Too Many cooks: Bayesian inference for coordinating Multi-agent

Advances in Neural Information Processing Systems

Goal-conditioned policies are generally understood to be" feed-forward" circuits, in the form of neural networks that map from the current state and the goal specification to the next action to take. However, under what circumstances such a policy can be learned and how efficient the policy will be are not well understood. In this paper, we present a circuit complexity analysis for relational neural networks (such as graph neural networks and transformers) representing policies for planning problems, by drawing connections with serialized goal regression search (S-GRS). We show that there are three general classes of planning problems, in terms of the growth of circuit width and depth as a function of the number of objects and planning horizon, providing constructive proofs. We also illustrate the utility of this analysis for designing neural networks for policy learning.

Research Policy

The COVID-19 pandemic necessitated social distancing at every level of society, including universities and research institutes, raising essential questions concerning the continuing importance of physical proximity for scientific and scholarly advance. Using customized author surveys about the intellectual influence of referenced work on scientists' own papers, combined with precise measures of geographical and semantic distance between focal and referenced works, we find that being at the same institution is strongly associated with intellectual influence on scientists' and scholars' published work. However, this influence increases with intellectual distance: the more different the referenced work done by colleagues at one's institution, the more influential it is on one's own. Universities worldwide constitute places where people doing very different work engage in sustained interactions through departments …

Advances in Neural Information Processing Systems

Contract design involves a principal who establishes contractual agreements about payments for outcomes that arise from the actions of an agent. In this paper, we initiate the study of deep learning for the automated design of optimal contracts. We introduce a novel representation: the Discontinuous ReLU (DeLU) network, which models the principal's utility as a discontinuous piecewise affine function of the design of a contract where each piece corresponds to the agent taking a particular action. DeLU networks implicitly learn closed-form expressions for the incentive compatibility constraints of the agent and the utility maximization objective of the principal, and support parallel inference on each piece through linear programming or interior-point methods that solve for optimal contracts. We provide empirical results that demonstrate success in approximating the principal's utility function with a small number of training samples and scaling to find approximately optimal contracts on problems with a large number of actions and outcomes.

Cognition

How do people hold others responsible? Responsibility judgments are affected not only by what actually happened, but also by what could have happened if things had turned out differently. Here, we look at how replaceability – the ease with which a person could have been replaced by someone else – affects responsibility. We develop the counterfactual replacement model, which runs simulations of alternative scenarios to determine the probability that the outcome would have differed if the person of interest had been replaced. The model predicts that a person is held more responsible, the more difficult it would have been to replace them. To test the model’s predictions, we design a paradigm that quantitatively varies replaceability by manipulating the number of replacements and the probability with which each replacement would have been available. Across three experiments featuring increasingly complex …

Bayesian models of cognition : reverse engineering the mind - WRAP: Warwick Research Archive Portal Skip to content Skip to navigation University of Warwick Study | Research | Business | Alumni | News | About University of Warwick Publications service & WRAP Highlight your research WRAP Home Search WRAP Browse by Warwick Author Browse WRAP by Year Browse WRAP by Subject Browse WRAP by Department Browse WRAP by Funder Browse Theses by Department Publications Service Home Search Publications Service Browse by Warwick Author Browse Publications service by Year Browse Publications service by Subject Browse Publications service by Department Browse Publications service by Funder Help & Advice University of Warwick The Library Login Admin Bayesian models of cognition : reverse engineering the mind Tools + Tools Griffiths, TL and Chater, Nick and Tenenbaum, Joshua B., …

arXiv preprint arXiv:2403.05334

Often, a good explanation for a program's unexpected behavior is a bug in the programmer's code. But sometimes, an even better explanation is a bug in the programmer's mental model of the language they are using. Instead of merely debugging our current code ("giving the programmer a fish"), what if our tools could directly debug our mental models ("teaching the programmer to fish")? In this paper, we apply ideas from computational cognitive science to do exactly that. Given a perplexing program, we use program synthesis techniques to automatically infer potential misconceptions that might cause the user to be surprised by the program's behavior. By analyzing these misconceptions, we provide succinct, useful explanations of the program's behavior. Our methods can even be inverted to synthesize pedagogical example programs for diagnosing and correcting misconceptions in students.

