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Students are, to various degrees, expected to present a context or explain what problem they are working on when they report on their problem-solving work. This expectation rarely comes with instructions on the best way to do this, and, as with most writing in mathematics, research on the issue or curricular standards is limited. This study uses multimodal analysis to investigate students’ strategies for presenting the problem, premises, and facts. The students are part of a research project focused on developing their ability to design written accounts of problem solving by addressing the efficiency and clarity of their writing. Findings suggest students use one of two strategies: integrating the problem and its premises with the calculations or separating the information from the rest of the account. Within these strategies, students approach demands for effective and clear communication in different ways using different semiotic resources.

It is well-known that a key to promoting students’ mathematics learning is to provide opportunities for problem solving and reasoning, but also that maintaining such opportunities in student–teacher interaction is challenging for teachers. In particular, teachers need support for identifying students’ specific difficulties, in order to select appropriate feedback that supports students’ mathematically founded reasoning without reducing students’ responsibility for solving the task. The aim of this study was to develop a diagnostic framework that is functional for identifying, characterising, and communicating about the difficulties students encounter when trying to solve a problem and needing help from the teacher to continue the construction of mathematically founded reasoning. We describe how we reached this aim by devising iterations of design experiments, including 285 examples of students’ difficulties from grades 1–12, related to 110 tasks, successively increasing the empirical grounding and theoretical refinement of the framework. The resulting framework includes diagnostic questions, definitions, and indicators for each diagnosis and structures the diagnostic process in two simpler steps with guidelines for difficult cases. The framework therefore has the potential to support teachers both in eliciting evidence about students’ reasoning during problem solving and in interpreting this evidence.

Problem solving at the crossroads between chemistry and mathematics presents significant challenges for students at all levels of education. This licentiate thesis aims to enhance our understanding of such problem solving, with a focus on how university students approach problems in the context of chemical kinetics. The thesis is based on two papers. The video data analysed in these papers were collected from problem-solving sessions where second-year chemistry students worked in pairs to solve tasks centred around a key concept in chemical kinetics. The first paper aimed to develop a framework suitable for analysing problem solving at the interface of chemistry and mathematics. Deductive and inductive analysis of the collected video data resulted in the extended mathematical modelling cycle (MMC). This empirically derived framework offers a fine-grained picture of the processes and resources at play during problem solving in chemical kinetics, suggesting that students: (1) engage in a range of (sub)processes beyond those typically outlined in the classical MMC; and (2) employ extra-mathematical resources (EMRs) in all stages of the MMC. The EMRs can be divided into chemical and other resources. While chemical resources are essential in translating chemical information into mathematical relationships, they also play a crucial role during mathematical work, offering guidance to the mathematical operations. The purpose of the second paper was to further characterise the nature and roles of other resources. Findings suggest that other resources can be divided into: implicit models of results, explicit examples from experience, and superficial procedural resources. Analysing their roles in problem solving revealed that implicit models primarily influence students’ ideas of where they are going, while explicit examples and superficial procedural resources provide a basis of strategies for how to get there.

It is well-known that a key to promoting students’ mathematics learning is to provide opportunities for problem solving and reasoning, but also that maintaining such opportunities in student–teacher interaction is challenging for teachers. In particular, teachers need support for identifying students’ specific difficulties, in order to select appropriate feedback that supports students’ mathematically founded reasoning without reducing students’ responsibility for solving the task. The aim of this study was to develop a diagnostic framework that is functional for identifying, characterising, and communicating about the difficulties students encounter when trying to solve a problem and needing help from the teacher to continue the construction of mathematically founded reasoning. We describe how we reached this aim by devising iterations of design experiments, including 285 examples of students’ difficulties from grades 1–12, related to 110 tasks, successively increasing the empirical grounding and theoretical refinement of the framework. The resulting framework includes diagnostic questions, definitions, and indicators for each diagnosis and structures the diagnostic process in two simpler steps with guidelines for difficult cases. The framework therefore has the potential to support teachers both in eliciting evidence about students’ reasoning during problem solving and in interpreting this evidence.

När elever står inför uppgifter inom problemlösning är det ett flertal steg som eleven ska ta sig igenom: Förstå uppgiften, göra någon form av plan, sedan genomföra planen och i bästa fall även gå tillbaka till uppgiften och reflektera över lösning och svar. Syftet med studien var att undersöka och belysa lösningsstrategier inom problemlösning ur ett elevperspektiv. För att besvara syftet har en kvalitativ ansats varit till grund för studien, som bestod av två datainsamlingar: ett elevtest och efterföljande gruppintervjuer. Resultatet för den första datainsamlingen analyserades med ett begreppsligt ramverk som definierar och begreppsliggör lösningsstrategier. Det visade att eleverna till stor del valde mellan algebraisk lösning och blockmodellen. Elevperspektivet har belysts genom att elever efter ett genomfört test ombetts reflektera gällande deras val av lösningsstrategi. Intervjuerna analyserades utifrån en induktiv ansats där data tematiserades och påvisade fem centrala motiveringar till att elever väljer de strategier de väljer. Dessa var: enkelhet att tolka, tid spenderad på varje uppgift, erfarenhet och självförtroende, uppgiftens karaktär och lärarens undervisning., When students face tasks in problem-solving, there are several steps they need to go through: understanding the task, devising some form of plan, executing the plan, and ideally, revisiting the task to reflect on the solution and answer. The purpose of the study was to investigate and illuminate problem-solving strategies from a student perspective. To address this purpose, a qualitative approach was employed, consisting of two data collection phases: a student test followed by group interviews. The results of the initial data collection were analyzed using a conceptual framework that defines and elucidates problem-solving strategies. It was found that students largely chose between algebraic solutions and block modeling. The student perspective was illuminated by having students reflect on their choice of solution strategy after completing the test. The interviews were analyzed using an inductive approach where data were themed, revealing six central motivations for why students choose the strategies they do. These were: simplicity of interpretation, time spent on each task, experience and self-confidence, the nature of the task, and teacher instruction.

