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Studies have found that when innovation involves recombining cultural traits, partially-connected populations produce higher levels of cultural complexity than fully-connected populations by avoiding cultural homogenization. However, population connectedness is only one of many factors that could promote cultural diversity and thus cultural complexity. Here, we examine whether people&#39;s preference for copying members of their own social group could also fill this role. Our simulations reveal that even in fully-connected populations, ingroup-biased transmission results in greater cultural complexity than unbiased transmission. Moreover, in partially-connected populations, this bias interacts with population structure to produce even higher levels of cultural complexity than population structure alone. Finally, by incorporating population turnover into our model, we shed light on the trade-off between promoting cultural diversity versus limiting cultural loss.

CogSci 2022

Ingroup-Biased Copying Promotes Cultural Diversity and Complexity

Studies have found that when innovation involves recombining cultural traits, partially-connected populations produce higher levels of cultural complexity than fully-connected populations by avoiding cultural homogenization. However, population connectedness is only one of many factors that could promote cultural diversity and thus cultural complexity. Here, we examine whether people's preference for copying members of their own social group could also fill this role. Our simulations reveal that even in fully-connected populations, ingroup-biased transmission results in greater cultural complexity than unbiased transmission. Moreover, in partially-connected populations, this bias interacts with population structure to produce even higher levels of cultural complexity than population structure alone. Finally, by incorporating population turnover into our model, we shed light on the trade-off between promoting cultural diversity versus limiting cultural loss.

technical paper

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Despite progress in understanding the sources of difficulty in solving insight problems, how new ideas are discovered, implemented, and learned is poorly understood. We report an experiment testing a theory of how individuals use failed attempts to discover new ideas. We compared performance on the nine-dot problem with a variant requiring solution using three lines rather than four. Results supported predictions that the three-line variant is easier than the four-line, and that transfer of solution knowledge from the three- to the four-line version is facilitative, but not vice-versa. Additionally, varying the spacing between dots facilitated discovery and transfer of solutions in both variants. Our theory specifies a priority order for seeking new ideas that offers a partial solution to the frame problem. Individuals first seek ideas from the problem statement and attempts they make. Only when these sources fail do they resort to searching memory or the external task environment.

Conceptual recoding of new ideas during and aer solution of an insight problem

The opportunistic assimilation hypothesis posits that struggling and failing to solve a problem creates failure indexes, or long-term memory traces of the problem, that creates sensitivity to environment hints that trigger insight experiences. Past laboratory research has cast doubt on the usefulness of such hints during incubation breaks, but laboratory work is limited in its ability to recreate the diversity of stimuli in everyday life the opportunistic assimilation hypothesis requires. The current paper evaluates the insight experiences of over 150 participants who solved an insight math puzzle outside the lab for the presence of incidental hints that aided with problem solving. Across two studies, participants reported that chance hints in the wild had helped them to solve the puzzle and triggered insight moments. This suggests that opportunistic assimilation may play a role in everyday insight experiences and should not be discounted in future research.

Diversity in Mathematical Insight Experiences in the Wild: Evidence of Opportunistic Assimilation

Where do mathematical insights come from? According to classic accounts, creativity is a multi-stage process that involves combining ideas in novel ways. Evidence for these accounts, however, is drawn from artificial lab-based settings or is zoomed out from the messy, moment-to-moment details of discovery. Here, we examine a video corpus of expert mathematicians generating proofs in an ecologically valid setting. We find that mathematicians begin by creating a variety of inscriptions. They then interact with these inscriptions through gaze, speech, gesture, and writing. When they experience an insight, however, their interactions become unpredictable, and they begin to connect inscriptions in novel ways (quantified by an information-theoretic measure, surprisal). Expert mathematical discovery, we conclude, exhibits the stages and combinatorial processing that have been proposed to characterize creativity. Even at the pinnacle of abstraction, at the highest levels of expertise, new ideas are born when the body discovers unexpected affinities among ideas.

Mathematical insights as novel connections: Evidence from expert mathematicians

Traditional laboratory studies have found that people are more likely to retrieve surface matches than distant analogs, suggesting that superficial similarities exert a stronger influence than structural similarities on retrieval. However, it has been contended that the observed supremacy of surface similarity may have originated in experimental conditions that are unfairly adverse for the retrieval of distant analogs, as well as in a faulty separation between surface and structural similarity during the construction of surface matches. In two experiments, we presented a target item that maintained only superficial similarities with one extra-experimental source and only structural similarities with another one. By using natural items, we were able to avoid the shallow processing often attributed to experimental analogs, while carefully controlling that surface matches did not maintain structural similarities. Converging with traditional results, our data showed a more frequent retrieval of surface matches than of distant analogs, indicating a supremacy of superficial similarities during retrieval.

Structural vs. Superficial Similarity During Unprompted Analogical Retrieval: Which one Exerts a Greater Force?

