The forms and meanings of grammatical markers support efficient communication.

Functionalist accounts of language suggest that forms are paired with meanings in ways that support efficient communication. Previous work on grammatical marking suggests that word forms have lengths that enable efficient production, and work on the semantic typology of the lexicon suggests that word meanings represent efficient partitions of semantic space. Here we establish a theoretical link between these two lines of work and present an information-theoretic analysis that captures how communicative pressures influence both form and meaning. We apply our approach to the grammatical features of number, tense, and evidentiality and show that the approach explains both which systems of feature values are attested across languages and the relative lengths of the forms for those feature values. Our approach shows that general information-theoretic principles can capture variation in both form and meaning across languages.

Immersive participatory design of assistive robots to support older adults.

Assistive robots have the potential to support independence, enhance safety, and lower healthcare costs for older adults, as well as alleviate the demands of their care partners. However, ensuring that these robots will effectively and reliably address end-user needs in the long term requires user-specific design factors to be considered during the robot development process. To identify these design factors, we embedded Stretch, a mobile manipulator created by Hello Robot Inc., in the home of an older adult with motor impairments and his care partner for four weeks to support them with everyday activities. An occupational therapist and a robotics engineer lived with them during this period, employing an immersive participatory design approach to co-design and customise the robot with them. We highlight the benefits of this immersive participatory design experience and provide insights into robot design that can be applied broadly to other assistive technologies.

Perceptual Grouping Explains Similarities in Constellations Across Cultures.

Cultures around the world organize stars into constellations, or asterisms, and these groupings are often considered to be arbitrary and culture specific. Yet there are striking similarities in asterisms across cultures, and groupings such as Orion, the Big Dipper, the Pleiades, and the Southern Cross are widely recognized across many different cultures. Psychologists have informally suggested that these shared patterns are explained by Gestalt laws of grouping, but there have been no systematic attempts to catalog asterisms that recur across cultures or to explain the perceptual basis of these groupings. Here, we compiled data from 27 cultures around the world and found that a simple computational model of perceptual grouping accounts for many of the recurring cross-cultural asterisms. Our results suggest that basic perceptual principles account for more of the structure of asterisms across cultures than previously acknowledged and highlight ways in which specific cultures depart from this shared baseline.

Efficient compression in color naming and its evolution.

We derive a principled information-theoretic account of cross-language semantic variation. Specifically, we argue that languages efficiently compress ideas into words by optimizing the information bottleneck (IB) trade-off between the complexity and accuracy of the lexicon. We test this proposal in the domain of color naming and show that (i) color-naming systems across languages achieve near-optimal compression; (ii) small changes in a single trade-off parameter account to a large extent for observed cross-language variation; (iii) efficient IB color-naming systems exhibit soft rather than hard category boundaries and often leave large regions of color space inconsistently named, both of which phenomena are found empirically; and (iv) these IB systems evolve through a sequence of structural phase transitions, in a single process that captures key ideas associated with different accounts of color category evolution. These results suggest that a drive for information-theoretic efficiency may shape color-naming systems across languages. This principle is not specific to color, and so it may also apply to cross-language variation in other semantic domains.

Communicative need in colour naming.

Colour naming across languages has traditionally been held to reflect the structure of colour perception. At the same time, it has often, and increasingly, been suggested that colour naming may be shaped by patterns of communicative need. However, much remains unknown about the factors involved in communicative need, how need interacts with perception, and how this interaction may shape colour naming. Here, we engage these open questions by building on general information-theoretic principles. We present a systematic evaluation of several factors that may reflect need, and that have been proposed in the literature: capacity constraints, linguistic usage, and the visual environment. Our analysis suggests that communicative need in colour naming is reflected more directly by capacity constraints and linguistic usage than it is by the statistics of the visual environment.

Small forces that differ with prior motor experience can communicate movement goals during human-human physical interaction.

