A Double Error Dynamic Asymptote Model of Associative Learning

In this paper a formal model of associative learning is presented which incorporates representational and computational mechanisms that, as a coherent corpus, empower it to make accurate predictions of a wide variety of phenomena that so far have eluded a unified account in learning theory. In particular, the Double Error Dynamic Asymptote (DDA) model introduces: 1) a fully-connected network architecture in which stimuli are represented as temporally clustered elements that associate to each other, so that elements of one cluster engender activity on other clusters, which naturally implements neutral stimuli associations and mediated learning; 2) a predictor error term within the traditional error correction rule (the double error), which reduces the rate of learning for expected predictors; 3) a revaluation associability rate that operates on the assumption that the outcome predictiveness is tracked over time so that prolonged uncertainty is learned, reducing the levels of attention to initially surprising outcomes; and critically 4) a biologically plausible variable asymptote, which encapsulates the principle of Hebbian learning, leading to stronger associations for similar levels of cluster activity. The outputs of a set of simulations of the DDA model are presented along with empirical results from the literature. Finally, the predictive scope of the model is discussed

Associative Learning Should Go Deep

Conditioning, how animals learn to associate two or more events, is one of the most influential paradigms in learning theory. It is nevertheless unclear how current models of associative learning can accommodate complex phenomena without ad hoc representational assumptions. We propose to embrace deep neural networks to negotiate this problem

A Double-Error Correction Computational Model of Learning

In this thesis, the Double Error model, a general computational model of real-time learning is presented. It builds upon previous real-time error-correction models and assumes that associative connections form not only between stimuli and reinforcers, but between all types of stimuli in a connectionist network. The stimulus representation uses temporally-distributed elements with memory traces, and a process of expectation-based attentional modulation for both reinforcers and non-reinforcing stimuli is introduced. A modified error-correction learning rule is proposed, which incorporates both an error-term for the predicted and predicting stimulus. The static asymptote of learning familiar from other models of learning is replaced by a similarity measure between the activities of said stimuli, resulting in more temporally correlated stimulus representations forming stronger associative links. Associative retrieval based on previously formed associative links result in the model predicting mediated learning and pre-exposure effects. As a general model of learning, it accounts for phenomena predicted by extant learning models. For instance, its usage of error-correction learning produces a natural account of cue-competition effects such as blocking and overshadowing. Its elemental framework, which incorporates overlapping sets of elements to represent stimuli, leads to it predicting non-linear discriminations including biconditional discriminations and negative patterning. The observation that adding a cue to an excitatory compound stimulus leads to a lower generalization decrement as compared to removing a cue from said compound also follows from this representational assumption. The model further makes a number of unique predictions. The apparent contradiction of mediated learning in backward blocking and mediated conditioning proceeding in opposite directions is predicted through the model’s dynamic asymptote. Latent inhibition is accounted for as occurring through both learning and selective attention. The selective attention of the model likewise produces emergent effects when instantiated in the real-time dynamics of the model, predicting that the relatively best predictor of an outcome can sustain the largest amount of attention when compared to poorer predictors of said outcome. The model is evaluated theoretically, through simulations of learning experiments, and mathematically to demonstrate its generality and formal validity. Further, a simplified version of the model is contrasted against other models on a simple artificial classification task, showcasing the power of the fully-connected nature of the model, as well as its second error term in enabling the model’s performance as a classifier. Finally, numerous avenues of future work have been explored. I have completed a proof-of-concept deep recurrent network extension of the model, instantiated with reference to machine learning theory, and applied the second error term of the model to modulating backpropagation in time of a vanilla RNN. Both the former and latter were applied to a natural language processing task

On the hygroscopic growth of ammoniated sulfate particles of non-stoichiometric composition

International audienceThe hygroscopic growth of ammoniated sulfate particles was studied by measurements and model calculations for particles with varying ammonium-to-sulfate ratio. In the measurements, the ammonium-to-sulfate ratio was adjusted by using mixtures of ammonium sulfate and ammonium bisulfate in generating the solid particles. The hygroscopic growth was measured using a tandem differential mobility analyzer. The measurements were simulated using a thermodynamical equilibrium model. The calculations indicated that the solid phases in particle with ammonium-to-sulfate ratio between 1.5?2, were ammonium sulfate and letovicite. Both in the calculations and in the experiments the hygroscopic growth was initiated at relative humidities less than the theoretical deliquescence relative humidity of these particles. This indicates that the particles were multi-phase particles including solids and liquids. The equilibrium model yielded a satisfactory prediction of the hygroscopic growth of particles generated from a solution with 1:1 mass ratio between dissolved ammonium sulfate and ammonium bisulfate. However, for particles with 3:1 and 10:1 mass ratios, the model predictions overestimated the growth at relative humidities between about 60% and the point of complete deliquescence (close to 80% RH). In contrast, a model, in which letovicite was allowed to dissolve only after complete dissolution of ammonium sulfate, reproduced the observations well. This indicates that the dry particles had a letovicite core surrounded by an ammonium sulfate shell

