The Impact of Prediction Errors on Memory Consolidation

2014 ◽  
Author(s):  
Esther De Loof ◽  
Lien Naert ◽  
Tom Verguts ◽  
Elger Abrahamse
Keyword(s):  
Memory Consolidation ◽  
Prediction Errors ◽  
The Impact

Serotonin depletion reduces both acquisition and expression of context-conditioned fear

2021 ◽  
pp. 1-8
Author(s):  
S. Melker Hagsäter ◽  
Robert Pettersson ◽  
Axel Holmäng ◽  
Elias Eriksson
Keyword(s):  
Unconditioned Stimulus ◽  
Memory Consolidation ◽  
Clinical Studies ◽  
Conditioned Fear ◽  
Sprague Dawley ◽  
Serotonin Synthesis ◽  
Synthesis Inhibitor ◽  
Serotonin Depletion ◽  
Sprague Dawley Rats ◽  
The Impact

Abstract Objective: Whereas numerous experimental and clinical studies suggest a complex involvement of serotonin in the regulation of anxiety, it remains to be clarified if the dominating impact of this transmitter is best described as anxiety-reducing or anxiety-promoting. The aim of this study was to assess the impact of serotonin depletion on acquisition, consolidation, and expression of conditioned fear. Methods: Male Sprague–Dawley rats were exposed to foot shocks as unconditioned stimulus and assessed with respect to freezing behaviour when re-subjected to context. Serotonin depletion was achieved by administration of a serotonin synthesis inhibitor, para-chlorophenylalanine (PCPA) (300 mg/kg daily × 3), (i) throughout the period from (and including) acquisition to (and including) expression, (ii) during acquisition but not expression, (iii) after acquisition only, and (iv) during expression only. Results: The time spent freezing was significantly reduced in animals that were serotonin-depleted during the entire period from (and including) acquisition to (and including) expression, as well as in those being serotonin-depleted during either acquisition only or expression only. In contrast, PCPA administrated immediately after acquisition, that is during memory consolidation, did not impact the expression of conditioned fear. Conclusion: Intact serotonergic neurotransmission is important for both acquisition and expression of context-conditioned fear.

scholarly journals Implicit signals in small group settings and their impact on the expression of cognitive capacity and associated brain responses

2012 ◽  
Vol 367 (1589) ◽  
pp. 704-716 ◽  
Author(s):  
Kenneth T. Kishida ◽  
Dongni Yang ◽  
Karen Hunter Quartz ◽  
Steven R. Quartz ◽  
P. Read Montague
Keyword(s):  
Social Status ◽  
Small Group ◽  
Cognitive Capacity ◽  
Cultural Norms ◽  
Prediction Errors ◽  
Group Setting ◽  
Group Settings ◽  
Brain Responses ◽  
Dorsolateral Prefrontal ◽  
The Impact

Measures of intelligence, when broadcast, serve as salient signals of social status, which may be used to unjustly reinforce low-status stereotypes about out-groups' cultural norms. Herein, we investigate neurobehavioural signals manifest in small ( n = 5) groups using functional magnetic resonance imaging and a ‘ranked group IQ task’ where implicit signals of social status are broadcast and differentiate individuals based on their expression of cognitive capacity. We report an initial overall decrease in the expression of cognitive capacity in the small group setting. However, the environment of the ‘ranked group IQ task’ eventually stratifies the population into two groups (‘high performers’, HP and ‘low performers’, LP) identifiable based on changes in estimated intelligence quotient and brain responses in the amygdala and dorsolateral prefrontal cortex. In addition, we demonstrate signals in the nucleus accumbens consistent with prediction errors in expected changes in status regardless of group membership. Our results suggest that individuals express diminished cognitive capacity in small groups, an effect that is exacerbated by perceived lower status within the group and correlated with specific neurobehavioural responses. The impact these reactions have on intergroup divisions and conflict resolution requires further investigation, but suggests that low-status groups may develop diminished capacity to mitigate conflict using non-violent means.

