Visual stimuli in the real world are often highly predictable on the basis of spatial context, transition probabilities and prior information from previous glances.
Normative Bayesian models dictate how expectations (that is, the prior) should be combined with incoming sensory evidence (that is, the likelihood) for optimal perceptual inference.
Prior information about upcoming percepts modulates baseline neural activity in sensory neurons encoding the expected stimulus and in decision-related neurons that integrate the sensory evidence.
Predictive coding is a neurobiologically plausible computational framework that seeks to explain how top-down priors and bottom-up inputs are combined.
Expectation and attention are often entangled but they are conceptually distinct. Expectation relates to the probability of a sensory event, whereas selective attention pertains to the relevance of a sensory event. A stimulus can be probable or improbable, irrespective of its behavioural relevance.
During decision making, expectation can alter the gain of information processing towards stimuli that are expected to occur.
Sensory signals are highly structured in both space and time. These structural regularities in visual information allow expectations to form about future stimulation, thereby facilitating decisions about visual features and objects. Here, we discuss how expectation modulates neural signals and behaviour in humans and other primates. We consider how expectations bias visual activity before a stimulus occurs, and how neural signals elicited by expected and unexpected stimuli differ. We discuss how expectations may influence decision signals at the computational level. Finally, we consider the relationship between visual expectation and related concepts, such as attention and adaptation.
Subscribe to Journal
Get full journal access for 1 year
only $22.08 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Rent or Buy article
Get time limited or full article access on ReadCube.
All prices are NET prices.
Gold, J. I. & Shadlen, M. N. The neural basis of decision making. Annu. Rev. Neurosci. 30, 535–574 (2007).
Heekeren, H. R., Marrett, S. & Ungerleider, L. G. The neural systems that mediate human perceptual decision making. Nature Rev. Neurosci. 9, 467–479 (2008).
Nienborg, H., Cohen, M. R. & Cumming, B. G. Decision-related activity in sensory neurons: correlations among neurons and with behavior. Annu. Rev. Neurosci. 35, 463–483 (2012).
Bar, M. Visual objects in context. Nature Rev. Neurosci. 5, 617–629 (2004).
Oliva, A. & Torralba, A. The role of context in object recognition. Trends Cogn. Sci. 11, 520–527 (2007).
Biederman, I. Perceiving real-world scenes. Science 177, 77–80 (1972).
Biederman, I., Mezzanotte, R. J. & Rabinowitz, J. C. Scene perception: detecting and judging objects undergoing relational violations. Cogn. Psychol. 14, 143–177 (1982).
Fiser, J. & Aslin, R. N. Statistical learning of new visual feature combinations by infants. Proc. Natl Acad. Sci. USA 99, 15822–15826 (2002).
Saffran, J. R., Aslin, R. N. & Newport, E. L. Statistical learning by 8-month-old infants. Science 274, 1926–1928 (1996). This study shows that young infants can quickly segment words from speech by picking up on the statistical regularities in the speech sound.
Turk-Browne, N. B., Junge, J. A. & Scholl, B. J. The automaticity of visual statistical learning. J. Exp. Psychol. Gen. 134, 552–564 (2005).
Turk-Browne, N. B., Scholl, B. J., Johnson, M. K. & Chun, M. M. Implicit perceptual anticipation triggered by statistical learning. J. Neurosci. 30, 11177–11187 (2010).
Chun, M. M. & Jiang, Y. Contextual cueing: implicit learning and memory of visual context guides spatial attention. Cogn. Psychol. 36, 28–71 (1998).
Meyer, D. E. & Schvaneveldt, R. W. Facilitation in recognizing pairs of words: evidence of a dependence between retrieval operations. J. Exp. Psychol. 90, 227–234 (1971).
Gregory, R. L. Perceptions as hypotheses. Phil. Trans. R. Soc. Lond. B 290, 181–197 (1980).
Green, D. M. & Swets, J. A. Signal Detection Theory and Psychophysics (Wiley, 1966).
Wald, A. & Wolfowitz, J. Bayes solutions of sequential decision problems. Proc. Natl Acad. Sci. USA 35, 99–102 (1949).
Edwards, W. Optimal strategies for seeking information: models for statistics, choice reaction times, and human information processing. J. Math. Psychol. 2, 312–329 (1965).
