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1. Resource‐rational Models of Human Goal Pursuit

   https://doi.org/10.1111/tops.12562  

   Prystawski, Ben ; Mohnert, Florian ; Tošić, Mateo ; Lieder, Falk ( August 2021 , Topics in Cognitive Science)

   Goal‐directed behavior is a deeply important part of human psychology. People constantly set goals for themselves and pursue them in many domains of life. In this paper, we develop computational models that characterize how humans pursue goals in a complex dynamic environment and test how well they describe human behavior in an experiment. Our models are motivated by the principle of resource rationality and draw upon psychological insights about people's limited attention and planning capacities. We find that human goal pursuit is qualitatively different and substantially less efficient than optimal goal pursuit in our simulated environment. Models of goal pursuit based on the principle of resource rationality capture human behavior better than both a model of optimal goal pursuit and heuristics that are not resource‐rational. We conclude that the way humans pursue goals is shaped by the need to achieve goals effectively as well as cognitive costs and constraints on planning and attention. Our findings are an important step toward understanding humans' goal pursuit as cognitive limitations play a crucial role in shaping people's goal‐directed behavior.

    

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2. Experimental study of the proton-transfer reaction C + H 2 + → CH + + H and its isotopic variant (D 2 + )

   https://doi.org/10.1039/D0CP04810K  

   Hillenbrand, Pierre-Michel ; Bowen, Kyle P. ; Dayou, Fabrice ; Miller, Kenneth A. ; de Ruette, Nathalie ; Urbain, Xavier ; Savin, Daniel W. ( December 2020 , Physical Chemistry Chemical Physics)

   We report absolute integral cross section (ICS) measurements using a dual-source merged-fast-beams apparatus to study the titular reactions over the relative translational energy range of E r ∼ 0.01–10 eV. We used photodetachment of C − to produce a pure beam of atomic C in the ground electronic 3 P term, with statistically populated fine-structure levels. The H 2 + and D 2 + were formed in an electron impact ionization source, with well known vibrational and rotational distributions. The experimental work is complemented by a theoretical study of the CH 2 + electronic system in the reactant and product channels, which helps to clarify the possible reaction mechanisms underlying the ICS measurements. Our measurements provide evidence that the reactions are barrierless and exoergic. They also indicate the apparent absence of an intermolecular isotope effect, to within the total experimental uncertainties. Capture models, taking into account either the charge-induced dipole interaction potential or the combined charge-quadrupole and charge-induced dipole interaction potentials, produce reaction cross sections that lie a factor of ∼4 above the experimental results. Based on our theoretical study, we hypothesize that the reaction is most likely to proceed adiabatically through the 1 4 A′ and 1 4 A′′ states of CH 2 + via the reaction C( 3 P) + H 2 + ( 2 Σ+g) → CH + ( 3 Π) + H( 2 S). We also hypothesize that at low collision energies only H 2 + ( v ≤ 2) and D 2 + ( v ≤ 3) contribute to the titular reactions, due to the onset of dissociative charge transfer for higher vibrational v levels. Incorporating these assumptions into the capture models brings them into better agreement with the experimental results. Still, for energies ≲0.1 eV where capture models are most relevant, the modified charge-induced dipole model yields reaction cross sections with an incorrect energy dependence and lying ∼10% below the experimental results. The capture cross section obtained from the combined charge-quadrupole and charge-induced dipole model better matches the measured energy dependence but lies ∼30–50% above the experimental results. These findings provide important guidance for future quasiclassical trajectory and quantum mechanical treatments of this reaction. 