A comparison among approaches for recommending learning objects through collaborative filtering algorithms

2017 ◽  
Vol 51 (1) ◽  
pp. 35-51
Author(s):  
Henrique Lemos dos Santos ◽  
Cristian Cechinel ◽  
Ricardo Matsumura Araújo
Keyword(s):  
Collaborative Filtering ◽  
Learning Objects ◽  
Learning Goals ◽  
Average Error ◽  
Neighborhood Size ◽  
Relevant Parameter ◽  
Prediction Errors ◽  
Content Type ◽  
User Coverage ◽  
The Impact

Purpose The purpose of this paper is to present the results of a comparison among three different approaches for recommending learning objects (LO) inside a repository. The comparison focuses not only on prediction errors but also on the coverage of each tested configuration. Design/methodology/approach The authors compared the offline evaluation by using pure collaborative filtering (CF) algorithms with two other different combinations of pre-processed data. The first approach for pre-processing data consisted of clustering users according to their disciplines resemblance, while the second approach consisted of clustering LO according to their textual similarity regarding title and description. The three methods were compared with respect to the mean average error between predicted values and real values. Moreover, we evaluated the impact of the number of clusters and neighborhood size on the user-coverage. Findings Clustering LO has improved the prediction error measure with a small loss on user-coverage when compared to the pure CF approach. On the other hand, the approach of clustering users failed in both the error and in user-space coverage. It also became clear that the neighborhood size is the most relevant parameter to determine how large the coverage will be. Research limitations The methods proposed here were not yet evaluated in a real-world scenario, with real users opinions about the recommendations and their respective learning goals. Future work is still required to evaluate users opinions. Originality/value This research provides evidence toward new recommendation methods directed toward LO repositories.

scholarly journals How accurate are modelled birth and pregnancy estimates? Comparison of four models using high resolution maternal health census data in southern Mozambique

2019 ◽  
Vol 4 (Suppl 5) ◽  
pp. e000894
Author(s):  
Yolisa Prudence Dube ◽  
Corrine Warren Ruktanonchai ◽  
Charfudin Sacoor ◽  
Andrew J Tatem ◽  
Khatia Munguambe ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Input Data ◽  
Census Data ◽  
Healthcare Services ◽  
Perinatal Health ◽  
Prediction Errors ◽  
The Sustainable Development ◽  
Administrative Unit ◽  
Development Goals ◽  
The Impact

BackgroundExistence of inequalities in quality and access to healthcare services at subnational levels has been identified despite a decline in maternal and perinatal mortality rates at national levels, leading to the need to investigate such conditions using geographical analysis. The need to assess the accuracy of global demographic distribution datasets at all subnational levels arises from the current emphasis on subnational monitoring of maternal and perinatal health progress, by the new targets stated in the Sustainable Development Goals.MethodsThe analysis involved comparison of four models generated using Worldpop methods, incorporating region-specific input data, as measured through the Community Level Intervention for Pre-eclampsia (CLIP) project. Normalised root mean square error was used to determine and compare the models’ prediction errors at different administrative unit levels.ResultsThe models’ prediction errors are lower at higher administrative unit levels. All datasets showed the same pattern for both the live birth and pregnancy estimates. The effect of improving spatial resolution and accuracy of input data was more prominent at higher administrative unit levels.ConclusionThe validation successfully highlighted the impact of spatial resolution and accuracy of maternal and perinatal health data in modelling estimates of pregnancies and live births. There is a need for more data collection techniques that conduct comprehensive censuses like the CLIP project. It is also imperative for such projects to take advantage of the power of mapping tools at their disposal to fill the gaps in the availability of datasets for populated areas.

Unjust Disparities? The Impact of Race on Juvenile Risk Assessment Outcomes

2016 ◽  
Vol 29 (5) ◽  
pp. 423-442 ◽  
Author(s):  
James T. McCafferty
Keyword(s):  
Risk Assessment ◽  
Black Youth ◽  
Attorney General ◽  
Assessment System ◽  
Risk Assessments ◽  
Prediction Errors ◽  
Racial Biases ◽  
Actuarial Risk ◽  
Robust Prediction ◽  
The Impact

Research on risk assessments has illustrated many utilitarian purposes of these tools, including the robust prediction of recidivism and uniformity in correctional decision making. Recently, however, Former U.S. Attorney General Eric Holder vocalized his position that actuarial risk assessments could be unintentionally contributing to disproportionate minority contact in the correctional system. This study used data from approximately 2,600 juvenile delinquents assessed with the Ohio Youth Assessment System–Disposition Instrument to examine these claims across subsamples of White and Black youth. Bivariate and multivariate analyses indicated that the instrument predicted recidivism similarly across the two groups. There were slightly more prediction errors for Black youth than White youth; however, these differences may be the result of methodological factors rather than empirical realities. The article concluded with a discussion of the implications that potential racial biases have on risk assessment research and practice.

