Causal inference in perception.

By Ladan Shams, Ulrik R Beierholm. 2010

389 citations recorded by the source corpus.

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.

Subfields: Cognitive Neuroscience, Experimental and Cognitive Psychology

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In Experimental and Cognitive Psychology

Citations in the corpus

Assessing the Role of the ‘Unity Assumption’ on Multisensory Integration: A Review (2017, methodology): “…and Spence (2007, 2008; Vatakis et al., 2008) were revisiting the unity effect in human behavior, other researchers were examining this issue using the computational modeling approach based on Bayes’ rule (e.g., Ernst, 2007; Körding et al., 2007; see Shams and Beierholm, 2010, 2011, for reviews).”
Cross-correlation between auditory and visual signals promotes multisensory integration. (2013, background): “Solving the crossmodal correspondence problem, that is, inferring which signals have a common underlying cause, and hence provide redundant information that should be integrated, is a crucial task for a perceptual system trying to make sense of multiple sensory inputs (Ernst and Bülthoff, 2004; Sham”
Sensory reliability shapes perceptual inference via two mechanisms. (2015, background): “…forcedfusion models that enforce signals to be integrated in a mandatory fashion (Ernst & Banks, 2002) but requires Bayesian Causal Inference (CI) that explicitly models the potential causal structures that could have generated the sensory signals (Koerding et al., 2007; Shams & Beierholm, 2010).”
How prior expectations shape multisensory perception (2016, methodology): “Based on models of Bayesian Causal Inference (Körding et al., 2007; Magnotti et al., 2013; Shams and Beierholm, 2010), we hypothesized that participants would form stronger congruency expectations (i.e. a higher common source prior) in congruent than incongruent contexts.”
The Brain’s Tendency to Bind Audiovisual Signals Is Stable but Not General (2016, background): “As we experience the surrounding world, our brains effortlessly process sights and sounds, inferring which visual sources produced which auditory signals (Parise, Spence, & Ernst, 2012; Shams & Beierholm, 2010; Shams, Ma, & Beierholm, 2005; Stein, 2012).”
Distinct Computational Principles Govern Multisensory Integration in Primary Sensory and Association Cortices. (2016, neutral): “Current Biology 26, 509 disparity) [4, 10, 11].”
The Curious Incident of Attention in Multisensory Integration: Bottom-up vs. Top-down (2016, positive): “In the following, I will explain how attention may influence this latter representational integration from the perspective of Bayesian causal inference (Körding et al., 2007; Rohe and Noppeney, 2015a, b; Shams and Beierholm, 2010).”
When correlation implies causation in multisensory integration. (2012, neutral): “Inferring which signals have a common underlying cause, and hence should be integrated, represents a primary challenge for a perceptual system dealing with multiple sensory inputs [1–3].”
Multisensory Causal Inference in the Brain (2015, background): “In addition, the brain needs to solve the “ causal inference problem ” [5 – 7]: it has to decide which sensory inputs likely originate from the same object and hence provide complementary evidence about this and which inputs originate from distinct objects and hence should be processed separately.”
Intersensory binding across space and time: A tutorial review (2013, background): “…noise in the environment or in the brain, whereas the prior captures the statistics of the events in the environment (Alais & Burr, 2004b; Battaglia, Jacobs & Aslin, 2003; Burr & Alais, 2006; Ernst & Banks, 2002; Sato et al., 2007; Shams & Beierholm, 2010; Shi et al., 2010; Witten & Knudsen, 2005).”
Rhythmic TMS as a Feasible Tool to Uncover the Oscillatory Signatures of Audiovisual Integration (2023, background): “First, they must ascertain whether such inputs share a common source [3,4].”