We see the external world as consisting not only of objects and their parts, but also of relations that hold between them. Visual analogy, which depends on similarities between relations, provides a clear example of how perception supports reasoning. Here we report an experiment in which we quantitatively measured the human ability to find analogical mappings between parts of different objects, where the objects to be compared were drawn either from the same category (e.g., images of two mammals, such as a dog and a horse), or from two dissimilar categories (e.g., a chair image mapped to a cat image). Humans showed systematic mapping patterns, but with greater variability in mapping responses when objects were drawn from dissimilar categories. We simulated the human response of analogical mapping using a computational model of mapping between 3D objects, visiPAM (visual Probabilistic Analogical Mapping). VisiPAM takes point-cloud representations of two 3D objects as inputs, and outputs the mapping between analogous parts of the two objects. VisiPAM consists of a visual module that constructs structural representations of individual objects, and a reasoning module that identifies a probabilistic mapping between parts of the two 3D objects. Model simulations not only capture the qualitative pattern of human mapping performance cross conditions, but also approach human-level reliability in solving visual analogy problems.

From Vision to Reasoning: Probabilistic Analogical Mapping Between 3D Objects

A key property of human cognition is its ability to generate novel predictions about unfamiliar situations by completing a partially-specified relation or an analogy. Here, we present a computational model capable of producing generative inferences from relations and analogs. This model, BART-Gen, operates on explicit representations of relations learned by BART (Bayesian Analogy with Relational Transformations), to achieve two related forms of generative inference: reasoning from a single relation, and reasoning from an analog. In the first form, a reasoner completes a partially-specified instance of a stated relation (e.g., robin is a type of ____). In the second, a reasoner completes a target analog based on a stated source analog (e.g., sedan:car :: robin:____). We compare the performance of BART-Gen with that of BERT, a popular model for Natural Language Processing (NLP) that is trained on sentence completion tasks and that does not rely on explicit representations of relations. Across simulations and human experiments, we show that BART-Gen produces more human-like responses for generative inferences from relations and analogs than does the NLP model. These results demonstrate the essential role of explicit relation representations in human generative reasoning.

Generative Inferences in Relational and Analogical Reasoning: A Comparison of Computational Models

The capacity to generate recursive sequences is argued to be a behavioral marker of rich, algorithmic cognition, like that found perhaps distinctively in humans. However, the precise mechanisms underlying recursive sequence generation remain mysterious. We investigate three potential building blocks of recursive pattern processing: hierarchical reasoning, ordinal reasoning, and associative chaining. We develop a Bayesian mixture model to quantify the extent to which these three cognitive mechanisms contribute to a center-embedded sequence generation task. We further test whether recursive rule discovery depends upon relational information (either perceptual or conceptual) present in the task stimuli. The presence of relational information facilitates hierarchical reasoning that drives the generation of recursive sequences across various depths of center-embedding. Without relational information, the use of ordinal reasoning predominates. Our results suggest that hierarchical reasoning is an important building block of recursive pattern processing and can be deployed across embedding depths and relational domains.

Building Blocks of Recursive Pattern Processing in Human Adults

Demonstratives---simple referential devices like this and that---are linguistic universals, but their meaning varies cross-linguistically. In languages like English and Italian, demonstratives are thought to encode the referent's distance from the producer (e.g., that one means ``the one far away from me"), while in others, like Portuguese and Spanish, they encode relative distance from both producer and receiver (e.g., aquel means ``the one far away from both of us"). Here we propose that demonstratives are also sensitive to the receiver's focus of attention, hence requiring a deeper form of social cognition than previously thought. We provide initial empirical and computational evidence for this idea, suggesting that producers use demonstratives to redirect the receiver's attention towards the intended referent, rather than only to indicate its physical distance.

Language universals rely on social cognition: Computational models of the use of this and that to redirect the receiver’s attention

“Each” and “every” can be used to express the same truth-conditions but differ in their contexts of use. We adopt a particular psycho-semantic proposal about the meanings of these universal quantifiers: “each” has a meaning that interfaces with the psychological system for representing object-files whereas “every” has a meaning that interfaces with the psychological system for representing ensembles. In five experiments (n=798 total) we demonstrate that this mentalistic account correctly predicts newly-observed constraints on how “each” and “every” are pragmatically used. More generally, these results demonstrate that canonical patterns of language use are affected in predictable ways by fine-grained differences in semantic representations and the cognitive systems to which those representations connect. By treating the output of semantics as mental representations that are more finely articulated than truth-conditions—and by taking seriously the relationship between linguistic meanings and non-linguistic cognitive systems—we can explain otherwise puzzling patterns of language use.

A Mentalistic Semantics Explains “Each” and “Every” Quantifier Use

How do humans produce and comprehend language in pragmatic ways? A variety of models of pragmatic inferences have been proposed, and these models are typically evaluated on their ability to account for human inferences in reference game experiments. However, these experiments are not tailored to target theoretical differences between models or clearly tease apart model predictions. We propose an optimal experiment design approach to systematically construct reference games that can optimally differentiate between models of human pragmatic reasoning. We demonstrate this approach and apply it to four models that have been debated in the literature: Grammar-based, Iterated Best Response (IBR), Rational Speech Act (RSA), and a recent variant of RSA grounded in rate-distortion theory (RD-RSA). Using these optimal reference game experiments, we find empirical evidence favoring iterated rationality models over the grammar-based model, as well as support for the relevance of rate-distortion theory to human pragmatic inferences. These results suggest that our optimal reference game design framework may help adjudicate between computational theories of pragmatic reasoning.

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Conceptual recoding of new ideas during and aer solution of an insight problem

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