A MIXTURE MODEL FOR PAYMENTS AND PAYMENT NUMBERS IN CLAIMS RESERVING

2016 ◽  
Vol 48 (1) ◽  
pp. 25-53 ◽  
Author(s):  
Patrizia Gigante ◽  
Liviana Picech ◽  
Luciano Sigalotti
Keyword(s):  
Random Effects ◽  
Linear Models ◽  
External Information ◽  
Parameter Estimates ◽  
Prediction Errors ◽  
Poisson Regression Model ◽  
External Data ◽  
Fixed And Random Effects ◽  
Likelihood Approach ◽  
The Impact

AbstractWe consider a Tweedie's compound Poisson regression model with fixed and random effects, to describe the payment numbers and the incremental payments, jointly, in claims reserving. The parameter estimates are obtained within the framework of hierarchical generalized linear models, by applying the h-likelihood approach. Regression structures are allowed for the means and also for the dispersions. Predictions and prediction errors of the claims reserves are evaluated. Through the parameters of the distributions of the random effects, some external information (e.g. a development pattern of industry wide-data) can be incorporated into the model. A numerical example shows the impact of external data on the reserve and prediction error evaluations.

scholarly journals Peripheral and central CB1 cannabinoid receptors control stress-induced impairment of memory consolidation

2016 ◽  
Vol 113 (35) ◽  
pp. 9904-9909 ◽  
Author(s):  
Arnau Busquets-Garcia ◽  
Maria Gomis-González ◽  
Raj Kamal Srivastava ◽  
Laura Cutando ◽  
Antonio Ortega-Alvaro ◽  
...  
Keyword(s):  
Memory Consolidation ◽  
Cannabinoid Receptors ◽  
Recognition Task ◽  
Underlying Mechanism ◽  
Stressful Events ◽  
Specific Cell ◽  
Cb1 Receptors ◽  
Emotional Memories ◽  
Stress Dependent ◽  
The Impact

Stressful events can generate emotional memories linked to the traumatic incident, but they also can impair the formation of nonemotional memories. Although the impact of stress on emotional memories is well studied, much less is known about the influence of the emotional state on the formation of nonemotional memories. We used the novel object-recognition task as a model of nonemotional memory in mice to investigate the underlying mechanism of the deleterious effect of stress on memory consolidation. Systemic, hippocampal, and peripheral blockade of cannabinoid type-1 (CB1) receptors abolished the stress-induced memory impairment. Genetic deletion and rescue of CB1 receptors in specific cell types revealed that the CB1 receptor population specifically in dopamine β-hydroxylase (DBH)-expressing cells is both necessary and sufficient for stress-induced impairment of memory consolidation, but CB1 receptors present in other neuronal populations are not involved. Strikingly, pharmacological manipulations in mice expressing CB1 receptors exclusively in DBH+ cells revealed that both hippocampal and peripheral receptors mediate the impact of stress on memory consolidation. Thus, CB1 receptors on adrenergic and noradrenergic cells provide previously unrecognized cross-talk between central and peripheral mechanisms in the stress-dependent regulation of nonemotional memory consolidation, suggesting new potential avenues for the treatment of cognitive aspects on stress-related disorders.

scholarly journals The impact of observing characteristics on the ability to predict ozone under varying polluted photochemical regimes

2015 ◽  
Vol 15 (18) ◽  
pp. 10645-10667 ◽  
Author(s):  
P. D. Hamer ◽  
K. W. Bowman ◽  
D. K. Henze ◽  
J.-L. Attié ◽  
V. Marécal
Keyword(s):  
Air Quality ◽  
Observation Time ◽  
Prediction Errors ◽  
Monitoring Networks ◽  
Source Inversion ◽  
Ozone Prediction ◽  
Air Quality Prediction ◽  
Set Up ◽  
Ozone Precursor ◽  
The Impact

Abstract. We conduct analyses to assess how characteristics of observations of ozone and its precursors affect air quality forecasting and research. To carry out this investigation, we use a photochemical box model and its adjoint integrated with a Lagrangian 4D-variational data assimilation system. Using this framework in conjunction with pseudo-observations, we perform an ozone precursor source inversion and estimate surface emissions. We then assess the resulting improvement in ozone air quality prediction. We use an analytical model to conduct uncertainty analyses. Using this analytical tool, we address some key questions regarding how the characteristics of observations affect ozone precursor emission inversion and in turn ozone prediction. These questions include what the effect is of choosing which species to observe, of varying amounts of observation noise, of changing the observing frequency and the observation time during the diurnal cycle, and of how these different scenarios interact with different photochemical regimes. In our investigation we use three observed species scenarios: CO and NO2; ozone, CO, and NO2; and HCHO, CO and NO2. The photochemical model was set up to simulate a range of summertime polluted environments spanning NOx-(NO and NO2)-limited to volatile organic compound (VOC)-limited conditions. We find that as the photochemical regime changes, here is a variation in the relative importance of trace gas observations to be able to constrain emission estimates and to improve the subsequent ozone forecasts. For example, adding ozone observations to an NO2 and CO observing system is found to decrease ozone prediction error under NOx- and VOC-limited regimes, and complementing the NO2 and CO system with HCHO observations would improve ozone prediction in the transitional regime and under VOC-limited conditions. We found that scenarios observing ozone and HCHO with a relative observing noise of lower than 33 % were able to achieve ozone prediction errors of lower than 5 ppbv (parts per billion by volume). Further, only observing intervals of 3 h or shorter were able to consistently achieve ozone prediction errors of 5 ppbv or lower across all photochemical regimes. Making observations closer to the prediction period and either in the morning or afternoon rush hour periods made greater improvements for ozone prediction: 0.2–0.3 ppbv for the morning rush hour and from 0.3 to 0.8 ppbv for the afternoon compared to only 0–0.1 ppbv for other times of the day. Finally, we made two complementary analyses that show that our conclusions are insensitive to the assumed diurnal emission cycle and to the choice of which VOC species emission to estimate using our framework. These questions will address how different types of observing platform, e.g. geostationary satellites or ground monitoring networks, could support future air quality research and forecasting.

