The cultural origins of human cognition

Relevance Communication And Cognition

Cambridge University Press v. Georgia State University

To what extent is the observed mispricing in experimental asset markets caused by strategic uncertainty and by confusion? We address this question by comparing subjects' initial price forecasts in two market environments: one with six human traders, and the other with one human and five computer traders. We find that both strategic uncertainty and confusion contribute equally to the median initial forecast deviation from the fundamental value. The effect of strategic uncertainty is greater for subjects with a perfect score in the Cognitive Reflection Test, and it is not significant for those with low scores.

/pdf/it-is-not-just-confusion-strategic-uncertainty-in-an-1r3fsikdh1.pdf

It is Not Just Confusion! Strategic Uncertainty in An Experimental Asset Market

Ranking valuations on the basis of observed choices Obliged to make a choice between two goals, we evaluate the benefits of achieving the goals compared with the costs of the actions required before deciding what to do. This seems perfectly straightforward, and it is unsurprising to learn that we can also apply this reasoning to others; that is, someone that we see choosing a goal that requires a more costly action must value that goal more highly. What is remarkable, as Liu et al. report, is that preverbal children can reason in this same fashion. Science, this issue p. 1038 Infants can assess how worthwhile or valuable an object or goal may be from others’ behaviors in achieving or acquiring it. Infants understand that people pursue goals, but how do they learn which goals people prefer? We tested whether infants solve this problem by inverting a mental model of action planning, trading off the costs of acting against the rewards actions bring. After seeing an agent attain two goals equally often at varying costs, infants expected the agent to prefer the goal it attained through costlier actions. These expectations held across three experiments that conveyed cost through different physical path features (height, width, and incline angle), suggesting that an abstract variable—such as “force,” “work,” or “effort”—supported infants’ inferences. We modeled infants’ expectations as Bayesian inferences over utility-theoretic calculations, providing a bridge to recent quantitative accounts of action understanding in older children and adults.

Ten-month-old infants infer the value of goals from the costs of actions

Cooperative transmission of data fosters rapid accumulation of knowledge by efficiently combining experiences across learners. Although well studied in human learning and increasingly in machine learning, we lack formal frameworks through which we may reason about the benefits and limitations of cooperative inference. We present such a framework. We introduce novel indices for measuring the effectiveness of probabilistic and cooperative information transmission. We relate our indices to the well-known Teaching Dimension in deterministic settings. We prove conditions under which optimal cooperative inference can be achieved, including a representation theorem that constrains the form of inductive biases for learners optimized for cooperative inference. We conclude by demonstrating how these principles may inform the design of machine learning algorithms and discuss implications for human and machine learning.

/pdf/optimal-cooperative-inference-3zrdif69jc.pdf

Optimal cooperative inference

Cooperative communication plays a central role in theories of human cognition, language, development, culture, and human-robot interaction. Prior models of cooperative communication are algorithmic in nature and do not shed light on why cooperation may yield effective belief transmission and what limitations may arise due to differences between beliefs of agents. Through a connection to the theory of optimal transport, we establishing a mathematical framework for cooperative communication. We derive prior models as special cases, statistical interpretations of belief transfer plans, and proofs of robustness and instability. Computational simulations support and elaborate our theoretical results, and demonstrate fit to human behavior. The results show that cooperative communication provably enables effective, robust belief transmission which is required to explain feats of human learning and improve human-machine interaction.

/pdf/a-mathematical-theory-of-cooperative-communication-4968d8yiai.pdf

A mathematical theory of cooperative communication

Cooperation information sharing is important to theories of human learning and has potential implications for machine learning. Prior work derived conditions for achieving optimal Cooperative Inference given strong, relatively restrictive assumptions. We relax these assumptions by demonstrating convergence for any discrete joint distribution, robustness through equivalence classes and stability under perturbation, and effectiveness by deriving bounds from structural properties of the original joint distribution. We provide geometric interpretations, connections to and implications for optimal transport, and connections to importance sampling, and conclude by outlining open questions and challenges to realizing the promise of Cooperative Inference.

/pdf/generalizing-the-theory-of-cooperative-inference-2c7z8vl3ql.pdf

Generalizing the theory of cooperative inference

Cooperation is often implicitly assumed when learning from other agents. Cooperation implies that the agent selecting the data, and the agent learning from the data, have the same goal, that the learner infer the intended hypothesis. Recent models in human and machine learning have demonstrated the possibility of cooperation. We seek foundational theoretical results for cooperative inference by Bayesian agents through sequential data. We develop novel approaches analyzing consistency, rate of convergence and stability of Sequential Cooperative Bayesian Inference (SCBI). Our analysis of the effectiveness, sample efficiency and robustness show that cooperation is not only possible in specific instances but theoretically well-founded in general. We discuss implications for human-human and human-machine cooperation.

/pdf/sequential-cooperative-bayesian-inference-2o3j7wfdns.pdf

Sequential Cooperative Bayesian Inference

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Papers

Optimal cooperative inference

A mathematical theory of cooperative communication

Generalizing the theory of cooperative inference

Generalizing the theory of cooperative inference

Sequential Cooperative Bayesian Inference