Webster, M. A. (2015). Visual Adaptation. Annual Review of Vision Science, 1(1), 547–567.

Rieke, F., & Rudd, M. E. (2009). The Challenges Natural Images Pose for Visual Adaptation. Neuron, 64(5), 605–616. doi:10.1016/j.neuron.2009.11.028

Kohn, A. (2007). Visual adaptation: physiology, mechanisms, and functional benefits. Journal of Neurophysiology, 97(5), 3155–3164. doi:10.1152/jn.00086.2007

Schwartz, O., Hsu, A., & Dayan, P. (2007). Space and time in visual context. Nature Reviews Neuroscience, 8(7), 522–535. doi:10.1038/nrn2155

Clifford, C. W. G. (2002). Perceptual adaptation: motion parallels orientation. Trends in cognitive sciences, 6(3), 136–143.



Carandini, M., Demb, J. B., Mante, V., Tolhurst, D. J., Dan, Y., Olshausen, B. A., Gallant, J. L., et al. (2005). Do we know what the early visual system does? Journal of Neuroscience, 25(46), 10577–10597. doi:10.1523/JNEUROSCI.3726-05.200

Bayesian decision making

Pouget et al. (2016). Confidence and certainty: distinct probabilistic quantities for different goals. Nat Neuro.

Ma, W. J., & Jazayeri, M. (2014). Neural coding of uncertainty and probability. Annual review of neuroscience, 37, 205-220.

Kording, K. P. (2014). Bayesian statistics: relevant for the brain? Current Opinion in Neurobiology, 25, 130–133. doi:10.1016/j.conb.2014.01.003

Pouget, A., Beck, J. M., Ma, W. J., & Latham, P. E. (2013). Probabilistic brains: knowns and unknowns. Nature Publishing Group, 16(9), 1170–1178. doi:10.1038/nn.3495

Fetsch, C. R., DeAngelis, G. C., & Angelaki, D. E. (2013). Bridging the gap between theories of sensory cue integration and the physiology of multisensory neurons. Nature Reviews Neuroscience, 14(6), 429–442. doi:10.1038/nrn3503

Ma, W. J. (2012). Organizing probabilistic models of perception. Trends in Cognitive Sciences, 16(10), 511–518. doi:10.1016/j.tics.2012.08.010

Maloney, L. T., & Zhang, H. (2010). Decision-theoretic models of visual perception and action. Vision Research, 50(23), 2362–2374.

Gold, J. I., & Shadlen, M. N. (2007). The neural basis of decision making. Annu. Rev. Neurosci., 30, 535-574.

Knill, D. C., & Pouget, A. (2004). The Bayesian brain: the role of uncertainty in neural coding and computation, 27(12), 712–719. doi:10.1016/j.tins.2004.10.007


Carandini, M., & Heeger, D. J. (2012). Normalization as a canonical neural computation. Nature Reviews Neuroscience, 13(1), 51–62. doi:10.1038/nrn3136

Reynolds, J. H., & Heeger, D. J. (2009). The normalization model of attention. Neuron, 61(2), 168–185.

Lee, J., & Maunsell, J. H. R. (2009). A normalization model of attentional modulation of single unit responses. PLoS ONE, 4(2), e4651.


Livingstone, M., & Hubel, D. (1988). Segregation of form, color, movement, and depth: anatomy, physiology, and perception. Science, 240(4853), 740–749.


Bradley, D. C., & Goyal, M. S. (2008). Velocity computation in the primate visual system. Nature Reviews Neuroscience, 9(9), 686–695. I think a very well-written review.

Born, R. T., & Bradley, D. C. (2005). Structure and function of visual area MT. Annual Review of Neuroscience, 28, 157–189. doi:10.1146/annurev.neuro.26.041002.131052

Motion position

Whitney, D. (2002). The influence of visual motion on perceived position. Trends in cognitive sciences, 6(5), 211–216.


Button, K. S., Ioannidis, J. P. A., Mokrysz, C., Nosek, B. A., Flint, J., Robinson, E. S. J., & Munafò, M. R. (2013). Power failure: why small sample size undermines the reliability of neuroscience. Nature Reviews Neuroscience. doi:10.1038/nrn3475


Matthews, W. J., & Meck, W. H. (2014). Time perception: the bad news and the good. Wiley Interdisciplinary Reviews: Cognitive Science, 5(4), 429–446. doi:10.1002/wcs.1298

Holcombe, A. O. (2009). Seeing slow and seeing fast: two limits on perception. Trends in cognitive sciences, 13(5), 216–221. doi:10.1016/j.tics.2009.02.005

Ivry, R. B., & Schlerf, J. E. (2008). Dedicated and intrinsic models of time perception. Trends in Cognitive Sciences, 12(7), 273–280. doi:10.1016/j.tics.2008.04.002