Multistability and Perceptual Inference

By Samuel J. Gershman, E. Vul, Josh Tenenbaum. 2012

147 citations recorded by the source corpus.

Subfields: Atomic and Molecular Physics, and Optics, Cognitive Neuroscience

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Atomic and Molecular Physics, and Optics

1. Eric P. Xing
2. Wolfgang Maass
3. Andrew Gordon Wilson
4. Samuel J. Gershman
5. John-Dylan Haynes

In Cognitive Neuroscience

1. Samuel J. Gershman
2. Noah D. Goodman
3. Peter S. Dayan
4. Eric P. Xing
5. Wolfgang Maass
6. T. Griffiths
7. David A. Lagnado
8. David J. Heeger

Citations in the corpus

• What can the brain teach us about building artificial intelligence? (2016, neutral): “, 2014) to garden-path effects in sentence processing (Levy, Reali, & Griffiths, 2009) and perceptual multistability (Gershman et al., 2012; MorenoBote, Knill, & Pouget, 2011).”
• Noise as a Resource for Computation and Learning in Networks of Spiking Neurons (2014, background): “states for ambiguous stimuli (see the discussion in [82] and [12]), imagination and movement planning [83] that are difficult to explain or model with a deterministic model for brain computation.”
• Amortized Inference in Probabilistic Reasoning (2014, background): “Currently one of the most promising hypotheses is that the brain uses some form of sample-based approximate inference, such as Markov chain Monte Carlo (MCMC; Gershman et al., 2012; Lieder et al., 2012) or importance sampling (Shi et al.”
• Formalizing Neurath’s Ship: Approximate Algorithms for Online Causal Learning (2016, background): “At the lower level the brain can average over many values, as in particle filtering (Abbott & Griffiths, 2011), with a whole fleet of Neurath’s ships, or via lots of long chains (Gershman et al., 2012; Lieder et al., 2012).”
• Building Machines that Learn and Think Like People (2018, unknown): “, 2014) to garden-path effects in sentence processing (Levy, Reali, & Griffiths, 2009) and perceptual multistability (Gershman et al., 2012; Moreno-Bote, Knill, & Pouget, 2011).”
• Theory of cortical function (2017, background): “However, in some cases, there are two or more interpretations that are roughly equally good (local minima) for bistable or multistable perceptual phenomena (9, 11, 12).”
• The Human Kernel (2015, background): “We assume the predictions y∗ are samples from the learner’s posterior distribution over possible functions, following results showing that human inferences and judgments resemble posterior samples across a wide range of perceptual and decision-making tasks [27, 28, 29].”