Problem solving at the crossroads between chemistry and mathematics presents significant challenges for students at all levels of education. This licentiate thesis aims to enhance our understanding of such problem solving, with a focus on how university students approach problems in the context of chemical kinetics. The thesis is based on two papers. The video data analysed in these papers were collected from problem-solving sessions where second-year chemistry students worked in pairs to solve tasks centred around a key concept in chemical kinetics. The first paper aimed to develop a framework suitable for analysing problem solving at the interface of chemistry and mathematics. Deductive and inductive analysis of the collected video data resulted in the extended mathematical modelling cycle (MMC). This empirically derived framework offers a fine-grained picture of the processes and resources at play during problem solving in chemical kinetics, suggesting that students: (1) engage in a range of (sub)processes beyond those typically outlined in the classical MMC; and (2) employ extra-mathematical resources (EMRs) in all stages of the MMC. The EMRs can be divided into chemical and other resources. While chemical resources are essential in translating chemical information into mathematical relationships, they also play a crucial role during mathematical work, offering guidance to the mathematical operations. The purpose of the second paper was to further characterise the nature and roles of other resources. Findings suggest that other resources can be divided into: implicit models of results, explicit examples from experience, and superficial procedural resources. Analysing their roles in problem solving revealed that implicit models primarily influence students’ ideas of where they are going, while explicit examples and superficial procedural resources provide a basis of strategies for how to get there.

Problem solving is a crucial aspect of the education curriculum, as it reflects the skills expected to be possessed by students. Indicating that the majority of students face difficulties in this process. Studies also indicate that numeracy literacy plays a significant role in enhancing mathematical problem-solving skills. This research aims to develop a numeracy literacy assessment that integrates Pandeglang Regency's local knowledge, with a particular emphasis on geometry, to enhance the mathematical problem-solving abilities of Grade IX. This study fits into the Tessmer formative research model, which is related to the category of development research. The study involved 35 ninth-grade students from MTsN 2 Pandeglang as participants. Various tools were used to gather data, such as validation sheets, interview guidelines, and numeracy literacy tests inspired by local knowledge in the Pandeglang Regency. The analysis process included assessing these tools' effectiveness, reliability, and precision through expert assessments and statistical methods. The study's conclusions show that the developed test's quality comprises moderate difficulty for each of the three items, good validity, and satisfactory item discrimination. &nbsp

Informatics is intricately intertwined with the mastery of problem-solving. Within this discipline, one of the fundamental areas of study revolves around the concepts of branching and looping. During field studies, several student problems were identified, such as difficulty solving programming problems presented in the form of stories. This resulted in a lack of problem-solving skills among students when it comes to story problems. Additionally, there were issues with instructional media and less interactive teaching methods. The aim of this research is to design multimedia that implements a problem-based learning model to enhance students' problem-solving abilities. The research method used is Research and Development (R&D) with the ADDIE development model. The study involved 36 students from class X-B RPL at SMKN 1 Cimahi. The data obtained are as follows: 1) The development of interactive multimedia achieved an average score of 88. 2) There is an increase in students' problem-solving indicators, with an average pretest score of 40.13 rising to 68.38 in the posttest. Additionally, an N-Gain of 0.47 was obtained, with details as follows: understanding the problem at 0.60, devising a plan at 0.41, carrying out the plan at 0.35, and looking back at 0.34. The overall N-Gain falls within the moderate category. 3) The response to the multimedia is 83, categorized as excellent. The N-Gain results and multimedia assessment indicate that both the multimedia and this research achieved good outcomes., Informatika sangat erat kaitannya dengan penguasaan pemecahan masalah. Dalam disiplin ini, salah satu area studi yang fundamental berkaitan dengan konsep-konsep percabangan dan perulangan.Pada studi lapangan ditemukan beberapa masalah siswa seperti kurang dapat menyelesaikan soal pemrograman dalam bentuk cerita. Hal itu mengakibatkan kurangnya kemampuan pemecahan masalah siswa pada soal cerita. Kemudian masalah lainnya adalah siswa yang masih belum dapat membedakan struktur perulangan. Selain itu juga ada masalah pada media pembelajaran dan metode pembelajaran yang kurang interaktif. Penelitian ini bertujuan untuk merancang multimedia yang menerapkan model pembelajaran problem based learning di dalamnya untuk meningkatkan kemampuan pemecahan masalah siswa. Metode penelitian yang digunakan dalam penelitian ini adalah R&D (Research and Development) dengan model pengembangan ADDIE. Penelitian dilakukan kepada 36 siswa kelas X-B RPL SMKN 1 Cimahi dengan memperoleh data: 1)Pengembangan multimedia interaktif mendapatkan nilai rata-rata 88  2)Terdapat kenaikan indikator pemecahan masalah siswa, rata-rata nilai pretest adalah 40,13 menjadi 68,38 pada posttest.  Didapatkan juga N-Gain sebesar 0.47 dengan rincian: memahami masalah sebesar 0.60, merencanakan pemecahan masalah sebesar 0.41, melaksanakan pemecahan masalah sebesar 0.35, dan memeriksa kembali sebesar 0.34. Seluruh N-Gain adalah pada kategori sedang. 3)Tanggapan terhadap multimedia adalah 83 atau pada kategori sangat baik.  &nbsp

