There is a strong, positive, and well-documented correlation between education and health outcomes. In this paper, we attempt to understand to what extent this relationship is causal Our approach ...exploits two changes to British compulsory schooling laws that generated sharp across-cohort differences in educational attainment. Using regression discontinuity methods, we find the reforms did not affect health although the reforms impacted educational attainment and wages. Our results suggest caution as to the likely health returns to educational interventions focused on increasing educational attainment among those at risk of dropping out of high school, a target of recent health policy efforts.
This paper studies a recent British reform that allowed public high schools to opt out of local authority control and become autonomous schools funded directly by the central government. Schools ...seeking autonomy had only to propose and win a majority vote among current parents. Almost one in three high schools voted on autonomy between 1988 and 1997, and using a version of the regression discontinuity design, I find large achievement gains at schools in which the vote barely won compared to schools in which it barely lost. Despite other reforms that ensured that the British education system was, by international standards, highly competitive, a comparison of schools in the geographic neighborhoods of narrow vote winners and narrow vote losers suggests that these gains did not spill over.
Visual motion provides rich geometrical cues about the three-dimensional configuration of the world. However, how brains decode the spatial information carried by motion signals remains poorly ...understood. Here, we study a collision-avoidance behavior in Drosophila as a simple model of motion-based spatial vision. With simulations and psychophysics, we demonstrate that walking Drosophila exhibit a pattern of slowing to avoid collisions by exploiting the geometry of positional changes of objects on near-collision courses. This behavior requires the visual neuron LPLC1, whose tuning mirrors the behavior and whose activity drives slowing. LPLC1 pools inputs from object and motion detectors, and spatially biased inhibition tunes it to the geometry of collisions. Connectomic analyses identified circuitry downstream of LPLC1 that faithfully inherits its response properties. Overall, our results reveal how a small neural circuit solves a specific spatial vision task by combining distinct visual features to exploit universal geometrical constraints of the visual world.
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•Walking flies sense object position and direction to avoid potential collisions•LPLC1 neurons are necessary and sufficient for this collision avoidance•LPLC1 detects collision trajectories using joint direction and position cues•PLP219 is a part of pathways downstream of LPLC1 that mediate collision avoidance
Visual motion contains rich information about space, but how brains decode spatial information to guide specific behaviors remains poorly understood. Tanaka and Clark show how Drosophila LPLC1 neurons implement a selective collision avoidance behavior by pooling outputs of motion and object detectors, as well as spatially biased inhibition.
Sensory systems use receptors to extract information from the environment and neural circuits to perform subsequent computations. These computations may be described as algorithms composed of ...sequential mathematical operations. Comparing these operations across taxa reveals how different neural circuits have evolved to solve the same problem, even when using different mechanisms to implement the underlying math. In this review, we compare how insect and mammalian neural circuits have solved the problem of motion estimation, focusing on the fruit fly Drosophila and the mouse retina. Although the two systems implement computations with grossly different anatomy and molecular mechanisms, the underlying circuits transform light into motion signals with strikingly similar processing steps. These similarities run from photoreceptor gain control and spatiotemporal tuning to ON and OFF pathway structures, motion detection, and computed motion signals. The parallels between the two systems suggest that a limited set of algorithms for estimating motion satisfies both the needs of sighted creatures and the constraints imposed on them by metabolism, anatomy, and the structure and regularities of the visual world.
In this review, Clark and Demb compare the mathematical operations that transform visual inputs into motion signals in the fly eye and in the mouse retina. The parallels in processing suggest that a limited set of operations may satisfy the needs of sighted creatures, even as the mechanisms that implement these operations differ substantially.
Visual systems are often equipped with neurons that detect small moving objects, which may represent prey, predators, or conspecifics. Although the processing properties of those neurons have been ...studied in diverse organisms, links between the proposed algorithms and animal behaviors or circuit mechanisms remain elusive. Here, we have investigated behavioral function, computational algorithm, and neurochemical mechanisms of an object-selective neuron, LC11, in Drosophila. With genetic silencing and optogenetic activation, we show that LC11 is necessary for a visual object-induced stopping behavior in walking flies, a form of short-term freezing, and its activity can promote stopping. We propose a new quantitative model for small object selectivity based on the physiology and anatomy of LC11 and its inputs. The model accurately reproduces LC11 responses by pooling fast-adapting, tightly size-tuned inputs. Direct visualization of neurotransmitter inputs to LC11 confirmed the model conjectures about upstream processing. Our results demonstrate how adaptation can enhance selectivity for behaviorally relevant, dynamic visual features.