BACKGROUND: Physical interactions between two people are ubiquitous in our daily lives, and an integral part of many forms of rehabilitation. However, few studies have investigated forces arising from physical interactions between humans during a cooperative motor task, particularly during overground movements. As such, the direction and magnitude of interaction forces between two human partners, how those forces are used to communicate movement goals, and whether they change with motor experience remains unknown. A better understanding of how cooperative physical interactions are achieved in healthy individuals of different skill levels is a first step toward understanding principles of physical interactions that could be applied to robotic devices for motor assistance and rehabilitation. METHODS: Interaction forces between expert and novice partner dancers were recorded while performing a forward-backward partnered stepping task with assigned "leader" and "follower" roles. Their position was recorded using motion capture. The magnitude and direction of the interaction forces were analyzed and compared across groups (i.e. expert-expert, expert-novice, and novice-novice) and across movement phases (i.e. forward, backward, change of direction). RESULTS: All dyads were able to perform the partnered stepping task with some level of proficiency. Relatively small interaction forces (10-30N) were observed across all dyads, but were significantly larger among expert-expert dyads. Interaction forces were also found to be significantly different across movement phases. However, interaction force magnitude did not change as whole-body synchronization between partners improved across trials. CONCLUSIONS: Relatively small interaction forces may communicate movement goals (i.e. "what to do and when to do it") between human partners during cooperative physical interactions. Moreover, these small interactions forces vary with prior motor experience, and may act primarily as guiding cues that convey information about movement goals rather than providing physical assistance. This suggests that robots may be able to provide meaningful physical interactions for rehabilitation using relatively small force levels.

Quantifying Bias in Hierarchical Category Systems.

Categorization is ubiquitous in human cognition and society, and shapes how we perceive and understand the world. Because categories reflect the needs and perspectives of their creators, no category system is entirely objective, and inbuilt biases can have harmful social consequences. Here we propose methods for measuring biases in hierarchical systems of categories, a common form of category organization with multiple levels of abstraction. We illustrate these methods by quantifying the extent to which library classification systems are biased in favour of western concepts and male authors. We analyze a large library data set including more than 3 million books organized into thousands of categories, and find that categories related to religion show greater western bias than do categories related to literature or history, and that books written by men are distributed more broadly across library classification systems than are books written by women. We also find that the Dewey Decimal Classification shows a greater level of bias than does the Library of Congress Classification. Although we focus on library classification as a case study, our methods are general, and can be used to measure biases in both natural and institutional category systems across a range of domains.

A Computational Approach to Identifying Cultural Keywords Across Languages.

Distinctive aspects of a culture are often reflected in the meaning and usage of words in the language spoken by bearers of that culture. Keywords such as душа (soul) in Russian, hati (heart) in Indonesian and Malay, and gezellig (convivial/cosy/fun) in Dutch are held to be especially culturally revealing, and scholars have identified a number of such keywords using careful linguistic analyses (Peeters, 2020b; Wierzbicka, 1990). Because keywords are expected to have different statistical properties than related words in other languages, we argue that a quantitative comparison of word usage across languages can help to identify cultural keywords. To support this claim, we describe a computational method that compares word frequencies across languages, and apply it to both linguistic corpora and word association data. The method identifies culturally specific words that range from "obvious" examples, such as Amsterdam in Dutch, to non-obvious yet independently proposed examples, such as hati (heart) in Indonesian. We show in addition that linguistic corpora and word association data provide converging evidence about culturally specific words. Our results therefore show how computational analyses and behavioral experiments can supplement the methods previously used by linguists to identify culturally salient words across languages.

Visual Perception Principles in Constellation Creation.

Many cultures share common constellations and common narratives about the stars in the night sky. Previous research has shown that this overlap in asterisms, minimal star groupings inside constellations, is clearly present across 27 distinct culture groups and can be explained in part by properties of individual stars (brightness) and properties of pairs of stars (proximity) (Kemp, Hamacher, Little, & Cropper, 2022). The same work, however, found no evidence that properties of triples (angle) and quadruples (good continuation) predicted constellation formation. We developed a behavioral experiment to explore how individuals form constellations under conditions that reduce cultural learning. We found that participants independently selected and connected similar stars, and that their responses were predicted by two properties of triples (angle and even spacing) in addition to the properties of brightness and proximity supported by previous work. Our findings lend further evidence to the theory that commonality of constellations across cultures is not a result of shared human history but rather stems from shared human nature.

A probabilistic account of exemplar and category generation.

People are capable of imagining and generating new category exemplars and categories. This ability has not been addressed by previous models of categorization, most of which focus on classifying category exemplars rather than generating them. We develop a formal account of exemplar and category generation which proposes that category knowledge is represented by probability distributions over exemplars and categories, and that new exemplars and categories are generated by sampling from these distributions. This sampling account of generation is evaluated in two pairs of behavioral experiments. In the first pair of experiments, participants were asked to generate novel exemplars of a category. In the second pair of experiments, participants were asked to generate a novel category after observing exemplars from several related categories. The results suggest that generation is influenced by both structural and distributional properties of the observed categories, and we argue that our data are better explained by the sampling account than by several alternative approaches.

BodyPressure - Inferring Body Pose and Contact Pressure From a Depth Image.

