Regional Specialization and Coordination Within the Network for Perceiving and Knowing About Others

Deep neural networks reveal topic-level representations of sentences in medial prefrontal cortex, lateral anterior temporal lobe, precuneus, and angular gyrus

When reading a sentence, individual words can be combined to create more complex meaning. In this study, we sought to uncover brain regions that reflect the representation of the meaning of sentences at the topic level, as opposed to the meaning of their individual constituent words when considered irrespective of their context. Using fMRI, we recorded the neural activity of participants while reading sentences. We constructed a topic-level sentence representations using the final layer of a convolutional neural network (CNN) trained to classify Wikipedia sentences into broad semantic categories. This model was contrasted with word-level sentence representations constructed using the average of the word embeddings constituting the sentence. Using representational similarity analysis, we found that the medial prefrontal cortex, lateral anterior temporal lobe, precuneus, and angular gyrus more strongly represent sentence topic-level, compared to word-level, meaning, uncovering the important role of these semantic system regions in the representation of topic-level meaning. Results were comparable when sentence meaning was modelled with a multilayer perceptron that was not sensitive to word order within a sentence, suggesting that the learning objective, in the terms of the topic being modelled, is the critical factor in capturing these neural representational spaces.

fMRI response to automatic and purposeful familiar-face processing in perceptual and nonperceptual cortical regions

Viewing the faces of familiar people selectively activates a distributed network of brain regions implicated in both the perceptual and nonperceptual processing of conspecifics. In this functional magnetic resonance imaging (fMRI) study, we investigate the influence of depth of famous-face processing on this network, comparing a passive incidental face processing to a task that required the extraction of identity and biographic information. We observed that the precuneus, ventromedial prefrontal cortex (vmPFC), anterior temporal face patch (ATFP), and the amygdala exhibit a selective response even during incidental face processing. At the same time, face selectivity was enhanced in the lateral anterior temporal lobe (latATL) and the posterior superior temporal sulcus (pSTS) when identity and information extraction was required. In addition, goal-directed identity and information extraction was associated with a recruitment of inferior frontal gyrus (IFG), whereas this region was deactivated during passive viewing. Collectively, these results show that: 1) in addition to active information extraction, the extended system is recruited by the passive retrieval of person-related knowledge and 2) active access to such knowledge modulates activity in latATL and pSTS, potentially mediated via control circuits in the IFG.NEW & NOTEWORTHY Information is extracted from familiar faces in both automatic and active modes. Using functional MRI, we show: 1) that automatic access results in the selective activation of nonperceptual brain regions, the precuneus, ventromedial prefrontal cortex, and the anterior face patch and amygdala, demonstrating the automaticity of access to information in these regions; 2) selective increases in the activation of the lateral anterior temporal lobe and posterior superior temporal gyrus when biographic information is actively extracted.

Profiles on the Orientation Discrimination Processing of Human Faces

Face recognition is a crucial subject for public health, as socialization is one of the main characteristics for full citizenship. However, good recognizers would be distinguished, not only by the number of faces they discriminate but also by the number of rejected stimuli as unfamiliar. When it comes to face recognition, it is important to remember that position, to some extent, would not entail a high cognitive cost, unlike other processes in similar areas of the brain. The aim of this paper was to examine participant’s recognition profiles according to face position. For this reason, a recognition task was carried out by employing the Karolinska Directed Emotional Faces. Reaction times and accuracy were employed as dependent variables and a cluster analysis was carried out. A total of two profiles were identified in participants’ performance, which differ in position in terms of reaction times but not accuracy. The results can be described as follows: first, it is possible to identify performance profiles in visual recognition of faces that differ in position in terms of reaction times, not accuracy; secondly, results suggest a bias towards the left. At the applied level, this could be of interest with a view to conducting training programs in face recognition.

General and feature-based semantic representations in the semantic network

How semantic representations are manifest over the brain remains a topic of active debate. A semantic representation may be determined by specific semantic features (e.g. sensorimotor information), or may abstract away from specific features and represent generalized semantic characteristics (general semantic representation). Here we tested whether nodes of the semantic system code for a general semantic representation and/or possess representational spaces linked to particular semantic features. In an fMRI study, eighteen participants performed a typicality judgment task with written words drawn from sixteen different categories. Multivariate pattern analysis (MVPA) and representational similarity analysis (RSA) were adopted to investigate the sensitivity of the brain regions to semantic content and the type of semantic representation coded (general or feature-based). We replicated previous findings of sensitivity to general semantic similarity in posterior middle/inferior temporal gyrus (pMTG/ITG) and precuneus (PC) and additionally observed general semantic representations in ventromedial prefrontal cortex (PFC). Finally, two brain regions of the semantic network were sensitive to semantic features: the left pMTG/ITG was sensitive to haptic perception and the left ventral temporal cortex (VTC) to size. This finding supports the involvement of both general semantic representation and feature-based representations in the brain's semantic system.