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•Small visual objects elicit brief freezing in Drosophila, mediated by LC11 neurons•LC11 responses are inconsistent with existing models for small object detection•Pooling of size-tuned, adapting units explains selectivity for object displacement•Visualized neurochemical inputs to LC11 are consistent with this pooling model
Tanaka and Clark show that Drosophila initiates brief freezing upon sighting small moving objects. This behavior depends on LC11 neurons, which show a high selectivity for translating objects that is not expected from existing models of object selective neurons. Spatial pooling of size-tuned, fast-adapting units explains the observed selectivity.
This paper distinguishes between the human capital and signaling theories by estimating the earnings return to a high school diploma. Unlike most indicators of education (e.g., a year of school), a ...diploma is essentially a piece of paper and, hence, by itself cannot affect productivity. Any earnings return to holding a diploma must therefore reflect the diploma’s signaling value. Using regression discontinuity methods to compare the earnings of workers who barely passed and barely failed high school exit exams—standardized tests that students must pass to earn a high school diploma—we find little evidence of diploma signaling effects.
In the visual system, peripheral processing circuits are often tuned to specific stimulus features. How this selectivity arises and how these circuits are organized to inform specific visual ...behaviors is incompletely understood. Using forward genetics and quantitative behavioral studies, we uncover an input channel to motion detecting circuitry in Drosophila. The second-order neuron L3 acts combinatorially with two previously known inputs, L1 and L2, to inform circuits specialized to detect moving light and dark edges. In vivo calcium imaging of L3, combined with neuronal silencing experiments, suggests a neural mechanism to achieve selectivity for moving dark edges. We further demonstrate that different innate behaviors, turning and forward movement, can be independently modulated by visual motion. These two behaviors make use of different combinations of input channels. Such modular use of input channels to achieve feature extraction and behavioral specialization likely represents a general principle in sensory systems.
•Specific genetic tools identify new inputs to motion vision•Combinatorial use of input channels achieves feature selectivity•Physiological specialization for motion detection emerges in the second-order neuron L3•Forward walking and turning responses to motion utilize distinct input modules
Silies et al. identify an input to circuits that detect motion and revise our understanding of how these circuits are organized. Different combinations of the input channels tune motion circuits to detect particular visual features and guide specific behaviors.
Will college students who set goals work harder and perform better? We report two field experiments that involved four thousand college students. One experiment asked treated students to set goals ...for performance in the course; the other asked treated students to set goals for a particular task (completing online practice exams). Task-based goals had robust positive effects on the level of task completion and marginally significant positive effects on course performance. Performance-based goals had positive but small and statistically insignificant effects on course performance. A theoretical framework that builds on present bias and loss aversion helps to interpret our results.
The algorithms and neural circuits that process spatio-temporal changes in luminance to extract visual motion cues have been the focus of intense research. An influential model, the ...Hassenstein-Reichardt correlator, relies on differential temporal filtering of two spatially separated input channels, delaying one input signal with respect to the other. Motion in a particular direction causes these delayed and non-delayed luminance signals to arrive simultaneously at a subsequent processing step in the brain; these signals are then nonlinearly amplified to produce a direction-selective response. Recent work in Drosophila has identified two parallel pathways that selectively respond to either moving light or dark edges. Each of these pathways requires two critical processing steps to be applied to incoming signals: differential delay between the spatial input channels, and distinct processing of brightness increment and decrement signals. Here we demonstrate, using in vivo patch-clamp recordings, that four medulla neurons implement these two processing steps. The neurons Mi1 and Tm3 respond selectively to brightness increments, with the response of Mi1 delayed relative to Tm3. Conversely, Tm1 and Tm2 respond selectively to brightness decrements, with the response of Tm1 delayed compared with Tm2. Remarkably, constraining Hassenstein-Reichardt correlator models using these measurements produces outputs consistent with previously measured properties of motion detectors, including temporal frequency tuning and specificity for light versus dark edges. We propose that Mi1 and Tm3 perform critical processing of the delayed and non-delayed input channels of the correlator responsible for the detection of light edges, while Tm1 and Tm2 play analogous roles in the detection of moving dark edges. Our data show that specific medulla neurons possess response properties that allow them to implement the algorithmic steps that precede the correlative operation in the Hassenstein-Reichardt correlator, revealing elements of the long-sought neural substrates of motion detection in the fly.
A new study explores how a population of neurons in the insect brain responds to different features of visual scenes and discovers an unusual topographic map that organizes the information they ...encode.
A new study explores how a population of neurons in the insect brain responds to different features of visual scenes and discovers an unusual topographic map that organizes the information they encode.
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