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Episodes, events, and models.

ABSTRACT: We describe a novel computational theory of how individuals segment perceptual information into representations of events. The theory is inspired by recent findings in the cognitive science and cognitive neuroscience of event segmentation. In line with recent theories, it holds that online event segmentation is automatic, and that event segmentation yields mental simulations of events. But it posits two novel principles as well: first, discrete episodic markers track perceptual and conceptual changes, and can be retrieved to construct event models. Second, the process of retrieving and reconstructing those episodic markers is constrained and prioritized. We describe a computational implementation of the theory, as well as a robotic extension of the theory that demonstrates the processes of online event segmentation and event model construction. The theory is the first unified computational account of event segmentation and temporal inference. We conclude by demonstrating now neuroimaging data can constrain and inspire the construction of process-level theories of human reasoning.

SUBMITTER: Khemlani SS

PROVIDER: S-EPMC4621428 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Episodes, events, and models.

Khemlani Sangeet S SS Harrison Anthony M AM Trafton J Gregory JG

Frontiers in human neuroscience 20151027

We describe a novel computational theory of how individuals segment perceptual information into representations of events. The theory is inspired by recent findings in the cognitive science and cognitive neuroscience of event segmentation. In line with recent theories, it holds that online event segmentation is automatic, and that event segmentation yields mental simulations of events. But it posits two novel principles as well: first, discrete episodic markers track perceptual and conceptual ch ...[more]

PMID: 26578934

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Project description:BackgroundRecurrent events data analysis is common in biomedicine. Literature review indicates that most statistical models used for such data are often based on time to the first event or consider events within a subject as independent. Even when taking into account the non-independence of recurrent events within subjects, data analyses are mostly done with continuous risk interval models, which may not be appropriate for treatments with sustained effects (e.g., drug treatments of malaria patients). Furthermore, results can be biased in cases of a confounding factor implying different risk exposure, e.g. in malaria transmission: if subjects are located at zones showing different environmental factors implying different risk exposures.MethodsThis work aimed to compare four different approaches by analysing recurrent malaria episodes from a clinical trial assessing the effectiveness of three malaria treatments [artesunate + amodiaquine (AS + AQ), artesunate + sulphadoxine-pyrimethamine (AS + SP) or artemether-lumefantrine (AL)], with continuous and discontinuous risk intervals: Andersen-Gill counting process (AG-CP), Prentice-Williams-Peterson counting process (PWP-CP), a shared gamma frailty model, and Generalized Estimating Equations model (GEE) using Poisson distribution. Simulations were also made to analyse the impact of the addition of a confounding factor on malaria recurrent episodes.ResultsUsing the discontinuous interval analysis, AG-CP and Shared gamma frailty models provided similar estimations of treatment effect on malaria recurrent episodes when adjusted on age category. The patients had significant decreased risk of recurrent malaria episodes when treated with AS + AQ or AS + SP arms compared to AL arm; Relative Risks were: 0.75 (95% CI (Confidence Interval): 0.62-0.89), 0.74 (95% CI: 0.62-0.88) respectively for AG-CP model and 0.76 (95% CI: 0.64-0.89), 0.74 (95% CI: 0.62-0.87) for the Shared gamma frailty model.With both discontinuous and continuous risk intervals analysis, GEE Poisson distribution models failed to detect the effect of AS + AQ arm compared to AL arm when adjusted for age category. The discontinuous risk interval analysis was found to be the more appropriate approach.ConclusionRepeated event in infectious diseases such as malaria can be analysed with appropriate existing models that account for the correlation between multiple events within subjects with common statistical software packages, after properly setting up the data structures.

Project description:BackgroundEmbolic events are a major cause of morbidity and mortality in patients with infective endocarditis. We analyzed the database of the prospective cohort study SEI in order to identify factors associated with the occurrence of embolic events and to develop a scoring system for the assessment of the risk of embolism.MethodsWe retrospectively analyzed 1456 episodes of infective endocarditis from the multicenter study SEI. Predictors of embolism were identified. Risk factors identified at multivariate analysis as predictive of embolism in left-sided endocarditis, were used for the development of a risk score: 1 point was assigned to each risk factor (total risk score range: minimum 0 points; maximum 2 points). Three categories were defined by the score: low (0 points), intermediate (1 point), or high risk (2 points); the probability of embolic events per risk category was calculated for each day on treatment (day 0 through day 30).ResultsThere were 499 episodes of infective endocarditis (34%) that were complicated by ≥ 1 embolic event. Most embolic events occurred early in the clinical course (first week of therapy: 15.5 episodes per 1000 patient days; second week: 3.7 episodes per 1000 patient days). In the total cohort, the factors associated with the occurrence of embolism at multivariate analysis were prosthetic valve localization (odds ratio, 1.84), right-sided endocarditis (odds ratio, 3.93), Staphylococcus aureus etiology (odds ratio, 2.23) and vegetation size ≥ 13 mm (odds ratio, 1.86). In left-sided endocarditis, Staphylococcus aureus etiology (odds ratio, 2.1) and vegetation size ≥ 13 mm (odds ratio, 2.1) were independently associated with embolic events; the 30-day cumulative incidence of embolism varied with risk score category (low risk, 12%; intermediate risk, 25%; high risk, 38%; p < 0.001).ConclusionsStaphylococcus aureus etiology and vegetation size are associated with an increased risk of embolism. In left-sided endocarditis, a simple scoring system, which combines etiology and vegetation size with time on antimicrobials, might contribute to a better assessment of the risk of embolism, and to a more individualized analysis of indications and contraindications for early surgery.

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Episodes, events, and models.

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Episodes, events, and models.

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