Peeling the Onion of Brain Representations.

The brain's function is to enable adaptive behavior in the world. To this end, the brain processes information about the world. The concept of representation links the information processed by the brain back to the world and enables us to understand what the brain does at a functional level. The appeal of making the connection between brain activity and what it represents has been irresistible to neuroscience, despite the fact that representational interpretations pose several challenges: We must define which aspects of brain activity matter, how the code works, and how it supports computations that contribute to adaptive behavior. It has been suggested that we might drop representational language altogether and seek to understand the brain, more simply, as a dynamical system. In this review, we argue that the concept of representation provides a useful link between dynamics and computational function and ask which aspects of brain activity should be analyzed to achieve a representational understanding. We peel the onion of brain representations in search of the layers (the aspects of brain activity) that matter to computation. The article provides an introduction to the motivation and mathematics of representational models, a critical discussion of their assumptions and limitations, and a preview of future directions in this area.

Predicting Cued and Oddball Visual Search Performance from fMRI, MEG, and DNN Neural Representational Similarity.

Capacity limitations in visual tasks can be observed when the number of task-related objects increases. An influential idea is that such capacity limitations are determined by competition at the neural level: two objects that are encoded by shared neural populations interfere more in behavior (e.g., visual search) than two objects encoded by separate neural populations. However, the neural representational similarity of objects varies across brain regions and across time, raising the questions of where and when competition determines task performance. Furthermore, it is unclear whether the association between neural representational similarity and task performance is common or unique across tasks. Here, we used neural representational similarity derived from fMRI, MEG, and a deep neural network (DNN) to predict performance on two visual search tasks involving the same objects and requiring the same responses but differing in instructions: cued visual search and oddball visual search. Separate groups of human participants (both sexes) viewed the individual objects in neuroimaging experiments to establish the neural representational similarity between those objects. Results showed that performance on both search tasks could be predicted by neural representational similarity throughout the visual system (fMRI), from 80 ms after onset (MEG), and in all DNN layers. Stepwise regression analysis, however, revealed task-specific associations, with unique variability in oddball search performance predicted by early/posterior neural similarity and unique variability in cued search task performance predicted by late/anterior neural similarity. These results reveal that capacity limitations in superficially similar visual search tasks may reflect competition at different stages of visual processing.

Visual Recognition Memory of Scenes Is Driven by Categorical, Not Sensory, Visual Representations.

When we perceive a scene, our brain processes various types of visual information simultaneously, ranging from sensory features, such as line orientations and colors, to categorical features, such as objects and their arrangements. Whereas the role of sensory and categorical visual representations in predicting subsequent memory has been studied using isolated objects, their impact on memory for complex scenes remains largely unknown. To address this gap, we conducted an fMRI study in which female and male participants encoded pictures of familiar scenes (e.g., an airport picture) and later recalled them, while rating the vividness of their visual recall. Outside the scanner, participants had to distinguish each seen scene from three similar lures (e.g., three airport pictures). We modeled the sensory and categorical visual features of multiple scenes using both early and late layers of a deep convolutional neural network. Then, we applied representational similarity analysis to determine which brain regions represented stimuli in accordance with the sensory and categorical models. We found that categorical, but not sensory, representations predicted subsequent memory. In line with the previous result, only for the categorical model, the average recognition performance of each scene exhibited a positive correlation with the average visual dissimilarity between the item in question and its respective lures. These results strongly suggest that even in memory tests that ostensibly rely solely on visual cues (such as forced-choice visual recognition with similar distractors), memory decisions for scenes may be primarily influenced by categorical rather than sensory representations.

The Time-Course of Food Representation in the Human Brain.

Humans make decisions about food every day. The visual system provides important information that forms a basis for these food decisions. Although previous research has focused on visual object and category representations in the brain, it is still unclear how visually presented food is encoded by the brain. Here, we investigate the time-course of food representations in the brain. We used time-resolved multivariate analyses of electroencephalography (EEG) data, obtained from human participants (both sexes), to determine which food features are represented in the brain and whether focused attention is needed for this. We recorded EEG while participants engaged in two different tasks. In one task, the stimuli were task relevant, whereas in the other task, the stimuli were not task relevant. Our findings indicate that the brain can differentiate between food and nonfood items from ∼112 ms after the stimulus onset. The neural signal at later latencies contained information about food naturalness, how much the food was transformed, as well as the perceived caloric content. This information was present regardless of the task. Information about whether food is immediately ready to eat, however, was only present when the food was task relevant and presented at a slow presentation rate. Furthermore, the recorded brain activity correlated with the behavioral responses in an odd-item-out task. The fast representation of these food features, along with the finding that this information is used to guide food categorization decision-making, suggests that these features are important dimensions along which the representation of foods is organized.