Systems and methods described herein relate to reconstructing a scene in three dimensions from a two-dimensional image. One embodiment processes an image using a detection transformer to detect an object in the scene and to generate a NOCS map of the object and a background depth map; uses MLPs to relate the object to a differentiable database of object priors (PriorDB); recovers, from the NOCS map, a partial 3D object shape; estimates an initial object pose; fits a PriorDB object prior to align in geometry and appearance with the partial 3D shape to produce a complete shape and refines the initial pose estimate; generates an editable and re-renderable 3D scene reconstruction based, at least in part, on the complete shape, the refined pose estimate, and the depth map; and controls the operation of a robot based, at least in part, on the editable and re-renderable 3D scene reconstruction.

Proceedings of the AAAI Conference on Artificial Intelligence

Multi-agent interactions, such as communication, teaching, and bluffing, often rely on higher-order social inference, i.e., understanding how others infer oneself. Such intricate reasoning can be effectively modeled through nested multi-agent reasoning. Nonetheless, the computational complexity escalates exponentially with each level of reasoning, posing a significant challenge. However, humans effortlessly perform complex social inferences as part of their daily lives. To bridge the gap between human-like inference capabilities and computational limitations, we propose a novel approach: leveraging neural networks to amortize high-order social inference, thereby expediting nested multi-agent reasoning. We evaluate our method in two challenging multi-agent interaction domains. The experimental results demonstrate that our method is computationally efficient while exhibiting minimal degradation in accuracy.

Advances in Neural Information Processing Systems

A single panel of a comic book can say a lot: it can depict not only where the characters currently are, but also their motions, their motivations, their emotions, and what they might do next. More generally, humans routinely infer complex sequences of past and future events from a static snapshot of a dynamic scene, even in situations they have never seen before. In this paper, we model how humans make such rapid and flexible inferences. Building on a long line of work in cognitive science, we offer a Monte Carlo algorithm whose inferences correlate well with human intuitions in a wide variety of domains, while only using a small, cognitively-plausible number of samples. Our key technical insight is a surprising connection between our inference problem and Monte Carlo path tracing, which allows us to apply decades of ideas from the computer graphics community to this seemingly-unrelated theory of mind task.

Advances in Neural Information Processing Systems

Nature evolves creatures with a high complexity of morphological and behavioral intelligence, meanwhile computational methods lag in approaching that diversity and efficacy. Co-optimization of artificial creatures' morphology and control in silico shows promise for applications in physical soft robotics and virtual character creation; such approaches, however, require developing new learning algorithms that can reason about function atop pure structure. In this paper, we present DiffuseBot, a physics-augmented diffusion model that generates soft robot morphologies capable of excelling in a wide spectrum of tasks.\name bridges the gap between virtually generated content and physical utility by (i) augmenting the diffusion process with a physical dynamical simulation which provides a certificate of performance, and (ii) introducing a co-design procedure that jointly optimizes physical design and control by leveraging information about physical sensitivities from differentiable simulation. We showcase a range of simulated and fabricated robots along with their capabilities. Check our website: https://diffusebot. github. io/

arXiv preprint arXiv:2402.12590

Large language models steer their behaviors based on texts generated by others. This capacity and their increasing prevalence in online settings portend that they will intentionally or unintentionally "program" one another and form emergent AI subjectivities, relationships, and collectives. Here, we call upon the research community to investigate these "society-like" properties of interacting artificial intelligences to increase their rewards and reduce their risks for human society and the health of online environments. We use a simple model and its outputs to illustrate how such emergent, decentralized AI collectives can expand the bounds of human diversity and reduce the risk of toxic, anti-social behavior online. Finally, we discuss opportunities for AI self-moderation and address ethical issues and design challenges associated with creating and maintaining decentralized AI collectives.

Proceedings of the AAAI Conference on Artificial Intelligence

We consider a platform in a two-sided market with unit-supply sellers and unit-demand buyers. Each buyer can transact with a subset of sellers it knows off platform and another seller that the platform recommends. Given the choice of sellers, transactions and prices form a competitive equilibrium. The platform selects one seller for each buyer, and charges a fixed percentage of prices to all transactions that it recommends. The platform seeks to maximize total revenue. We show that the platform's problem is NP-hard, even when each buyer knows at most two buyers off platform. Finally, when each buyer values all sellers equally and knows only one buyer off platform, we provide a polynomial time algorithm that optimally solves the problem.

