Index systems are central to our everyday and intellectual lives. Their ubiquity and diversity make them an important class of cognitive artifacts, the study of which has implications for our understanding of representational systems in general. This paper builds schema-theoretic network models of the nature of the memory structures, that underpin the interpretation of indexing systems. We identify four common classes of index systems. Using Representation Interpretation Structure Theory, we ex-plain how the four basic classes can be responsible for the substantial diversity among index systems.

Research in psychology on reasoning has often been restrictedto relatively inexpressive statements involving quantifiers.This is limited to situations that typically do not arise inpractical settings, such as ontology engineering. In orderto provide an analysis of inference, we focus on reasoningtasks presented in external graphic representations wherestatements correspond to those involving multiple quantifiersand unary and binary relations. Our experiment measuredparticipants’ performance when reasoning with two notations.The first used topology to convey information via node-linkdiagrams (i.e. graphs). The second used topological andspatial constraints to convey information (Euler diagrams withadditional graph-like syntax). We found that topological-spatial representations were more effective than topologicalrepresentations. Unlike topological-spatial representations,reasoning with topological representations was harder wheninvolving multiple quantifiers and binary relations than singlequantifiers and unary relations. These findings are comparedto those for sentential reasoning tasks.

In order to communicate, humans flatten complex ideas and their attributes into a sequence of words. Humans can use this ability to express and understand complex hierarchical and relational concepts, such as kinship relations and logical deduction chains. We simulate communication of relational and hierarchical concepts using artificial agents. We propose a new set of graph communication games, which show that agents parametrized by graph neural networks develop a more compositional language compared to bag-of-words and sequence models. Graph-based agents are also more successful at systematic generalization to new combinations of familiar features. We release the implementation to probe research on emergent communication over complex data.

We present a framework to assess the relative cognitive cost of alternative representational systems for problem solving.The framework consists of 19 cognitive properties of representational systems, which are distributed across 4 dimensions(registration, semantic encoding, inference, and solution) and three scales of granularity (symbol, expression, and sub-representations). It examines components and processes spanning the internal mental representation and external physicaldisplay, and also addresses heterogenous representations of problems. We provide functions to evaluate the cost of eachcognitive property by examining, for example, types of matches between display symbols and concepts, the arity ofrelations, or the depth of solution trees. The cognitive costs for each property are combined to estimate the overallcognitive cost, and hence the relative effectiveness, of a representation. The frameworks development is motivated byour goal of engineering an automated system that will select representations suited to specific classes of problems forindividual users.

Transcription with Incremental Presentation of the Stimulus (TIPS) is a novel approach relying on micro-behaviours proposed by Colarusso and colleagues (2023) to study users' cognition with data visualizations. The study in this paper has two primary objectives: (a) investigate whether TIPS can measure an individual's competence with data visualizations; and (b) explore the potential enhancement of TIPS measures by normalizing them with the individual's performance on tests of visuo-spatial abilities and memory capacity. We test 30 participants with different expertise and cognitive skills. Results reveal that TIPS provides some promise for individual competence assessment, but only when normalized with the individual's performance on a test of rigid transformation of mental images. Other tests measuring visuospatial abilities or memory capacity did not produce effective normalizations.