Animacy Dimensions Ratings and Approach for Decorrelating Stimuli Dimensions

The distinction between animate and inanimate objects plays an important role in object recognition. The following 5 dimensions were shown in previous studies to be important for animacy perception independently: “being alive”, “looking like an animal”, “having mobility”, “having agency” and “being unpredictable”. However, it is not known how these dimensions in combination determine how we perceive animacy. To investigate, we created a stimulus set (M = 300) with almost all dimension combinations for which we acquired behavioural ratings on the 5 dimensions. We show that subjects (N = 26) are consistent in animacy ratings (r = 0.6) and that “being alive” and “having agency” dimensions are highly correlated (r = 0.62). To design a stimulus sub-set that is decorrelated on animacy dimensions for future fMRI and EGG experiments we used a genetic algorithm. Our approach proved to be successful in stimuli selection (max r = 0.35, compared to max r = 0.59 when using a random search). In summary, our study systematically investigates animacy dimensions, provides new insights in animacy perception, and presents an approach for decorrelating stimuli dimensions that can be useful for other studies

Extracting the Groupwise Core Structural Connectivity Network: Bridging Statistical and Graph-Theoretical Approaches

Finding the common structural brain connectivity network for a given population is an open problem, crucial for current neuro-science. Recent evidence suggests there's a tightly connected network shared between humans. Obtaining this network will, among many advantages , allow us to focus cognitive and clinical analyses on common connections, thus increasing their statistical power. In turn, knowledge about the common network will facilitate novel analyses to understand the structure-function relationship in the brain. In this work, we present a new algorithm for computing the core structural connectivity network of a subject sample combining graph theory and statistics. Our algorithm works in accordance with novel evidence on brain topology. We analyze the problem theoretically and prove its complexity. Using 309 subjects, we show its advantages when used as a feature selection for connectivity analysis on populations, outperforming the current approaches

Similarity, Not Complexity, Determines Visual Working Memory Performance

A number of studies have shown that visual working memory (WM) is poorer for complex versus simple items, traditionally accounted for by higher information load placing greater demands on encoding and storage capacity limits. Other research suggests that it may not be complexity that determines WM performance per se, but rather increased perceptual similarity between complex items as a result of a large amount of overlapping information. Increased similarity is thought to lead to greater comparison errors between items encoded into WM and the test item(s) presented at retrieval. However, previous studies have used different object categories to manipulate complexity and similarity, raising questions as to whether these effects are simply due to cross-category differences. For the first time, here the relationship between complexity and similarity in WM using the same stimulus category (abstract polygons) are investigated. The authors used a delayed discrimination task to measure WM for 1–4 complex versus simple simultaneously presented items and manipulated the similarity between the single test item at retrieval and the sample items at encoding. WM was poorer for complex than simple items only when the test item was similar to 1 of the encoding items, and not when it was dissimilar or identical. The results provide clear support for reinterpretation of the complexity effect in WM as a similarity effect and highlight the importance of the retrieval stage in governing WM performance. The authors discuss how these findings can be reconciled with current models of WM capacity limits

Semantic data set construction from human clustering and spatial arrangement

Abstract Research into representation learning models of lexical semantics usually utilizes some form of intrinsic evaluation to ensure that the learned representations reflect human semantic judgments. Lexical semantic similarity estimation is a widely used evaluation method, but efforts have typically focused on pairwise judgments of words in isolation, or are limited to specific contexts and lexical stimuli. There are limitations with these approaches that either do not provide any context for judgments, and thereby ignore ambiguity, or provide very specific sentential contexts that cannot then be used to generate a larger lexical resource. Furthermore, similarity between more than two items is not considered. We provide a full description and analysis of our recently proposed methodology for large-scale data set construction that produces a semantic classification of a large sample of verbs in the first phase, as well as multi-way similarity judgments made within the resultant semantic classes in the second phase. The methodology uses a spatial multi-arrangement approach proposed in the field of cognitive neuroscience for capturing multi-way similarity judgments of visual stimuli. We have adapted this method to handle polysemous linguistic stimuli and much larger samples than previous work. We specifically target verbs, but the method can equally be applied to other parts of speech. We perform cluster analysis on the data from the first phase and demonstrate how this might be useful in the construction of a comprehensive verb resource. We also analyze the semantic information captured by the second phase and discuss the potential of the spatially induced similarity judgments to better reflect human notions of word similarity. We demonstrate how the resultant data set can be used for fine-grained analyses and evaluation of representation learning models on the intrinsic tasks of semantic clustering and semantic similarity. In particular, we find that stronger static word embedding methods still outperform lexical representations emerging from more recent pre-training methods, both on word-level similarity and clustering. Moreover, thanks to the data set’s vast coverage, we are able to compare the benefits of specializing vector representations for a particular type of external knowledge by evaluating FrameNet- and VerbNet-retrofitted models on specific semantic domains such as “Heat” or “Motion.”</jats:p

