Co-learning companionship benefits word learning in a new language: Evidence from a dual-brain EEG examination.

Companionship refers to one's being in the presence of another individual. For adults, acquiring a new language is a highly social activity that often involves learning in the context of companionship. However, the effects of companionship on new language learning have gone relatively underexplored, particularly with respect to word learning. Using a within-subject design, the current study employs electroencephalography to examine how two types of companionship (monitored and co-learning) affect word learning (semantic and lexical) in a new language. Dyads of Chinese speakers of English as a second language participated in a pseudo-word-learning task during which they were placed in monitored and co-learning companionship contexts. The results showed that exposure to co-learning companionship affected the early attention stage of word learning. Moreover, in this early stage, evidence of a higher representation similarity between co-learners showed additional support that co-learning companionship influenced attention. Observed increases in delta and theta interbrain synchronization further revealed that co-learning companionship facilitated semantic access. In all, the similar neural representations and interbrain synchronization between co-learners suggest that co-learning companionship offers important benefits for learning words in a new language.

Observing the suppression of individual aversive memories from conscious awareness.

When reminded of an unpleasant experience, people often try to exclude the unwanted memory from awareness, a process known as retrieval suppression. Here we used multivariate decoding (MVPA) and representational similarity analyses on EEG data to track how suppression unfolds in time and to reveal its impact on item-specific cortical patterns. We presented reminders to aversive scenes and asked people to either suppress or to retrieve the scene. During suppression, mid-frontal theta power within the first 500 ms distinguished suppression from passive viewing of the reminder, indicating that suppression rapidly recruited control. During retrieval, we could discern EEG cortical patterns relating to individual memories-initially, based on theta-driven visual perception of the reminders (0 to 500 ms) and later, based on alpha-driven reinstatement of the aversive scene (500 to 3000 ms). Critically, suppressing retrieval weakened (during 360 to 600 ms) and eventually abolished item-specific cortical patterns, a robust effect that persisted until the reminder disappeared (780 to 3000 ms). Representational similarity analyses provided converging evidence that retrieval suppression weakened the representation of target scenes during the 500 to 3000 ms reinstatement window. Together, rapid top-down control during retrieval suppression abolished cortical patterns of individual memories, and precipitated later forgetting. These findings reveal a precise chronometry on the voluntary suppression of individual memories.

Neural synchronization as a function of engagement with the narrative.

We can all agree that a good story engages us, however, agreeing which story is good is far more debatable. In this study, we explored whether engagement with a narrative synchronizes listeners' brain responses, by examining individual differences in engagement to the same story. To do so, we pre-registered and re-analyzed a previously collected dataset by Chang et al. (2021) of functional Magnetic Resonance Imaging (fMRI) scans of 25 participants who listened to a one-hour story and answered questionnaires. We assessed the degree of their overall engagement with the story and their engagement with the main characters. The questionnaires revealed individual differences in engagement with the story, as well as different valence towards specific characters. Neuroimaging data showed that the auditory cortex, the default mode network (DMN) and language regions were involved in processing the story. Increased engagement with the story was correlated with increased neural synchronization within regions in the DMN (especially the medial prefrontal cortex), as well as regions outside the DMN such as the dorso-lateral prefrontal cortex and the reward system. Interestingly, positively and negatively engaging characters elicited different patterns of neural synchronization. Finally, engagement increased functional connectivity within and between the DMN, the ventral attention network and the control network. Taken together, these findings suggest that engagement with a narrative synchronizes listeners' responses in regions involved in mentalizing, reward, working memory and attention. By examining individual differences in engagement, we revealed that these synchronization patterns are due to engagement, and not due to differences in the narrative's content.

Healthcare experience affects pain-specific responses to others' suffering in the anterior insula.

