The Neural Representational Space of Social Memory.
Ontology highlight
ABSTRACT:
SUBMITTER: Dziura SL
PROVIDER: S-EPMC8412184 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Categorizing and understanding other people's actions is a key human capability. Whereas there exists a growing literature regarding the organization of objects, the representational space underlying the organization of observed actions remains largely unexplored. Here we examined the organizing principles of a large set of actions and the corresponding neural representations. Using multiple regression representational similarity analysis of fMRI data, in which we accounted for variability due to major action components (body parts, scenes, movements, objects, sociality, transitivity) and three control models (distance between observer and actor, number of people, HMAX-C1), we found that the semantic dissimilarity structure was best captured by patterns of activation in the lateral occipitotemporal cortex (LOTC). Together, our results demonstrate that the organization of observed actions in the LOTC resembles the organizing principles used by participants to classify actions behaviorally, in line with the view that this region is crucial for accessing the meaning of actions.
| S-EPMC6894926 | biostudies-literature
Project description:Neural activity in medial prefrontal cortex (MPFC), identified as engaging in self-related processing, predicts later health behavior change. However, it is unknown to what extent individual differences in neural representation of content and lived experience influence this brain-behavior relationship. We examined whether the strength of content-specific representations during persuasive messaging relates to later behavior change, and whether these relationships change as a function of individuals' social network composition. In our study, smokers viewed anti-smoking messages while undergoing fMRI and we measured changes in their smoking behavior one month later. Using representational similarity analyses, we found that the degree to which message content (i.e. health, social, or valence information) was represented in a self-related processing MPFC region was associated with later smoking behavior, with increased representations of negatively valenced (risk) information corresponding to greater message-consistent behavior change. Furthermore, the relationship between representations and behavior change depended on social network composition: smokers who had proportionally fewer smokers in their network showed increases in smoking behavior when social or health content was strongly represented in MPFC, whereas message-consistent behavior (i.e., less smoking) was more likely for those with proportionally more smokers in their social network who represented social or health consequences more strongly. These results highlight the dynamic relationship between representations in MPFC and key outcomes such as health behavior change; a complete understanding of the role of MPFC in motivation and action should take into account individual differences in neural representation of stimulus attributes and social context variables such as social network composition.
| S-EPMC5821423 | biostudies-other
Project description:Observed touch interactions provide useful information on how others communicate with the external world. Previous studies revealed shared neural circuits between the direct experience and the passive observation of simple touch, such as being stroked/slapped. Here, we investigate the complexity of the neural representations underlying the understanding of others' socio-affective touch interactions. Importantly, we use a recently developed touch database that contains a larger range of more complex social and non-social touch interactions. Participants judged affective aspects of each touch event and were scanned while watching the same videos. Using correlational multivariate pattern analysis methods, we obtained neural similarity matrices in 18 regions of interest from five different networks: somatosensory, pain, the theory of mind, visual and motor regions. Among them, four networks except motor cortex represent the social nature of the touch, whereas fine-detailed affective information is reflected in more targeted areas such as social brain regions and somatosensory cortex. Lastly, individual social touch preference at the behavioral level was correlated with the involvement of somatosensory areas on representing affective information, suggesting that individuals with higher social touch preference exhibit stronger vicarious emotional responses to others' social touch experiences. Together, these results highlight the overall complexity and the individual modulation of the distributed neural representations underlying the processing of observed socio-affective touch.
| S-EPMC5971215 | biostudies-literature
Project description:In an increasingly aging society, age has become a foundational dimension of social grouping broadly targeted by advertising and governmental policies. However, perception of old age induces mainly strong negative social biases. To characterize their cognitive and perceptual foundations, we modeled the mental representations of faces associated with three age groups (young age, middle age, and old age), in younger and older participants. We then validated the accuracy of each mental representation of age with independent validators. Using statistical image processing, we identified the features of mental representations that predict perceived age. Here, we show that whereas younger people mentally dichotomize aging into two groups, themselves (younger) and others (older), older participants faithfully represent the features of young age, middle age, and old age, with richer representations of all considered ages. Our results demonstrate that, contrary to popular public belief, older minds depict socially relevant information more accurately than their younger counterparts.
| S-EPMC4251953 | biostudies-literature
Project description:Understanding the neural basis of the remarkable human cognitive capacity to learn novel concepts from just one or a few sensory experiences constitutes a fundamental problem. We propose a simple, biologically plausible, mathematically tractable, and computationally powerful neural mechanism for few-shot learning of naturalistic concepts. We posit that the concepts that can be learned from few examples are defined by tightly circumscribed manifolds in the neural firing-rate space of higher-order sensory areas. We further posit that a single plastic downstream readout neuron learns to discriminate new concepts based on few examples using a simple plasticity rule. We demonstrate the computational power of our proposal by showing that it can achieve high few-shot learning accuracy on natural visual concepts using both macaque inferotemporal cortex representations and deep neural network (DNN) models of these representations and can even learn novel visual concepts specified only through linguistic descriptors. Moreover, we develop a mathematical theory of few-shot learning that links neurophysiology to predictions about behavioral outcomes by delineating several fundamental and measurable geometric properties of neural representations that can accurately predict the few-shot learning performance of naturalistic concepts across all our numerical simulations. This theory reveals, for instance, that high-dimensional manifolds enhance the ability to learn new concepts from few examples. Intriguingly, we observe striking mismatches between the geometry of manifolds in the primate visual pathway and in trained DNNs. We discuss testable predictions of our theory for psychophysics and neurophysiological experiments.
