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1. Infants rationally infer the goals of other people's reaches in the absence of first‐person experience with reaching actions

   https://doi.org/10.1111/desc.13453  

   Woo, Brandon M. ; Liu, Shari ; Spelke, Elizabeth S. ( November 2023 , Developmental Science)

   Does knowledge of other people's minds grow from concrete experience to abstract concepts? Cognitive scientists have hypothesized that infants’ first‐person experience, acting on their own goals, leads them to understand others’ actions and goals. Indeed, classic developmental research suggests that before infants reach for objects, they do not see others’ reaches as goal‐directed. In five experiments (N = 117), we test an alternative hypothesis: Young infants view reaching as undertaken for a purpose but are open‐minded about the specific goals that reaching actions are aimed to achieve. We first show that 3‐month‐old infants, who cannot reach for objects, lack the expectation that observed acts of reaching will be directed to objects rather than to places. Infants at the same age learned rapidly, however, that a specific agent's reaching action was directed either to an object or to a place, after seeing the agent reach for the same object regardless of where it was, or to the same place regardless of what was there. In a further experiment, 3‐month‐old infants did not demonstrate such inferences when they observed an actor engaging in passive movements. Thus, before infants have learned to reach and manipulate objects themselves, they infer that reaching actions are goal‐directed, and they are open to learning that the goal of an action is either an object or a place.

   In the present experiments, 3‐month‐old prereaching infants learned to attribute either object goals or place goals to other people's reaching actions.

   Prereaching infants view agents’ actions as goal‐directed, but do not expect these acts to be directed to specific objects, rather than to specific places.

   Prereaching infants are open‐minded about the specific goal states that reaching actions aim to achieve.

    

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2. Leveraging Unstructured Statistical Knowledge in a Probabilistic Language of Thought

   Lew, Alexander K. ; Tessler, Michael Henry ; Mansinghka, Vikash K. ; Tenenbaum, Joshua B. ( January 2020 , Proceedings of the Annual Conference of the Cognitive Science Society)

   One hallmark of human reasoning is that we can bring to bear a diverse web of common-sense knowledge in any situation. The vastness of our knowledge poses a challenge for the practical implementation of reasoning systems as well as for our cognitive theories – how do people represent their common-sense knowledge? On the one hand, our best models of sophisticated reasoning are top-down, making use primarily of symbolically-encoded knowledge. On the other, much of our understanding of the statistical properties of our environment may arise in a bottom-up fashion, for example through asso- ciationist learning mechanisms. Indeed, recent advances in AI have enabled the development of billion-parameter language models that can scour for patterns in gigabytes of text from the web, picking up a surprising amount of common-sense knowledge along the way—but they fail to learn the structure of coherent reasoning. We propose combining these approaches, by embedding language-model-backed primitives into a state- of-the-art probabilistic programming language (PPL). On two open-ended reasoning tasks, we show that our PPL models with neural knowledge components characterize the distribution of human responses more accurately than the neural language models alone, raising interesting questions about how people might use language as an interface to common-sense knowledge, and suggesting that building probabilistic models with neural language-model components may be a promising approach for more human-like AI. 

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3. Emergence of relational reasoning

   https://doi.org/doi.org/10.1016/j.cobeha.2020.11.012  

   Holyoak, K. J. ; Lu, H. ( February 2021 , Current opinion in behavioral sciences)

   null (Ed.)

   We review recent theoretical and empirical work on the emergence of relational reasoning, drawing connections among the fields of comparative psychology, developmental psychology, cognitive neuroscience, cognitive science, and machine learning. Relational learning appears to involve multiple systems: a suite of Early Systems that are available to human infants and are shared to some extent with nonhuman animals; and a Late System that emerges in humans only, at approximately age three years. The Late System supports reasoning with explicit role-governed relations, and is closely tied to the functions of a frontoparietal network in the human brain. Recent work in cognitive science and machine learning suggests that humans (and perhaps machines) may acquire abstract relations from nonrelational inputs by means of processes that enable re-representation. 

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4. “Dave...I can assure you ...that it’s going to be all right ...” A Definition, Case for, and Survey of Algorithmic Assurances in Human-Autonomy Trust Relationships

   https://doi.org/10.1145/3267338  

   Israelsen, Brett W. ; Ahmed, Nisar R. ( February 2019 , ACM Computing Surveys)

   null (Ed.)

   People who design, use, and are affected by autonomous artificially intelligent agents want to be able to trust such agents—that is, to know that these agents will perform correctly, to understand the reasoning behind their actions, and to know how to use them appropriately. Many techniques have been devised to assess and influence human trust in artificially intelligent agents. However, these approaches are typically ad hoc and have not been formally related to each other or to formal trust models. This article presents a survey of algorithmic assurances , i.e., programmed components of agent operation that are expressly designed to calibrate user trust in artificially intelligent agents. Algorithmic assurances are first formally defined and classified from the perspective of formally modeled human-artificially intelligent agent trust relationships. Building on these definitions, a synthesis of research across communities such as machine learning, human-computer interaction, robotics, e-commerce, and others reveals that assurance algorithms naturally fall along a spectrum in terms of their impact on an agent’s core functionality, with seven notable classes ranging from integral assurances (which impact an agent’s core functionality) to supplemental assurances (which have no direct effect on agent performance). Common approaches within each of these classes are identified and discussed; benefits and drawbacks of different approaches are also investigated. 

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5. Two Computational Approaches to Visual Analogy: Task‐Specific Models Versus Domain‐General Mapping

   https://doi.org/10.1111/cogs.13347  

   Ichien, Nicholas ; Liu, Qing ; Fu, Shuhao ; Holyoak, Keith J. ; Yuille, Alan L. ; Lu, Hongjing ( September 2023 , Cognitive Science)

   Advances in artificial intelligence have raised a basic question about human intelligence: Is human reasoning best emulated by applying task‐specific knowledge acquired from a wealth of prior experience, or is it based on the domain‐general manipulation and comparison of mental representations? We address this question for the case of visual analogical reasoning. Using realistic images of familiar three‐dimensional objects (cars and their parts), we systematically manipulated viewpoints, part relations, and entity properties in visual analogy problems. We compared human performance to that of two recent deep learning models (Siamese Network and Relation Network) that were directly trained to solve these problems and to apply their task‐specific knowledge to analogical reasoning. We also developed a new model using part‐based comparison (PCM) by applying a domain‐general mapping procedure to learned representations of cars and their component parts. Across four‐term analogies (Experiment 1) and open‐ended analogies (Experiment 2), the domain‐general PCM model, but not the task‐specific deep learning models, generated performance similar in key aspects to that of human reasoners. These findings provide evidence that human‐like analogical reasoning is unlikely to be achieved by applying deep learning with big data to a specific type of analogy problem. Rather, humans do (and machines might) achieve analogical reasoning by learning representations that encode structural information useful for multiple tasks, coupled with efficient computation of relational similarity.

    

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