Following the seminal structure mapping theory by Dedre Gentner, the process of mapping the corresponding structures of relations defining two analogs has been understood as a key component of analogy making. However, not without a merit, in recent years some semantic, pragmatic, and perceptual aspects of analogy mapping attracted primary attention of analogy researchers. For almost a decade, our team have been re-focusing on relational structure mapping, investigating its potential mechanisms across various analogy tasks, both abstract (semantically-lean) and more concrete (semantically-rich), using diverse methods (behavioral, correlational, eye-tracking, EEG). I will present the overview of our main findings. They suggest that structure mapping (1) consists of an incremental construction of the ultimate mental representation, (2) which strongly depends on working memory resources and reasoning ability, (3) even if as little as a single trivial relation needs to be represented mentally. The effective mapping (4) is related to the slowest brain rhythm – the delta band (around 2-3 Hz) – suggesting its highly integrative nature. Finally, we have developed a new task – Graph Mapping – which involves pure mapping of two explicit relational structures. This task allows for precise investigation and manipulation of the mapping process in experiments, as well as is one of the best proxies of individual differences in reasoning ability. Structure mapping is as crucial to analogy as Gentner advocated, and perhaps it is crucial to cognition in general.

Relational thinking involves comparing abstract relationships between mental representations that vary in complexity; however, this complexity is rarely made explicit during everyday comparisons. This study explored how people naturally navigate relational complexity and interference using a novel relational match-to-sample (RMTS) task with both minimal and relationally directed instruction to observe changes in performance across three levels of relational complexity: perceptual, analogy, and system mappings. Individual working memory and relational abilities were examined to understand RMTS performance and susceptibility to interfering relational structures. Trials were presented without practice across four blocks and participants received feedback after each attempt to guide learning. Experiment 1 instructed participants to select the target that best matched the sample, while Experiment 2 additionally directed participants’ attention to same and different relations. Participants in Experiment 2 demonstrated improved performance when solving analogical mappings, suggesting that directing attention to relational characteristics affected behavior. Higher performing participants—those above chance performance on the final block of system mappings—solved more analogical RMTS problems and had greater visuospatial working memory, abstraction, verbal analogy, and scene analogy scores compared to lower performers. Lower performers were less dynamic in their performance across blocks and demonstrated negative relationships between analogy and system mapping accuracy, suggesting increased interference between these relational structures. Participant performance on RMTS problems did not change monotonically with relational complexity, suggesting that increases in relational complexity places nonlinear demands on working memory. We argue that competing relational information causes additional interference, especially in individuals with lower executive function abilities.

Previous work suggests that children’s ability to understand metaphors emerges late in development. Researchers argue that children’s initial failure to understand metaphors is due to an inability to reason about shared relational structures between concepts. However, recent work demonstrates that preschoolers, toddlers, and even infants are already capable of relational reasoning. Might preschoolers also be capable of understanding metaphors, given more sensitive experimental paradigms? I explore whether preschoolers (N = 200, ages 4-5) understand functional metaphors, namely metaphors based on functional similarities. In Experiment 1a, preschoolers rated functional metaphors (e.g. “Roofs are hats”; “Clouds are sponges”) as “smarter” than nonsense statements. In Experiment 1b, adults (N = 48) also rated functional metaphors as “smarter” than nonsense statements (e.g. “Dogs are scissors”; “Boats are skirts”). In Experiment 2, preschoolers preferred functional explanations (e.g. “Both hold water”) over perceptual explanations (e.g. “Both are fluffy”) when interpreting a functional metaphor (e.g. “Clouds are sponges”). In Experiment 3, preschoolers preferred functional metaphors over nonsense statements in a dichotomous-choice task. Overall, this work demonstrates preschoolers’ early-emerging ability to understand functional metaphors.

Analogy-making is considered to be one of the cognitive processes which are hard to be accounted for in connectionist terms. A number of models have been proposed, but they are either tailed for specific analogical tasks or require complicated mechanisms which don’t fit into the mainstream connectionist modelling paradigm. In this talk I will present a new connectionist account of analogy-making based on the vector approach to representing symbols (VARS). This approach allows representing relational structures of varying complexity by numeric vectors with fixed dimensionality. I will also present a simple and computationally efficient mechanism of aligning VARS representations, which integrates both semantic similarity and structural constraints. The results of a series of simulations will demonstrate that VARS can account for basic analogical phenomena.