Generative discriminative models for multivariate inference and statistical mapping in medical imaging

This paper presents a general framework for obtaining interpretable multivariate discriminative models that allow efficient statistical inference for neuroimage analysis. The framework, termed generative discriminative machine (GDM), augments discriminative models with a generative regularization term. We demonstrate that the proposed formulation can be optimized in closed form and in dual space, allowing efficient computation for high dimensional neuroimaging datasets. Furthermore, we provide an analytic estimation of the null distribution of the model parameters, which enables efficient statistical inference and p-value computation without the need for permutation testing. We compared the proposed method with both purely generative and discriminative learning methods in two large structural magnetic resonance imaging (sMRI) datasets of Alzheimer's disease (AD) (n=415) and Schizophrenia (n=853). Using the AD dataset, we demonstrated the ability of GDM to robustly handle confounding variations. Using Schizophrenia dataset, we demonstrated the ability of GDM to handle multi-site studies. Taken together, the results underline the potential of the proposed approach for neuroimaging analyses.Comment: To appear in MICCAI 2018 proceeding

Faces and voices in the brain: a modality-general person-identity representation in superior temporal sulcus

Leverhulme Trus

Affective iconic words benefit from additional sound–meaning integration in the left amygdala

Recent studies have shown that a similarity between sound and meaning of a word (i.e., iconicity) can help more readily access the meaning of that word, but the neural mechanisms underlying this beneficial role of iconicity in semantic processing remain largely unknown. In an fMRI study, we focused on the affective domain and examined whether affective iconic words (e.g., high arousal in both sound and meaning) activate additional brain regions that integrate emotional information from different domains (i.e., sound and meaning). In line with our hypothesis, affective iconic words, compared to their non‐iconic counterparts, elicited additional BOLD responses in the left amygdala known for its role in multimodal representation of emotions. Functional connectivity analyses revealed that the observed amygdalar activity was modulated by an interaction of iconic condition and activations in two hubs representative for processing sound (left superior temporal gyrus) and meaning (left inferior frontal gyrus) of words. These results provide a neural explanation for the facilitative role of iconicity in language processing and indicate that language users are sensitive to the interaction between sound and meaning aspect of words, suggesting the existence of iconicity as a general property of human language

Interpreting BOLD: towards a dialogue between cognitive and cellular neuroscience

Cognitive neuroscience depends on the use of blood oxygenation level-dependent (BOLD) functional magnetic resonance imaging (fMRI) to probe brain function. Although commonly used as a surrogate measure of neuronal activity, BOLD signals actually reflect changes in brain blood oxygenation. Understanding the mechanisms linking neuronal activity to vascular perfusion is, therefore, critical in interpreting BOLD. Advances in cellular neuroscience demonstrating differences in this neurovascular relationship in different brain regions, conditions or pathologies are often not accounted for when interpreting BOLD. Meanwhile, within cognitive neuroscience, increasing use of high magnetic field strengths and the development of model-based tasks and analyses have broadened the capability of BOLD signals to inform us about the underlying neuronal activity, but these methods are less well understood by cellular neuroscientists. In 2016, a Royal Society Theo Murphy Meeting brought scientists from the two communities together to discuss these issues. Here we consolidate the main conclusions arising from that meeting. We discuss areas of consensus about what BOLD fMRI can tell us about underlying neuronal activity, and how advanced modelling techniques have improved our ability to use and interpret BOLD. We also highlight areas of controversy in understanding BOLD and suggest research directions required to resolve these issues