Aerosol effects on clouds are concealed by natural cloud heterogeneity and satellite retrieval errors

One major source of uncertainty in the cloud-mediated aerosol forcing arises from the magnitude of the cloud liquid water path (LWP) adjustment to aerosol-cloud interactions, which is poorly constrained by observations. Many of the recent satellite-based studies have observed a decreasing LWP as a function of cloud droplet number concentration (CDNC) as the dominating behavior. Estimating the LWP response to the CDNC changes is a complex task since various confounding factors need to be isolated. However, an important aspect has not been sufficiently considered: the propagation of natural spatial variability and errors in satellite retrievals of cloud optical depth and cloud effective radius to estimates of CDNC and LWP. Here we use satellite and simulated measurements to demonstrate that, because of this propagation, even a positive LWP adjustment is likely to be misinterpreted as negative. This biasing effect therefore leads to an underestimate of the aerosol-cloud-climate cooling and must be properly considered in future studies

The AeroCom evaluation and intercomparison of organic aerosol in global models

This paper evaluates the current status of global modeling of the organic aerosol (OA) in the troposphere and analyzes the differences between models as well as between models and observations. Thirty-one global chemistry transport models (CTMs) and general circulation models (GCMs) have participated in this intercomparison, in the framework of AeroCom phase II. The simulation of OA varies greatly between models in terms of the magnitude of primary emissions, secondary OA (SOA) formation, the number of OA species used (2 to 62), the complexity of OA parameterizations (gas-particle partitioning, chemical aging, multiphase chemistry, aerosol microphysics), and the OA physical, chemical and optical properties. The diversity of the global OA simulation results has increased since earlier AeroCom experiments, mainly due to the increasing complexity of the SOA parameterization in models, and the implementation of new, highly uncertain, OA sources. Diversity of over one order of magnitude exists in the modeled vertical distribution of OA concentrations that deserves a dedicated future study. Furthermore, although the OA / OC ratio depends on OA sources and atmospheric processing, and is important for model evaluation against OA and OC observations, it is resolved only by a few global models. The median global primary OA (POA) source strength is 56 Tg a−1 (range 34–144 Tg a−1) and the median SOA source strength (natural and anthropogenic) is 19 Tg a−1 (range 13–121 Tg a−1). Among the models that take into account the semi-volatile SOA nature, the median source is calculated to be 51 Tg a−1 (range 16–121 Tg a−1), much larger than the median value of the models that calculate SOA in a more simplistic way (19 Tg a−1; range 13–20 Tg a−1, with one model at 37 Tg a−1). The median atmospheric burden of OA is 1.4 Tg (24 models in the range of 0.6–2.0 Tg and 4 between 2.0 and 3.8 Tg), with a median OA lifetime of 5.4 days (range 3.8–9.6 days). In models that reported both OA and sulfate burdens, the median value of the OA/sulfate burden ratio is calculated to be 0.77; 13 models calculate a ratio lower than 1, and 9 models higher than 1. For 26 models that reported OA deposition fluxes, the median wet removal is 70 Tg a−1 (range 28–209 Tg a−1), which is on average 85% of the total OA deposition. Fine aerosol organic carbon (OC) and OA observations from continuous monitoring networks and individual field campaigns have been used for model evaluation. At urban locations, the model–observation comparison indicates missing knowledge on anthropogenic OA sources, both strength and seasonality. The combined model–measurements analysis suggests the existence of increased OA levels during summer due to biogenic SOA formation over large areas of the USA that can be of the same order of magnitude as the POA, even at urban locations, and contribute to the measured urban seasonal pattern. Global models are able to simulate the high secondary character of OA observed in the atmosphere as a result of SOA formation and POA aging, although the amount of OA present in the atmosphere remains largely underestimated, with a mean normalized bias (MNB) equal to −0.62 (−0.51) based on the comparison against OC (OA) urban data of all models at the surface, −0.15 (+0.51) when compared with remote measurements, and −0.30 for marine locations with OC data. The mean temporal correlations across all stations are low when compared with OC (OA) measurements: 0.47 (0.52) for urban stations, 0.39 (0.37) for remote stations, and 0.25 for marine stations with OC data. The combination of high (negative) MNB and higher correlation at urban stations when compared with the low MNB and lower correlation at remote sites suggests that knowledge about the processes that govern aerosol processing, transport and removal, on top of their sources, is important at the remote stations. There is no clear change in model skill with increasing model complexity with regard to OC or OA mass concentration. However, the complexity is needed in models in order to distinguish between anthropogenic and natural OA as needed for climate mitigation, and to calculate the impact of OA on climate accurately

