel (random), or balanced (creative) response. Results revealed that children exhibited greater cognitive flexibility in the creative condition compared to the control conditions, as revealed by the structure and resiliency of the semantic networks. Moreover, responses' word embeddings extracted from pretrained deep neural networks showed that semantic distance and category switching index increased in the creative condition with respect to the ordinary condition and decreased compared to the random condition. Critically, we showed how children efficiently solved the exploration/exploitation trade-off to generate creative associations by fitting a computational reinforcement learning (RL) model that simulates semantic search strategies. Our findings provide compelling evidence that children balance novelty and appropriateness to generate creative associations by optimally regulating the level of exploration in the semantic search. This corroborates previous findings on the adult population and highlights the crucial contribution of both components to the overall creative process. In conclusion, these results shed light on the connections between theoretical concepts such as bottom-up/top-down modes of thinking in creativity research and the exploration/exploitation trade-off in human RL research.

Rapid adaptation of predictive models during language comprehension: Aperiodic EEG slope, individual alpha frequency and idea density modulate individual differences in real-time model updating

Predictive coding provides a compelling, unified theory of neural information processing, including for language. However, there is insufficient understanding of how predictive models adapt to changing contextual and environmental demands and the extent to which such adaptive processes differ between individuals. Here, we used electroencephalography (EEG) to track prediction error responses during a naturalistic language processing paradigm. In Experiment 1, 45 native speakers of English listened to a series of short passages. Via a speaker manipulation, we introduced changing intra-experimental adjective order probabilities for two-adjective noun phrases embedded within the passages and investigated whether prediction error responses adapt to reflect these intra-experimental predictive contingencies. To this end, we calculated a novel measure of speaker-based, intra-experimental surprisal (“speaker-based surprisal”) as defined on a trial-by-trial basis and by clustering together adjectives with a similar meaning. N400 amplitude at the position of the critical second adjective was used as an outcome measure of prediction error. Results showed that N400 responses attuned to speaker-based surprisal over the course of the experiment, thus indicating that listeners rapidly adapt their predictive models to reflect local environmental contingencies (here: the probability of one type of adjective following another when uttered by a particular speaker). Strikingly, this occurs in spite of the wealth of prior linguistic experience that participants bring to the laboratory. Model adaptation effects were strongest for participants with a steep aperiodic (1/f) slope in resting EEG and low individual alpha frequency (IAF), with idea density (ID) showing a more complex pattern. These results were replicated in a separate sample of 40 participants in Experiment 2, which employed a highly similar design to Experiment 1. Overall, our results suggest that individuals with a steep aperiodic slope adapt their predictive models most strongly to context-specific probabilistic information. Steep aperiodic slope is thought to reflect low neural noise, which in turn may be associated with higher neural gain control and better cognitive control. Individuals with a steep aperiodic slope may thus be able to more effectively and dynamically reconfigure their prediction-related neural networks to meet current task demands. We conclude that predictive mechanisms in language are highly malleable and dynamic, reflecting both the affordances of the present environment as well as intrinsic information processing capabilities of the individual.

The verb-self link: An implicit association test study

Agency is defined as the ability to assign and pursue goals. Given people’s focus on achieving their own goals, agency has been found to be strongly linked to the self. In two studies (N = 168), we examined whether this self–agency link is visible from a linguistic perspective. As the preferred grammatical category to convey agency is verbs, we hypothesize that, in the Implicit Association Test (IAT), verbs (vs. nouns) would be associated more strongly with the self (vs. others). Our results confirmed this hypothesis. Participants exhibited particularly fast responses when reading self-related stimuli (e.g., “me” or “my”) and verb stimuli (e.g., “deflect” or “contemplate”) both necessitated pressing an identical rather than different response keys in the IAT (d = .25). The finding connects two streams of literature—on the link between agency and verbs and on the link between self and agency—suggesting a triad between self, agency, and verbs. We argue that this verb–self link (1) opens up new perspectives for understanding linguistic expressions of agency and (2) expands our understanding of how word choice impacts socio-cognitive processing.

Behavioral and Brain Responses Highlight the Role of Usage in the Preparation of Multiword Utterances for Production

Usage-based theories assume that all aspects of language processing are shaped by the distributional properties of the language. The frequency not only of words but also of larger chunks plays a major role in language processing. These theories predict that the frequency of phrases influences the time needed to prepare these phrases for production and their acoustic duration. By contrast, dominant psycholinguistic models of utterance production predict no such effects. In these models, the system keeps track of the frequency of individual words but not of co-occurrences. This study investigates the extent to which the frequency of phrases impacts naming latencies and acoustic duration with a balanced design, where the same words are recombined to build high- and low-frequency phrases. The brain signal of participants is recorded so as to obtain information on the electrophysiological bases and functional locus of frequency effects. Forty-seven participants named pictures using high- and low-frequency adjective-noun phrases. Naming latencies were shorter for high-frequency than low-frequency phrases. There was no evidence that phrase frequency impacted acoustic duration. The electrophysiological signal differed between high- and low-frequency phrases in time windows that do not overlap with conceptualization or articulation processes. These findings suggest that phrase frequency influences the preparation of phrases for production, irrespective of the lexical properties of the constituents, and that this effect originates at least partly when speakers access and encode linguistic representations. Moreover, this study provides information on how the brain signal recorded during the preparation of utterances changes with the frequency of word combinations.