Neural Systems Underlying the Implementation of Working Memory Removal Operations.

Recently, multi-voxel pattern analysis has verified that information can be removed from working memory (WM) via three distinct operations replacement, suppression, or clearing compared to information being maintained ( Kim et al., 2020). While univariate analyses and classifier importance maps in Kim et al. (2020) identified brain regions that contribute to these operations, they did not elucidate whether these regions represent the operations similarly or uniquely. Using Leiden-community-detection on a sample of 55 humans (17 male), we identified four brain networks, each of which has a unique configuration of multi-voxel activity patterns by which it represents these WM operations. The visual network (VN) shows similar multi-voxel patterns for maintain and replace, which are highly dissimilar from suppress and clear, suggesting this network differentiates whether an item is held in WM or not. The somatomotor network (SMN) shows a distinct multi-voxel pattern for clear relative to the other operations, indicating the uniqueness of this operation. The default mode network (DMN) has distinct patterns for suppress and clear, but these two operations are more similar to each other than to maintain and replace, a pattern intermediate to that of the VN and SMN. The frontoparietal control network (FPCN) displays distinct multi-voxel patterns for each of the four operations, suggesting that this network likely plays an important role in implementing these WM operations. These results indicate that the operations involved in removing information from WM can be performed in parallel by distinct brain networks, each of which has a particular configuration by which they represent these operations.

Hippocampal Functions Modulate Transfer-Appropriate Cortical Representations Supporting Subsequent Memory.

The hippocampus plays a central role as a coordinate system or index of information stored in neocortical loci. Nonetheless, it remains unclear how hippocampal processes integrate with cortical information to facilitate successful memory encoding. Thus, the goal of the current study was to identify specific hippocampal-cortical interactions that support object encoding. We collected fMRI data while 19 human participants (7 female and 12 male) encoded images of real-world objects and tested their memory for object concepts and image exemplars (i.e., conceptual and perceptual memory). Representational similarity analysis revealed robust representations of visual and semantic information in canonical visual (e.g., occipital cortex) and semantic (e.g., angular gyrus) regions in the cortex, but not in the hippocampus. Critically, hippocampal functions modulated the mnemonic impact of cortical representations that are most pertinent to future memory demands, or transfer-appropriate representations Subsequent perceptual memory was best predicted by the strength of visual representations in ventromedial occipital cortex in coordination with hippocampal activity and pattern information during encoding. In parallel, subsequent conceptual memory was best predicted by the strength of semantic representations in left inferior frontal gyrus and angular gyrus in coordination with either hippocampal activity or semantic representational strength during encoding. We found no evidence for transfer-incongruent hippocampal-cortical interactions supporting subsequent memory (i.e., no hippocampal interactions with cortical visual/semantic representations supported conceptual/perceptual memory). Collectively, these results suggest that diverse hippocampal functions flexibly modulate cortical representations of object properties to satisfy distinct future memory demands.Significance Statement The hippocampus is theorized to index pieces of information stored throughout the cortex to support episodic memory. Yet how hippocampal processes integrate with cortical representation of stimulus information remains unclear. Using fMRI, we examined various forms of hippocampal-cortical interactions during object encoding in relation to subsequent performance on conceptual and perceptual memory tests. Our results revealed novel hippocampal-cortical interactions that utilize semantic and visual representations in transfer-appropriate manners: conceptual memory supported by hippocampal modulation of frontoparietal semantic representations, and perceptual memory supported by hippocampal modulation of occipital visual representations. These findings provide important insights into the neural mechanisms underlying the formation of information-rich episodic memory and underscore the value of studying the flexible interplay between brain regions for complex cognition.

The neural representation of body part concepts.