Advances in Neural Information Processing Systems

The data market design problem is a problem in economic theory to find a set of signaling schemes (statistical experiments) to maximize expected revenue to the information seller, where each experiment reveals some of the information known to a seller and has a corresponding price. Each buyer has their own decision to make in a world environment, and their subjective expected value for the information associated with a particular experiment comes from the improvement in this decision and depends on their prior and value for different outcomes. In a setting with multiple buyers, a buyer's expected value for an experiment may also depend on the information sold to others. We introduce the application of deep learning for the design of revenue-optimal data markets, looking to expand the frontiers of what can be understood and achieved. Relative to earlier work on deep learning for auction design, we must learn signaling schemes rather than allocation rules and handle obedience constraints—these arising from modeling the downstream actions of buyers—in addition to incentive constraints on bids. Our experiments demonstrate that this new deep learning framework can almost precisely replicate all known solutions from theory, expand to more complex settings, and be used to establish the optimality of new designs for data markets and make conjectures in regard to the structure of optimal designs.

Intuitive physics as probabilistic inference - WRAP: Warwick Research Archive Portal Skip to content Skip to navigation University of Warwick Study | Research | Business | Alumni | News | About University of Warwick Publications service & WRAP Highlight your research WRAP Home Search WRAP Browse by Warwick Author Browse WRAP by Year Browse WRAP by Subject Browse WRAP by Department Browse WRAP by Funder Browse Theses by Department Publications Service Home Search Publications Service Browse by Warwick Author Browse Publications service by Year Browse Publications service by Subject Browse Publications service by Department Browse Publications service by Funder Help & Advice University of Warwick The Library Login Admin Intuitive physics as probabilistic inference Tools + Tools Smith, KA, Hamrick, JB, Sanborn, Adam N., Battaglia, PW, Gerstenberg, T., Ullman, TD and …

CogSci

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.

ICLR

Recent advances in high-fidelity virtual environments serve as one of the major driving forces for building intelligent embodied agents to perceive, reason and interact with the physical world. Typically, these environments remain unchanged unless agents interact with them. However, in real-world scenarios, agents might also face dynamically changing environments characterized by unexpected events and need to rapidly take action accordingly. To remedy this gap, we propose a new simulated embodied benchmark, called HAZARD, specifically designed to assess the decision-making abilities of embodied agents in dynamic situations. HAZARD consists of three unexpected disaster scenarios, including fire, flood, and wind, and specifically supports the utilization of large language models (LLMs) to assist common sense reasoning and decision-making. This benchmark enables us to evaluate autonomous agents' decision-making capabilities across various pipelines, including reinforcement learning (RL), rule-based, and search-based methods in dynamically changing environments. As a first step toward addressing this challenge using large language models, we further develop an LLM-based agent and perform an in-depth analysis of its promise and challenge of solving these challenging tasks. HAZARD is available at https://vis-www.cs.umass.edu/hazard/.

Proceedings of the AAAI Conference on Artificial Intelligence

Recent work has considered whether large language models (LLMs) can function as planners: given a task, generate a plan. We investigate whether LLMs can serve as generalized planners: given a domain and training tasks, generate a program that efficiently produces plans for other tasks in the domain. In particular, we consider PDDL domains and use GPT-4 to synthesize Python programs. We also consider (1) Chain-of-Thought (CoT) summarization, where the LLM is prompted to summarize the domain and propose a strategy in words before synthesizing the program; and (2) automated debugging, where the program is validated with respect to the training tasks, and in case of errors, the LLM is re-prompted with four types of feedback. We evaluate this approach in seven PDDL domains and compare it to four ablations and four baselines. Overall, we find that GPT-4 is a surprisingly powerful generalized planner. We also conclude that automated debugging is very important, that CoT summarization has non-uniform impact, that GPT-4 is far superior to GPT-3.5, and that just two training tasks are often sufficient for strong generalization.

