Do teachers matter? Measuring the variation in teacher effectiveness in England

Using a unique primary dataset for the UK, we estimate the effect of individual teachers on student outcomes, and the variability in teacher quality. This links over 7000 pupils to the individual teachers who taught them, in each of their compulsory subjects in the high-stakes exams at age 16. We use point-in-time fixed effects and prior attainment to control for pupil heterogeneity. We find considerable variability in teacher effectiveness, a little higher than the estimates found in the few US studies. We also corroborate recent findings that observed teachers’ characteristics explain very little of the differences in estimated effectiveness.education, test scores, teacher effectiveness

Anterior Hippocampus and Goal-Directed Spatial Decision Making

Contains fulltext : 115487.pdf (publisher's version ) (Open Access

Establishing the boundaries: the hippocampal contribution to imagining scenes

When we visualize scenes, either from our own past or invented, we impose a viewpoint for our “mind's eye” and we experience the resulting image as spatially coherent from that viewpoint. The hippocampus has been implicated in this process, but its precise contribution is unknown. We tested a specific hypothesis based on the spatial firing properties of neurons in the hippocampal formation of rats, that this region supports the construction of spatially coherent mental images by representing the locations of the environmental boundaries surrounding our viewpoint. Using functional magnetic resonance imaging, we show that hippocampal activation increases parametrically with the number of enclosing boundaries in the imagined scene. In contrast, hippocampal activity is not modulated by a nonspatial manipulation of scene complexity nor to increasing difficulty of imagining the scenes in general. Our findings identify a specific computational role for the hippocampus in mental imagery and episodic recollection

Consolidation of complex events via reinstatement in posterior cingulate cortex

It is well-established that active rehearsal increases the efficacy of memory consolidation. It is also known that complex events are interpreted with reference to prior knowledge. However, comparatively little attention has been given to the neural underpinnings of these effects. In healthy adult humans, we investigated the impact of effortful, active rehearsal on memory for events by showing people several short video clips and then asking them to recall these clips, either aloud (Experiment 1) or silently while in an MRI scanner (Experiment 2). In both experiments, actively rehearsed clips were remembered in far greater detail than unrehearsed clips when tested a week later. In Experiment 1, highly similar descriptions of events were produced across retrieval trials, suggesting a degree of semanticization of the memories had taken place. In Experiment 2, spatial patterns of BOLD signal in medial temporal and posterior midline regions were correlated when encoding and rehearsing the same video. Moreover, the strength of this correlation in the posterior cingulate predicted the amount of information subsequently recalled. This is likely to reflect a strengthening of the representation of the video's content. We argue that these representations combine both new episodic information and stored semantic knowledge (or "schemas"). We therefore suggest that posterior midline structures aid consolidation by reinstating and strengthening the associations between episodic details and more generic schematic information. This leads to the creation of coherent memory representations of lifelike, complex events that are resistant to forgetting, but somewhat inflexible and semantic-like in nature

Computational methodology for modelling the dynamics of statistical arbitrage

Recent years have seen the emergence of a multi-disciplinary research area known as "Computational Finance". In many cases the data generating processes of financial and other economic time-series are at best imperfectly understood. By allowing restrictive assumptions about price dynamics to be relaxed, recent advances in computational modelling techniques offer the possibility to discover new "patterns" in market activity. This thesis describes an integrated "statistical arbitrage" framework for identifying, modelling and exploiting small but consistent regularities in asset price dynamics. The methodology developed in the thesis combines the flexibility of emerging techniques such as neural networks and genetic algorithms with the rigour and diagnostic techniques which are provided by established modelling tools from the fields of statistics, econometrics and time-series forecasting. The modelling methodology which is described in the thesis consists of three main parts. The first part is concerned with constructing combinations of time-series which contain a significant predictable component, and is a generalisation of the econometric concept of cointegration. The second part of the methodology is concerned with building predictive models of the mispricing dynamics and consists of low-bias estimation procedures which combine elements of neural and statistical modelling. The third part of the methodology controls the risks posed by model selection and performance instability through actively encouraging diversification across a "portfolio of models". A novel population-based algorithm for joint optimisation of a set of trading strategies is presented, which is inspired both by genetic and evolutionary algorithms and by modern portfolio theory. Throughout the thesis the performance and properties of the algorithms are validated by means of experimental evaluation on synthetic data sets with known characteristics. The effectiveness of the methodology is demonstrated by extensive empirical analysis of real data sets, in particular daily closing prices of FTSE 100 stocks and international equity indices

Using Grid Cells for Navigation

SummaryMammals are able to navigate to hidden goal locations by direct routes that may traverse previously unvisited terrain. Empirical evidence suggests that this “vector navigation” relies on an internal representation of space provided by the hippocampal formation. The periodic spatial firing patterns of grid cells in the hippocampal formation offer a compact combinatorial code for location within large-scale space. Here, we consider the computational problem of how to determine the vector between start and goal locations encoded by the firing of grid cells when this vector may be much longer than the largest grid scale. First, we present an algorithmic solution to the problem, inspired by the Fourier shift theorem. Second, we describe several potential neural network implementations of this solution that combine efficiency of search and biological plausibility. Finally, we discuss the empirical predictions of these implementations and their relationship to the anatomy and electrophysiology of the hippocampal formation

Successor-Predecessor Intrinsic Exploration

Exploration is essential in reinforcement learning, particularly in environments where external rewards are sparse. Here we focus on exploration with intrinsic rewards, where the agent transiently augments the external rewards with self-generated intrinsic rewards. Although the study of intrinsic rewards has a long history, existing methods focus on composing the intrinsic reward based on measures of future prospects of states, ignoring the information contained in the retrospective structure of transition sequences. Here we argue that the agent can utilise retrospective information to generate explorative behaviour with structure-awareness, facilitating efficient exploration based on global instead of local information. We propose Successor-Predecessor Intrinsic Exploration (SPIE), an exploration algorithm based on a novel intrinsic reward combining prospective and retrospective information. We show that SPIE yields more efficient and ethologically plausible exploratory behaviour in environments with sparse rewards and bottleneck states than competing methods. We also implement SPIE in deep reinforcement learning agents, and show that the resulting agent achieves stronger empirical performance than existing methods on sparse-reward Atari games