Predicting the behavior of human participants in strategic settings is an important problem in many domains. Most existing work either assumes that participants are perfectly rational, or attempts to directly model each participant's cognitive processes based on insights from cognitive psychology and experimental economics. In this work, we present an alternative, a deep learning approach that automatically performs cognitive modeling without relying on such expert knowledge. We introduce a novel architecture that allows a single network to generalize across different input and output dimensions by using matrix units rather than scalar units, and show that its performance significantly outperforms that of the previous state of the art, which relies on expert-constructed features.

Large language models generate complex, open-ended outputs: instead of outputting a class label they write summaries, generate dialogue, or produce working code. In order to asses the reliability of these open-ended generation systems, we aim to identify qualitative categories of erroneous behavior, beyond identifying individual errors. To hypothesize and test for such qualitative errors, we draw inspiration from human cognitive biases--systematic patterns of deviation from rational judgement. Specifically, we use cognitive biases as motivation to (i) generate hypotheses for problems that models may have, and (ii) develop experiments that elicit these problems. Using code generation as a case study, we find that OpenAI's Codex errs predictably based on how the input prompt is framed, adjusts outputs towards anchors, and is biased towards outputs that mimic frequent training examples. We then use our framework to elicit high-impact errors such as incorrectly deleting files. Our results indicate that experimental methodology from cognitive science can help characterize how machine learning systems behave.

A lack of corpora has so far limited advances in integrating human gaze data as a supervisory signal in neural attention mechanisms for natural language processing (NLP). We propose a novel hybrid text saliency model (TSM) that, for the first time, combines a cognitive model of reading with explicit human gaze supervision in a single machine learning framework. On four different corpora we demonstrate that our hybrid TSM duration predictions are highly correlated with human gaze ground truth. We further propose a novel joint modeling approach to integrate TSM predictions into the attention layer of a network designed for a specific upstream NLP task without the need for any task-specific human gaze data. We demonstrate that our joint model outperforms the state of the art in paraphrase generation on the Quora Question Pairs corpus by more than 10% in BLEU-4 and achieves state of the art performance for sentence compression on the challenging Google Sentence Compression corpus. As such, our work introduces a practical approach for bridging between data-driven and cognitive models and demonstrates a new way to integrate human gaze-guided neural attention into NLP tasks.

Binary human choice feedback is widely used in interactive preference learning for its simplicity, but it provides limited information about preference strength. To overcome this limitation, we leverage human response times, which inversely correlate with preference strength, as complementary information. Our work integrates the EZ-diffusion model, which jointly models human choices and response times, into preference-based linear bandits. We introduce a computationally efficient utility estimator that reformulates the utility estimation problem using both choices and response times as a linear regression problem. Theoretical and empirical comparisons with traditional choice-only estimators reveal that for queries with strong preferences ("easy" queries), choices alone provide limited information, while response times offer valuable complementary information about preference strength. As a result, incorporating response times makes easy queries more useful. We demonstrate this advantage in the fixed-budget best-arm identification problem, with simulations based on three real-world datasets, consistently showing accelerated learning when response times are incorporated.

Purpose: Industrial robots allow manufacturing companies to increase productivity and remain competitive. For robots to be used, they must be accepted by operators on the one hand and bought by decision-makers on the other. The roles involved in such organizational processes have very different perspectives. It is therefore essential for suppliers and robot customers to understand these motives so that robots can successfully be integrated on manufacturing shopfloors. Methodology: We present findings of a qualitative study with operators and decision-makers from two Swiss manufacturing SMEs. Using laddering interviews and means-end analysis, we compare operators' and deciders' relevant elements and how these elements are linked to each other on different abstraction levels. These findings represent drivers and barriers to the acquisition, integration and acceptance of robots in the industry. Findings: We present the differing foci of operators and deciders, and how they can be used by demanders as well as suppliers of robots to achieve robot acceptance and deployment. First, we present a list of relevant attributes, consequences and values that constitute robot acceptance and/or rejection. Second, we provide quantified relevancies for these elements, and how they differ between operators and deciders. And third, we demonstrate how the elements are linked with each other on different abstraction levels, and how these links differ between the two groups.

For public health programs with limited resources, the ability to Public health programs play an essential role in improving the predict how behaviors change over time and in response to interventions health outcomes of individuals and communities, often through education is crucial for deciding when and to whom interventions and subsequent behavioral change. Some health programs should be allocated. Using data from a real-world maternal interact with their intended beneficiaries in a broad and infrequent health program, we demonstrate how a cognitive model based on manner. For example, a campaign about the health risks of smoking Instance-Based Learning (IBL) Theory can augment existing purely may address a general population of smokers through scattered computational approaches. Our findings show that, compared to advertisements in the media [18]. Others rely on repeated direct interactions general time-series forecasters (e.g., LSTMs), IBL models, which with their intended beneficiaries. For example, maternal reflect human decision-making processes, better predict how individuals' health programs that send automated messages about exercise and behaviors change over time (transition-consistency) and nutrition to enrolled expectant mothers [13]. In this case, it is crucial in response to receiving an intervention (intervention-sensitivity).

A game developer called Amazing Seasun Games is demonstrating NVIDIA's Avatar Cloud Engine (ACE) technology through its upcoming multiplayer mecha game Mecha BREAK at Gamescom this year. NVIDIA unveiled ACE at Computer 2023, presenting it as a "custom AI model foundry service" that developers can use to make their games more interactive. Specifically, it will allow players to interact with NPCs without the constraints of pre-programmed conversations and will be able to give them appropriate responses. When NVIDIA launched the technology, it showed a player talking to an NPC called Jin at a ramen shop. The player asked how the character was, and Jin was able to respond naturally to tell them about his worries about his city's rising crime rates.

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 (AI) systems satisfy some of these criteria, some of the time. In this Perspective, we show how the science of collaborative cognition can be put to work to engineer systems that really can be called ``thought partners,'' systems built to meet our expectations and complement our limitations. We lay out several modes of collaborative thought in which humans and AI thought partners can engage and propose desiderata for human-compatible thought partnerships. Drawing on motifs from computational cognitive science, we motivate an alternative scaling path for the design of thought partners and ecosystems around their use through a Bayesian lens, whereby the partners we construct actively build and reason over models of the human and world.

Extracting time-varying latent variables from computational cognitive models is a key step in model-based neural analysis, which aims to understand the neural correlates of cognitive processes. However, existing methods only allow researchers to infer latent variables that explain subjects' behavior in a relatively small class of cognitive models. For example, a broad class of relevant cognitive models with analytically intractable likelihood is currently out of reach from standard techniques, based on Maximum a Posteriori parameter estimation. Here, we present an approach that extends neural Bayes estimation to learn a direct mapping between experimental data and the targeted latent variable space using recurrent neural networks and simulated datasets. We show that our approach achieves competitive performance in inferring latent variable sequences in both tractable and intractable models. Furthermore, the approach is generalizable across different computational models and is adaptable for both continuous and discrete latent spaces. We then demonstrate its applicability in real world datasets. Our work underscores that combining recurrent neural networks and simulation-based inference to identify latent variable sequences can enable researchers to access a wider class of cognitive models for model-based neural analyses, and thus test a broader set of theories.