Medical students and professional healthcare providers often underestimate patients' pain, together with decreased neural responses to pain information in the anterior insula (AI), a brain region implicated in self-pain processing and negative affect. However, the functional significance and specificity of these neural changes remains debated. Across two experiments, we recruited university medical students and emergency nurses to test the role of healthcare experience on the brain reactivity to other's pain, emotions, and beliefs, using both pictorial and verbal cues. Brain responses to self-pain was also assessed and compared with those to observed pain. Our results confirmed that healthcare experience decreased the activity in AI in response to others' suffering. This effect was independent from stimulus modality (pictures or texts), but specific for pain, as it did not generalize to inferences about other mental or affective states. Furthermore, representational similarity and multivariate pattern analysis revealed that healthcare experience impacted specifically a component of the neural representation of others' pain that is shared with that of first-hand nociception, and related more to AI than to other pain-responsive regions. Taken together, our study suggests a decreased propensity to appraise others' suffering as one's own, associated with a reduced recruitment of pain-specific information in AI. These findings provide new insights into neural mechanisms leading to pain underestimation by caregivers in clinical settings.

The emergence of task-relevant representations in a nonlinear decision-making task.

This paper describes the relationship between performance in a decision-making task and the emergence of task-relevant representations. Participants learnt two tasks in which the appropriate response depended on multiple relevant stimuli and the underlying stimulus-outcome associations were governed by a latent feature that participants could discover. We divided participants into good and bad performers based on their overall classification rate and computed behavioural accuracy for each feature value. We found that participants with better performance had a better representation of the latent feature space. We then used representation similarity analysis on Electroencephalographic (EEG) data to identify when these representations emerge. We were able to decode task-relevant representations in a time window emerging 700 ms after stimulus presentation, but only for participants with good task performance. Our findings suggest that, in order to make good decisions, it is necessary to create and extract a low-dimensional representation of the task at hand.

Decoding the temporal dynamics of affective scene processing.

Natural images containing affective scenes are used extensively to investigate the neural mechanisms of visual emotion processing. Functional fMRI studies have shown that these images activate a large-scale distributed brain network that encompasses areas in visual, temporal, and frontal cortices. The underlying spatial and temporal dynamics, however, remain to be better characterized. We recorded simultaneous EEG-fMRI data while participants passively viewed affective images from the International Affective Picture System (IAPS). Applying multivariate pattern analysis to decode EEG data, and representational similarity analysis to fuse EEG data with simultaneously recorded fMRI data, we found that: (1) ∼80 ms after picture onset, perceptual processing of complex visual scenes began in early visual cortex, proceeding to ventral visual cortex at ∼100 ms, (2) between ∼200 and ∼300 ms (pleasant pictures: ∼200 ms; unpleasant pictures: ∼260 ms), affect-specific neural representations began to form, supported mainly by areas in occipital and temporal cortices, and (3) affect-specific neural representations were stable, lasting up to ∼2 s, and exhibited temporally generalizable activity patterns. These results suggest that affective scene representations in the brain are formed temporally in a valence-dependent manner and may be sustained by recurrent neural interactions among distributed brain areas.

A simple rule to describe interactions between visual categories.

Humans can rapidly categorise visual objects when presented in isolation. However, in everyday life we encounter multiple objects at the same time. Far less is known about how simultaneously active object representations interact. We examined such interactions by asking participants to categorise a target object at the basic (Experiment 1) or the superordinate (Experiment 2) level while the representation of another object was still active. We found that the "prime" object strongly modulated the response to the target implying that the prime's category was rapidly and automatically accessed, influencing subsequent categorical processing. Using drift diffusion modelling, we show that a prime, whose category is different from that of the target, interferes with target processing primarily during the evidence accumulation stage. This suggests that the state of category-processing neurons is altered by an active representation and this modifies the processing of other categories. Interestingly, the strength of interference increases with the similarity between the distractor and the target category. Considering these results and previous studies, we propose a general principle that category interactions are determined by the distance from a distractor's representation to the target's task-relevant categorical boundary. We argue that this principle arises from the specific architectural organisation of categories in the brain.

How the Human Brain Represents Perceived Dangerousness or "Predacity" of Animals.