Ratcliff, R. & McKoon, G. The diffusion decision model: theory and data for two-choice decision tasks. Neural Comput. 20, 873–922 (2008).
Mulder, M. J., Wagenmakers, E. J., Ratcliff, R., Boekel, W. & Forstmann, B. U. Bias in the brain: a diffusion model analysis of prior probability and potential payoff. J. Neurosci. 32, 2335–2343 (2012).
Ratcliff, R. & Smith, P. L. A comparison of sequential sampling models for two-choice reaction time. Psychol. Rev. 111, 333–367 (2004).
Ratcliff, R. Theoretical interpretations of the speed and accuracy of positive and negative responses. Psychol. Rev. 92, 212–225 (1985).
Hanes, D. P. & Schall, J. D. Neural control of voluntary movement initiation. Science 274, 427–430 (1996).
Hanks, T. D., Mazurek, M. E., Kiani, R., Hopp, E. & Shadlen, M. N. Elapsed decision time affects the weighting of prior probability in a perceptual decision task. J. Neurosci. 31, 6339–6352 (2011).
Basso, M. A. & Wurtz, R. H. Modulation of neuronal activity in superior colliculus by changes in target probability. J. Neurosci. 18, 7519–7534 (1998).
Donner, T. H., Siegel, M., Fries, P. & Engel, A. K. Buildup of choice-predictive activity in human motor cortex during perceptual decision making. Curr. Biol. 19, 1581–1585 (2009).
de Lange, F. P., Rahnev, D. A., Donner, T. H. & Lau, H. Prestimulus oscillatory activity over motor cortex reflects perceptual expectations. J. Neurosci. 33, 1400–1410 (2013).
Erickson, C. A. & Desimone, R. Responses of macaque perirhinal neurons during and after visual stimulus association learning. J. Neurosci. 19, 10404–10416 (1999).
Sakai, K. & Miyashita, Y. Neural organization for the long-term memory of paired associates. Nature 354, 152–155 (1991).
Schlack, A. & Albright, T. D. Remembering visual motion: neural correlates of associative plasticity and motion recall in cortical area MT. Neuron 53, 881–890 (2007). This single-unit recording study in monkeys shows that when a monkey expects visual motion in a certain direction, there is increased spiking activity in the relevant direction-selective cells in the motion-sensitive area MT.
Kok, P., Brouwer, G. J., van Gerven, M. A. & de Lange, F. P. Prior expectations bias sensory representations in visual cortex. J. Neurosci. 33, 16275–16284 (2013).
Albright, T. D. On the perception of probable things: neural substrates of associative memory, imagery, and perception. Neuron 74, 227–245 (2012).
Puri, A. M., Wojciulik, E. & Ranganath, C. Category expectation modulates baseline and stimulus-evoked activity in human inferotemporal cortex. Brain Res. 1301, 89–99 (2009).
Esterman, M. & Yantis, S. Perceptual expectation evokes category-selective cortical activity. Cereb. Cortex 20, 1245–1253 (2010).
Bar, M. et al. Cortical mechanisms specific to explicit visual object recognition. Neuron 29, 529–535 (2001).
Hesselmann, G., Kell, C. A., Eger, E. & Kleinschmidt, A. Spontaneous local variations in ongoing neural activity bias perceptual decisions. Proc. Natl Acad. Sci. USA 105, 10984–10989 (2008).
Hesselmann, G., Kell, C. A. & Kleinschmidt, A. Ongoing activity fluctuations in hMT+ bias the perception of coherent visual motion. J. Neurosci. 28, 14481–14485 (2008).
Kok, P., Failing, M. F. & de Lange, F. P. Prior expectations evoke stimulus templates in the primary visual cortex. J. Cogn. Neurosci. 26, 1546–1554 (2014).
Summerfield, C. & Koechlin, E. A neural representation of prior information during perceptual inference. Neuron 59, 336–347 (2008).
Huk, A. C. & Shadlen, M. N. Neural activity in macaque parietal cortex reflects temporal integration of visual motion signals during perceptual decision making. J. Neurosci. 25, 10420–10436 (2005).
Bogacz, R., Brown, E., Moehlis, J., Holmes, P. & Cohen, J. D. The physics of optimal decision making: a formal analysis of models of performance in two-alternative forced-choice tasks. Psychol. Rev. 113, 700–765 (2006).
