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1. Estimating cognitive biases with attention-aware inverse planning

   Banerjee, Sounak; Cornelisse, Daphne; Gopinath, Deepak; Sumner, Emily; DeCastro, Jonathan; Rosman, Guy; Vinitsky, Eugene; Ho, Mark K (January 2026, Curran Associates, Inc.)

   People's goal-directed behaviors are influenced by their cognitive biases, and autonomous systems that interact with people should be aware of this. For example, people's attention to objects in their environment will be biased in a way that systematically affects how they perform everyday tasks such as driving to work. Here, building on recent work in computational cognitive science, we formally articulate the attention-aware inverse planning problem, in which the goal is to estimate a person's attentional biases from their actions. We demonstrate how attention-aware inverse planning systematically differs from standard inverse reinforcement learning and how cognitive biases can be inferred from behavior. Finally, we present an approach to attention-aware inverse planning that combines deep reinforcement learning with computational cognitive modeling. We use this approach to infer the attentional strategies of RL agents in real-life driving scenarios selected from the Waymo Open Dataset, demonstrating the scalability of estimating cognitive biases with attention-aware inverse planning. 

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2. When 0 is good: instrumental learning with counterintuitive goals decreases working memory engagement

   Pan, Ti-Fen; Molinaro, Gaia; Collins, Anne GE (July 2025, Proceedings of the Annual Meeting of the Cognitive Science Society)

   Humans are adept at setting goals quickly and flexibly in their daily lives. Previous research has shown that people can assign rewarding properties to abstract or novel outcomes and use them to guide behavior. However, the mechanisms supporting this flexibility and their impact on learning processes, such as working memory (WM) or slower incremental systems, remain unclear. To address this, we designed an instrumental learning task in which participants learned stimulus-action associations by pursuing either standard goals (+1) or counterintuitive goals (+0) under varying WM loads. Our behavioral and modeling results revealed that when pursuing counterintuitive goals, humans learned more slowly and shifted their reliance from WM to habit-like associative processes, despite both processes remaining functionally intact. Additionally, we replicated previous findings showing that humans do not rely on reinforcement learning (RL) processes but instead integrate WM and habit-like processes to learn the associations. This interplay between WM and habit-like processes may allow a more resource-efficient approach to pursuing diverse goals. Our findings shed light on the breadth and cost of people's ability to flexibly learn and pursue any goal. 

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3. SPNets: Human-like Navigation Behaviors with Uncertain Goals

   https://doi.org/10.1145/3424636.3426911  

   Sohre, Nicholas; Guy, Stephen J. (October 2020, SPNets: Human-like Navigation Behaviors with Uncertain Goals)

   null (Ed.)

   Most path planning techniques use exact, global information of the environment to make optimal or near-optimal plans. In contrast, humans navigate using only local information, which they must augment with their understanding of typical building layouts to guess what lies ahead, while integrating what they have seen already to form mental representations of building structure. Here, we propose Scene Planning Networks (SPNets), a neural network based approach for formulating the long-range navigation problem as a series of local decisions similar to what humans face when navigating. Agents navigating using SPNets build additive neural representations of previous observations to understand local obstacle structure, and use a network-based planning approach to plan the next steps towards a fuzzy goal region. Our approach reproduces several important aspects of human behavior that are not captured by either full global planning or simple local heuristics. 

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4. A Quantitative Analysis of Rational Decisions Under Uncertainty in Engineering Systems Design

   https://doi.org/10.1017/pds.2025.10039  

   Mutlu, Kaan; Javadpour, Nikoo; Sha, Zhenghui; Bayrak, Alparslan Emrah (August 2025, Proceedings of the Design Society)

   Rational decision-making is crucial in the later stages of engineering system design to allocate resources efficiently and minimize costs. However, human rationality is bounded by cognitive biases and limitations. Understanding how humans deviate from rationality is critical for guiding designers toward better design outcomes. In this paper, we quantify designer rationality in competitive scenarios based on utility theory. Using an experiment inspired by crowd-sourced contests, we show that designers employ varied search strategies. Some participants approximate a Bayesian agent that aimed to maximize its expected utility. Those with higher rationality reduce uncertainty more effectively. Furthermore, rationality correlates with both the proximity to optimal design and design iteration costs, with winning participants exhibiting greater rationality than losing participants. 

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5. Building Human-Like Artificial Agents: A General Cognitive Algorithm for Emulating Human Decision-Making in Dynamic Environments

   https://doi.org/10.1177/17456916231196766  

   Gonzalez, Cleotilde (September 2024, Perspectives on Psychological Science)

   One of the early goals of artificial intelligence (AI) was to create algorithms that exhibited behavior indistinguishable from human behavior (i.e., human-like behavior). Today, AI has diverged, often aiming to excel in tasks inspired by human capabilities and outperform humans, rather than replicating human cogntion and action. In this paper, I explore the overarching question of whether computational algorithms have achieved this initial goal of AI. I focus on dynamic decision-making, approaching the question from the perspective of computational cognitive science. I present a general cognitive algorithm that intends to emulate human decision-making in dynamic environments, as defined in instance-based learning theory (IBLT). I use the cognitive steps proposed in IBLT to organize and discuss current evidence that supports some of the human-likeness of the decision-making mechanisms. I also highlight the significant gaps in research that are required to improve current models and to create higher fidelity in computational algorithms to represent human decision processes. I conclude with concrete steps toward advancing the construction of algorithms that exhibit human-like behavior with the ultimate goal of supporting human dynamic decision-making. 

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