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People generally prefer functional explanations over mechanistic ones. Why? One possibility is that people value functional information more. But another possibility is that people don&#39;t have an overall preference for functional explanations; Instead, people might just expect this information to precede mechanistic information. Here, we ask whether people have preferences for the order of functional and mechanistic information in explanations. In a first set of studies, we show that adults do in fact prefer that functional information precedes mechanistic. In a second set of studies, we show that people have a more general preference for explanations to address the whole before parts. Finally, we show that the preference for function to precede mechanism may be related to the broader whole-before-parts preference.

CogSci 2022

A Right Way to Explain? Function, Mechanism, and the Order of Explanations

People generally prefer functional explanations over mechanistic ones. Why? One possibility is that people value functional information more. But another possibility is that people don't have an overall preference for functional explanations; Instead, people might just expect this information to precede mechanistic information. Here, we ask whether people have preferences for the order of functional and mechanistic information in explanations. In a first set of studies, we show that adults do in fact prefer that functional information precedes mechanistic. In a second set of studies, we show that people have a more general preference for explanations to address the whole before parts. Finally, we show that the preference for function to precede mechanism may be related to the broader whole-before-parts preference.

technical paper

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The 44th Annual Meeting of the Cognitive Science Society highlighted research on the theme Cognitive Diversity in addition to the full breadth and diversity of research topics offered by the society’s membership. 





What kinds of arguments do people make, and what effect do they have on others? Normative constraints on argument-making are as old as philosophy itself, but little is known about the diversity of arguments made in practice. We use NLP tools to extract patterns of argument-making from the Reddit site "Change My View'' (r/CMV). This reveals six distinct argument patterns: not just the familiar deductive and inductive forms, but also arguments about definitions, relevance, possibility and cause, and personal experience. Data from r/CMV also reveal differences in efficacy: personal experience and, to a lesser extent, arguments about causation and examples, are most likely to shift a person's view, while arguments about relevance and presumption are the least. Finally, our methods reveal a gradient of argument-making preferences among users: a two-axis model, of "personal—impersonal'' and "concrete—abstract'', can account for nearly 80% of the strategy variance between individuals.

The Diversity of Argument-Making in the Wild: from Assumptions and Definitions to Causation and Anecdote in Reddit's ``Change My View''

We introduce purely visual paradigms that convey the logical structure of illusory inferences from disjunction: (a AND b) OR c, a |- b. Although the logical information was conveyed entirely via non-linguistic means, we found that the visual paradigms induce reasoning fallacies, though less attractive than their linguistic counterparts. The visual paradigms highlight the role of alternative-based reasoning, or question-answer dynamics, as they control for narrowly interpretive processes that confound the study of their linguistic counterparts.  To our knowledge, this is the first work to develop visual paradigms that represent reasoning fallacies committed by adults and involve multiple logical operators non-trivially embedded.  Previous studies focused on pre-verbal children or non-human animals, and for this reason limited the scope of research to visually representing logically simple, valid inferences.

Question-answer dynamics in deductive fallacies without language

Investigation of the relationship between cognitive function and academic performance has recently pivoted from differences in intelligence to executive function. To date, these studies have focused disproportionately on samples recruited from Western countries, despite evidence in support of cultural differences in these putative relationships. To address this gap, the present study investigated whether differences in inhibitory and/or attentional control could predict academic performance in a sample of Chinese adolescents (n=42). Participants reported on demographic details and completed both the Simon task and Attention Network Test. Data were analyzed using multiple linear regression controlling for gender, age, SES, English language proficiency, processing speed, and fluid intelligence. Results showed that one index of inhibitory control derived from flanker task performance explained a significant amount of unique variance in academic performance. Our findings provide evidence that executive function, specifically inhibitory control, plays a significant role in academic performance.

Inhibitory Control Predicts Academic Performance Beyond Fluid Intelligence and Processing Speed in English-Immersed Chinese High Schoolers

Parents and teachers often encourage students (e.g., "You can do it!") when they encounter challenges, but these messages are not always effective. Whose encouragement motivates students the most, and why? Here we tested the hypothesis that others’ domain knowledge (e.g., knowledge about course materials) and ability knowledge (e.g., knowledge about students’ abilities in the course) each inform how students evaluate their encouragement. In a large-scale survey, we find that middle school students (n=288) and high school students (n=425) are most likely to seek out and be motivated by encouragement from someone with both domain and ability knowledge, rather than only one or the other. This effect emerged both when students reasoned about hypothetical classmates (Study 1a) and real people in their lives (Study 1b). Moreover, we find that confidence in others' performance estimates linearly increases when they have greater ability and domain knowledge (Study 1c). Collectively, this work suggests that students do not find all encouragement equally motivating. Rather, students find encouragement most motivating when the speaker has knowledge of their abilities and the domain.