Neuropsychological and neuroimaging studies provide evidence for a degree of category-related organization of conceptual knowledge in the brain. Some of this evidence indicates that body part concepts are distinctly represented from other categories; yet, the neural correlates and mechanisms underlying these dissociations are unclear. We expand on the limited prior data by measuring functional magnetic resonance imaging responses induced by body part words and performing a series of analyses investigating the cortical representation of this semantic category. Across voxel-level contrasts, pattern classification, representational similarity analysis, and vertex-wise encoding analyses, we find converging evidence that the posterior middle temporal gyrus, the supramarginal gyrus, and the ventral premotor cortex in the left hemisphere play important roles in the preferential representation of this category compared to other concrete objects.

Contributions of repeated learning to memory in humans: insights from single-neuron recordings in the hippocampus and amygdala.

Despite the well-established phenomenon of improved memory performance through repeated learning, studies investigating the associated neural mechanisms have yielded complex and sometimes contradictory findings, and direct evidence from human neuronal recordings has been lacking. This study employs single-neuron recordings with exceptional spatial-temporal resolution, combined with representational similarity analysis, to explore the neural dynamics within the hippocampus and amygdala during repeated learning. Our results demonstrate that in the hippocampus, repetition enhances both representational specificity and fidelity, with these features predicting learning times. Conversely, the amygdala exhibits heightened representational specificity and fidelity during initial learning but does not show improvement with repetition, suggesting functional specialization of the hippocampus and amygdala during different stages of the learning repetition. Specifically, the hippocampus appears to contribute to sustained engagement necessary for benefiting from repeated learning, while the amygdala may play a role in the representation of novel items. These findings contribute to a comprehensive understanding of the intricate interplay between these brain regions in memory processes. Significance statement  For over a century, understanding how repetition contributes to memory enhancement has captivated researchers, yet direct neuronal evidence has been lacking, with a primary focus on the hippocampus and a neglect of the neighboring amygdala. Employing advanced single-neuron recordings and analytical techniques, this study unveils a nuanced functional specialization within the amygdala-hippocampal circuit during various learning repetition. The results highlight the hippocampus's role in sustaining engagement for improved memory with repetition, contrasting with the amygdala's superior ability in representing novel items. This exploration not only deepens our comprehension of memory enhancement intricacies but also sheds light on potential interventions to optimize learning and memory processes.

What makes an event significant: an fMRI study on self-defining memories.

Self-defining memories are highly significant personal memories that contribute to an individual's life story and identity. Previous research has identified 4 key subcomponents of self-defining memories: content, affect, specificity, and self-reflection. However, these components were not tested under functional neuroimaging. In this study, we first explored how self-defining memories distinguish themselves from everyday memories (non-self-defining) through their associated brain activity. Next, we evaluated the different self-defining memory subcomponents through their activity in the underlying brain system. Participants recalled both self-defining and non-self-defining memories under functional MRI and evaluated the 4 subcomponents for each memory. Multivoxel pattern analysis uncovered a brain system closely related to the default mode network to discriminate between self-defining and non-self-defining memories. Representational similarity analysis revealed the neural coding of each subcomponent. Self-reflection was coded mainly in the precuneus, middle and inferior frontal gyri, and cingulate, lateral occipital, and insular cortices. To a much lesser extent, content coding was primarily in the left angular gyrus and fusiform gyrus. No region was found to represent information on affect and specificity. Our findings highlight the marked difference in brain processing between significant and non-significant memories, and underscore self-reflection as a predominant factor in the formation and maintenance of self-defining memories, inviting a reassessment of what constitutes significant memories.

Neural representations of others' traits predict social decisions.

To guide social interaction, people often rely on expectations about the traits of other people, based on markers of social group membership (i.e., stereotypes). Although the influence of stereotypes on social behavior is widespread, key questions remain about how traits inferred from social-group membership are instantiated in the brain and incorporated into neural computations that guide social behavior. Here, we show that the human lateral orbitofrontal cortex (OFC) represents the content of stereotypes about members of different social groups in the service of social decision-making. During functional MRI scanning, participants decided how to distribute resources across themselves and members of a variety of social groups in a modified Dictator Game. Behaviorally, we replicated our recent finding that inferences about others' traits, captured by a two-dimensional framework of stereotype content (warmth and competence), had dissociable effects on participants' monetary-allocation choices: recipients' warmth increased participants' aversion to advantageous inequity (i.e., earning more than recipients), and recipients' competence increased participants' aversion to disadvantageous inequity (i.e., earning less than recipients). Neurally, representational similarity analysis revealed that others' traits in the two-dimensional space were represented in the temporoparietal junction and superior temporal sulcus, two regions associated with mentalizing, and in the lateral OFC, known to represent inferred features of a decision context outside the social domain. Critically, only the latter predicted individual choices, suggesting that the effect of stereotypes on behavior is mediated by inference-based decision-making processes in the OFC.