Advances in Neural Information Processing Systems

Given a visual scene, humans have strong intuitions about how a scene can evolve over time under given actions. The intuition, often termed visual intuitive physics, is a critical ability that allows us to make effective plans to manipulate the scene to achieve desired outcomes without relying on extensive trial and error. In this paper, we present a framework capable of learning 3D-grounded visual intuitive physics models from videos of complex scenes with fluids. Our method is composed of a conditional Neural Radiance Field (NeRF)-style visual frontend and a 3D point-based dynamics prediction backend, using which we can impose strong relational and structural inductive bias to capture the structure of the underlying environment. Unlike existing intuitive point-based dynamics works that rely on the supervision of dense point trajectory from simulators, we relax the requirements and only assume access to multi-view RGB images and (imperfect) instance masks acquired using color prior. This enables the proposed model to handle scenarios where accurate point estimation and tracking are hard or impossible. We generate datasets including three challenging scenarios involving fluid, granular materials, and rigid objects in the simulation. The datasets do not include any dense particle information so most previous 3D-based intuitive physics pipelines can barely deal with that. We show our model can make long-horizon future predictions by learning from raw images and significantly outperforms models that do not employ an explicit 3D representation space. We also show that once trained, our model can achieve strong generalization in complex …

Advances in Neural Information Processing Systems

Recent works such as VisProg and ViperGPT have smartly composed foundation models for visual reasoning—using large language models (LLMs) to produce programs that can be executed by pre-trained vision-language models. However, they operate in limited domains, such as 2D images, not fully exploiting the generalization of language: abstract concepts like “left” can also be grounded in 3D, temporal, and action data, as in moving to your left. This limited generalization stems from these inference-only methods’ inability to learn or adapt pre-trained models to a new domain. We propose the Logic-Enhanced FoundaTion Model (LEFT), a unified framework that learns to ground and reason with concepts across domains with a differentiable, domain-independent, first-order logic-based program executor. LEFT has an LLM interpreter that outputs a program represented in a general, logic-based reasoning language, which is shared across all domains and tasks. LEFT’s executor then executes the program with trainable domain-specific grounding modules. We show that LEFT flexibly learns concepts in four domains: 2D images, 3D scenes, human motions, and robotic manipulation. It exhibits strong reasoning ability in a wide variety of tasks, including those that are complex and not seen during training, and can be easily applied to new domains.

Topics in Cognitive Science

Successful spoken discourse requires a speaker to be informative to deliver a coherent, meaningful message. The informativeness of discourse can be conveyed by the variety of vocabulary produced (i.e., lexical diversity [LD]), the typicality of vocabulary items used (i.e., core lexicon [CL]), and the amount of relevant content produced (i.e., information units). Yet, it is well documented that older adults produce less informative content compared to younger adults despite relatively subtle changes to LD. The typicality of core lexical items has not been assessed in healthy aging. Paradoxically, these results indicate that some aspects of discourse informativeness remain stable or even improve across the adult lifespan, while other aspects decline. The purpose of the current study is to understand how microlinguistic processes of informativeness change across the adult lifespan. The cross‐sectional study included …

Topics in Cognitive Science

Collaboration requires agents to coordinate their behavior on the fly, sometimes cooperating to solve a single task together and other times dividing it up into sub‐tasks to work on in parallel. Underlying the human ability to collaborate is theory‐of‐mind (ToM), the ability to infer the hidden mental states that drive others to act. Here, we develop Bayesian Delegation, a decentralized multi‐agent learning mechanism with these abilities. Bayesian Delegation enables agents to rapidly infer the hidden intentions of others by inverse planning. We test Bayesian Delegation in a suite of multi‐agent Markov decision processes inspired by cooking problems. On these tasks, agents with Bayesian Delegation coordinate both their high‐level plans (e.g., what sub‐task they should work on) and their low‐level actions (e.g., avoiding getting in each other's way). When matched with partners that act using the same algorithm …