The aim of this study was to enhance scientific competencies using the Polya method among 11th-grade students at Juan Jacobo Aragón Educational Institution in Fonseca - La Guajira, Colombia. To achieve this, a qualitative approach and action research design were implemented through a six-session didactic sequence based on the Polya method in the physics subject. Additionally, to gather information on the implementation's effects, a survey technique was employed involving two physics teachers and two parents of students from the institution, along with a focus group including 10 students. Ultimately, it was found that competencies related to problem analysis were indeed strengthened, leading to a more reflective and analytical attitude among students when facing subject-related problems. Furthermore, both parents and teachers, as well as students, reported an improved relationship with the subject and enhanced academic performance following the implementation, El objetivo del presente trabajo fue fortalecer las competencias científicas con el método de Pólya en los estudiantes del grado 11 de la Institución Educativa Juan Jacobo Aragón del municipio de Fonseca – La Guajira, Colombia. Para lo cual se procede a implementar una metodología de enfoque cualitativo y diseño investigación-acción a partir de la aplicación de una secuencia didáctica de seis sesiones basado en el método Pólya en la asignatura física. Adicionalmente, para levantar información sobre el efecto de la implementación, se recurre a la técnica de encuesta a dos docentes del área de física y a dos padres de familia de estudiantes de la institución y a la del grupo focal dónde participaron 10 estudiantes. Finalmente, se encuentra que efectivamente se fortalecen las competencias relacionadas con el análisis de problemas, consiguiendo generar una actitud más reflexiva y analítica en los estudiantes ante problemas de la asignatura, así mismo, tanto padres, como docentes y estudiantes manifiestan una mejor relación con la materia y también un mejor desempeño académico posterior a la implementación.

This article focused on analyzing different theories related to the playful games workshop and its influence on mathematical problem solving in elementary school students. For this purpose, a qualitative documentary approach study was applied. In addition, 15 articles were selected from Scielo and Redalyc; therefore, the population was made up of the main theoretical contents related to the students' learning levels in the area of mathematics. As a technique, documentary analysis and file analysis were used. As a result, it was pointed out that it is of vital importance to implement playful games programs in order to raise learning levels in educational institutions where elementary school is taught. Therefore, it was concluded that the playful activities provide principles, processes and practical tools that lead the participants to control more technically the use of mathematics in their daily actions., Este artículo se enfocó en analizar diversas teorías relacionadas con el Taller de actividades lúdicas y su influencia en la resolución de problemas matemáticos en estudiantes del nivel primario. Para tal fin, se aplicó un estudio de enfoque cualitativo de tipo documental. Además, se seleccionaron 15 artículos de Scielo y Redalyc; por lo tanto, la población la conformaron los principales contenidos teóricos relacionados con los niveles de aprendizaje en el área de matemática de los estudiantes. Como técnica, se utilizó el análisis documental y el fichaje. Como resultado, se precisó que es de vital importancia ejecutar programas de juegos lúdicos para elevar los niveles de aprendizaje en las instituciones educativas donde se imparte la primaria. Por ende, se concluyó que las actividades lúdicas, suministran principios, procesos y herramientas prácticas que conducen a los participantes a controlar más técnicamente el empleo de la matemática en sus acciones diarias.

Existing research has investigated student problem-solving strategies across science, technology, engineering, and mathematics; however, there is limited work in undergraduate biology education on how various aspects that influence learning combine to generate holistic approaches to problem solving. Through the lens of situated cognition, we consider problem solving as a learning phenomenon that involves the interactions between internal cognition of the learner and the external learning environment. Using phenomenography as a methodology, we investigated undergraduate student approaches to problem solving in biology through interviews. We identified five aspects of problem solving (including knowledge, strategy, intention, metacognition, and mindset) that define three qualitatively different approaches to problem solving; each approach is distinguishable by variations across the aspects. Variations in the knowledge and strategy aspects largely aligned with previous work on how the use or avoidance of biological knowledge informed both concept-based and nonconcept-based strategies. Variations in the other aspects revealed intentions spanning complete disengagement to deep interest with the course material, different degrees of metacognitive reflections, and a continuum of fixed to growth mindsets. We discuss implications for how these characterizations can improve instruction and efforts to support development of problem-solving skills.

In term series problems where multiple mental models can be constructed, partial-order models can be created as mental representations, which make it easier to perceive the symmetry of the terms. To test these hypotheses, we categorized multi-model (indeterminate) term series problems according to the patterns of partial-order models that could be constructed, and analyzed the reasoning performance for each pattern. These results suggest that reasoners tend to use the symmetry of terms to reduce the cognitive load of reasoning. Analysis of the patterns of incorrect answers also suggests that attempts to exploit the symmetry of the term may be biased, leading to errors in reasoning.

