10,660 research outputs found
An investigation of entorhinal spatial representations in self-localisation behaviours
Spatial-modulated cells of the medial entorhinal cortex (MEC) and neighbouring cortices are thought to provide the neural substrate for self-localisation behaviours. These cells include grid cells of the MEC which are thought to compute path integration operations to update self-location estimates. In order to read this grid code, downstream cells are thought to reconstruct a positional estimate as a simple rate-coded representation of space.
Here, I show the coding scheme of grid cell and putative readout cells recorded from mice performing a virtual reality (VR) linear location task which engaged mice in both beaconing and path integration behaviours. I found grid cells can encode two unique coding schemes on the linear track, namely a position code which reflects periodic grid fields anchored to salient features of the track and a distance code which reflects periodic grid fields without this anchoring. Grid cells were found to switch between these coding schemes within sessions. When grid cells were encoding position, mice performed better at trials that required path integration but not on trials that required beaconing. This result provides the first mechanistic evidence linking grid cell activity to path integration-dependent behaviour.
Putative readout cells were found in the form of ramp cells which fire proportionally as a function of location in defined regions of the linear track. This ramping activity was found to be primarily explained by track position rather than other kinematic variables like speed and acceleration. These representations were found to be maintained across both trial types and outcomes indicating they likely result from recall of the track structure.
Together, these results support the functional importance of grid and ramp cells for self-localisation behaviours. Future investigations will look into the coherence between these two neural populations, which may together form a complete neural system for coding and decoding self-location in the brain
Decomposing Triple-Differences Regression under Staggered Adoption
The triple-differences (TD) design is a popular identification strategy for
causal effects in settings where researchers do not believe the parallel trends
assumption of conventional difference-in-differences (DiD) is satisfied. TD
designs augment the conventional 2x2 DiD with a "placebo" stratum --
observations that are nested in the same units and time periods but are known
to be entirely unaffected by the treatment. However, many TD applications go
beyond this simple 2x2x2 and use observations on many units in many "placebo"
strata across multiple time periods. A popular estimator for this setting is
the triple-differences regression (TDR) fixed-effects estimator -- an extension
of the common "two-way fixed effects" estimator for DiD. This paper decomposes
the TDR estimator into its component two-group/two-period/two-strata
triple-differences and illustrates how interpreting this parameter causally in
settings with arbitrary staggered adoption requires strong effect homogeneity
assumptions as many placebo DiDs incorporate observations under treatment. The
decomposition clarifies the implied identifying variation behind the
triple-differences regression estimator and suggests researchers should be
cautious when implementing these estimators in settings more complex than the
2x2x2 case. Alternative approaches that only incorporate "clean placebos" such
as direct imputation of the counterfactual may be more appropriate. The paper
concludes by demonstrating the utility of this imputation estimator in an
application of the "gravity model" to the estimation of the effect of the
WTO/GATT on international trade
On regular copying languages
This paper proposes a formal model of regular languages enriched with unbounded copying. We augment finite-state machinery with the ability to recognize copied strings by adding an unbounded memory buffer with a restricted form of first-in-first-out storage. The newly introduced computational device, finite-state buffered machines (FS-BMs), characterizes the class of regular languages and languages de-rived from them through a primitive copying operation. We name this language class regular copying languages (RCLs). We prove a pumping lemma and examine the closure properties of this language class. As suggested by previous literature (Gazdar and Pullum 1985, p.278), regular copying languages should approach the correct characteriza-tion of natural language word sets
Relaxing the symmetry assumption in participation games: A specification test for cluster heterogeneity
Published online: 05 April 2023.
OnlinePublWe propose a novel approach to check whether individual behaviour in binary-choice participation games is consistent with the restrictions imposed by symmetric models. This approach allows in particular an assessment of how much cluster-heterogeneity a symmetric model can tolerate to remain consistent with its behavioural restrictions. We assess our approach with data from market-entry experiments which we analyse through the lens of âExploration versus Explorationâ (EvE, which is equivalent to Logit-QRE) or of Impulse Balance Equilibrium (IBE). We find that when the symmetry assumption is imposed, both models are typically rejected when assuming pooled data and IBE yields more data-consistent estimates than EvE, i.e., IBEâs estimates of session and pooled data are more consistent than those of EvE. When relaxing symmetry, EvE (IBE) is rejected for 17% (42%) of the time. Although both models support cluster-heterogeneity, IBE is much less likely to yield over-parametrised specifications and insignificant estimates so it outperforms EvE in accommodating a model-consistent cluster-heterogeneity. The use of regularisation procedures in the estimations partially addresses EvEâs shortcomings but leaves our overall conclusions unchanged.Alan Kirman, François Laisney, Paul Pezanis, Christo
Bayesian Forecasting in Economics and Finance: A Modern Review
The Bayesian statistical paradigm provides a principled and coherent approach
to probabilistic forecasting. Uncertainty about all unknowns that characterize
any forecasting problem -- model, parameters, latent states -- is able to be
quantified explicitly, and factored into the forecast distribution via the
process of integration or averaging. Allied with the elegance of the method,
Bayesian forecasting is now underpinned by the burgeoning field of Bayesian
computation, which enables Bayesian forecasts to be produced for virtually any
problem, no matter how large, or complex. The current state of play in Bayesian
forecasting in economics and finance is the subject of this review. The aim is
to provide the reader with an overview of modern approaches to the field, set
in some historical context; and with sufficient computational detail given to
assist the reader with implementation.Comment: The paper is now published online at:
https://doi.org/10.1016/j.ijforecast.2023.05.00
Human wellbeing responses to speciesâ traits