Topics in Cognitive Science

As organizations gravitate to group‐based structures, the problem of improving performance through judicious selection of group members has preoccupied scientists and managers alike. However, which individual attributes best predict group performance remains poorly understood. Here, we describe a preregistered experiment in which we simultaneously manipulated four widely studied attributes of group compositions: skill level, skill diversity, social perceptiveness, and cognitive style diversity. We find that while the average skill level of group members, skill diversity, and social perceptiveness are significant predictors of group performance, skill level dominates all other factors combined. Additionally, we explore the relationship between patterns of collaborative behavior and performance outcomes and find that any potential gains in solution quality from additional communication between the group members …

Topics in Cognitive Science

One of the most heated debates in psychological science concerns the concept of repressed memory. We discuss how the debate on repressed memories continues to surface in legal settings, sometimes even to suggest avenues of legal reform. In the past years, several European countries have extended or abolished the statute of limitations for the prosecution of sexual crimes. Such statutes force legal actions (e.g., prosecution of sexual abuse) to be applied within a certain period of time. One of the reasons for the changes in statutes of limitations concerns the idea of repressed memory. We argue that from a psychological standpoint, these law reforms can be detrimental, particularly when they are done to endorse unfounded psychological theories. The validity of testimonies is compromised many years after the alleged facts, and abolishing the statute of limitations increases the chance that even more (false …

Topics in Cognitive Science

One of the most heated debates in psychological science concerns the concept of repressed memory. We discuss how the debate on repressed memories continues to surface in legal settings, sometimes even to suggest avenues of legal reform. In the past years, several European countries have extended or abolished the statute of limitations for the prosecution of sexual crimes. Such statutes force legal actions (e.g., prosecution of sexual abuse) to be applied within a certain period of time. One of the reasons for the changes in statutes of limitations concerns the idea of repressed memory. We argue that from a psychological standpoint, these law reforms can be detrimental, particularly when they are done to endorse unfounded psychological theories. The validity of testimonies is compromised many years after the alleged facts, and abolishing the statute of limitations increases the chance that even more (false …

Topics in Cognitive Science

Motivation is the driving force that influences people's behaviors and interacts with many cognitive functions. Computationally, motivation is represented as a cost−benefit analysis that weighs efforts and rewards in order to choose the optimal actions. Shenhav and colleagues proposed an elegant theory, the Expected Value of Control (EVC), which describes the relationship between cognitive efforts, costs, and rewards. In this paper, we propose a more fine‐grained and detailed motivation framework that incorporates the principles of EVC into the ACT‐R cognitive architecture. Specifically, motivation is represented as a specific slot in the Goal buffer with a corresponding scalar value, M, that is translated into the reward value Rt that is delivered when the goal is reached. This implementation is tested in two models. The first model is a high‐level model that reproduces the EVC predictions with abstract actions. The …

Topics in Cognitive Science

Decades of research have established that learners benefit when instruction includes hand gestures. This benefit is seen when learners watch an instructor gesture, as well as when they are taught or encouraged to gesture themselves. However, there is substantial individual variability with respect to this phenomenon—not all individuals benefit equally from gesture instruction. In the current paper, we explore the sources of this variability. First, we review the existing research on individual differences that do or do not predict learning from gesture instruction, including differences that are either context‐dependent (linked to the particular task at hand) or context‐independent (linked to the learner across multiple tasks). Next, we focus on one understudied measure of individual difference: the learner's own spontaneous gesture rate. We present data showing rates of “non‐gesturers” across a number of studies and we …

Topics in Cognitive Science

Adverse early life experiences can have remarkably enduring negative consequences on mental health, with numerous, varied psychiatric conditions sharing this developmental origin. Yet, the mechanisms linking adverse experiences to these conditions remain poorly understood. Here, we draw on a principled model of interval timing to propose that statistically optimal adaptation of temporal representations to an unpredictable early life environment can produce key characteristics of anhedonia, a transdiagnostic symptom associated with affective disorders like depression and anxiety. The core observation is that early temporal unpredictability produces broader, more imprecise temporal expectations. As a result, reward anticipation is diminished, and associative learning is slowed. When agents with such representations are later introduced to more stable environments, they demonstrate a negativity bias …