Contact pressure between the human body and its surroundings has important implications. For example, it plays a role in comfort, safety, posture, and health. We present a method that infers contact pressure between a human body and a mattress from a depth image. Specifically, we focus on using a depth image from a downward facing camera to infer pressure on a body at rest in bed occluded by bedding, which is directly applicable to the prevention of pressure injuries in healthcare. Our approach involves augmenting a real dataset with synthetic data generated via a soft-body physics simulation of a human body, a mattress, a pressure sensing mat, and a blanket. We introduce a novel deep network that we trained on an augmented dataset and evaluated with real data. The network contains an embedded human body mesh model and uses a white-box model of depth and pressure image generation. Our network successfully infers body pose, outperforming prior work. It also infers contact pressure across a 3D mesh model of the human body, which is a novel capability, and does so in the presence of occlusion from blankets.

An integrated account of generalization across objects and features.

Humans routinely make inductive generalizations about unobserved features of objects. Previous accounts of inductive reasoning often focus on inferences about a single object or feature: accounts of causal reasoning often focus on a single object with one or more unobserved features, and accounts of property induction often focus on a single feature that is unobserved for one or more objects. We explore problems where people must make inferences about multiple objects and features, and propose that people solve these problems by integrating knowledge about features with knowledge about objects. We evaluate three computational methods for integrating multiple systems of knowledge: the output combination approach combines the outputs produced by these systems, the distribution combination approach combines the probability distributions captured by these systems, and the structure combination approach combines a graph structure over features with a graph structure over objects. Three experiments explore problems where participants make inferences that draw on causal relationships between features and taxonomic relationships between animals, and we find that the structure combination approach provides the best account of our data.

Simplification Is Not Dominant in the Evolution of Chinese Characters.

Linguistic systems are hypothesised to be shaped by pressures towards communicative efficiency that drive processes of simplification. A longstanding illustration of this idea is the claim that Chinese characters have progressively simplified over time. Here we test this claim by analyzing a dataset with more than half a million images of Chinese characters spanning more than 3,000 years of recorded history. We find no consistent evidence of simplification through time, and contrary to popular belief we find that modern Chinese characters are higher in visual complexity than their earliest known counterparts. One plausible explanation for our findings is that simplicity trades off with distinctiveness, and that characters have become less simple because of pressures towards distinctiveness. Our findings are therefore compatible with functional accounts of language but highlight the diverse and sometimes counterintuitive ways in which linguistic systems are shaped by pressures for communicative efficiency.

The Design of Stretch: A Compact, Lightweight Mobile Manipulator for Indoor Human Environments.

Mobile manipulators for indoor human environments can serve as versatile devices that perform a variety of tasks, yet adoption of this technology has been limited. Reducing size, weight, and cost could facilitate adoption, but risks restricting capabilities. We present a novel design that reduces size, weight, and cost, while supporting a variety of tasks. The core design consists of a two-wheeled differential-drive mobile base, a lift, and a telescoping arm configured to achieve Cartesian motion at the end of the arm. Design extensions include a 1 degree-of-freedom (DOF) wrist to stow a tool, a 2-DOF dexterous wrist to pitch and roll a tool, and a compliant gripper. We justify our design with anthropometry and mathematical models of static stability. We also provide empirical support from teleoperating and autonomously controlling a commercial robot based on our design (the Stretch RE1 from Hello Robot Inc.) to perform tasks in real homes.

The discovery of structural form.

Algorithms for finding structure in data have become increasingly important both as tools for scientific data analysis and as models of human learning, yet they suffer from a critical limitation. Scientists discover qualitatively new forms of structure in observed data: For instance, Linnaeus recognized the hierarchical organization of biological species, and Mendeleev recognized the periodic structure of the chemical elements. Analogous insights play a pivotal role in cognitive development: Children discover that object category labels can be organized into hierarchies, friendship networks are organized into cliques, and comparative relations (e.g., "bigger than" or "better than") respect a transitive order. Standard algorithms, however, can only learn structures of a single form that must be specified in advance: For instance, algorithms for hierarchical clustering create tree structures, whereas algorithms for dimensionality-reduction create low-dimensional spaces. Here, we present a computational model that learns structures of many different forms and that discovers which form is best for a given dataset. The model makes probabilistic inferences over a space of graph grammars representing trees, linear orders, multidimensional spaces, rings, dominance hierarchies, cliques, and other forms and successfully discovers the underlying structure of a variety of physical, biological, and social domains. Our approach brings structure learning methods closer to human abilities and may lead to a deeper computational understanding of cognitive development.

Material Recognition via Heat Transfer Given Ambiguous Initial Conditions.