People who have no experience with programming can create informal programs to rearrange the order of cars in trains. To find out whether they rely on kinematic mental simulations, the current studies examined participants' eye movements in two experiments in which participants performed various moves and rearrangements on a railway consisting of a main track running from left to right and a siding entered from and exited to the left track. In Experiment 1, they had to imagine different sorts of single moves of cars on the railway. The sequences of their fixations resembled iconic gestures: they tended to look at the starting location of the imagined move, and then at its final location. In Experiment 2, the task was to create descriptions of how to solve four sorts of rearrangements that differ in their Kolmogorov complexity. It predicted the time to find the correct solution and the relative number and duration of fixations recorded during the description of each move for rearrangements of different complexity. Participants were more likely to fixate on the symbols on the cars than anything else, and they fixated longer when the rearrangement was more difficult. They also tended to fixate regions of the tracks where a car's movement began or ended, as if they were imagining a car moving along the tracks. The results suggest that humans rely on a kinematic mental simulation when creating informal algorithms.

Math anxiety is a pervasive issue in higher education that is often associated with poor performance outcomes. A hypothesized reason for this association is that individuals with math anxiety experience negative and intrusive thoughts related to the situation, their performance, and its consequences. These distractions are thought to be specific to math-related contexts. However, recent empirical evidence from the test anxiety literature calls the anxiety-distraction association into question. Here, we demonstrate that (a) math anxiety is associated with higher average reports of negative distraction, (b) that math anxiety-induced distraction is specific to the math problem-solving domain, and (c) that test anxiety also accounts for higher ratings of math-specific negative distraction. Investigating potential mechanisms underlying the math anxiety–poor math performance relationship is necessary for implementing effective interventions that foster math success, both in educational settings and in everyday life.

We explore different aspects of cognitive diversity and its effect on the success of group deliberation. To evaluate this, we use 500 dialogues from small, online groups discussing the Wason Card Selection task - the DeliData corpus. Leveraging the corpus, we perform quantitative analysis evaluating three different measures of cognitive diversity. First, we analyse the effect of group size as a proxy measure for diversity. Second, we evaluate the effect of the size of the initial idea pool. Finally, we look into the content of the discussion by analysing discussed solutions, discussion patterns, and how conversational probing can improve those characteristics. Despite the reputation of groups for compounding bias, we show that small groups can, through dialogue, overcome intuitive biases and improve individual decision-making. Across a large sample and different operationalisations, we consistently find that greater cognitive diversity is associated with more successful group deliberation. Code and data used for the analysis are available in the repository: https://github.com/gkaradzhov/cognitive-diversity-groups-cogsci24

We can evaluate features of problems and their potential solutions well before we can effectively solve them. When considering a game we have never played, for instance, we might infer whether it is likely to be challenging, fair, or fun simply from hearing the game rules, prior to deciding whether to invest time in learning the game or trying to play it well. Many studies of game play have focused on optimality and expertise, characterizing how people and computational models play based on moderate to extensive search and after playing a game dozens (if not thousands or millions) of times. Here, we study how people reason about a range of simple but novel connect-n style board games. We ask people to judge how fair and how fun the games are from very little experience: just thinking about the game for a minute or so, before they have ever actually played with anyone else, and we propose a resource-limited model that captures their judgments using only a small number of partial game simulations and almost no lookahead search. For more information about this project, see https://sites.google.com/view/intuitive-game-theory

Human learning is sensitive to rule-like structure and the curriculum of examples used for training. In tasks governed by succinct rules, learning is more robust when related examples are blocked across trials, but in the absence of such rules, interleaving is more effective. To date, no neural model has simultaneously captured these seemingly contradictory effects. Here we show that this same tradeoff spontaneously emerges with “in-context learning” (ICL) both in neural networks trained with metalearning and in large language models (LLMs). ICL is the ability to learn new tasks “in context” — without weight changes — via an inner-loop algorithm implemented in activation dynamics. Experiments with pretrained LLMs and metalearning transformers show that ICL exhibits the blocking advantage demonstrated in humans on a task involving rule-like structure, and conversely, that concurrent in-weight learning reproduces the interleaving advantage observed in humans on tasks lacking such structure.

Most recent research on human planning attempts to adjudicate between a small set of hypothesized models based on their ability to predict participants' choices, using carefully designed experiments and/or model comparison. Here, we propose an alternative approach. We designed a task in which gaze is highly indicative of participants' planning operations, allowing us to discover properties of human planning from eye-tracking data in a data-driven way. Our results reveal ways that people's planning strategies have both similarities and differences with classical planning algorithms like best-first search and Monte Carlo tree search. They also provide a more nuanced perspective on previously proposed properties of human planning like pruning and depth limits. We conclude that planning research would benefit greatly from an increased use of rich sources of data that provide more direct evidence about the internal processes underlying sequential decision-making.

Salient features of a task play an important role in how people create task representations which then influence strategy selection for accomplishing the task. We examined two theories, Represent-Construct-Choose-Learn (RCCL) and Rational Metareasoning (RM), both of which incorporate task features into their models of strategy selection. RCCL theory posits that when a strategy's success rate is low, it indicates that the task representation is not useful and those represented features are irrelevant in this case so people tend to drop these features from the task representation. Conversely, RM theory posits that strategy selection is based on consideration of all available features, with no discrete changes in the features incorporated into the task representation. A study was conducted to examine how participants changed their strategy choices based on the success rate of using a specific task feature. The results showed that neither theory aligned closely with empirical data.

Programming is a complex problem-solving domain, requiring the coordination of different types of knowledge and skills. The present study investigates expert and novice programming problem solving by analyzing talk-aloud transcripts and the code generated. Based on this analysis a set of basic goal and step components used by novice and expert programmers are identified, which will inform on the generation of cognitive models in the next phase of this research.