Topics in Cognitive Science

I present a computational‐level model of language production in terms of a combination of information theory and control theory in which words are chosen incrementally in order to maximize communicative value subject to an information‐theoretic capacity constraint. The theory generally predicts a tradeoff between ease of production and communicative accuracy. I apply the theory to two cases of apparent availability effects in language production, in which words are selected on the basis of their accessibility to a speaker who has not yet perfectly planned the rest of the utterance. Using corpus data on English relative clause complementizer dropping and experimental data on Mandarin noun classifier choice, I show that the theory reproduces the observed phenomena, providing an alternative account to Uniform Information Density and a promising general model of language production which is tightly linked to …

Topics in Cognitive Science

Over two decades have passed since the publication of van Gelder's (1998) “dynamical hypothesis.” In that paper, van Gelder proposed that cognitive agents were not digital computers—per the representational computational approach—but dynamical systems. The evolution of the dynamical hypothesis was driven by parallel advances in three areas. Theoretically, a deeper understanding of genetics, biology, neuroscience, and cognitive science inspired questions about how systems within each domain dynamically interact and extend their effects across spatiotemporal scales. Methodologically, more sophisticated and domain‐general tools allowed researchers to discover, model, and quantify system dynamics, structure, and patterns across multiple scales to generate a more comprehensive system‐level understanding of behaviors. Empirically, we can analyze a system's behavior while preserving its natural …

Topics in Cognitive Science

Over two decades have passed since the publication of van Gelder's (1998) “dynamical hypothesis.” In that paper, van Gelder proposed that cognitive agents were not digital computers—per the representational computational approach—but dynamical systems. The evolution of the dynamical hypothesis was driven by parallel advances in three areas. Theoretically, a deeper understanding of genetics, biology, neuroscience, and cognitive science inspired questions about how systems within each domain dynamically interact and extend their effects across spatiotemporal scales. Methodologically, more sophisticated and domain‐general tools allowed researchers to discover, model, and quantify system dynamics, structure, and patterns across multiple scales to generate a more comprehensive system‐level understanding of behaviors. Empirically, we can analyze a system's behavior while preserving its natural …

Topics in Cognitive Science

Performance on the psychomotor vigilance test (PVT; Dinges & Powell, 1985)—a common index of sustained attention—is affected by the opposing forces of fatigue and sustained effort, where reaction times and error rates typically increase across trials and are sometimes offset by additional efforts deployed toward the end of the task (i.e., an “end‐spurt”; cf. Bergum & Klein, 1961). In ACT‐R (Adaptive Control of Thought‐Rational; Anderson et al., 2004), these influences on task performance have been modeled as latent variables that are inferred from performance (e.g., Jongman, 1998; Veksler & Gunzelmann, 2018) without connections to directly observable variables. We propose the use of frontal gamma (γ) spectral power as a direct measure of vigilant effort and demonstrate its efficacy in modeling performance on the PVT in both the aggregate and in individuals.

Topics in Cognitive Science

One of the most heated debates in psychological science concerns the concept of repressed memory. We discuss how the debate on repressed memories continues to surface in legal settings, sometimes even to suggest avenues of legal reform. In the past years, several European countries have extended or abolished the statute of limitations for the prosecution of sexual crimes. Such statutes force legal actions (e.g., prosecution of sexual abuse) to be applied within a certain period of time. One of the reasons for the changes in statutes of limitations concerns the idea of repressed memory. We argue that from a psychological standpoint, these law reforms can be detrimental, particularly when they are done to endorse unfounded psychological theories. The validity of testimonies is compromised many years after the alleged facts, and abolishing the statute of limitations increases the chance that even more (false …

Topics in Cognitive Science

This paper presents two studies in which a peer‐assisted learning condition was compared to an individual learning condition. The first study used the paired‐associates learning task and the second study used an incrementally more complex task—the remote associate test. Participants in the peer‐assisted learning condition worked in groups of four. They had to solve a given problem individually and give a first answer before being able to request to see their peers’ solutions; then, a second answer was issued. After six sessions of peer‐assisted practice, a final individual test was administered. Peer interaction was found to benefit learning in both studies but the benefit transferred to the final test only in the second study. Fine‐grained behavioral analyses and computational modeling suggested that the benefits of peer interaction were (partially) offset by its costs, particularly increased cognitive load and error …