Decoding the information structure underlying the neural representation of concepts.

The nature of the representational code underlying conceptual knowledge remains a major unsolved problem in cognitive neuroscience. We assessed the extent to which different representational systems contribute to the instantiation of lexical concepts in high-level, heteromodal cortical areas previously associated with semantic cognition. We found that lexical semantic information can be reliably decoded from a wide range of heteromodal cortical areas in the frontal, parietal, and temporal cortex. In most of these areas, we found a striking advantage for experience-based representational structures (i.e., encoding information about sensory-motor, affective, and other features of phenomenal experience), with little evidence for independent taxonomic or distributional organization. These results were found independently for object and event concepts. Our findings indicate that concept representations in the heteromodal cortex are based, at least in part, on experiential information. They also reveal that, in most heteromodal areas, event concepts have more heterogeneous representations (i.e., they are more easily decodable) than object concepts and that other areas beyond the traditional "semantic hubs" contribute to semantic cognition, particularly the posterior cingulate gyrus and the precuneus.

Morphology, Connectivity, and Encoding Features of Tactile and Motor Representations of the Fingers in the Human Precentral and Postcentral Gyrus.

Despite the tight coupling between sensory and motor processing for fine manipulation in humans, it is not yet totally clear which specific properties of the fingers are mapped in the precentral and postcentral gyrus. We used fMRI to compare the morphology, connectivity, and encoding of the motor and tactile finger representations (FRs) in the precentral and postcentral gyrus of 25 5-fingered participants (8 females). Multivoxel pattern and structural and functional connectivity analyses demonstrated the existence of distinct motor and tactile FRs within both the precentral and postcentral gyrus, integrating finger-specific motor and tactile information. Using representational similarity analysis, we found that the motor and tactile FRs in the sensorimotor cortex were described by the perceived structure of the hand better than by the actual hand anatomy or other functional models (finger kinematics, muscles synergies). We then studied a polydactyly individual (i.e., with a congenital 6-fingered hand) showing superior manipulation abilities and divergent anatomic-functional hand properties. The perceived hand model was still the best model for tactile representations in the precentral and postcentral gyrus, while finger kinematics better described motor representations in the precentral gyrus. We suggest that, under normal conditions (i.e., in subjects with a standard hand anatomy), the sensorimotor representations of the 5 fingers in humans converge toward a model of perceived hand anatomy, deviating from the real hand structure, as the best synthesis between functional and structural features of the hand.SIGNIFICANCE STATEMENT Distinct motor and tactile finger representations exist in both the precentral and postcentral gyrus, supported by a finger-specific pattern of anatomic and functional connectivity across modalities. At the representational level, finger representations reflect the perceived structure of the hand, which might result from an adapting process harmonizing (i.e., uniformizing) the encoding of hand function and structure in the precentral and postcentral gyrus. The same analyses performed in an extremely rare polydactyly subject showed that the emergence of such representational geometry is also found in neuromechanical variants with different hand anatomy and function. However, the harmonization process across the precentral and postcentral gyrus might not be possible because of divergent functional-structural properties of the hand and associated superior manipulation abilities.

The Spatiotemporal Neural Dynamics of Object Recognition for Natural Images and Line Drawings.