Whether travelling, playing games, or debugging code, any situation where an agent desires change can be framed as a problem. Despite this ubiquity, there is no unifying framework describing how people reason backwards when solving problems. We introduce AND/OR trees, which chain together subgoals and actions to attain them, as a way to represent this process. To investigate whether actions from AND/OR trees were predictive of human behavior, we conducted a study in which participants solved deterministic, long-horizon puzzles. AND/OR trees were able to explain most of the actions the participants took. Next, we modeled search through these trees using a psychologically plausible, single-parameter search algorithm. We fit this model to the data of individual participants and found that it captures trends in summary statistics of human play. Our results show the promise of AND/OR trees as a representation for backward reasoning in problem solving.

The present study examines how natural language aptitude subskills predict individual differences in learning Python and Java. Past work has demonstrated that overall performance on the Modern Language Aptitude Test (MLAT), a standardized measure of language aptitude, is a strong predictor of both the speed and accuracy with which individuals learn Python. However, language aptitude is a broad multidimensional construct made up of individual subskills. In the present study, we examine how two of these subskills - sensitivity to form and meaning mapping - relate to programming outcomes in both Python and Java. Results indicate that both sensitivity to form (MLAT IV) and meaning mapping (MLAT V) are related to programming acquisition in both languages - this relationship remains even after controlling for fluid intelligence. We also examined how programming skills tied to semantics and syntax related between Python and Java in a subset of learners who learned both languages. These results demonstrated that proficiency in Python predicted individual differences in both syntactic and semantic knowledge in Java. Taken together, these results further elucidate the role of natural language aptitude in programming learning and suggest that semantic and syntactic content may transfer across programming languages.

We applied eye tracking and semantically rich four-term analogies with a broad range of distractor types to investigate strategies of analogical reasoning. We adopted the operationalization of strategies proposed in previous eye-tracking studies and introduced an alternative, more fine-grained method of presenting gaze dynamics across a trial in a four-term analogy (A:B::C:D). Our analysis of fixations and transitions between Areas of Interest provided support for existing research findings, suggesting that the primary and most effective strategy when solving four-term analogies is the so-called projection-first strategy, which focuses on the source-domain relation and its generalization to the target domain.

Questions combine our mastery of language with our remarkable facility for reasoning about uncertainty. How do people navigate vast hypothesis spaces to pose informative questions given limited cognitive resources? We study these tradeoffs in a classic grounded question-asking task based on the board game Battleship. Our language-informed program sampling (LIPS) model uses large language models (LLMs) to generate natural language questions, translate them into symbolic programs, and evaluate their expected information gain. We find that with a surprisingly modest resource budget, this simple Monte Carlo optimization strategy yields informative questions that mirror human performance across varied Battleship board scenarios. In contrast, LLM-only baselines struggle to ground questions in the board state; notably, GPT-4V provides no improvement over non-visual baselines. Our results illustrate how Bayesian models of question-asking can leverage the statistics of language to capture human priors, while highlighting some shortcomings of pure LLMs as grounded reasoners.

Efficient algorithms can enhance problem-solving in many cognitive domains but can be difficult to discover and use. For example, classical studies of seriation suggest that children struggle to apply algorithmic strategies in a simple sorting problem. We investigate the spontaneous discovery of algorithmic solutions across development. We gave children a variant of the sorting problem with hidden object ranks. Children sort animated bunnies into the right order, from the shortest to the tallest, when the bunnies are standing behind a wall so their heights are not visible. Children performed far above chance on this difficult sorting task, potentially because higher demands in memory and reasoning incentivized strategic behaviors. Children independently discovered at least two efficient algorithmic solutions to the sorting problem, Selection sort and Shaker sort. This result suggests that children are far more competent at sorting tasks than previous research would suggest. Additionally, older children were more efficient sorters than younger children. This suggests that competent performance on sorting tasks improves throughout development.

Question-asking is a crucial aspect of human interaction. Questions fuel engagement, stimulate thought processes, foster learning, and facilitate information seeking behavior. Yet, scarce empirical research on question-asking, or its relation to related cognitive capacities such as creativity and intelligence, exists. We empirically investigate how people ask questions and the connections between question-asking and creativity through the domain of interactive gaming. To do so, we developed an online game—Spot the Spy—where players are required to find a hidden spy amidst a crowded room, by asking questions that guide them in their investigation. Thus, we dive into the very essence of how creative and strategic thinking collaborate to shape the queries we formulate. We find that players' gameplay correlates with their cognitive abilities, especially with intelligence measures. As such, our game captures insights into the profound ways creative cognition shapes the questions we articulate and navigate within dynamic gaming environments.

This paper seeks to understand designers' abstraction in ill- structured problem-solving. We utilize a protocol study with expert designers to empirically analyze the abstraction process in the latent need problem setting. A logic-based abstraction schema is found to model the process the designers employed. The study reveals how designers utilize this schema, detailing, developing, and evaluating solutions for ill-structured problems. It highlights the recursive nature of abstraction and raises questions about the termination of the process in ill- structured domains. We conclude by proposing a computational model to further evaluate abstraction in complex problem-solving scenarios.

