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In recent years, scientists have increasingly taken to investigate the predictive nature of cognition. We argue that prediction relies on abstraction, and thus theories of predictive cognition need an explicit theory of abstract representation. We propose such a theory of the abstract representational capacities that allow humans to transcend the “here-and-now”. Consistent with the predictive cognition literature, we suggest that the representational substrates of the mind are built as a hierarchy, ranging from the concrete to the abstract; however, we argue that there are qualitative differences between elements along this hierarchy, generating meaningful, often unacknowledged, diversity. Echoing views from philosophy, we suggest that the representational hierarchy can be parsed into: modality-specific representations, instantiated on perceptual similarity; multimodal representations, primarily instantiated on the discovery of spatiotemporal contiguity; and categorical representations, primarily instantiated on social interaction. These elements serve as the building blocks of complex structures discussed in cognitive psychology (e.g., episodes, scripts) and are the inputs for mental representations that behave like functions, typically discussed in linguistics (i.e., predicators). We support our argument for representational diversity by explaining how the elements in our ontology are all required in order to account for humans’ predictive cognition (e.g., in subserving logic-based prediction; in optimizing the trade-off between accurate and detailed predictions) and by examining how the neuroscientific evidence coheres with our account. In doing so, we provide a testable model of the neural bases of conceptual cognition, and highlight several important implications to research on self-projection, reinforcement learning, and predictive-processing models of psychopathology.
Eleni Gregoromichelaki and Ruth Kempson present a range of arguments and data, including cases of split utterances, in support of their position that even syntax should be construed in terms of the linguistic underspecification of utterance content and incremental context-relative processing. This approach to language (which they call ‘Dynamic Syntax’) is fundamentally different from orthodox generative grammar and conceptualises syntax as procedures for interaction.
Jary and Kissine examine the meaning of imperative sentences, taking the existing relevance-theoretic semantic analysis, in terms of the desirability and potentiality of the described state of affairs, as their point of departure. In their view, a complete account of the interpretation of imperatives has to explain how they can result in the addressee forming an intention to perform an action, and this requires the theory to make room for ‘action representations’ (in addition to factual representations, such as assumptions). They claim that the imperative form is uniquely specified to interface with such action representations.
To evaluate the association between novel pre- and post-operative biomarker levels and 30-day unplanned readmission or mortality after paediatric congenital heart surgery.
Children aged 18 years or younger undergoing congenital heart surgery (n = 162) at Johns Hopkins Hospital from 2010 to 2014 were enrolled in the prospective cohort. Collected novel pre- and post-operative biomarkers include soluble suppression of tumorgenicity 2, galectin-3, N-terminal prohormone of brain natriuretic peptide, and glial fibrillary acidic protein. A model based on clinical variables from the Society of Thoracic Surgery database was developed and evaluated against two augmented models.
Unplanned readmission or mortality within 30 days of cardiac surgery occurred among 21 (13%) children. The clinical model augmented with pre-operative biomarkers demonstrated a statistically significant improvement over the clinical model alone with a receiver-operating characteristics curve of 0.754 (95% confidence interval: 0.65–0.86) compared to 0.617 (95% confidence interval: 0.47–0.76; p-value: 0.012). The clinical model augmented with pre- and post-operative biomarkers demonstrated a significant improvement over the clinical model alone, with a receiver-operating characteristics curve of 0.802 (95% confidence interval: 0.72–0.89; p-value: 0.003).
Novel biomarkers add significant predictive value when assessing the likelihood of unplanned readmission or mortality after paediatric congenital heart surgery. Further exploration of the utility of these novel biomarkers during the pre- or post-operative period to identify early risk of mortality or readmission will aid in determining the clinical utility and application of these biomarkers into routine risk assessment.
Major depressive disorder (MDD) represents a leading cause of disability. This study examines the course of disability in patients with chronic, recurrent and remitting MDD compared to healthy controls and identifies predictors of disability in remitting MDD.
We included 914 participants from the Netherlands Study of Depression and Anxiety (NESDA). DSM-IV MDD and WHO DAS II disability were assessed at baseline and at 2, 4 and 6 years. Six-year total and domain-specific disability were analysed and compared in participants with chronic (n = 57), recurrent (n = 120), remitting (n = 127) MDD and in healthy controls (n = 430). Predictors of residual disability were identified using linear regression analysis.
