We test the hypothesis that discourse predictability influences Hindi syntactic choice. While prior work has shown that a number of factors (e.g., information status, dependency length, and syntactic surprisal) influence Hindi word order preferences, the role of discourse predictability is underexplored in the literature. Inspired by prior work on syntactic priming, we investigate how the words and syntactic structures in a sentence influence the word order of the following sentences. Specifically, we extract sentences from the Hindi-Urdu Treebank corpus (HUTB), permute the preverbal constituents of those sentences, and build a classifier to predict which sentences actually occurred in the corpus against artificially generated distractors. The classifier uses a number of discourse-based features and cognitive features to make its predictions, including dependency length, surprisal, and information status. We find that information status and LSTM-based discourse predictability influence word order choices, especially for non-canonical object-fronted orders. We conclude by situating our results within the broader syntactic priming literature.

To achieve desirable performance, current AI systems often require huge amounts of training data. This is especially problematic in domains where collecting data is both expensive and time-consuming, e.g., where AI systems require having numerous interactions with humans, collecting feedback from them. In this work, we substantiate the idea of $\textit{cognitive models as simulators}$, which is to have AI systems interact with, and collect feedback from, cognitive models instead of humans, thereby making their training process both less costly and faster. Here, we leverage this idea in the context of moral decision-making, by having reinforcement learning (RL) agents learn about fairness through interacting with a cognitive model of the Ultimatum Game (UG), a canonical task in behavioral and brain sciences for studying fairness. Interestingly, these RL agents learn to rationally adapt their behavior depending on the emotional state of their simulated UG responder. Our work suggests that using cognitive models as simulators of humans is an effective approach for training AI systems, presenting an important way for computational cognitive science to make contributions to AI.

This paper proposes a perceptual visual analysis regarding the personality of virtual humans. Many studies have presented findings regarding the way human beings perceive virtual humans with respect to their faces, body animation, motion in the virtual environment and etc. We are interested in investigating the way people perceive visual manifestations of virtual humans' personality traits when they are interactive and organized in groups. Many applications in games and movies can benefit from the findings regarding the perceptual analysis with the main goal to provide more realistic characters and improve the users' experience. We provide experiments with subjects and obtained results indicate that, although is very subtle, people perceive more the extraversion (the personality trait that we measured), into the crowds of virtual humans, when interacting with virtual humans behaviors, than when just observing as a spectator camera.

Inclusion and Identity – Learn how we internalize group values and goals and how our social groups become part of the way we identify ourselves. We'll explore how these identity processes influence our behavior and how they can lead to a sense of group cohesion. Group Formation Principles – Learn what types of people are attracted to group settings and what types of factors contribute to attraction and relationship formation. We'll also explore the different individual motivations that drive people into group settings and explore ways of overcoming social anxiety and loneliness. Group Development and Group Cohesion – Learn how all groups go through a predictable set of stages and how these stages influence behavior.

Some languages allow arguments to be omitted in certain contexts. Yet human language comprehenders reliably infer the intended referents of these zero pronouns, in part because they construct expectations about which referents are more likely. We ask whether Neural Language Models also extract the same expectations. We test whether 12 contemporary language models display expectations that reflect human behavior when exposed to sentences with zero pronouns from five behavioral experiments conducted in Italian by Carminati (2005). We find that three models - XGLM 2.9B, 4.5B, and 7.5B - capture the human behavior from all the experiments, with others successfully modeling some of the results. This result suggests that human expectations about coreference can be derived from exposure to language, and also indicates features of language models that allow them to better reflect human behavior.

