The Experts below are selected from a list of 43422 Experts worldwide ranked by ideXlab platform
Ladan Shams - One of the best experts on this subject based on the ideXlab platform.
-
Multisensory Causal Inference in the Brain
PLoS biology, 2015Co-Authors: Christoph Kayser, Ladan ShamsAbstract:At any given moment, our brain processes multiple inputs from its different sensory modalities (vision, hearing, touch, etc.). In deciphering this array of sensory information, the brain has to solve two problems: (1) which of the inputs originate from the same object and should be integrated and (2) for the sensations originating from the same object, how best to integrate them. Recent behavioural studies suggest that the human brain solves these problems using optimal probabilistic Inference, known as Bayesian Causal Inference. However, how and where the underlying computations are carried out in the brain have remained unknown. By combining neuroimaging-based decoding techniques and computational modelling of behavioural data, a new study now sheds light on how multisensory Causal Inference maps onto specific brain areas. The results suggest that the complexity of neural computations increases along the visual hierarchy and link specific components of the Causal Inference process with specific visual and parietal regions.
-
Causal Inference in perception
Trends in cognitive sciences, 2010Co-Authors: Ladan Shams, Ulrik BeierholmAbstract:Until recently, the question of how the brain performs Causal Inference has been studied primarily in the context of cognitive reasoning. However, this problem is at least equally crucial in perceptual processing. At any given moment, the perceptual system receives multiple sensory signals within and across modalities and, for example, has to determine the source of each of these signals. Recently, a growing number of studies from various fields of cognitive science have started to address this question and have converged to very similar computational models. Therefore, it seems that a common computational strategy, which is highly consistent with a normative model of Causal Inference, is exploited by the perceptual system in a variety of domains.
-
Causal Inference in Multisensory Perception
PLOS ONE, 2007Co-Authors: Ladan ShamsAbstract:Perceptual events derive their significance to an animal from their meaning about the world, that is from the information they carry about their causes. The brain should thus be able to efficiently infer the causes underlying our sensory events. Here we use multisensory cue combination to study Causal Inference in perception. We formulate an ideal-observer model that infers whether two sensory cues originate from the same location and that also estimates their location(s). This model accurately predicts the nonlinear integration of cues by human subjects in two auditory-visual localization tasks. The results show that indeed humans can efficiently infer the Causal structure as well as the location of causes. By combining insights from the study of Causal Inference with the ideal-observer approach to sensory cue combination, we show that the capacity to infer Causal structure is not limited to conscious, high-level cognition; it is also performed continually and effortlessly in perception.
Miguel A. Hernán - One of the best experts on this subject based on the ideXlab platform.
-
Win-win: Reconciling Social Epidemiology and Causal Inference.
American journal of epidemiology, 2019Co-Authors: Sandro Galea, Miguel A. HernánAbstract:Social epidemiology is concerned with the health effects of forces that are "above the skin." Although Causal Inference should be a key goal for social epidemiology, social epidemiology and quantitative Causal Inference have been seemingly at odds over the years. This does not have to be the case and, in fact, both fields stand to gain through a closer engagement of social epidemiology with formal Causal Inference approaches. We discuss the misconceptions that have led to an uneasy relationship between these 2 fields, propose a way forward that illustrates how the 2 areas can come together to inform Causal questions, and discuss the implications of this approach. We argue that quantitative Causal Inference in social epidemiology is an opportunity to do better science that matters, a win-win for both fields.
Charles Poole - One of the best experts on this subject based on the ideXlab platform.
-
Causal Inference from randomized trials in social epidemiology.
Social science & medicine (1982), 2003Co-Authors: Jay S. Kaufman, Sol Kaufman, Charles PooleAbstract:Social epidemiology is the study of relations between social factors and health status in populations. Although recent decades have witnessed a rapid development of this research program in scope and sophistication, Causal Inference has proven to be a persistent dilemma due to the natural assignment of exposure level based on unmeasured attributes of individuals, which may lead to substantial confounding. Some optimism has been expressed about randomized social interventions as a solution to this long-standing inferential problem. We review the Causal Inference problem in social epidemiology, and the potential for Causal Inference in randomized social interventions. Using the example of a currently on-going intervention that randomly assigns families to non-poverty housing, we review the limitations to Causal Inference even under experimental conditions and explain which Causal effects become identifiable. We note the benefit of using the randomized trial as a conceptual model, even for design and interpretation of observational studies in social epidemiology.
Sandro Galea - One of the best experts on this subject based on the ideXlab platform.
-
Win-win: Reconciling Social Epidemiology and Causal Inference.
American journal of epidemiology, 2019Co-Authors: Sandro Galea, Miguel A. HernánAbstract:Social epidemiology is concerned with the health effects of forces that are "above the skin." Although Causal Inference should be a key goal for social epidemiology, social epidemiology and quantitative Causal Inference have been seemingly at odds over the years. This does not have to be the case and, in fact, both fields stand to gain through a closer engagement of social epidemiology with formal Causal Inference approaches. We discuss the misconceptions that have led to an uneasy relationship between these 2 fields, propose a way forward that illustrates how the 2 areas can come together to inform Causal questions, and discuss the implications of this approach. We argue that quantitative Causal Inference in social epidemiology is an opportunity to do better science that matters, a win-win for both fields.
Jay S. Kaufman - One of the best experts on this subject based on the ideXlab platform.
-
Causal Inference from randomized trials in social epidemiology.
Social science & medicine (1982), 2003Co-Authors: Jay S. Kaufman, Sol Kaufman, Charles PooleAbstract:Social epidemiology is the study of relations between social factors and health status in populations. Although recent decades have witnessed a rapid development of this research program in scope and sophistication, Causal Inference has proven to be a persistent dilemma due to the natural assignment of exposure level based on unmeasured attributes of individuals, which may lead to substantial confounding. Some optimism has been expressed about randomized social interventions as a solution to this long-standing inferential problem. We review the Causal Inference problem in social epidemiology, and the potential for Causal Inference in randomized social interventions. Using the example of a currently on-going intervention that randomly assigns families to non-poverty housing, we review the limitations to Causal Inference even under experimental conditions and explain which Causal effects become identifiable. We note the benefit of using the randomized trial as a conceptual model, even for design and interpretation of observational studies in social epidemiology.