Drawings offer a simple and efficient way to communicate meaning. While line drawings capture only coarsely how objects look in reality, we still perceive them as resembling real-world objects. Previous work has shown that this perceived similarity is mirrored by shared neural representations for drawings and natural images, which suggests that similar mechanisms underlie the recognition of both. However, other work has proposed that representations of drawings and natural images become similar only after substantial processing has taken place, suggesting distinct mechanisms. To arbitrate between those alternatives, we measured brain responses resolved in space and time using fMRI and MEG, respectively, while human participants (female and male) viewed images of objects depicted as photographs, line drawings, or sketch-like drawings. Using multivariate decoding, we demonstrate that object category information emerged similarly fast and across overlapping regions in occipital, ventral-temporal, and posterior parietal cortex for all types of depiction, yet with smaller effects at higher levels of visual abstraction. In addition, cross-decoding between depiction types revealed strong generalization of object category information from early processing stages on. Finally, by combining fMRI and MEG data using representational similarity analysis, we found that visual information traversed similar processing stages for all types of depiction, yet with an overall stronger representation for photographs. Together, our results demonstrate broad commonalities in the neural dynamics of object recognition across types of depiction, thus providing clear evidence for shared neural mechanisms underlying recognition of natural object images and abstract drawings.SIGNIFICANCE STATEMENT When we see a line drawing, we effortlessly recognize it as an object in the world despite its simple and abstract style. Here we asked to what extent this correspondence in perception is reflected in the brain. To answer this question, we measured how neural processing of objects depicted as photographs and line drawings with varying levels of detail (from natural images to abstract line drawings) evolves over space and time. We find broad commonalities in the spatiotemporal dynamics and the neural representations underlying the perception of photographs and even abstract drawings. These results indicate a shared basic mechanism supporting recognition of drawings and natural images.

Lingering Neural Representations of Past Task Features Adversely Affect Future Behavior.

During goal-directed behavior, humans purportedly form and retrieve so-called event files, conjunctive representations that link context-specific information about stimuli, their associated actions, and the expected action outcomes. The automatic formation, and later retrieval, of such conjunctive representations can substantially facilitate efficient action selection. However, recent behavioral work suggests that these event files may also adversely affect future behavior, especially when action requirements have changed between successive instances of the same task context (e.g., during task switching). Here, we directly tested this hypothesis with a recently developed method for measuring the strength of the neural representations of context-specific stimulus-action conjunctions (i.e., event files). Thirty-five male and female adult humans performed a task switching paradigm while undergoing EEG recordings. Replicating previous behavioral work, we found that changes in action requirements between two spaced repetitions of the same task incurred a significant reaction time cost. By combining multivariate pattern analysis and representational similarity analysis of the EEG recordings with linear mixed-effects modeling of trial-to-trial behavior, we then found that the magnitude of this behavioral cost was directly proportional to the strength of the conjunctive representation formed during the most recent previous exposure to the same task, that is, the most recent event file. This confirms that the formation of conjunctive representations of specific task contexts, stimuli, and actions in the brain can indeed adversely affect future behavior. Moreover, these findings demonstrate the potential of neural decoding of complex task set representations toward the prediction of behavior beyond the current trial.SIGNIFICANCE STATEMENT Understanding how the human brain organizes individual components of complex tasks is paramount for understanding higher-order cognition. During complex tasks, the brain forms conjunctive representations that link individual task features (contexts, stimuli, actions), which aids future performance of the same task. However, this can have adverse effects when the required sequence of actions within a task changes. We decoded conjunctive representations from electroencephalographic recordings during a task that included frequent changes to the rules determining the response. Indeed, stronger initial conjunctive representations predicted significant future response-time costs when task contexts repeated with changed response requirements. Showing that the formation of conjunctive task representations can have negative future effects generates novel insights into complex behavior and cognition, including task switching, planning, and problem solving.

Effects of Aging on Successful Object Encoding: Enhanced Semantic Representations Compensate for Impaired Visual Representations.