Topics in Cognitive Science

Successful spoken discourse requires a speaker to be informative to deliver a coherent, meaningful message. The informativeness of discourse can be conveyed by the variety of vocabulary produced (i.e., lexical diversity [LD]), the typicality of vocabulary items used (i.e., core lexicon [CL]), and the amount of relevant content produced (i.e., information units). Yet, it is well documented that older adults produce less informative content compared to younger adults despite relatively subtle changes to LD. The typicality of core lexical items has not been assessed in healthy aging. Paradoxically, these results indicate that some aspects of discourse informativeness remain stable or even improve across the adult lifespan, while other aspects decline. The purpose of the current study is to understand how microlinguistic processes of informativeness change across the adult lifespan. The cross‐sectional study included …

Topics in Cognitive Science

As organizations gravitate to group‐based structures, the problem of improving performance through judicious selection of group members has preoccupied scientists and managers alike. However, which individual attributes best predict group performance remains poorly understood. Here, we describe a preregistered experiment in which we simultaneously manipulated four widely studied attributes of group compositions: skill level, skill diversity, social perceptiveness, and cognitive style diversity. We find that while the average skill level of group members, skill diversity, and social perceptiveness are significant predictors of group performance, skill level dominates all other factors combined. Additionally, we explore the relationship between patterns of collaborative behavior and performance outcomes and find that any potential gains in solution quality from additional communication between the group members …

Topics in Cognitive Science

Children's testimonial learning often occurs in epistemic collaborations with others. In this paper, we will discuss ways in which cultural learning emerges in social and interpersonal contexts, and is intrinsically supported and guided by children's collaborative capacities. Much work in cultural learning has focused on children's examination of speaker and model characteristics, but more recent research has investigated the interactive aspects of testimonial exchanges. We will review evidence that children (1) participate in the interpersonal commitments that are shared in testimonial transactions by way of direct address and epistemic buck passing, (2) participate in social groups that affect their selective learning in nuanced ways, and (3) may detect epistemic harms by listeners who refuse to believe sincere and accurate speakers. Implications for conceptualizing children's testimonial learning as an interactive …

Topics in Cognitive Science

Ruminative thinking, characterized by a recurrent focus on negative and self‐related thought, is a key cognitive vulnerability marker of depression and, therefore, a key individual difference variable. This study aimed to develop a computational cognitive model of rumination focusing on the organization and retrieval of information in memory, and how these mechanisms differ in individuals prone to rumination and individuals less prone to rumination. Adaptive Control of Thought‐Rational (ACT‐R) was used to develop a rumination model by adding memory chunks with negative valence to the declarative memory. In addition, their strength of association was increased to simulate recurrent negative focus, thereby making it harder to disengage from. The ACT‐R models were validated by comparing them against two empirical datasets containing data from control and depressed participants. Our general and …

Topics in Cognitive Science

Collaboration requires agents to coordinate their behavior on the fly, sometimes cooperating to solve a single task together and other times dividing it up into sub‐tasks to work on in parallel. Underlying the human ability to collaborate is theory‐of‐mind (ToM), the ability to infer the hidden mental states that drive others to act. Here, we develop Bayesian Delegation, a decentralized multi‐agent learning mechanism with these abilities. Bayesian Delegation enables agents to rapidly infer the hidden intentions of others by inverse planning. We test Bayesian Delegation in a suite of multi‐agent Markov decision processes inspired by cooking problems. On these tasks, agents with Bayesian Delegation coordinate both their high‐level plans (e.g., what sub‐task they should work on) and their low‐level actions (e.g., avoiding getting in each other's way). When matched with partners that act using the same algorithm …

Topics in Cognitive Science

Humans routinely form groups to achieve goals that no individual can accomplish alone. Group coordination often brings to mind synchrony and alignment, where all individuals do the same thing (e.g., driving on the right side of the road, marching in lockstep, or playing musical instruments on a regular beat). Yet, effective coordination also typically involves differentiation, where specialized roles emerge for different members (e.g., prep stations in a kitchen or positions on an athletic team). Role specialization poses a challenge for computational models of group coordination, which have largely focused on achieving synchrony. Here, we present the CARMI framework, which characterizes role specialization processes in terms of five core features that we hope will help guide future model development: Communication, Adaptation to feedback, Repulsion, Multi‐level planning, and Intention modeling. Although there …