At baseline, most disability was found in chronic MDD, followed by recurrent MDD, remitting MDD and healthy controls. Across diagnostic groups, most disability was found in household activities, interpersonal functioning, participation in society and cognition. A chronic course was associated with chronic disability. Symptom remission was associated with a decrease in disability, but some disability remained. In remitting MDD, higher residual disability was predicted by older age, more severe avoidance symptoms, higher disability at baseline and late symptom remission. Severity of residual disability correlated with the severity of residual depressive symptoms.
Symptomatic remission is a prerequisite for improvements in disability. However, disability persists despite symptom remission. Therefore, treatment of MDD should include an explicit focus on disability, especially on the more complex domains. To this end, treatments should promote behavioural activation and address subthreshold depressive symptoms in patients with remitted MDD.
Attrition modeling is a direct application of extant turnover research that can favorably impact workforce planning and action planning. However, while academic research enables practitioners insights into understanding turnover phenomena, there is no single document that comprehensively translates this work to give guidance as to the many practical decisions that must be made when modeling turnover, as well as how to apply psychological research to messier operational data. This focal article introduces and provides guidance on attrition modeling by outlining early considerations when planning a study, describing how to mesh theory with operational considerations when identifying turnover predictors within organizational settings, highlighting analytical strategies to model turnover, and considering how to appropriately share results. Collectively, this article serves as a guide to conducting attrition modeling within organizations and offers suggestions for future research to inform best practices.
This chapter examines the uses of academic approaches to history in discussing energy policy. It sets out a case that the value of history is not simply in the past as a source of empirical data on policy and behaviour (which is accessible to any discipline), but a style of synthetic thinking and evaluation particular to the study of History as a disciplines. History may provide analogue situations for current dilemmas, and a long-term view on change, but does not necessarily work in large-scale or long-term phenomena. Rather, it is the blending of perspectives and the assumption of causal complexity, as opposed to methodological and explanatory parsimony, that marks the value of historical approaches. This is exemplified in the history of prediction, asking not whether predictions were accurate (generally they were not), but why demand for them arose and how they were constructed so as to be plausible to actors.
Political science does not offer a distinct subdiscipline to address the subject of energy. Insofar as political science has addressed energy, it has focused on issues often neglected by other disciplines, notably the role of geopolitics and international relations, and the domestic politics of resource-rich states. Apart from the different subfields, we examine different approaches including realism, constructivism, liberalism and Marxism. The rise and fall and rise again of academic articles on energy in leading political science journals is reviewed and linked to exogenous forces such as the price of oil. Two distinct energy topics which have received attention are nuclear power and the oil crises of 1973–79 because of their wider geopolitical ramifications. Perhaps the most prominent or consistent thread through studies of the politics of energy is the question of energy security or energy independence. Finally, in recent years, energy has increasingly emerged as a focus for study in environmental politics and climate change politics in particular.
Guangxi, a province in southwestern China, has the second highest reported number of HIV/AIDS cases in China. This study aimed to develop an accurate and effective model to describe the tendency of HIV and to predict its incidence in Guangxi. HIV incidence data of Guangxi from 2005 to 2016 were obtained from the database of the Chinese Center for Disease Control and Prevention. Long short-term memory (LSTM) neural network models, autoregressive integrated moving average (ARIMA) models, generalised regression neural network (GRNN) models and exponential smoothing (ES) were used to fit the incidence data. Data from 2015 and 2016 were used to validate the most suitable models. The model performances were evaluated by evaluating metrics, including mean square error (MSE), root mean square error, mean absolute error and mean absolute percentage error. The LSTM model had the lowest MSE when the N value (time step) was 12. The most appropriate ARIMA models for incidence in 2015 and 2016 were ARIMA (1, 1, 2) (0, 1, 2)12 and ARIMA (2, 1, 0) (1, 1, 2)12, respectively. The accuracy of GRNN and ES models in forecasting HIV incidence in Guangxi was relatively poor. Four performance metrics of the LSTM model were all lower than the ARIMA, GRNN and ES models. The LSTM model was more effective than other time-series models and is important for the monitoring and control of local HIV epidemics.