Although episodic memory and visual processing decline substantially with healthy aging, semantic knowledge is generally spared. There is evidence that older adults' spared semantic knowledge can support episodic memory. Here, we used functional magnetic resonance imaging (fMRI) combined with representational similarity analyses (RSAs) to examine how novel visual and preexisting semantic representations at encoding predict subjective memory vividness at retrieval. Eighteen young and seventeen older adults (female and male participants) encoded images of objects during fMRI scanning and recalled these images while rating the vividness of their memories. After scanning, participants discriminated between studied images and similar lures. RSA based on a deep convolutional neural network and normative concept feature data were used to link patterns of neural activity during encoding to visual and semantic representations. Relative to young adults, the specificity of activation patterns for visual features was reduced in older adults, consistent with dedifferentiation. However, the specificity of activation patterns for semantic features was enhanced in older adults, consistent with hyperdifferentiation. Despite dedifferentiation, visual representations in early visual cortex (EVC) predicted high memory vividness in both age groups. In contrast, semantic representations in lingual gyrus (LG) and fusiform gyrus (FG) were associated with high memory vividness only in the older adults. Intriguingly, data suggests that older adults with lower specificity of visual representations in combination with higher specificity of semantic representations tended to rate their memories as more vivid. Our findings suggest that memory vividness in aging relies more on semantic representations over anterior regions, potentially compensating for age-related dedifferentiation of visual information in posterior regions.SIGNIFICANCE STATEMENT Normal aging is associated with impaired memory for events while semantic knowledge might even improve. We investigated the effects of aging on the specificity of visual and semantic information in the brain when viewing common objects and how this information enables subsequent memory vividness for these objects. Using functional magnetic resonance imaging (fMRI) combined with modeling of the stimuli we found that visual information was represented with less specificity in older than young adults while still supporting memory vividness. In contrast semantic information supported memory vividness only in older adults and especially in those individuals that had the lowest specificity of visual information. These findings provide evidence for a spared semantic memory system increasingly recruited to compensate for degraded visual representations in older age.

Establishing the relationship between subjective perception and neural responses: Insights from correlation analysis and representational similarity analysis.

Exploring the relationship between sensory perception and brain responses holds important theoretical and clinical implications. However, commonly used methodologies like correlation analysis performed either intra- or inter- individually often yield inconsistent results across studies, limiting their generalizability. Representational similarity analysis (RSA), a method that assesses the perception-response relationship by calculating the correlation between behavioral and neural patterns, may offer a fresh perspective to reveal novel findings. Here, we delivered a series of graded sensory stimuli of four modalities (i.e., nociceptive somatosensory, non-nociceptive somatosensory, visual, and auditory) to/near the left or right hand of 107 healthy subjects and collected their single-trial perceptual ratings and electroencephalographic (EEG) responses. We examined the relationship between sensory perception and brain responses using within- and between-subject correlation analysis and RSA, and assessed their stability across different numbers of subjects and trials. We found that within-subject and between-subject correlations yielded distinct results: within-subject correlation revealed strong and reliable correlations between perceptual ratings and most brain responses, while between-subject correlation showed weak correlations that were vulnerable to the change of subject number. In addition to verifying the correlation results, RSA revealed some novel findings, i.e., correlations between behavioral and neural patterns were observed in some additional neural responses, such as "γ-ERS" in the visual modality. RSA results were sensitive to the trial number, but not to the subject number, suggesting that consistent results could be obtained for studies with relatively small sample sizes. In conclusion, our study provides a novel perspective on establishing the relationship between behavior and brain activity, emphasizing that RSA holds promise as a method for exploring this pattern relationship in future research.

Happy people are always similar: The evidence from brain morphological and functional inter-subject correlations.

A fundamental question in the study of happiness is whether there is neural evidence to support a well-known hypothesis that happy people are always similar while unfortunate people have their own misfortunes. To investigate this, we employed several happiness-related questionnaires to identify potential components of happiness, and further investigated and confirmed their associations with personality, mood, aggressive behaviors, and amygdala reactivity to fearful faces within a substantial sample size of college students (n = 570). Additionally, we examined the functional and morphological similarities and differences among happy individuals using the inter-subject representational similarity analysis (IS-RSA). IS-RSA emphasizes the geometric properties in a high-dimensional space constructed by brain or behavioral patterns and focuses on individual subjects. Our behavioral findings unveiled two factors of happiness: individual and social, both of which mediated the effect of personality traits on individual aggression. Subsequently, mood mediated the impact of happiness on aggressive behaviors across two subgroup splits. Functional imaging data revealed that individuals with higher levels of happiness exhibited reduced amygdala reactivity to fearful faces, as evidenced by a conventional face-matching task (n = 104). Moreover, IS-RSA demonstrated that these participants manifested similar neural activation patterns when processing fearful faces within the visual pathway, but not within the emotional network (e.g., amygdala). Morphological observations (n = 425) indicated that individuals with similar high happiness levels exhibited comparable gray matter volume patterns within several networks, including the default mode network, fronto-parietal network, visual network, and attention network. Collectively, these findings offer early neural evidence supporting the proposition that happy individuals may share common neural characteristics.

