Samuel J. Cheyette

People learn languages, music, games, mathematics, and a seemingly limitless assortment ofother structures across domains. How do we learn such a large variety of richly structuredrepresentations efficiently? One possibility is that people "learn by programming," synthesizingdata-generating algorithms to explain what they observe. We examine the nature andorigins of structure learning mechanisms in human adults, human children, and nonhumanprimates (rhesus macaques), using a highly unconstrained sequence prediction task. Humanadults and older children (4-7 y.o.) learned many richly structured sequences, while monkeysand younger children (3 y.o.) succeeded mostly on simple, continuously-varying sequences(e.g. linear or approximately linear patterns). We test multiple learning models and findthat adults and older children are best explained by an inference model that generates programsin a "Language of Thought" with motor and geometry primitives, while monkeys arebest explained by local linear extrapolation strategies. Younger children exhibit variationin strategies but pattern more closely with monkeys than adults. By age 4, children showstrong program-like inductive biases similar to adults and are best fit in aggregate by theLoT model.

Many real-world problems are defined by complex systems of interlocking constraints. How people are able to solve these problems with such limited working memory capacity remains poorly understood.We propose a formal model of human problem-solving that uses metacognitive knowledge of its own memory limits and imperfect reasoning to guide subproblem choice. We compare our model to human gameplay in two experiments using a variant of the classic game Minesweeper. In Experiment 1, we find that participants' accuracy was influenced both by the order of subproblems and their ability to externalize intermediate results, indicative of a memory bottleneck in reasoning. In Experiment 2, we used a mouse-tracking paradigm to assess participants' subproblem choice and time allocation. The model captures key patterns of subproblem ordering, error, and time allocation. Our results point toward memory limits and strategies for navigating those limits --- including the careful choice of subproblems and memory-offloading --- as central elements of human problem-solving.

Human learners often seek information rationally: they prefer more informative over less informative data, and ask questions that optimally discriminate between competing hypotheses. But this picture holds only in simple, controlled settings: in more complex tasks, the computations needed for optimal information seeking become intractable and human inquiry empirically is often far from optimal. How do people effectively navigate the information landscapes the real world presents? We propose a model of information-seeking under representational constraints, which simplifies complex data used to make inferences and seeks information rationally with respect to its costs. Six behavioral experiments using two popular search games support this account: we find that people reason imprecisely and make queries in ways that are objectively uninformative under standard models, but are actually efficient given their limited representational capacity. Participants also increasingly prefer simpler, less informative queries as they gain experience in a task, suggesting rapid adaptation to their cognitive limits. Our findings challenge theories of human information-seeking based on an idealized notion of rationality and instead support a more nuanced and realistic view: people are limited in what information they can represent but have information-seeking strategies that are efficient given those limits.

Humans and other animals are able to perceive and represent a number of objects present in a scene, a core cognitive ability thought to underlie the development of mathematics. However, the perceptual mechanisms that underpin this capacity remain poorly understood. Here, we show that our visual sense of number derives from a visual system designed to efficiently encode the location of objects in scenes. Using a mathematical model, we demonstrate that an efficient but information-limited encoding of objects' locations can explain many key aspects of number psychophysics, including subitizing, Weber's law, underestimation, and effects of exposure time. In two experiments (N = 100 each), we find that this model of visual encoding captures human performance in both a change-localization task and a number estimation task. In a third experiment (N = 100), we find that individual differences in change-localization performance are highly predictive of differences in number estimation, both in terms of overall performance and inferred model parameters, with participants having numerically indistinguishable inferred information capacities across tasks. Our results therefore indicate that key psychophysical features of numerical cognition do not arise from separate modules or capacities specific to number, but rather as by-products of lower level constraints on perception. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

In a large (N = 300), pre-registered experiment and data analysis model, we find that individual variation in overall performance on Raven’s Progressive Matrices is substantially driven by differential strategizing in the face of difficulty. Some participants choose to spend more time on hard problems while others choose to spend less and these differences explain about 42% of the variance in overall performance. In a data analysis jointly predicting participants’ reaction times and accuracy on each item, we find that the Raven’s task captures at most half of participants’ variation in time-controlled ability (48%) down to almost none (3%), depending on which notion of ability is assumed. Our results highlight the role that confounding factors such as motivation play in explaining individuals’ differential performance in IQ testing.