Intercomparison and evaluation of global aerosol microphysical properties among AeroCom models of a range of complexity

Many of the next generation of global climate models will include aerosol schemes which explicitly simulate the microphysical processes that determine the particle size distribution. These models enable aerosol optical properties and cloud condensation nuclei (CCN) concentrations to be determined by fundamental aerosol processes, which should lead to a more physically based simulation of aerosol direct and indirect radiative forcings. This study examines the global variation in particle size distribution simulated by 12 global aerosol microphysics models to quantify model diversity and to identify any common biases against observations. Evaluation against size distribution measurements from a new European network of aerosol supersites shows that the mean model agrees quite well with the observations at many sites on the annual mean, but there are some seasonal biases common to many sites. In particular, at many of these European sites, the accumulation mode number concentration is biased low during winter and Aitken mode concentrations tend to be overestimated in winter and underestimated in summer. At high northern latitudes, the models strongly underpredict Aitken and accumulation particle concentrations compared to the measurements, consistent with previous studies that have highlighted the poor performance of global aerosol models in the Arctic. In the marine boundary layer, the models capture the observed meridional variation in the size distribution, which is dominated by the Aitken mode at high latitudes, with an increasing concentration of accumulation particles with decreasing latitude. Considering vertical profiles, the models reproduce the observed peak in total particle concentrations in the upper troposphere due to new particle formation, although modelled peak concentrations tend to be biased high over Europe. Overall, the multi-model-mean data set simulates the global variation of the particle size distribution with a good degree of skill, suggesting that most of the individual global aerosol microphysics models are performing well, although the large model diversity indicates that some models are in poor agreement with the observations. Further work is required to better constrain size-resolved primary and secondary particle number sources, and an improved understanding of nucleation and growth (e.g. the role of nitrate and secondary organics) will improve the fidelity of simulated particle size distributions

Post-load glucose subgroups and associated metabolic traits in individuals with type 2 diabetes:An IMI-DIRECT study

AIM: Subclasses of different glycaemic disturbances could explain the variation in characteristics of individuals with type 2 diabetes (T2D). We aimed to examine the association between subgroups based on their glucose curves during a five-point mixed-meal tolerance test (MMT) and metabolic traits at baseline and glycaemic deterioration in individuals with T2D. METHODS: The study included 787 individuals with newly diagnosed T2D from the Diabetes Research on Patient Stratification (IMI-DIRECT) Study. Latent class trajectory analysis (LCTA) was used to identify distinct glucose curve subgroups during a five-point MMT. Using general linear models, these subgroups were associated with metabolic traits at baseline and after 18 months of follow up, adjusted for potential confounders. RESULTS: At baseline, we identified three glucose curve subgroups, labelled in order of increasing glucose peak levels as subgroup 1-3. Individuals in subgroup 2 and 3 were more likely to have higher levels of HbA1c, triglycerides and BMI at baseline, compared to those in subgroup 1. At 18 months (n = 651), the beta coefficients (95% CI) for change in HbA1c (mmol/mol) increased across subgroups with 0.37 (-0.18-1.92) for subgroup 2 and 1.88 (-0.08-3.85) for subgroup 3, relative to subgroup 1. The same trend was observed for change in levels of triglycerides and fasting glucose. CONCLUSIONS: Different glycaemic profiles with different metabolic traits and different degrees of subsequent glycaemic deterioration can be identified using data from a frequently sampled mixed-meal tolerance test in individuals with T2D. Subgroups with the highest peaks had greater metabolic risk