Adolescents are most motivated by encouragement from someone who knows their abilities and the domain

Research in cognitive science indicates that the effects of corrective feedback are powerful, but quite variable. Stemming from the Feedback Intervention Theory, we tested two features of feedback that may influence the learner’s attention to the self and ultimately their performance. In this zoom-based experiment, 6 to 8 year old children (N = 102) completed an online learning activity focused on solving mathematical equivalence problems. During the learning activity, children were assigned to different conditions which varied both feedback source (person vs. computer) and feedback type (correct-answer vs. correct-answer+verification). Feedback source was found to affect accuracy, where computer-based feedback resulted in higher accuracy compared to person-based feedback. Additionally, feedback type was found to affect strategy variability. Low knowledge children were more likely to use a variety of different strategies when they received correct-answer only feedback, while high knowledge children were less affected by the presence or absence of verification cues.

The source and type of feedback influence children's mathematics performance

We describe UMA (Unified Model of Arithmetic), a theory of children’s arithmetic implemented as a computational model. UMA extends a theory of fraction arithmetic (Braithwaite et al., 2017) to include arithmetic with whole numbers and decimals. We evaluated UMA in the domain of decimal arithmetic by training the model on problems from a math textbook series, then testing it on decimal arithmetic problems that were solved by 6th and 8th graders in a previous study. UMA’s test performance closely matched that of children, supporting three assumptions of the theory: (1) most errors reflect small deviations from standard procedures, (2) between-problem variations in error rates reflect the distribution of input that learners receive, and (3) individual differences in strategy use reflect underlying variation in learning parameters.

Testing a Unified Model of Arithmetic

Common critiques of natural language processing (NLP)
methods cite their lack of multimodal sensory information,
claiming an inability to learn situated, action-oriented relations
through language alone. Barsalou’s (1983) theory of
ad hoc categories, which are formed from to achieve goals in
real-world scenarios, correspond theoretically to those types
of relations with which language models ought to have great
difficulty. Recent NLP models have developed dynamic approaches
to word representations, where the same word can
have different encodings depending on the context in which it
appears. Testing these models using categorization tasks with
human response data demonstrates that situated properties may
be partially captured through semantic analysis. We discuss
possible ways in which different notions of situatedness may
be distinguished for future development and testing of NLP
models.

Getting Situated: Comparative Analysis of Language Models With Experimental Categorization Tasks

Semantic features have been playing a central role in investigating the nature of our conceptual representations. Yet the time and effort required to sample features from human raters has restricted their use to a limited set of manually curated concepts. Given recent success of transformer-based language models, we asked whether it was possible to use such models to automatically generate meaningful lists of properties for arbitrary object concepts and whether these models would produce features similar to those found in humans. We probed a GPT-3 model to generate semantic features for 1,854 objects and compared them to existing human feature norms. GPT-3 showed a similar distribution in the types of features and similar performance in predicting similarity, relatedness, and category membership. Together these results highlight the potential of large language models to capture important facets of human knowledge and yield a new approach for automatically generating interpretable feature sets.

Semantic features of object concepts generated with GPT-3

We present a neural dynamic model that perceptually grounds nested noun phrases, i.e., noun phrases that contain further (possibly also nested) noun phrases as parts. The model receives input from the visual array and a representation of a noun phrase from language processing. It organizes a search for the denoted object in the visual scene. The model is a neural dynamic architecture of interacting neural populations which has clear interfaces with perceptual processes. It solves a set of theoretical challenges, including the problem of keeping a nested structure in short-term memory in a way that solves the problem of 2 and massive binding problem emphasized by Jackendoff (2002). The model organizes a search for the objects that are referenced in that structure. We motivate the model, demonstrate simulation results, and discuss how it differs from related models.

A Neural Dynamic Model Perceptually Grounds Nested Noun Phrases

Human language offers a powerful window into our thoughts -- we tell stories, give explanations, and express our beliefs and goals through words. Abundant evidence also suggests that language plays a developmental role in structuring our learning. Here, we ask: how much of human-like thinking can be captured by learning statistical patterns in language alone? We first contribute a new challenge benchmark for comparing humans and distributional large language models (LLMs). Our benchmark contains two problem-solving domains (planning and explanation generation) and is designed to require generalization to new, out-of-distribution problems expressed in language. We find that humans are far more robust than LLMs on this benchmark. Next, we propose a hybrid Parse-and-Solve model, which augments distributional LLMs with a structured symbolic reasoning module. We find that this model shows more robust adaptation to out-of-distribution planning problems, demonstrating the promise of hybrid AI models for more human-like reasoning.

Structured, flexible, and robust: benchmarking and improving large language models towards more human-like behavior in out-of-distribution reasoning tasks

A Right Way to Explain? Function, Mechanism, and the Order of Explanations

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The Diversity of Argument-Making in the Wild: from Assumptions and Definitions to Causation and Anecdote in Reddit's ``Change My View''

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