Conceptualization
conceptualization
Brain network communication: concepts, models and applications
Understanding communication and information processing in nervous systems is a central goal of neuroscience. Over the past two decades, advances in connectomics and network neuroscience have opened new avenues for investigating polysynaptic communication in complex brain networks. Recent work has brought into question the mainstay assumption that connectome signalling occurs exclusively via shortest paths, resulting in a sprawling constellation of alternative network communication models. This Review surveys the latest developments in models of brain network communication. We begin by drawing a conceptual link between the mathematics of graph theory and biological aspects of neural signalling such as transmission delays and metabolic cost. We organize key network communication models and measures into a taxonomy, aimed at helping researchers navigate the growing number of concepts and methods in the literature. The taxonomy highlights the pros, cons and interpretations of different conceptualizations of connectome signalling. We showcase the utility of network communication models as a flexible, interpretable and tractable framework to study brain function by reviewing prominent applications in basic, cognitive and clinical neurosciences. Finally, we provide recommendations to guide the future development, application and validation of network communication models.
Where do problem spaces come from? On metaphors and representational change
The challenges of problem solving do not exclusively lie in how to perform heuristic search, but they begin with how we understand a given task: How to cognitively represent the task domain and its components can determine how quickly someone is able to progress towards a solution, whether advanced strategies can be discovered, or even whether a solution is found at all. While this challenge of constructing and changing representations has been acknowledged early on in problem solving research, for the most part it has been sidestepped by focussing on simple, well-defined problems whose representation is almost fully determined by the task instructions. Thus, the established theory of problem solving as heuristic search in problem spaces has little to say on this. In this talk, I will present a study designed to explore this issue, by virtue of finding and refining an adequate problem representation being its main challenge. In this exploratory case study, it was investigated how pairs of participants acquaint themselves with a complex spatial transformation task in the domain of iterated mental paper folding over the course of several days. Participants have to understand the geometry of edges which occurs when repeatedly mentally folding a sheet of paper in alternating directions without the use of external aids. Faced with the difficulty of handling increasingly complex folds in light of limited cognitive capacity, participants are forced to look for ways in which to represent folds more efficiently. In a qualitative analysis of video recordings of the participants' behaviour, the development of their conceptualisation of the task domain was traced over the course of the study, focussing especially on their use of gesture and the spontaneous occurrence and use of metaphors in the construction of new representations. Based on these observations, I will conclude the talk with several theoretical speculations regarding the roles of metaphor and cognitive capacity in representational change.
NMC4 Short Talk: Untangling Contributions of Distinct Features of Images to Object Processing in Inferotemporal Cortex
How do humans perceive daily objects of various features and categorize these seemingly intuitive and effortless mental representations? Prior literature focusing on the role of the inferotemporal region (IT) has revealed object category clustering that is consistent with the semantic predefined structure (superordinate, ordinate, subordinate). It has however been debated whether the neural signals in the IT regions are a reflection of such categorical hierarchy [Wen et al.,2018; Bracci et al., 2017]. Visual attributes of images that correlated with semantic and category dimensions may have confounded these prior results. Our study aimed to address this debate by building and comparing models using the DNN AlexNet, to explain the variance in representational dissimilarity matrix (RDM) of neural signals in the IT region. We found that mid and high level perceptual attributes of the DNN model contribute the most to neural RDMs in the IT region. Semantic categories, as in predefined structure, were moderately correlated with mid to high DNN layers (r = [0.24 - 0.36]). Variance partitioning analysis also showed that the IT neural representations were mostly explained by DNN layers, while semantic categorical RDMs brought little additional information. In light of these results, we propose future works should focus more on the specific role IT plays in facilitating the extraction and coding of visual features that lead to the emergence of categorical conceptualizations.
Children's relational noun generalization strategies
A common result is that comparison settings (i.e., several stimuli introduced simultaneously) favor conceptualization and generalization. However still little is known of the solving strategies used by children to compare and generalize novel words. Understanding the temporal dynamics of children’s solving strategies may help assess which processes underlie generalization. We tested children in noun and relational noun generalization tasks and collected eye tracking data. To analyze and interpret the data we followed predictions made by existing models of analogical reasoning and generalization. The data reveals clear patterns of exploration in which participants compare learning items before searching for a solution. Analyses of the beginning of trials show that early comparisons favor generalization and that errors may be caused by a lake of early comparison. Children then pursue their search in different ways according to the task. In this presentation I will present the generalization strategies revealed by eye tracking, compare the strategies from both tasks and confront them to existing models.