UNLABELLED: Common or folk knowledge about animals is dominated by three dimensions: (1) level of cognitive complexity or "animacy;" (2) dangerousness or "predacity;" and (3) size. We investigated the neural basis of the perceived dangerousness or aggressiveness of animals, which we refer to more generally as "perception of threat." Using functional magnetic resonance imaging (fMRI), we analyzed neural activity evoked by viewing images of animal categories that spanned the dissociable semantic dimensions of threat and taxonomic class. The results reveal a distributed network for perception of threat extending along the right superior temporal sulcus. We compared neural representational spaces with target representational spaces based on behavioral judgments and a computational model of early vision and found a processing pathway in which perceived threat emerges as a dominant dimension: whereas visual features predominate in early visual cortex and taxonomy in lateral occipital and ventral temporal cortices, these dimensions fall away progressively from posterior to anterior temporal cortices, leaving threat as the dominant explanatory variable. Our results suggest that the perception of threat in the human brain is associated with neural structures that underlie perception and cognition of social actions and intentions, suggesting a broader role for these regions than has been thought previously, one that includes the perception of potential threat from agents independent of their biological class. SIGNIFICANCE STATEMENT: For centuries, philosophers have wondered how the human mind organizes the world into meaningful categories and concepts. Today this question is at the core of cognitive science, but our focus has shifted to understanding how knowledge manifests in dynamic activity of neural systems in the human brain. This study advances the young field of empirical neuroepistemology by characterizing the neural systems engaged by an important dimension in our cognitive representation of the animal kingdom ontological subdomain: how the brain represents the perceived threat, dangerousness, or "predacity" of animals. Our findings reveal how activity for domain-specific knowledge of animals overlaps the social perception networks of the brain, suggesting domain-general mechanisms underlying the representation of conspecifics and other animals.

Multi-Connection Pattern Analysis: Decoding the representational content of neural communication.

The lack of multivariate methods for decoding the representational content of interregional neural communication has left it difficult to know what information is represented in distributed brain circuit interactions. Here we present Multi-Connection Pattern Analysis (MCPA), which works by learning mappings between the activity patterns of the populations as a factor of the information being processed. These maps are used to predict the activity from one neural population based on the activity from the other population. Successful MCPA-based decoding indicates the involvement of distributed computational processing and provides a framework for probing the representational structure of the interaction. Simulations demonstrate the efficacy of MCPA in realistic circumstances. In addition, we demonstrate that MCPA can be applied to different signal modalities to evaluate a variety of hypothesis associated with information coding in neural communications. We apply MCPA to fMRI and human intracranial electrophysiological data to provide a proof-of-concept of the utility of this method for decoding individual natural images and faces in functional connectivity data. We further use a MCPA-based representational similarity analysis to illustrate how MCPA may be used to test computational models of information transfer among regions of the visual processing stream. Thus, MCPA can be used to assess the information represented in the coupled activity of interacting neural circuits and probe the underlying principles of information transformation between regions.

Evaluating the roles of left middle frontal gyrus in word production using electrocorticography.

To assess the specific roles of left middle frontal gyrus (LMFG) in word production, electrocorticography signals were recorded from an epilepsy patient when he participated in language tasks. We found three sites of LMFG showed high-gamma perturbations with distinct patterns across tasks; and neural activities elicited in the same tasks shared similar patterns, while those elicited by stimuli leading to the same articulations did not. These findings confirmed that the LMFG takes active parts in word production, and suggested that it may serve as a temporal perceptual information storage space, supporting the hierarchical state feedback control model of word production.

Complementary role of frontoparietal activity and cortical pattern similarity in successful episodic memory encoding.