Are older adults less generous? Age differences in emotion-related social decision making.

In social interaction, age-related differences in emotional processing may lead to varied social decision making between young and older adults. However, previous studies of social decision making have paid less attention to the interactants' emotions, leaving age differences and underlying neural mechanisms unexplored. To address this gap, the present study combined functional and structural magnetic resonance imaging, employing a modified dictator game task with recipients displaying either neutral or sad facial expressions. Behavioral results indicated that although older adults' overall allocations did not differ significantly from those of young adults, older adults' allocations showing a decrease in emotion-related generosity compared to young adults. Using representational similarity analysis, we found that older adults showed reduced neural representations of recipients' emotions and gray matter volume in the right anterior cingulate gyrus (ACC), right insula, and left dorsomedial prefrontal cortex (DMPFC) compared to young adults. More importantly, mediation analyses indicated that age influenced allocations not only through serial mediation of neural representations of the right insula and left DMPFC, but also through serial mediation of the mean gray matter volume of the right ACC and left DMPFC. This study identifies the potential neural pathways through which age affects emotion-related social decision making, advancing our understanding of older adults' social interaction behavior that they may not be less generous unless confronted with individuals with specific emotions.

Uncovering individual variations in bystander intervention of injustice through intrinsic brain connectivity patterns.

When confronted with injustice, individuals often intervene as third parties to restore justice by either punishing the perpetrator or helping the victim, even at their own expense. However, little is known about how individual differences in third-party intervention propensity are related to inter-individual variability in intrinsic brain connectivity patterns and how these associations vary between help and punishment intervention. To address these questions, we employed a novel behavioral paradigm in combination with resting-state fMRI and inter-subject representational similarity analysis (IS-RSA). Participants acted as third-party bystanders and needed to decide whether to maintain the status quo or intervene by either helping the disadvantaged recipient (Help condition) or punishing the proposer (Punish condition) at a specific cost. Our analyses focused on three brain networks proposed in the third-party punishment (TPP) model: the salience (e.g., dorsal anterior cingulate cortex, dACC), central executive (e.g., dorsolateral prefrontal cortex, dlPFC), and default mode (e.g., dorsomedial prefrontal cortex, dmPFC; temporoparietal junction, TPJ) networks. IS-RSA showed that individual differences in resting-state functional connectivity (rs-FC) patterns within these networks were associated with the general third-party intervention propensity. Moreover, rs-FC patterns of the right dlPFC and right TPJ were more strongly associated with individual differences in the helping propensity rather than the punishment propensity, whereas the opposite pattern was observed for the dmPFC. Post-hoc predictive modeling confirmed the predictive power of rs-FC in these regions for intervention propensity across individuals. Collectively, these findings shed light on the shared and distinct roles of key regions in TPP brain networks at rest in accounting for individual variations in justice-restoring intervention behaviors.

Unraveling lexical semantics in the brain: Comparing internal, external, and hybrid language models.

To explain how the human brain represents and organizes meaning, many theoretical and computational language models have been proposed over the years, varying in their underlying computational principles and in the language samples based on which they are built. However, how well they capture the neural encoding of lexical semantics remains elusive. We used representational similarity analysis (RSA) to evaluate to what extent three models of different types explained neural responses elicited by word stimuli: an External corpus-based word2vec model, an Internal free word association model, and a Hybrid ConceptNet model. Semantic networks were constructed using word relations computed in the three models and experimental stimuli were selected through a community detection procedure. The similarity patterns between language models and neural responses were compared at the community, exemplar, and word node levels to probe the potential hierarchical semantic structure. We found that semantic relations computed with the Internal model provided the closest approximation to the patterns of neural activation, whereas the External model did not capture neural responses as well. Compared with the exemplar and the node levels, community-level RSA demonstrated the broadest involvement of brain regions, engaging areas critical for semantic processing, including the angular gyrus, superior frontal gyrus and a large portion of the anterior temporal lobe. The findings highlight the multidimensional semantic organization in the brain which is better captured by Internal models sensitive to multiple modalities such as word association compared with External models trained on text corpora.