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Significance As mature speakers of a language, we are able to produce and understand an indefinite number of sentences. This ability comes from a powerful cognitive system—syntax—whose properties reveal the types of computations that human minds can engage in. One core property is the capacity to encode abstract grammatical dependencies that can hold at a distance. When does this property emerge in development? We identify a nonlocal dependency that 18-mo-old infants, but not younger infants, represent syntactically. These findings suggest that the second year of life is a period of active syntactic development and that even before they regularly produce full sentences of their own, infants demonstrate some of the core computational capacities that syntax relies on., The human ability to produce and understand an indefinite number of sentences is driven by syntax, a cognitive system that can combine a finite number of primitive linguistic elements to build arbitrarily complex expressions. The expressive power of syntax comes in part from its ability to encode potentially unbounded dependencies over abstract structural configurations. How does such a system develop in human minds? We show that 18-mo-old infants are capable of representing abstract nonlocal dependencies, suggesting that a core property of syntax emerges early in development. Our test case is English wh-questions, in which a fronted wh-phrase can act as the argument of a verb at a distance (e.g., What did the chef burn?). Whereas prior work has focused on infants’ interpretations of these questions, we introduce a test to probe their underlying syntactic representations, independent of meaning. We ask when infants know that an object wh-phrase and a local object of a verb cannot co-occur because they both express the same argument relation (e.g., *What did the chef burn the pizza). We find that 1) 18 mo olds demonstrate awareness of this complementary distribution pattern and thus represent the nonlocal grammatical dependency between the wh-phrase and the verb, but 2) younger infants do not. These results suggest that the second year of life is a period of active syntactic development, during which the computational capacities for representing nonlocal syntactic dependencies become evident.

Significance The finding that human hippocampal-cortical functional connectivity is nonunitary, separated along functional network borders (default mode network [DMN], self-oriented; parietal memory network [PMN], goal-oriented) in the anterior–posterior axis, raises various possibilities as to why this organization might be beneficial and could inform updates to current models of human hippocampal function, memory, and the self., The hippocampus is critically important for a diverse range of cognitive processes, such as episodic memory, prospective memory, affective processing, and spatial navigation. Using individual-specific precision functional mapping of resting-state functional MRI data, we found the anterior hippocampus (head and body) to be preferentially functionally connected to the default mode network (DMN), as expected. The hippocampal tail, however, was strongly preferentially functionally connected to the parietal memory network (PMN), which supports goal-oriented cognition and stimulus recognition. This anterior–posterior dichotomy of resting-state functional connectivity was well-matched by differences in task deactivations and anatomical segmentations of the hippocampus. Task deactivations were localized to the hippocampal head and body (DMN), relatively sparing the tail (PMN). The functional dichotomization of the hippocampus into anterior DMN-connected and posterior PMN-connected parcels suggests parallel but distinct circuits between the hippocampus and medial parietal cortex for self- versus goal-oriented processing.

Significance Four waves of longitudinal survey data demonstrate that contact with law enforcement predicts increases in black and Latino adolescents’ self-reported criminal behaviors 6, 12, and 18 months later. These results are partially mediated by psychological distress. The younger boys are when stopped for the first time, the stronger these relationships. Boys’ race and prior engagement in delinquent behaviors did not moderate the effect. These findings fill a gap in the research literature on labeling, life course, general strain, and deterrence theories. To our knowledge, the relationships among police contact, psychological strain, and subsequent criminal behavior for young boys had not been tested quantitatively before. These findings raise policy questions about the influence of proactive policing on the trajectory of children., Proactive policing, the strategic targeting of people or places to prevent crimes, is a well-studied tactic that is ubiquitous in modern law enforcement. A 2017 National Academies of Sciences report reviewed existing literature, entrenched in deterrence theory, and found evidence that proactive policing strategies can reduce crime. The existing literature, however, does not explore what the short and long-term effects of police contact are for young people who are subjected to high rates of contact with law enforcement as a result of proactive policing. Using four waves of longitudinal survey data from a sample of predominantly black and Latino boys in ninth and tenth grades, we find that adolescent boys who are stopped by police report more frequent engagement in delinquent behavior 6, 12, and 18 months later, independent of prior delinquency, a finding that is consistent with labeling and life course theories. We also find that psychological distress partially mediates this relationship, consistent with the often stated, but rarely measured, mechanism for adolescent criminality hypothesized by general strain theory. These findings advance the scientific understanding of crime and adolescent development while also raising policy questions about the efficacy of routine police stops of black and Latino youth. Police stops predict decrements in adolescents’ psychological well-being and may unintentionally increase their engagement in criminal behavior.

Significance The ability to learn and make inferences based on relations is central to intelligence, underlying the distinctively human ability to reason by analogy across dissimilar situations. We have developed a computational model demonstrating that abstract relations, such as synonymy and antonymy, can be learned efficiently from semantic feature vectors for individual words and can be used to solve simple verbal analogy problems with close to human-level accuracy. The approach illustrates the potential synergy between deep learning from “big data” and supervised learning from “small data.” Core properties of high-level intelligence can emerge from relatively simple computations coupled with rich semantics. The model illustrates how operations on nonrelational inputs can give rise to protosymbolic relational representations., By middle childhood, humans are able to learn abstract semantic relations (e.g., antonym, synonym, category membership) and use them to reason by analogy. A deep theoretical challenge is to show how such abstract relations can arise from nonrelational inputs, thereby providing key elements of a protosymbolic representation system. We have developed a computational model that exploits the potential synergy between deep learning from “big data” (to create semantic features for individual words) and supervised learning from “small data” (to create representations of semantic relations between words). Given as inputs labeled pairs of lexical representations extracted by deep learning, the model creates augmented representations by remapping features according to the rank of differences between values for the two words in each pair. These augmented representations aid in coping with the feature alignment problem (e.g., matching those features that make “love-hate” an antonym with the different features that make “rich-poor” an antonym). The model extracts weight distributions that are used to estimate the probabilities that new word pairs instantiate each relation, capturing the pattern of human typicality judgments for a broad range of abstract semantic relations. A measure of relational similarity can be derived and used to solve simple verbal analogies with human-level accuracy. Because each acquired relation has a modular representation, basic symbolic operations are enabled (notably, the converse of any learned relation can be formed without additional training). Abstract semantic relations can be induced by bootstrapping from nonrelational inputs, thereby enabling relational generalization and analogical reasoning.