People rely on well-functioning ecosystems to provide critical services that underpin human health and wellbeing. Consequently, biodiversity loss has profound negative implications for humanity. Human-biodiversity interactions can deliver individual-level wellbeing gains, equating to substantial healthcare cost-savings when scaled-up across populations. However, critical questions remain about which species and/or traits (e.g. colours, sounds, smells) elicit wellbeing responses. The traits that influence wellbeing can be considered âeffectâ traits. Using techniques from community ecology, we analyse a database of speciesâ effect traits articulated by people, to identify those that generate different types of wellbeing (physical, emotional, cognitive, social, spiritual and âglobalâ wellbeing, the latter being akin to âwhole-person healthâ). Effect traits have a predominately positive impact on wellbeing, influenced by the identity and taxonomic kingdom of each species. Different sets of effect traits deliver different types of wellbeing. However, traits cannot be considered independently of species because multiple traits can be supported by a single species. Indeed, we find numerous effect traits from across the ecological community can elicit multiple types of wellbeing, illustrating the complexity of biodiversity experiences. Our empirical approach can help implement interdisciplinary thinking for biodiversity conservation and nature-based public health interventions designed to support human wellbeing
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Essays in Macroeconomics
This dissertation consists of three chapters, each containing a distinct research paper in the field of macroeconomics. In the first chapter, I estimate the impact of mutual fund flows on corporate bond prices, issuance and firm investment. I leverage variation caused by the COVID-19 induced financial panic of March 2020 and find that safer firms suffered a larger impact in the component of bond spreads that does not compensate for expected default risk. However, I do not detect impacts of fund flows on issuance or investment.
A simple model predicts liquidation decisions and price responses as being driven by demand and liquidation elasticities, which depend on the characteristics of the bond return processes. In the second chapter, we ask: what is the importance of firm and bank credit factors in determining investment responses to monetary policy? We decompose variation in corporate loan growth rates into purely firm-level and bank-level variation. The estimated factors are correlated with a set of variables that proxy for the firmâs and bankâs financial health. Firms with a higher borrowing factor experience relatively larger investment responses to an unexpected interest rate shock; the effect is muted when the shock is the reveal of central bank information. The bank factor does not induce similar heterogeneity in investment responses.
In the third chapter, we ask: what is the nature of optimal monetary policy and central bank disclosure when the monetary authority is uncertain about the economic state? We consider a model in which firms make nominal pricing decisions and the central bank sets the nominal interest rate under incomplete information. We find that implementing flexible-price allocations is both feasible and optimal despite the existence of numerous measurability constraints; we explore a series of different implementations. When monetary policy is sub-optimal, public information disclosure by the central bank is welfare-improving as long as either firm or central bank information is sufficiently precise
The Transition into Higher Education for Students with Autism Spectrum Disorders
There are more students with autism spectrum disorders (ASD) in higher education now than ever before and these students need to be supported in ways that will guarantee their success. The purpose of this qualitative study was to examine the experiences of six students with ASD as they transition into higher education through the use of semi-structured interviews. What was discovered is that students with ASD typically overestimate the difficulty of academics and underestimate the amount of social interaction they will encounter at the college level. These students are able to identify aspects of themselves that help them to succeed while also developing strategies to manage their stress. This research found that on-campus supports are underutilized by students with ASD due to a lack of knowledge of the supports, difficulties attaining support, or fear of stigmas associated with support. The participants of this study discuss their experiences before and during college, as well as their decision-making process regarding disclosing their diagnosis of an ASD. This study developed a theoretical model to visually understand the experiences of this population. Real-world implications are discussed along with recommendations for those supporting these students in transition, from a variety of different perspectives
Endogenous measures for contextualising large-scale social phenomena: a corpus-based method for mediated public discourse
This work presents an interdisciplinary methodology for developing endogenous measures of group membership through analysis of pervasive linguistic patterns in public discourse. Focusing on political discourse, this work critiques the conventional approach to the study of political participation, which is premised on decontextualised, exogenous measures to characterise groups. Considering the theoretical and empirical weaknesses of decontextualised approaches to large-scale social phenomena, this work suggests that contextualisation using endogenous measures might provide a complementary perspective to mitigate such weaknesses.
This work develops a sociomaterial perspective on political participation in mediated discourse as affiliatory action performed through language. While the affiliatory function of language is often performed consciously (such as statements of identity), this work is concerned with unconscious features (such as patterns in lexis and grammar). This work argues that pervasive patterns in such features that emerge through socialisation are resistant to change and manipulation, and thus might serve as endogenous measures of sociopolitical contexts, and thus of groups.
In terms of method, the work takes a corpus-based approach to the analysis of data from the Twitter messaging service whereby patterns in usersâ speech are examined statistically in order to trace potential community membership. The method is applied in the US state of Michigan during the second half of 2018â6 November having been the date of midterm (i.e. non-Presidential) elections in the United States. The corpus is assembled from the original posts of 5,889 users, who are nominally geolocalised to 417 municipalities. These users are clustered according to pervasive language features. Comparing the linguistic clusters according to the municipalities they represent finds that there are regular sociodemographic differentials across clusters. This is understood as an indication of social structure, suggesting that endogenous measures derived from pervasive patterns in language may indeed offer a complementary, contextualised perspective on large-scale social phenomena
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