Background: Cervical spondylotic myelopathy (CSM) is the leading cause of spinal cord impairment. In a public healthcare system, wait times to see spine specialists and eventually access surgical treatment for CSM can be substantial. The goals of this study were to determine consultation wait times (CWT) and surgical wait times (SWT), and identify predictors of wait time length. Methods: Consecutive patients enrolled in the Canadian Spine Outcomes and Research Network (CSORN) prospective and observational CSM study from March 2015 to July 2017 were included. A data-splitting technique was used to develop and internally validate multivariable models of potential predictors. Results: A CSORN query returned 264 CSM patients for CWT. The median was 46 days. There were 31% mild, 35% moderate, and 33% severe CSM. There was a statistically significant difference in median CWT between moderate and severe groups; 207 patients underwent surgical treatment. Median SWT was 42 days. There was a statistically significant difference in SWT between mild/moderate and severe groups. Short symptom duration, less pain, lower BMI, and lower physical component score of SF-12 were predictive of shorter CWT. Only baseline pain and medication duration were predictive of SWT. Both CWT and SWT were shorter compared to a concurrent cohort of lumbar stenosis patients (p <0.001). Conclusions: Patients with shorter duration (either symptoms or medication) and less neck pain waited less to see a spine specialist in Canada and to undergo surgical treatment. This study highlights some of the obstacles to overcome in expedited care for this patient population.
Introduction: The quick Sepsis-related Organ Failure Assessment (qSOFA) score was developed to provide clinicians with a quick assessment for patients with latent organ failure possibly consistent with sepsis at high-risk for mortality. With the clinical heterogeneity of patients presenting with sepsis, a Bayesian validation approach may provide a better understanding of its clinical utility. This study used a Bayesian analysis to assess the prediction of hospital mortality by the qSOFA score among patients with infection transported by paramedics. Methods: A one-year cohort of adult patients transported by paramedics in a large, provincial EMS system was linked to Emergency Department (ED) and hospital administrative databases, then restricted to those patients with an ED diagnosed infection. A Bayesian binomial regression model was constructed using Hamiltonian Markov-Chain Monte-Carlo sampling, normal priors for each parameter, the calculated score, age and sex as the predictors, and hospital mortality as the outcome. Discrimination was assessed using posterior predictions to calculate a “Bayesian” C statistic, and calibration was assessed with calibration plots of the observed and predicted probability distributions. The independent predictive ability of each measure was tested by including each component measure (respiratory rate, Glasgow Coma Scale, and systolic blood pressure) as continuous predictors in a second model. Results: A total of 9,920 patients with ED diagnosed infection were included. 264 (2.7%) patients were admitted directly to the ICU, and 955 (9.6%) patients died in-hospital. As independent predictors, the probability of mortality increased as each measure became more extreme, with the Glasgow Coma Scale predicting the greatest change in mortality risk from a high to low score; however, no dramatic change in the probability supporting a single decision threshold was seen for any measure. For the calculated score, the C statistic for predicting mortality was 0.728. The calibration curve had no overlap of predictions, with a probability of 0.5 (50% credible interval 0.47-0.53) for patients with a qSOFA score of 3. Conclusion: Although no single decision threshold was identified for each component measure, a calculated qSOFA score provides good prediction of mortality for patients with ED diagnosed infection. When validating clinical prediction scores, a Bayesian approach may be used to assess probabilities of interest for clinicians to support better clinical decision making. Character count 2494
Relational event models are becoming increasingly popular in modeling temporal dynamics of social networks. Due to their nature of combining survival analysis with network model terms, standard methods of assessing model fit are not suitable to determine if the models are specified sufficiently to prevent biased estimates. This paper tackles this problem by presenting a simple procedure for model-based simulations of relational events. Predictions are made based on survival probabilities and can be used to simulate new event sequences. Comparing these simulated event sequences to the original event sequence allows for in depth model comparisons (including parameter as well as model specifications) and testing of whether the model can replicate network characteristics sufficiently to allow for unbiased estimates.
Previous research by Goldstone et al. (2010) generated a highly accurate predictive model of state-level political instability. Notably, this model identifies political institutions – and partial democracy with factionalism, specifically – as the most compelling factors explaining when and where instability events are likely to occur. This article reassesses the model’s explanatory power and makes three related points: (1) the model’s predictive power varies substantially over time; (2) its predictive power peaked in the period used for out-of-sample validation (1995–2004) in the original study and (3) the model performs relatively poorly in the more recent period. The authors find that this decline is not simply due to the Arab Uprisings, instability events that occurred in autocracies. Similar issues are found with attempts to predict nonviolent uprisings (Chenoweth and Ulfelder 2017) and armed conflict onset and continuation (Hegre et al. 2013). These results inform two conclusions: (1) the drivers of instability are not constant over time and (2) care must be exercised in interpreting prediction exercises as evidence in favor or dispositive of theoretical mechanisms.
