Mark Ho
New York
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computational cog sci • problem solving and social cognition • asst prof @NYUPsych • markkho.bsky.social
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Articles by Mark Ho
“Foi um desespero total”: irmão do papa Leão fala sobre câncer do pontífice
O irmão do papa Leão XIV, Louis Prevost, afirmou no domingo (18) que a família entrou em “desespero total” ao saber que o pontífice estava com câncer de pulmão, mas disse que o papa agora está se recuperando bem e que os médicos conseguiram remover o tumor canceroso. Ele deu entrevista ao apresentador Peter Doocy, da Fox News, neste domingo (18). “Tentamos conversar todos os dias desde que isso aconteceu. E ele está muito bem.
Mentalizing and heuristics as distinct cognitive strategies in human teaching
Abstract Teaching is a foundational social behaviour that can result from mentally effortful reasoning or cognitively frugal heuristics. When do people use these strategies to teach? Here we investigated this using behavioural experiments and computational modelling with adult participants recruited via Prolific.
Naufrágio de balsa na Guiana deixa dezenas de desaparecidos
Autoridades temem que dezenas de pessoas tenham morrido após uma balsa que transportava 133 viajantes virar na costa da Guiana, no final do sábado (18). Oficiais sugeriram que uma lista de passageiros imprecisa e o uso de drogas pela tripulação podem ter contribuído para a situação. Nenhum sobrevivente foi encontrado até tarde de domingo (19), quando as autoridades guianesas anunciaram o resgate de 67 pessoas, incluindo 15 crianças.
Author Correction: Adaptive mechanisms of social and asocial learning in immersive collective foraging - Nature Communications
Correction to: Nature Communications https://doi.org/10.1038/s41467-025-58365-6, published online 25 April 2025 In this article the author name ‘Mark K. Ho’ was incorrectly written as ‘Mark H. Ho’. The original article has been corrected.
Adaptive mechanisms of social and asocial learning in immersive collective foraging - Nature Communications
Abstract Human cognition is distinguished by our ability to adapt to different environments and circumstances. Yet the mechanisms driving adaptive behavior have predominantly been studied in separate asocial and social contexts, with an integrated framework remaining elusive. Here, we use a collective foraging task in a virtual Minecraft environment to integrate these two fields, by leveraging automated transcriptions of visual field data combined with high-resolution spatial trajectories.
Configurable Graph-Based Task Solving with the Marco Multi-AI Agent Framework for Chip Design
Chip and hardware design presents numerous challenges stemming from its complexity and advancing technologies. These challenges result in longer turn-around time (TAT) for optimizing performance, power, area, and cost (PPAC) during synthesis, verification, physical design, and reliability loops. Large language models (LLMs) have shown a remarkable capacity to comprehend and generate natural language at a massive scale, leading to many potential applications and benefits across various domains.
Building machines that learn and think with people - Nature Human Behaviour
Abstract What do we want from machine intelligence? We envision machines that are not just tools for thought but partners in thought: reasonable, insightful, knowledgeable, reliable and trustworthy systems that think with us. Current artificial intelligence systems satisfy some of these criteria, some of the time.
Using games to understand the mind - Nature Human Behaviour
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Abstract Board, card or video games have been played by virtually every individual in the world. Games are popular because they are intuitive and fun. These distinctive qualities of games also make them ideal for studying the mind. By being intuitive, games provide a unique vantage point for understanding the inductive biases that support behaviour in more complex, ecological settings than traditional laboratory experiments.
By Kelsey Allen Verified, Franziska Brändle, Matthew Botvinick, Judith E. Fan, Samuel J. Gershman, Alison Gopnik, Thomas L. Griffiths, Joshua K. Hartshorne, Tobias U. Hauser, Mark Ho, Wei Ji Ma, Kou Murayama, Jonathan Nelson, Iyad Rahwan, Robb B. Rutledge, Jacob Sherson, Hugo Spiers, Christopher Summerfield, Joshua B. Tenenbaum, Özgür Şimşek
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Nature
Verified
Humans decompose tasks by trading off utility and computational cost
Comparing subgoal choice to theories. Having established that participants learned the task, as well as the validity of their probe responses, we now turn to our central claim, namely that subgoal choice is driven by the computational costs of hierarchical planning. Letting participants’ responses to the subgoal probes stand as reasonable proxies of subgoal choice, we relate the predictions of normative accounts and heuristics to participant probe choice across the three probes.
People construct simplified mental representations to plan - Nature
Abstract One of the most striking features of human cognition is the ability to plan. Two aspects of human planning stand out—its efficiency and flexibility. Efficiency is especially impressive because plans must often be made in complex environments, and yet people successfully plan solutions to many everyday problems despite having limited cognitive resources1,2,3.
[2111.00876] On the Expressivity of Markov Reward
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[2009.02476] Using Machine Teaching to Investigate Human Assumptions when Teaching Reinforcement Learners
[Submitted on 5 Sep 2020 ( v1 ), last revised 29 Jun 2023 (this version, v4)] Title:Using Machine Teaching to Investigate Human Assumptions when Teaching Reinforcement Learners Download a PDF of the paper titled Using Machine Teaching to Investigate Human Assumptions when Teaching Reinforcement Learners, by Yun-Shiuan Chuang and 5 other authors Download PDF Abstract: Successful teaching requires an assumption of how the learner learns - how the learner uses experiences from the world to...
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