Todorovic, A. & de Lange, F. P. Repetition suppression and expectation suppression are dissociable in time in early auditory evoked fields. J. Neurosci. 32, 13389–13395 (2012).
Näätänen, R., Paavilainen, P., Alho, K., Reinikainen, K. & Sams, M. Do event-related potentials reveal the mechanism of the auditory sensory memory in the human brain? Neurosci. Lett. 98, 217–221 (1989). In this study, loud tones were interspersed by rare softer tones. By showing a larger brain response to the unexpected softer sound, this study demonstrates that the amplitude of neural responses to sensory stimuli can be strongly dictated by the expectation of a sensory event, rather than its physical properties.
Strange, B. A., Duggins, A., Penny, W., Dolan, R. J. & Friston, K. J. Information theory, novelty and hippocampal responses: unpredicted or unpredictable? Neural Netw. 18, 225–230 (2005).
den Ouden, H. E., Friston, K. J., Daw, N. D., McIntosh, A. R. & Stephan, K. E. A dual role for prediction error in associative learning. Cereb. Cortex 19, 1175–1185 (2009).
Egner, T., Monti, J. M. & Summerfield, C. Expectation and surprise determine neural population responses in the ventral visual stream. J. Neurosci. 30, 16601–16608 (2010).
Bekinschtein, T. A. et al. Neural signature of the conscious processing of auditory regularities. Proc. Natl Acad. Sci. USA 106, 1672–1677 (2009).
Summerfield, C., Trittschuh, E. H., Monti, J. M., Mesulam, M. M. & Egner, T. Neural repetition suppression reflects fulfilled perceptual expectations. Nature Neurosci. 11, 1004–1006 (2008). This study was the first to propose a new interpretation of repetition suppression, arguing that stimulus expectation strongly modulates the neural adaptation for repeated stimuli.
Meyer, T. & Olson, C. R. Statistical learning of visual transitions in monkey inferotemporal cortex. Proc. Natl Acad. Sci. USA 108, 19401–19406 (2011). This single-unit recording study provides evidence for the notion that neurons in the visual system make experience-based predictions and respond more strongly when these predictions are violated.
Tanaka, K. Inferotemporal cortex and object vision. Annu. Rev. Neurosci. 19, 109–139 (1996).
Angelucci, A. et al. Circuits for local and global signal integration in primary visual cortex. J. Neurosci. 22, 8633–8646 (2002).
Grossberg, S. Adaptive Resonance Theory: how a brain learns to consciously attend, learn, and recognize a changing world. Neural Netw. 37, 1–47 (2013).
Dayan, P., Hinton, G. E. & Neal, R. M. The Helmholtz machine. Neural Comput. 7, 889–904 (1995).
Kersten, D., Mamassian, P. & Yuille, A. Object perception as Bayesian inference. Annu. Rev. Psychol. 55, 271–304 (2004).
Mumford, D. On the computational architecture of the neocortex. II. The role of cortico-cortical loops. Biol. Cybern. 66, 241–251 (1992).
Rao, R. P. & Ballard, D. H. Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects. Nature Neurosci. 2, 79–87 (1999).
Kalman, R. E. A new approach to linear filtering and prediction problems. J. Fluids Eng. 82, 35–45 (1960).
Hubel, D. H. & Wiesel, T. N. Receptive fields and functional architecture in two nonstriate visual areas (18 and 19) of the cat. J. Neurophysiol. 28, 229–289 (1965).
von der Heydt, R., Peterhans, E. & Baumgartner, G. Illusory contours and cortical neuron responses. Science 224, 1260–1262 (1984).
Lee, T. S. & Nguyen, M. Dynamics of subjective contour formation in the early visual cortex. Proc. Natl Acad. Sci. USA 98, 1907–1911 (2001).
Kok, P. & de Lange, F. P. Shape perception simultaneously up- and down-regulates neural activity in the primary visual cortex. Curr. Biol. 24, 1531–1535 (2014).
Smith, F. W. & Muckli, L. Nonstimulated early visual areas carry information about surrounding context. Proc. Natl Acad. Sci. USA 107, 20099–20103 (2010). This study shows that even in the absence of any bottom-up input, V1 contains information about the global scene, suggesting that the surrounding context may bias activity patterns in V1 to reflect expected input.