One central goal in cognitive neuroscience of learning and memory is to characterize the neural processes that lead to long-lasting episodic memory. In addition to the stronger frontoparietal activity, greater category- or item-specific cortical representation during encoding, as measured by pattern similarity (PS), is also associated with better subsequent episodic memory. Nevertheless, it is unknown whether frontoparietal activity and cortical PS reflect distinct mechanisms. To address this issue, we reanalyzed previous data (Xue G, Dong Q, Chen C, Lu ZL, Mumford JA, Poldrack RA. 2010. Greater neural pattern similarity across repetitions is associated with better memory. Science. 330:97, Experiment 3) using a novel approach based on combined activation-based and information-based analyses. The results showed that across items, stronger frontoparietal activity was associated with greater PS in distributed brain regions, including those where the PS was predictive of better subsequent memory. Nevertheless, the item-specific PS was still associated with later episodic memory after controlling the effect of frontoparietal activity. Our results suggest that one possible mechanism of frontoparietal activity on episodic memory encoding is via enhancing PS, resulting in more unique and consistent input to the medial temporal lobe. In addition, they suggest that PS might index additional processes, such as pattern reinstatement as a result of study-phase retrieval, that contribute to episodic memory encoding.

Individual uniqueness in trust profiles and well-being: Understanding the role of cultural tightness-looseness from a representation similarity perspective.

This paper provides a unique perspective for understanding cultural differences: representation similarity-a computational technique that uses pairwise comparisons of units to reveal their representation in higher-order space. By combining individual-level measures of trust across domains and well-being from 13,823 participants across 15 nations with a measure of society-level tightness-looseness, we found that any two countries with more similar tightness-looseness tendencies exhibit higher degrees of representation similarity in national interpersonal trust profiles. Although each individual's trust profile is generally similar to their nation's trust profile, the greater similarity between an individual's and their society's trust profile predicted a higher level of individual life satisfaction only in loose cultures but not in tight cultures. Using the framework of representation similarity to explore cross-cultural differences from a multidimensional, multi-national perspective provide a comprehensive picture of how culture is related to the human activities.

Neural representations underlying mental imagery as unveiled by representation similarity analysis.

It is commonly acknowledged that visual imagery and perception rely on the same content-dependent brain areas in the high-level visual cortex (HVC). However, the way in which our brain processes and organizes previous acquired knowledge to allow the generation of mental images is still a matter of debate. Here, we performed a representation similarity analysis of three previous fMRI experiments conducted in our laboratory to characterize the neural representation underlying imagery and perception of objects, buildings and faces and to disclose possible dissimilarities in the neural structure of such representations. To this aim, we built representational dissimilarity matrices (RDMs) by computing multivariate distances between the activity patterns associated with each pair of stimuli in the content-dependent areas of the HVC and HC. We found that spatial information is widely coded in the HVC during perception (i.e. RSC, PPA and OPA) and imagery (OPA and PPA). Also, visual information seems to be coded in both preferred and non-preferred regions of the HVC, supporting a distributed view of encoding. Overall, the present results shed light upon the spatial coding of imagined and perceived exemplars in the HVC.

Visual dictionaries as intermediate features in the human brain.

The human visual system is assumed to transform low level visual features to object and scene representations via features of intermediate complexity. How the brain computationally represents intermediate features is still unclear. To further elucidate this, we compared the biologically plausible HMAX model and Bag of Words (BoW) model from computer vision. Both these computational models use visual dictionaries, candidate features of intermediate complexity, to represent visual scenes, and the models have been proven effective in automatic object and scene recognition. These models however differ in the computation of visual dictionaries and pooling techniques. We investigated where in the brain and to what extent human fMRI responses to short video can be accounted for by multiple hierarchical levels of the HMAX and BoW models. Brain activity of 20 subjects obtained while viewing a short video clip was analyzed voxel-wise using a distance-based variation partitioning method. Results revealed that both HMAX and BoW explain a significant amount of brain activity in early visual regions V1, V2, and V3. However, BoW exhibits more consistency across subjects in accounting for brain activity compared to HMAX. Furthermore, visual dictionary representations by HMAX and BoW explain significantly some brain activity in higher areas which are believed to process intermediate features. Overall our results indicate that, although both HMAX and BoW account for activity in the human visual system, the BoW seems to more faithfully represent neural responses in low and intermediate level visual areas of the brain.