Cassie Freeman

What constitutes a language? Natural languages share features with other domains: from math, to music, to gesture. However, the brain mechanisms that process linguistic input are highly specialized, showing little response to diverse nonlinguistic tasks. Here, we examine constructed languages (conlangs) to ask whether they draw on the same neural mechanisms as natural languages or whether they instead pattern with domains like math and programming languages. Using individual-subject fMRI analyses, we show that understanding conlangs recruits the same brain areas as natural language comprehension. This result holds for Esperanto (n = 19 speakers) and four fictional conlangs [Klingon (n = 10), Na’vi (n = 9), High Valyrian (n = 3), and Dothraki (n = 3)]. These findings suggest that conlangs and natural languages share critical features that allow them to draw on the same representations and computations, implemented in the left-lateralized network of brain areas. The features of conlangs that differentiate them from natural languages—including recent creation by a single individual, often for an esoteric purpose, the small number of speakers, and the fact that these languages are typically learned in adulthood—appear to not be consequential for the reliance on the same cognitive and neural mechanisms. We argue that the critical shared feature of conlangs and natural languages is that they are symbolic systems capable of expressing an open-ended range of meanings about our outer and inner worlds.

What constitutes a language? Natural languages share features with other domains: from math, to music, to gesture. However, the brain mechanisms that process linguistic input are highly specialized, showing little response to diverse non-linguistic tasks. Here, we examine constructed languages (conlangs) to ask whether they draw on the same neural mechanisms as natural languages, or whether they instead pattern with domains like math and programming languages. Using individual-subject fMRI analyses, we show that understanding conlangs recruits the same brain areas as natural language comprehension. This result holds for Esperanto (n=19 speakers) and four fictional conlangs (Klingon (n=10), Na’vi (n=9), High Valyrian (n=3), and Dothraki (n=3)). These findings suggest that conlangs and natural languages share critical features that allow them to draw on the same representations and computations, implemented in the left-lateralized network of brain areas. The features of conlangs that differentiate them from natural languages—including recent creation by a single individual, often for an esoteric purpose, the small number of speakers, and the fact that these languages are typically learned in adulthood— appear to not be consequential for the reliance on the same cognitive and neural mechanisms. We argue that the critical shared feature of conlangs and natural languages is that they are symbolic systems capable of expressing an open-ended range of meanings about our outer and inner worlds. Significance Statement: What constitutes a language has been of interest to diverse disciplines – from philosophy and linguistics to psychology, anthropology, and sociology. An empirical approach is to test whether the system in question recruits the brain system that processes natural languages. In spite of their similarity to natural languages, math and programming languages recruit a distinct brain system. Using fMRI, we test brain responses to stimuli not previously investigated—constructed languages (conlangs)—and find that they are processed by the same brain network as natural languages. Thus, an ability for a symbolic system to express diverse meanings about the world— but not the recency, manner, and purpose of its creation, or a large user base—is a defining characteristic of a language.

This article describes research that focuses on the concern that researchers are unable to fully realize the potential value of their collective efforts because they do not have shared conceptual or operational tools for communicating assumptions, ideas, research strategies, or findings with others outside, or even within their disciplines. This research, through the lens of measuring implementation of educational programs, has taken steps toward bringing researchers’ varied pictures of understanding into a coherent landscape. This article describes a conceptual framework for describing aspects of implementation, a conceptual framework for describing the factors that affect implementation, and tools for measuring each. It describes the challenges addressed in the development of these approaches, and the application of these approaches to current studies in education and other fields in the social sciences. In doing so, it demonstrates that meaningful communication between researchers and accumulation of knowledge across fields is possible, and necessary.

This chapter describes an ecological approach to treatment called EcoFit that was inspired by research suggesting that the most successful integrative interventions are those which target multiple domains of children and families. In addition to describing the intervention model and its effectiveness in decreasing youth problem behavior, the chapter also discusses issues relevant to implementation in the school context. Students' disruptive behaviors at school can cause serious problems for teachers, parents, and society. The EcoFIT model was designed for implementation in schools and comprises specific components that facilitate parent involvement and family-school connectedness. The chapter talks about the components of EcoFIT that include the family resource center (FRC), the family checkup (FCU), and a structured menu of intervention options. Successful adoption and implementation of the EcoFIT model within schools involves several requisite components, such as school resources and infrastructure, proper training and implementation manuals, and program fidelity checks.

There is a growing recognition of the value of measuring fidelity of implementation (FOI) as a necessary part of evaluating interventions. However, evaluators do not have a shared conceptual understanding of what FOI is and how to measure it. Thus, the creation of FOI measures is typically a secondary focus and based on specific contexts and programs. This article describes a project that holds the development of FOI measures as its primary goal and has developed a suite of data collection tools designed to be used across multiple programs. It describes the foundation of the suite—a conceptual framework for clearly and specifically describing FOI and the need for the framework. It also describes where the framework resides in existing literature and how it can be used to support measurement of interventions in education and other fields.

It was recently demonstrated that capuchin monkeys notice and respond to distributional inequity, a trait that has been proposed to support the evolution of cooperation in the human species. However, it is unknown how capuchins react to inequitable rewards in an unrestricted cooperative paradigm in which they may freely choose both whether to participate and, within the bounds of their partner's behavior, which reward they will receive for their participation. We tested capuchin monkeys with such a design, using a cooperative barpull, which has been used with great success in the past. Contrary to our expectations, the equity of the reward distribution did not affect success or pulling behavior. However, the behavior of the partner in an unequal situation did affect overall success rates: pairs that had a tendency to alternate which individual received the higher‐value food in unequal reward situations were more than twice as successful in obtaining rewards than pairs in which one individual dominated the higher‐value food. This ability to equitably distribute rewards in inherently biased cooperative situations has profound implications for activities such as group hunts, in which multiple individuals work together for a single, monopolizable reward. Am. J. Primatol. 68:713–724, 2006.

Formal testing of sensation as part of the neurological examination followed the improvements in examination techniques as well as advances in neuroscience. By the 1890s, the observation that temperature sense was frequently impaired at the same time that pain was appreciated led to the supposition that the two paths traveled closely. Through the works of Brown-Séquard and Edinger the existence of a crossed afferent tract was verified. The distinction between two sensory pathways was clear by 1898, when van Gehuchten reported a case of syringomyelia and suggested that the pain and temperature fibers were carried anterolaterally and the position sense fibers carried posteriorly in the spinal cord. Many authors describing patients with tabes dorsalis suspected the posterior columns of the spinal cord played a key role in position sense. It is difficult to determine in the 19th century who first employed the use of movements of joints as a test for proprioceptive function; however, Bell in 1826 recognized what he termed a sixth sense, which later was characterized as proprioceptive function. Goldscheider went on to report the degrees of movement that were considered normal for each joint. Although vibratory sense had been described by Cardano and Ingrassia in the 16th century and tests had been developed by Rinne and Rumpf by the 19th century, it was not until 1903 that Rydel and Seiffer found that vibratory sense and proprioceptive sense were closely related and that both senses were carried in the posterior columns of the spinal cord. By 1955, the sensory examination included tests for light-touch, superficial pain, temperature, position sense, vibration, muscle (deep pain), and two-point discrimination. Tests for these sensibilities still remain in use. We will review the origins of the understanding of sensation, which ultimately led to the development of the sensory examination. We will highlight individuals who made important discoveries and observations, as well as review the history of each of the elements of the sensory examination.