| S-EPMC9618072 | biostudies-literature
Project description:Neuronal plasticity that develops in the cortex during learning is assumed to represent memory content, but the functions of such plasticity are actually unknown. The shift in spectral tuning in primary auditory cortex (A1) to the frequency of a tone signal is a compelling candidate for a substrate of memory because it has all of the cardinal attributes of associative memory: associativity, specificity, rapid induction, consolidation, and long-term retention. Tuning shifts increase the representational area of the signal in A1, as an increasing function of performance level, suggesting that area encodes the magnitude of acquired stimulus significance. The present study addresses the question of the specific function of learning-induced associative representational plasticity. We tested the hypothesis that specific increases in A1 representational area for an auditory signal serve the mnemonic function of enhancing memory strength for that signal. Rats were trained to bar-press for reward contingent on the presence of a signal tone (5.0 kHz), and assessed for memory strength during extinction. The amount of representational area gain for the signal frequency band was significantly positively correlated with resistance to extinction to the signal frequency in two studies that spanned the range of task difficulty. These findings indicate that specific gain in cortical representational area underlies the strength of the behaviorally-relevant contents of memory. Thus, mnemonic functions of cortical plasticity are determinable.
| S-EPMC2840533 | biostudies-literature
Project description:Numerous studies of insight problem solving are focused on both the control and storage systems of working memory. We obtained contradictory data about how working memory systems are involved in insight problem solving process. We argue that measuring the dynamics of the control system and storage systems through the course of problem solving can provide a more refined view on the processes involved, as a whole, and explain the existing controversies. We theorize that specific insight mechanisms require varying working memory capacities at different stages of the problem solving process. Our study employed a dual task paradigm to track the dynamics of working memory systems load during problem solving by measuring the reaction time in a secondary probe-task during different stages of problem solving. We varied the modality (verbal, visual) and the complexity of the probe-task during insight and non-insight problem solving. The results indicated that the dynamics of working memory load in insight problems differs from those in non-insight problems. Our first experiment shows that the complexity of the probe-task affects overall probe-task reaction times in both insight and non-insight problem solving. Our second experiment demonstrates that the solution of a non-insight problem is primarily associated with the working memory control system, while insight problems rely on relevant storage systems. Our results confirm that insight process requires access to various systems of working memory throughout the solution. We found that working memory load in non-insight problems increases from stage to stage due to allocation of the attentional control resources to interim calculations. The nature of the dynamics of working memory load in insight problems remains debatable. We claim that insight problem solving demands working memory storage during the entire problem solving process and that control system plays an important role just prior to the solution.
| S-EPMC6174229 | biostudies-literature
Project description:The activity of neural populations in the brains of humans and animals can exhibit vastly different spatial patterns when faced with different tasks or environmental stimuli. The degrees of similarity between these neural activity patterns in response to different events are used to characterize the representational structure of cognitive states in a neural population. The dominant methods of investigating this similarity structure first estimate neural activity patterns from noisy neural imaging data using linear regression, and then examine the similarity between the estimated patterns. Here, we show that this approach introduces spurious bias structure in the resulting similarity matrix, in particular when applied to fMRI data. This problem is especially severe when the signal-to-noise ratio is low and in cases where experimental conditions cannot be fully randomized in a task. We propose Bayesian Representational Similarity Analysis (BRSA), an alternative method for computing representational similarity, in which we treat the covariance structure of neural activity patterns as a hyper-parameter in a generative model of the neural data. By marginalizing over the unknown activity patterns, we can directly estimate this covariance structure from imaging data. This method offers significant reductions in bias and allows estimation of neural representational similarity with previously unattained levels of precision at low signal-to-noise ratio, without losing the possibility of deriving an interpretable distance measure from the estimated similarity. The method is closely related to Pattern Component Model (PCM), but instead of modeling the estimated neural patterns as in PCM, BRSA models the imaging data directly and is suited for analyzing data in which the order of task conditions is not fully counterbalanced. The probabilistic framework allows for jointly analyzing data from a group of participants. The method can also simultaneously estimate a signal-to-noise ratio map that shows where the learned representational structure is supported more strongly. Both this map and the learned covariance matrix can be used as a structured prior for maximum a posteriori estimation of neural activity patterns, which can be further used for fMRI decoding. Our method therefore paves the way towards a more unified and principled analysis of neural representations underlying fMRI signals. We make our tool freely available in Brain Imaging Analysis Kit (BrainIAK).
| S-EPMC6553797 | biostudies-literature
Project description:We present a high-dimensional model of the representational space in human ventral temporal (VT) cortex in which dimensions are response-tuning functions that are common across individuals and patterns of response are modeled as weighted sums of basis patterns associated with these response tunings. We map response-pattern vectors, measured with fMRI, from individual subjects' voxel spaces into this common model space using a new method, "hyperalignment." Hyperalignment parameters based on responses during one experiment--movie viewing--identified 35 common response-tuning functions that captured fine-grained distinctions among a wide range of stimuli in the movie and in two category perception experiments. Between-subject classification (BSC, multivariate pattern classification based on other subjects' data) of response-pattern vectors in common model space greatly exceeded BSC of anatomically aligned responses and matched within-subject classification. Results indicate that population codes for complex visual stimuli in VT cortex are based on response-tuning functions that are common across individuals.
| S-EPMC3201764 | biostudies-literature
Project description: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.
| S-EPMC5705443 | biostudies-literature