The precise mechanisms precipitating the process of representational change in problem solving have been investigated for nearly a century. One current hypothesis is that analyzing the unchanging elements of previous attempts may facilitate restructuring. We investigated this hypothesis by providing solvers with three common examples of unsuccessful problem attempts, their own problem attempts, or no previous attempts. The prior attempts conditions eliminated the need to rely on working memory to access previous unsuccessful attempts. While there was no evidence for an overall effect of the prior attempts conditions, cognitive reflection was identified as a reliable predictor of restructuring and solving. Eye-tracking data were collected to further investigate the contributions of these systems to fixations while solving. The current study is an exploratory analysis of this data, with analyses focusing on participants' fixations on problem-irrelevant space and unsuccessful attempts.

Several recent contributions to the research on group problem solving suggest that reducing the connectivity between agents in a social network may be epistemically beneficial. This notion stems from the idea that collective problem-solving behavior may benefit from the transient diversity in agents' beliefs due to increased individual exploration and decreased social influence. At the same time, however, lower connectivity hinders the diffusion of good solutions between network members. Our simulation findings shed light on this trade-off. We identify conditions under which the less-is-more effect is likely to manifest. Our findings suggest that a community consisting of semi-isolated groups could provide an answer to the tension between diversity and diffusion.

Analogical transfer between source and target problems ought to be a major contributor to problem-solving and learning. Yet, data from laboratory studies show that successful spontaneous analogical transfer does not reliably occur in the absence of explicit hints to analogize, in the presence of a delay between source and target, or when there are extensive filler tasks, a finding attributed to the complexity of analogy retrieval and mapping. Here, we show that participants solving variants of the Cards problem often failed to show transfer between source and target problems that shared both conceptual and superficial similarities. Frequency of re-inspecting the task instructions was a significant predictor of transfer, with participants successful at T2 requiring fewer re-inspections. The results suggest that analogical transfer may be limited, not just by the difficulty of mapping between source and target, but by a lack of conceptual understanding of the source and its solution, even when the source is solved.

There are two foundational assumptions that underlie research in interactivity. First, that resources external to the human agent should support problem-solving and other cognitive activities and second, that human agents naturally engage in this form of offloading when they are allowed to. We aimed to test whether participants would naturally engage with external resources, without prompting, in four types of simple verbal problems and whether the level of engagement was affected by expertise or the experience of impasse. We found that very few people naturally engaged external resources apart from with mathematical problems where it had a benefit. There was no difference in expertise in problem-solving between those who did and those who did not use external props and nor was there a significant difference in the proportion of people using external resources as a function of experiencing impasse. These results suggest that researchers in interactivity need to focus on how and when interactivity is both engaged and provides a benefit.

Cognitive development has been hypothesised to be stagelike between the ages of 5-8 years (e.g., Piaget). Yet, cognition varies from moment to moment, in every task, for every child. Studies have demonstrated that cognitive variability is non-trivial, non-random, and meaningful, but attempts for systematic and large-scale longitudinal measurements of cognitive variability have scarcely been undertaken. This project's goal is to create a more detailed empirical record and dynamical account of intra-individual variability in cognitive development of children. We aim to do this with a 3-year longitudinal and multimodal data collection starting at 5 years of age. Half-yearly measurements will be complemented with periods of daily measurements. Our ultimate aim is to build a variability corpus in which we can study variability patterns and developmental transitions, and to connect our findings to “Piaget's new theory”. Our poster will present our methodology and findings from a pilot study.

Conventions are arbitrary and self-sustaining practices that emerge in a population and facilitate solving coordination problems. A recent study (Formaux et al. 2021) traced the formation of simple conventions in captive baboons in a touch-screen-based colour-matching ‘game'. We replicated this task with human pairs under different conditions (varying the instructions given, visual access to partner's screen, and subjects' previous experience) to assess their effects on convention formation. We found that more information delayed the formation of conventions (arbitrary rankings of colours). Interestingly, pairs maintained their conventions even when given visual access to their partner's screen, despite the availability of a potentially simpler strategy (copying). Although experienced subjects did not transmit their conventions to naïve subjects, they enabled more rapid establishment of a new convention. We hypothesise that these effects are rooted in whether human subjects are prompted to employ cognitively less or more sophisticated processes during behavioural coordination.

Classic base-rate neglect studies have been consistently criticised for lacking ecological validity. A study by Welsh & Navarro (2012) found this heuristic was significantly reduced when participants perceived the base rate as more relevant. The present study aims to study this phenomenon through a more realistic scenario while simultaneously capturing participants' written reasoning. Using mixed-methods, participants (N = 2,052) read an engaging scenario regarding a person who committed infidelity and containing a base-rate and specific information where the contextual information regarding the base-rate was manipulated. They were then asked to provide an estimate of the person's likelihood to cheat in the future. Results show that each of our three manipulations to the context of the base rate are significant in affecting participants' estimates, supporting Welsh and Navarro's findings. Analysis of participants' written reasoning demonstrates the sophistication and nuance of participants' engagement with the base-rate, challenging the original view of this supposed heuristic.

Acute stress triggers a cascade of physiological and psychological changes including heightened cortisol levels, perspiration, and anxiety. Existing research has focused on acute stress's effect on cognition in basic tasks of executive functioning, but its effect on computationally harder tasks is not well understood. Here, in a within-participants laboratory experiment (n=42, mostly college students), we test for an effect of acute stress on decision-making at varying levels of computational hardness in the 0-1 Knapsack Decision Problem. We find that acute stress, induced via the Trier Social Stress Test, leads to impaired decision quality irrespective of the level of computational hardness. Among cortisol responders, higher cortisol levels were associated with lower decision quality and higher time on task. Our findings help bridge the gap between research on executive functioning tasks and `real-world decisions', building a more nuanced understanding of how acute stress affects decision-making.