Two of the most important ideas underpinning contemporary cognitive science-and the closely related AI subfield of computational cognitive modeling-are the suppositions that the human mind uses cognitive structures and that progress in understanding the mind can come from modeling those structures and the algorithms which operate on them. The semantic fluency task (SFT), sometimes called the verbal fluency task Welsh et al. [1991], is commonly employed in service of those goals. In SFT, participants name as many items belonging to a particular semantic category (animals, fruits, etc.) as they can in a fixed amount of time (typically 40-180 seconds). Despite this task's simplicity, the lists generated by participants (which we call semantic fluency lists or SFLs) offer insights into the structure of human knowledge and the heuristics used for memory retrieval. For example, words sharing semantic features tend to group in clusters, and there is often a temporal delay before a participant switches from one cluster to another. Multiple approaches to computationally modeling behaviors in SFT have been proposed Hills et al. [2012], Abbott et al. [2015], Zemla et al. [2016], Zemla and Austerweil [2017], Avery and Jones [2018], most relying on graph-based representations in which words are represented as nodes, and edges correspond to some meaningful semantic relationship between the nodes. However, to date, no work has explored whether transformer-based language models (TLMs) can be any better at modeling the generation of SFLs. And there are multiple reasons, at least from an exploratory perspective, to suspect TLMs might do well in this regard, e.g.: (1) a large body of literature demonstrates why semantic memory can not be sufficiently represented purely by fixed associative links between lexical nodes--at minimum, representations must allow for dynamic role binding, hierarchical (or otherwise unidirectional) activations, and enough richness to carry out structure-sensitive similarity assessments Holyoak and Hummel [2000], Sun [2002]; (2) TLMs perform unexpectedly well on human-oriented linguistic benchmarks Wang et al. [2019], and they are typically pre-trained using a lengthy process designed to embed deep semantic knowledge, resulting in a dense encoding of semantic relationships Cui et al. [2020]; (3) The pre-training process often proceeds by optimizing LMs to perform well on the MLM (masked language modeling) task, which shares more than a passing resemblance to the kind of word prediction that some

Manipulability ellipsoids efficiently capture the human pose and reveal information about the task at hand. Their use in task-dependent robot teaching - particularly their transfer from a teacher to a learner - can advance emulation of human-like motion. Although in recent literature focus is shifted towards manipulability transfer between two robots, the adaptation to the capabilities of the other kinematic system is to date not addressed and research in transfer from human to robot is still in its infancy. This work presents a novel manipulability domain adaptation method for the transfer of manipulability information to the domain of another kinematic system. As manipulability matrices/ellipsoids are symmetric positive-definite (SPD) they can be viewed as points on the Riemannian manifold of SPD matrices. We are the first to address the problem of manipulability transfer from the perspective of point cloud registration. We propose a manifold-aware Iterative Closest Point algorithm (ICP) with parallel transport initialization. Furthermore, we introduce a correspondence matching heuristic for manipulability ellipsoids based on inherent geometric features. We confirm our method in simulation experiments with 2-DoF manipulators as well as 7-DoF models representing the human-arm kinematics.

Much like most of cognition research, music cognition is an interdisciplinary field, which attempts to apply methods of cognitive science (neurological, computational and experimental) to understand the perception and process of composition of music. In this paper, we first motivate why music is relevant to cognitive scientists and give an overview of the approaches to computational modelling of music cognition. We then review literature on the various models of music perception, including non-computational models, computational non-cognitive models and computational cognitive models. Lastly, we review literature on modelling the creative behaviour and on computer systems capable of composing music. Since a lot of technical terms from music theory have been used, we have appended a list of relevant terms and their definitions at the end.

Nvidia has introduced a new platform for building virtual beings to interact with in the digital realms of the metaverse, which Nvidia refers to as its Omniverse. The Nvidia Omniverse Avatar Cloud Engine (ACE) provides a collection of AI models and related tools for users to design the AI creations that will populate their virtual worlds, including synthetic voices and visual media. The cloud-based ACE catalog streamlines building virtual beings and applies Nvidia's computing power to setting up and embedding the AI avatars in digital worlds. The resulting synthetic being can converse in multiple languages, offering recommendations based on the conversation and even process its digital environment enough to interact with objects around it. The system used Nvidia's Unified Compute Framework of software products, including the Riva speech AI technology and the NeMo Megatron natural language understanding using large language models.

"This is it," said SCOTUSblog media editor Katie Barlow on TikTok, posting live from outside the court. Barlow was one of the few correspondents on camera the moment the opinion was released. She was silent for a few seconds, glancing down at her phone, nodding, before looking up again and succinctly announcing the crux of it: "The Constitution does not confer a right to abortion." A reader on TikTok commented that it was hard to watch live as Barlow silently read the opinion, "to see the reality of the decision wash over you," adding: "Thank you for your work." It was a fitting way to enter the official post-Roe age: on platforms that can feel so personal to their publics, even as history unfolds.