Do judges telegraph their preferences during oral arguments? Using the U.S. Supreme Court as our example, we demonstrate that Justices implicitly reveal their leanings during oral arguments, even before arguments and deliberations have concluded. Specifically, we extract the emotional content of over 3,000 hours of audio recordings spanning 30 years of oral arguments before the Court. We then use the level of emotional arousal, as measured by vocal pitch, in each of the Justices’ voices during these arguments to accurately predict many of their eventual votes on these cases. Our approach yields predictions that are statistically and practically significant and robust to including a range of controls; in turn, this suggests that subconscious vocal inflections carry information that legal, political, and textual information do not.
In business the future is not predetermined, and the unexpected often happens. So how should entrepreneurs (and businesses) try to address that future uncertainty? This paper suggests that there are two main options:
1.The often-preferred approach seeks to reduce uncertainty by forecasting and planning, using ‘left-brained’ logic and analysis.
2.The alternative way seeks to live with, and to benefit from, uncertainty by using ideas derived from exploration, effectuation, antifragility and ‘trial and error’.
This paper compares the two approaches and considers their rationales and potential effectiveness. It suggests that forecasting and planning has many drawbacks and is often not the best way to operate in uncertain conditions. Nevertheless, it is often advocated and its thinking seems to have been adopted as the default philosophy for business. Therefore if, as has been suggested, uncertainty is the norm, do we need to advocate adopting a different way of thinking?
Intra coding is an essential part of all video coding algorithms and applications. Additionally, intra coding algorithms are predestined for an efficient still image coding. To overcome limitations in existing intra coding algorithms (such as linear directional extrapolation, only one direction per block, small reference area), we propose non-linear Contour-based Multidirectional Intra Coding. This coding mode is based on four different non-linear contour models, on the connection of intersecting contours and on a boundary recall-based contour model selection algorithm. The different contour models address robustness against outliers for the detected contours and evasive curvature changes. Additionally, the information for the prediction is derived from already reconstructed pixels in neighboring blocks. The achieved coding efficiency is superior to those of related works from the literature. Compared with the closest related work, BD rate gains of 2.16% are achieved on average.
When language users predict upcoming speech, they generate pluralistic expectations, weighted by likelihood (Kuperberg & Jaeger, 2016). Many variables influence the prediction of highly likely sentential outcomes, but less is known regarding variables affecting the prediction of less-likely outcomes. Here we explore how English vocabulary size and self-identification as a native speaker (NS) of English modulate adult bi-/multilinguals’ preactivation of less-likely sentential outcomes in two visual-world experiments. Participants heard transitive sentences containing an agent, action, and theme (The pirate chases the ship) while viewing four referents varying in expectancy by relation to the agent and action. In Experiment 1 (N=70), spoken themes referred to highly expected items (e.g., ship). Results indicate lower skill (smaller vocabulary size) and less confident (not identifying as NS) bi-/multilinguals activate less-likely action-related referents more than their higher skill/confidence peers. In Experiment 2 (N=65), themes were one of two less-likely items (The pirate chases the bone/cat). Results approaching significance indicate an opposite but similar size effect: higher skill/confidence listeners activate less-likely action-related (e.g., bone) referents slightly more than lower skill/confidence listeners. Results across experiments suggest higher skill/confidence participants more flexibly modulate their linguistic predictions per the demands of the task, with similar but not identical patterns emerging when bi-/multilinguals are grouped by self-ascribed NS status versus vocabulary size.
Persisting symptoms after treatment for major depressive disorder (MDD) contribute to ongoing impairment and relapse risk. Whether cognitive behavior therapy (CBT) or antidepressant medications result in different profiles of residual symptoms after treatment is largely unknown.
Three hundred fifteen adults with MDD randomized to treatment with either CBT or antidepressant medication in the Predictors of Remission in Depression to Individual and Combined Treatments (PReDICT) study were analyzed for the frequency of residual symptoms using the Montgomery Asberg Depression Rating Scale (MADRS) item scores at the end of the 12-week treatment period. Separate comparisons were made for treatment responders and non-responders.
Among treatment completers (n = 250) who responded to CBT or antidepressant medication, there were no significant differences in the persistence of residual MADRS symptoms. However, non-responders treated with medication were significantly less likely to endorse suicidal ideation (SI) at week 12 compared with those treated with CBT (non-responders to medication: 0/54, 0%, non-responders to CBT: 8/30, 26.7%; p = .001). Among patients who terminated the trial early (n = 65), residual MADRS item scores did not significantly differ between the CBT- and medication-treated groups.
Depressed adults who respond to CBT or antidepressant medication have similar residual symptom profiles. Antidepressant medications reduce SI, even among patients for whom the medication provides little overall benefit.