Large language models (LLMs) have performed well on several reasoning benchmarks, including ones that test analogical reasoning abilities. However, it has been debated whether they are actually performing humanlike abstract reasoning or instead employing less general processes that rely on similarity to what has been seen in their training data. Here we investigate the generality of analogy-making abilities previously claimed for LLMs (Webb, Holyoak, & Lu, 2023). We take one set of analogy problems used to evaluate LLMs and create a set of “counterfactual” variants—versions that test the same abstract reasoning abilities but that are likely dissimilar from any pre-training data. We test humans and three GPT models on both the original and counterfactual problems, and show that, while the performance of humans remains high for all the problems, the GPT models' performance declines sharply on the counterfactual set. This work provides evidence that, despite previously reported successes of LLMs on analogical reasoning, these models lack the robustness and generality of human analogy-making.

When people solve problems, they may try multiple invalid solutions before finally having an insight about the correct solution. Insight problem-solving is an example of the flexibility of the human mind which remains unmatched by machines. In this paper, we present a novel experimental paradigm for studying insight problem-solving behavior in a physical reasoning domain. Using this paradigm and several data-driven analyses, we seek to quantify what it means to have an insight during physical problem-solving and identify behavioral traces that predict subjective insight ratings collected from human participants. This project aims to provide the first steps towards a computationally informed theory of insight problems solving.

The Traveling Salesperson Problem (TSP) is a combinatorial optimization problem originally of interest to mathematicians, but more recently used also in the context of cognitive and comparative psychology. Humans perform extremely well on spatial versions of this task, despite its mathematical complexity, making it an appealing tool for the study of spatial and mathematical cognition. We presented participants with three versions of a TSP in navigational space; one that could be solved visually, one with visual distractors, and one that also required the use of memory. The task was preceded by instructions that promoted either a ‘growth mindset' or ‘fixed mindset' approach. Results indicated that performance on this navigational version of the TSP is generally good, though not quite as efficient as solutions reported in the traditional pencil-and-paper version of the task. The effects of visual distractors and of memory requirements were greater in problems with a larger number of targets. Instructions had no significant effect on performance.

We build a computational model of how humans actively infer hidden rules by doing experiments. The basic principles behind the model is that, even if the rule is deterministic, the learner considers a broader space of fuzzy probabilistic rules, which it represents in natural language, and updates its hypotheses online after each experiment according to approximately Bayesian principles. In the same framework we also model experiment design according to information-theoretic criteria. We find that the combination of these three principles -- explicit hypotheses, probabilistic rules, and online updates -- can explain human performance on a Zendo-style task, and that removing any of these components leaves the model unable to account for the data.

As adults, we readily work through alternative possibilities and their potential consequences in our minds before acting. This capacity for mental simulation enables us to internally explore alternatives without incurring costs of acting in reality. Young children are highly exploratory in the real world, but little is known about their ability to engage in internal exploration via mental simulation. This preregistered study (1) examines developmental changes in the use of mental simulation when solving problems with multiple options, and (2) investigates the influence of resource availability on the tendency to simulate. Adults (N=30) and 4-to 7-year-olds (target N=120; data collection ongoing) completed computer-based puzzles where they chose where to drop balls into a vertical maze to hit a goal. Accuracy and latency to act were measured as indices of mental simulation. Our findings will contribute to understanding children's problem-solving, and could lead to a new conceptualization of their exploratory behaviour.

The space of human goals is tremendously vast; and yet, from just a few moments of watching a scene or reading a story, we seem to spontaneously infer a range of plausible motivations for the people and characters involved. What explains this remarkable capacity for intuiting other agents' goals, despite the infinitude of ends they might pursue? And how does this cohere with our understanding of other people as approximately rational agents? In this paper, we introduce a sequential Monte Carlo model of open-ended goal inference, which combines top-down Bayesian inverse planning with bottom-up sampling based on the statistics of co-occurring subgoals. By proposing goal hypotheses related to the subgoals achieved by an agent, our model rapidly generates plausible goals without exhaustive search, then filters out goals that would be irrational given the actions taken so far. We validate this model in a goal inference task called Block Words, where participants try to guess the word that someone is stacking out of lettered blocks. In comparison to both heuristic bottom-up guessing and exact Bayesian inference over hundreds of goals, our model better predicts the mean, variance, efficiency, and resource rationality of human goal inferences, achieving similar accuracy to the exact model at a fraction of the cognitive cost, while also explaining garden-path effects that arise from misleading bottom-up cues. Our experiments thus highlight the importance of uniting top-down and bottom-up models for explaining the speed, accuracy, and generality of human theory-of-mind.

Classical approaches to studying insight problem-solving typically use specialized problems (e.g., nine-dot problem, compound-remote associates task) as stimuli together with verbal reports from subjects during problem-solving to reveal their thought processes, possibly adding other task-related metrics such as completion rate and physiological measures like eye fixation and neural activity. This approach has led to the claims that insight and creative thought require impasse and mental restructuring. What is missing from this literature is a cognitive process model of insight, and one reason for the lack of such a model is the lack of a unified, scalable, and tunable experimental framework with which to study human creative problem-solving with higher fidelity. In this paper, we introduce ESCAPE, an experimental paradigm using puzzle video games as stimuli which allow for the collection of process data that can serve as a basis for computational models. We have specifically developed a set of puzzle games based on this paradigm and conducted experiments that demonstrate the utility of the approach by revealing a set of computational principles that need to be accounted for by a theory of creative problems and the computational models based on it.