scholarly journals Evaluation of the impact of explanatory variables on the accuracy of prediction of daily inflow to the sewage treatment plant by selected models nonlinear

2017 ◽  
Vol 43 (3) ◽  
pp. 74-81 ◽  
Author(s):  
Bartosz Szeląg ◽  
Lidia Bartkiewicz ◽  
Jan Studziński ◽  
Krzysztof Barbusiński
Keyword(s):  
Prediction Models ◽  
Sewage Treatment ◽  
Sewage Treatment Plant ◽  
Treatment Plant ◽  
Municipal Sewage ◽  
Support Vector ◽  
Prediction Errors ◽  
Explanatory Variables ◽  
Input Variables ◽  
The Impact

AbstractThe aim of the study was to evaluate the possibility of applying different methods of data mining to model the inflow of sewage into the municipal sewage treatment plant. Prediction models were elaborated using methods of support vector machines (SVM), random forests (RF), k-nearest neighbour (k-NN) and of Kernel regression (K). Data consisted of the time series of daily rainfalls, water level measurements in the clarified sewage recipient and the wastewater inflow into the Rzeszow city plant. Results indicate that the best models with one input delayed by 1 day were obtained using the k-NN method while the worst with the K method. For the models with two input variables and one explanatory one the smallest errors were obtained if model inputs were sewage inflow and rainfall data delayed by 1 day and the best fit is provided using RF method while the worst with the K method. In the case of models with three inputs and two explanatory variables, the best results were reported for the SVM and the worst for the K method. In the most of the modelling runs the smallest prediction errors are obtained using the SVM method and the biggest ones with the K method. In the case of the simplest model with one input delayed by 1 day the best results are provided using k-NN method and by the models with two inputs in two modelling runs the RF method appeared as the best.

scholarly journals Frontal Theta Oscillatory Activity Is a Common Mechanism for the Computation of Unexpected Outcomes and Learning Rate

2014 ◽  
Vol 26 (3) ◽  
pp. 447-458 ◽  
Author(s):  
Ernest Mas-Herrero ◽  
Josep Marco-Pallarés
Keyword(s):  
Prediction Error ◽  
Learning Task ◽  
Learning Rate ◽  
Oscillatory Activity ◽  
Common Mechanism ◽  
Prediction Errors ◽  
Reward Prediction Error ◽  
Reward Prediction ◽  
Medial Pfc ◽  
The Impact

In decision-making processes, the relevance of the information yielded by outcomes varies across time and situations. It increases when previous predictions are not accurate and in contexts with high environmental uncertainty. Previous fMRI studies have shown an important role of medial pFC in coding both reward prediction errors and the impact of this information to guide future decisions. However, it is unclear whether these two processes are dissociated in time or occur simultaneously, suggesting that a common mechanism is engaged. In the present work, we studied the modulation of two electrophysiological responses associated to outcome processing—the feedback-related negativity ERP and frontocentral theta oscillatory activity—with the reward prediction error and the learning rate. Twenty-six participants performed two learning tasks differing in the degree of predictability of the outcomes: a reversal learning task and a probabilistic learning task with multiple blocks of novel cue–outcome associations. We implemented a reinforcement learning model to obtain the single-trial reward prediction error and the learning rate for each participant and task. Our results indicated that midfrontal theta activity and feedback-related negativity increased linearly with the unsigned prediction error. In addition, variations of frontal theta oscillatory activity predicted the learning rate across tasks and participants. These results support the existence of a common brain mechanism for the computation of unsigned prediction error and learning rate.

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