Humans and other animals are able to perceive and represent the number of objects present in a scene, a core cognitive ability thought to underlie the development of mathematics. However, the perceptual mechanisms that underpin this capacity remain poorly understood. Here, we show that our visual sense of number derives from a visual system designed to efficiently encode the location of objects in scenes. Using a mathematical model, we demonstrate that an efficient but information-limited encoding of objects' locations can explain many key aspects of number psychophysics, including subitizing, Weber's law, underestimation, and effects of exposure time. In two experiments (N=100 each), we find that this model of visual encoding captures human performance in both a change-localization task and a number estimation task. In a third experiment (N=100), we find that individual differences in change-localization performance are highly predictive of differences in number estimation, both in terms of overall performance and inferred model parameters, with participants having numerically indistinguishable inferred information capacities across tasks. Our results therefore indicate that key psychophysical features of numerical cognition do not arise from separate modules or capacities specific to number, but rather as byproducts of lower-level constraints on perception.

People can identify the number of objects in small sets rapidly and without error but become increasingly noisy for larger sets. However, the cognitive mechanisms underlying these ubiquitous psychophysics are poorly understood. We present a model of a limited-capacity visual system optimized to individuate and remember the location of objects in a scene which gives rise to all key aspects of number psychophysics, including error-free small number perception and scalar variability for larger numbers. We therefore propose that number psychophysics can be understood as an emergent property of primitive perceptual mechanisms --- namely, the process of identifying and representing individual objects in a scene. To test our theory, we ran two experiments: a change-localization task to measure participants' memory for the locations of objects (Experiment 1) and a numerical estimation task (Experiment 2). Our model accounts well for participants' performance in both experiments, despite only being optimized to efficiently encode where objects are present in a scene. Our results demonstrate that the key psychophysical features of numerical cognition do not arise from separate modules or capacities specific to number, but rather from lower-level constraints on perception which are manifested even in non-numerical tasks.

People can identify the number of objects in sets of four or fewer items with near-perfect accuracy but exhibit linearly increasing error for larger sets. Some researchers have taken this discontinuity as evidence of two distinct representational systems. Here, we present a mathematical derivation showing that this behaviour is an optimal representation of cardinalities under a limited informational capacity, indicating that this behaviour can emerge from a single system. Our derivation predicts how the amount of information accessible to viewers should influence the perception of quantity for both large and small sets. In a series of four preregistered experiments (N = 100 each), we varied the amount of information accessible to participants in number estimation. We find tight alignment between the model and human performance for both small and large quantities, implicating efficient representation as the common origin behind key phenomena of human and animal numerical cognition.

The ability to represent recursive sequences is early developing and found across cultures, but is not unique to human thought.

The approximate number system (ANS) has attracted broad interest due to its potential importance in early mathematical development and the fact that it is conserved across species. Models of the ANS and behavioral measures of ANS acuity both assume that quantity estimation is computed rapidly and in parallel across an entire view of the visual scene. We present evidence instead that ANS estimates are largely the product of a serial accumulation mechanism operating across visual fixations. We used an eye-tracker to collect data on participants’ visual fixations while they performed quantity-estimation and -discrimination tasks. We were able to predict participants’ numerical estimates using their visual fixation data: As the number of dots fixated increased, mean estimates also increased, and estimation error decreased. A detailed model-based analysis shows that fixated dots contribute twice as much as peripheral dots to estimated quantities; people do not “double count” multiply fixated dots; and they do not adjust for the proportion of area in the scene that they have fixated. The accumulation mechanism we propose explains reported effects of display time on estimation and earlier findings of a bias to underestimate quantities.