Problem-solving and sequential decision-making research have a long-standing tradition of utilizing various tasks in experiments to gain insights into different aspects of human behavior. Choosing the right task for investigating these aspects is crucial since human solution approaches depend on features and dynamics of tasks. For a complete theory of sequential decision-making, we must consider this relationship between behavior and task features. We developed a taxonomy and identified nine structural task features that allow us to describe the relationship between tasks and the behavior in the tasks. We categorize sequential decision-making tasks and show how their features link to the demands on solution approaches that leverage their structure. We argue that this taxonomic view on tasks can guide research processes as it can help select the right task for a research question at hand and can be used to relate the results of behavioral studies to each other.

Planning in complex environments is crucial in everyday life, yet the underlying cognitive abilities remain unclear. We investigated this through an online experiment (n=476) where participants completed nine cognitive tasks: Raven's Matrices, Mental Rotation, Corsi Block Task, Change-Detection Task, Pattern Recognition Task, Wisconsin Card Sorting Task, a complex two-player game called Four-in-a-Row, and two simpler planning tasks. We found moderate correlations across most metrics, aligning with existing literature on cognitive interconnectivity. Notably, performance in the Four-in-a-Row game significantly correlated with all other tasks, implying a shared cognitive basis for planning, regardless of task complexity. Additionally, latent variable analysis revealed distinct factors underlying planning in different state spaces, with working memory capacity playing a crucial role in navigating larger spaces. These findings shed light on the cognitive architecture of complex planning.

Decision frames influence how people act. These frames and the resulting decisions can be changed by manipulating how a problem is described. Here, we ask if people themselves can induce frame changes when thinking about a problem and how these frame changes affect decision-making and choice satisfaction. In our experiment, participants (N > 700) generated as many options as they would like for day to day scenarios as choosing a costume for a party or finding a gift for a friend. Then, participants selected one of the options they generated and reported their choice satisfaction. We found that choice satisfaction was higher when the option selected was more semantically dissimilar to the rest of the option set. We argue that this suggests that participants use a novel strategy to facilitate decision-making: Participants aimed to construct decision frames by generating options sets with a uniquely dissimilar option, which facilitated choice and increased satisfaction.

Having better knowledge of fractions is causally related to the ability to learn algebra, so what happens when teaching and learning about fractions is disrupted, as was the case during the COVID-19 pandemic? In this study, we examine how educational disruption caused by a pandemic differentially impacted students' fraction learning relative to students who were learning other mathematics content during that time. This study provides results from a cross-sequential project examining various facets of mathematics knowledge for students in 4th-10th grades over three years (2021, 2022, 2023; N=903 students). We investigate differences in fractions and algebra knowledge based on students' grade levels across cohorts to determine if there are particular periods at which students' learning was differentially affected by the disruption. Individual differences in students' self-regulation, self-efficacy, and personality will also be explored as potential buffers.

Humans are born with an intuitive representation of the physics world. How accurate is intuitive physics? Researchers from education focus on the failures, students' errors and misconceptions while cognitive psychologists argue humans anticipate and manipulate physical environments in ways betraying veridical knowledge of classical mechanics. One solution is to hypothesize there are distinct systems of “cognitive physics” with different limitations and deployment in the tasks favored by the two literatures. The goal of current study is to gather evidence from psychometric studies by estimating how many distinct factors explain performance on intuitive physics assessments. We build an augmented concept inventory including several previously-validated concept inventories, around 120 items. The pilot study indicated that participants recruited online from Prolific displayed expected accuracy on the tasks. We are now collecting around 1,000 participants and applying multidimensional item response theory (MIRT) analyzes to identify the latent structure.

Studying physical rotation (i.e., rotation tasks during which figures can be physically rotated, such as through gestures) can offer insights also into problem solving processes at work during mental rotation. We present a novel method for behavioral pattern analysis which we applied to data from 2,999 physical rotation tasks gathered in-class from 50 secondary school students. The method uses normalized, resampled, time-dependent data on angular offsets between figures over time and agglomerative, correlation-based clustering. Each cluster represents a distinct behavioral pattern and its respective prototype a problem solving strategy. Results indicate that multiple strategies were employed: The dominant strategy matches the classical model of mental rotation, in which angular offsets between figures are decreased over time. For the secondary strategy, angular offsets were actually increased. A subsequent analysis shows that the secondary strategy was more frequently used for symmetric figures, possibly indicating problems with correctly matching segments across figures.

When children solve cognitive problems together, they coordinate their speech, hand movements and head movements. Previous studies with adults have shown that such multimodal coordination is related to better collaboration. We do not know whether this is true for children, however. In this study, dyads of children (6-10 years) discussed and solved balance scale problems together. To investigate children's multimodal coordination, we measured their speech, hand movements and head movements throughout their bouts of discussion, and applied multidimensional Recurrence Quantification Analysis (MdRQA) on these timeseries. We coded the type of collaboration the children engaged in during these bouts of discussion. We measured performance regarding predicting to which side the balance scale would tilt. We will analyse how children's multimodal coordination is related to the type of collaboration and to their performance on the balance scale problems. Our results will show how successful collaboration between children emerges from their multimodal coordination.