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3. Modeling Human Trust and Reliance in AI-Assisted Decision Making: A Markovian Approach

   https://doi.org/10.1609/aaai.v37i5.25748  

   Li, Zhuoyan ; Lu, Zhuoran ; Yin, Ming ( June 2023 , Proceedings of the AAAI Conference on Artificial Intelligence)

   The increased integration of artificial intelligence (AI) technologies in human workflows has resulted in a new paradigm of AI-assisted decision making,in which an AI model provides decision recommendations while humans make the final decisions. To best support humans in decision making, it is critical to obtain a quantitative understanding of how humans interact with and rely on AI. Previous studies often model humans' reliance on AI as an analytical process, i.e., reliance decisions are made based on cost-benefit analysis. However, theoretical models in psychology suggest that the reliance decisions can often be driven by emotions like humans' trust in AI models. In this paper, we propose a hidden Markov model to capture the affective process underlying the human-AI interaction in AI-assisted decision making, by characterizing how decision makers adjust their trust in AI over time and make reliance decisions based on their trust. Evaluations on real human behavior data collected from human-subject experiments show that the proposed model outperforms various baselines in accurately predicting humans' reliance behavior in AI-assisted decision making. Based on the proposed model, we further provide insights into how humans' trust and reliance dynamics in AI-assisted decision making is influenced by contextual factors like decision stakes and their interaction experiences. 

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4. How smart your smartphone is in lie detection?

   https://doi.org/10.1145/3360774.3360788  

   Rahman, Md. Mizanur ; Shome, Atanu ; Chellappan, Sriram ; Islam, A. B. ( November 2019 , MobiQuitous '19: Proceedings of the 16th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services)

   Lying is a (practically) unavoidable component of our day to day interactions with other people, and it includes both oral and textual communications (e.g. text entered via smartphones). Detecting when a person is lying has important applications, especially with the ubiquity of messaging via smart-phones, coupled with rampant increases in (intentional) spread of mis-information today. In this paper, we design a technique to detect whether or not a person's textual inputs when typed via a smartphone indicate lying. To do so, first, we judiciously develop a smartphone based survey that guarantees any participant to provide a mix of true and false responses. While the participant is texting out responses to each question, the smartphone measures readings from its inbuilt inertial sensors, and then computes features like shaking, acceleration, tilt angle, typing speed etc. experienced by it. Subsequently, for each participant (47 in total), we glean the true and false responses using our own experiences with them, and also via informal discussions with each participant. By comparing the responses of each participant, along with the corresponding motion features computed by the smartphone, we implement several machine learning algorithms to detect when a participant is lying, and our accuracy is around 70% in the most stringent leave-one-out evaluation strategy. Later, utilizing findings of our analysis, we develop an architecture for real-time lie detection using smartphones. Yet another user evaluation of our lie detection system yields 84%-90% accuracy in detecting false responses. 

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5. Overt visual attention and value computation in complex risky choice

   Xinhao Fan, Jacob Elsey ( December 2020 , bioRxiv)

   Traditional models of decision making under uncertainty explain human behavior in simple situations with a minimal set of alternatives and attributes. Some of them, such as prospect theory, have been proven successful and robust in such simple situations. Yet, less is known about the preference formation during decision making in more complex cases. Furthermore, it is generally accepted that attention plays a role in the decision process but most theories make simplifying assumptions about where attention is deployed. In this study, we replace these assumptions by measuring where humans deploy overt attention, i.e. where they fixate. To assess the influence of task complexity, participants perform two tasks. The simpler of the two requires participants to choose between two alternatives with two attributes each (four items to consider). The more complex one requires a choice between four alternatives with four attributes each (16 items to consider). We then compare a large set of model classes, of different levels of complexity, by considering the dynamic interactions between uncertainty, attention and pairwise comparisons between attribute values. The task of all models is to predict what choices humans make, using the sequence of observed eye movements for each participant as input to the model. We find that two models outperform all others. The first is the two-layer leaky accumulator which predicts human choices on the simpler task better than any other model. We call the second model, which is introduced in this study, TNPRO. It is modified from a previous model from management science and designed to deal with highly complex decision problems. Our results show that this model performs well in the simpler of our two tasks (second best, after the accumulator model) and best for the complex task. Our results suggest that, when faced with complex choice problems, people prefer to accumulate preference based on attention-guided pairwise comparisons. 

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