Humans and robots can recognize materials with distinct thermal effusivities by making physical contact and observing temperatures during heat transfer. This works well with room temperature materials, yet research has shown that contact with distinct materials can result in similar temperatures and confusion when one material is heated or cooled. To thoroughly investigate this form of ambiguity, we designed a psychophysical experiment in which a participant discriminates between two materials given initial conditions that result in similar temperatures (i.e., ambiguous initial conditions). In this article, we conducted a study with 32 human participants and a robot. Humans and the robot confused the materials. We also found that robots can overcome this ambiguity using two temperature sensors with different temperatures prior to contact. We support this conclusion based on a mathematical proof using a heat transfer model and empirical results in which a robot achieved 100% accuracy compared to 5% human accuracy. Our results also indicate that robots with a single temperature sensor can use subtle cues to outperform humans. Overall, our work provides insights into challenging conditions for material recognition via heat transfer, and suggests methods by which robots can overcome these challenges.

Chaining and the growth of linguistic categories.

We explore how linguistic categories extend over time as novel items are assigned to existing categories. As a case study we consider how Chinese numeral classifiers were extended to emerging nouns over the past half century. Numeral classifiers are common in East and Southeast Asian languages, and are prominent in the cognitive linguistics literature as examples of radial categories. Each member of a radial category is linked to a central prototype, and this view of categorization therefore contrasts with exemplar-based accounts that deny the existence of category prototypes. We explore these competing views by evaluating computational models of category growth that draw on existing psychological models of categorization. We find that an exemplar-based approach closely related to the Generalized Context Model provides the best account of our data. Our work suggests that numeral classifiers and other categories previously described as radial categories may be better understood as exemplar-based categories, and thereby strengthens the connection between cognitive linguistics and psychological models of categorization.

Structured statistical models of inductive reasoning.

Everyday inductive inferences are often guided by rich background knowledge. Formal models of induction should aim to incorporate this knowledge and should explain how different kinds of knowledge lead to the distinctive patterns of reasoning found in different inductive contexts. This article presents a Bayesian framework that attempts to meet both goals and describes [corrected] 4 applications of the framework: a taxonomic model, a spatial model, a threshold model, and a causal model. Each model makes probabilistic inferences about the extensions of novel properties, but the priors for the 4 models are defined over different kinds of structures that capture different relationships between the categories in a domain. The framework therefore shows how statistical inference can operate over structured background knowledge, and the authors argue that this interaction between structure and statistics is critical for explaining the power and flexibility of human reasoning.

In-home and remote use of robotic body surrogates by people with profound motor deficits.

By controlling robots comparable to the human body, people with profound motor deficits could potentially perform a variety of physical tasks for themselves, improving their quality of life. The extent to which this is achievable has been unclear due to the lack of suitable interfaces by which to control robotic body surrogates and a dearth of studies involving substantial numbers of people with profound motor deficits. We developed a novel, web-based augmented reality interface that enables people with profound motor deficits to remotely control a PR2 mobile manipulator from Willow Garage, which is a human-scale, wheeled robot with two arms. We then conducted two studies to investigate the use of robotic body surrogates. In the first study, 15 novice users with profound motor deficits from across the United States controlled a PR2 in Atlanta, GA to perform a modified Action Research Arm Test (ARAT) and a simulated self-care task. Participants achieved clinically meaningful improvements on the ARAT and 12 of 15 participants (80%) successfully completed the simulated self-care task. Participants agreed that the robotic system was easy to use, was useful, and would provide a meaningful improvement in their lives. In the second study, one expert user with profound motor deficits had free use of a PR2 in his home for seven days. He performed a variety of self-care and household tasks, and also used the robot in novel ways. Taking both studies together, our results suggest that people with profound motor deficits can improve their quality of life using robotic body surrogates, and that they can gain benefit with only low-level robot autonomy and without invasive interfaces. However, methods to reduce the rate of errors and increase operational speed merit further investigation.

Color Naming Reflects Both Perceptual Structure and Communicative Need.

Gibson et al. () argued that color naming is shaped by patterns of communicative need. In support of this claim, they showed that color naming systems across languages support more precise communication about warm colors than cool colors, and that the objects we talk about tend to be warm-colored rather than cool-colored. Here, we present new analyses that alter this picture. We show that greater communicative precision for warm than for cool colors, and greater communicative need, may both be explained by perceptual structure. However, using an information-theoretic analysis, we also show that color naming across languages bears signs of communicative need beyond what would be predicted by perceptual structure alone. We conclude that color naming is shaped both by perceptual structure, as has traditionally been argued, and by patterns of communicative need, as argued by Gibson et al. -although for reasons other than those they advanced.