Alink, A., Schwiedrzik, C. M., Kohler, A., Singer, W. & Muckli, L. Stimulus predictability reduces responses in primary visual cortex. J. Neurosci. 30, 2960–2966 (2010).
Muckli, L., Kohler, A., Kriegeskorte, N. & Singer, W. Primary visual cortex activity along the apparent-motion trace reflects illusory perception. PLoS Biol. 3, e265 (2005).
Murray, S. O., Kersten, D., Olshausen, B. A., Schrater, P. & Woods, D. L. Shape perception reduces activity in human primary visual cortex. Proc. Natl Acad. Sci. USA 99, 15164–15169 (2002).
Harrison, L. M., Stephan, K. E., Rees, G. & Friston, K. J. Extra-classical receptive field effects measured in striate cortex with fMRI. Neuroimage 34, 1199–1208 (2007).
Sandell, J. H. & Schiller, P. H. Effect of cooling area 18 on striate cortex cells in the squirrel monkey. J. Neurophysiol. 48, 38–48 (1982).
Hupe, J. M. et al. Cortical feedback improves discrimination between figure and background by V1, V2 and V3 neurons. Nature 394, 784–787 (1998).
Vetter, P., Grosbras, M. H. & Muckli, L. TMS over V5 disrupts motion prediction. Cereb. Cortex http://dx.doi.org/10.1093/cercor/bht297 (2013).
Summerfield, C. & Egner, T. Expectation (and attention) in visual cognition. Trends Cogn. Sci. 13, 403–409 (2009).
Zhao, J., Al-Aidroos, N. & Turk-Browne, N. B. Attention is spontaneously biased toward regularities. Psychol. Sci. 24, 667–677 (2013).
Vo, M. L. & Henderson, J. M. The time course of initial scene processing for eye movement guidance in natural scene search. J. Vis. 10, 1–13 (2010).
Desimone, R. & Duncan, J. Neural mechanisms of selective visual attention. Annu. Rev. Neurosci. 18, 193–222 (1995).
Posner, M. I., Snyder, C. R. & Davidson, B. J. Attention and the detection of signals. J. Exp. Psychol. 109, 160–174 (1980).
Naatanen, R. The role of attention in auditory information processing as revealed by event-related potentials and other brain measure of cognitive function. Behav. Brain Sci. 13, 201–288 (1990).
Nakano, T., Homae, F., Watanabe, H. & Taga, G. Anticipatory cortical activation precedes auditory events in sleeping infants. PloS One 3, e3912 (2008).
Yaron, A., Hershenhoren, I. & Nelken, I. Sensitivity to complex statistical regularities in rat auditory cortex. Neuron 76, 603–615 (2012).
Feldman, H. & Friston, K. J. Attention, uncertainty, and free-energy. Front. Hum. Neurosci. 4, 215 (2010).
Friston, K. The free-energy principle: a unified brain theory? Nature Rev. Neurosci. 11, 127–138 (2010).
Carrasco, M. Visual attention: the past 25 years. Vision Res. 51, 1484–1525 (2011).
Wyart, V., Nobre, A. C. & Summerfield, C. Dissociable prior influences of signal probability and relevance on visual contrast sensitivity. Proc. Natl Acad. Sci. USA 109, 3593–3598 (2012).
Rao, R. P. & Ballard, D. H. in Neurobiology of Attention (eds Itti, L., Rees, G. & Tsotsos, J. K.) 553–561 (Elsevier Academic Press., 2005).
Yu, A. J. & Dayan, P. in Advances in Neural Information Processing Systems (eds Saul, L. K. & Bottou, L.) 1577–1584 (MIT Press, 2005).
Rao, R. P. Bayesian inference and attentional modulation in the visual cortex. Neuroreport 16, 1843–1848 (2005).
Spratling, M. W. Reconciling predictive coding and biased competition models of cortical function. Front. Comput. Neurosci. 2, 4 (2008). This mathematical modelling study shows that predictive-coding and biased-competition models of cortical processing, which are often assumed to be incompatible, are formally equivalent under some simplifying assumptions.
Kok, P., Rahnev, D., Jehee, J. F., Lau, H. C. & de Lange, F. P. Attention reverses the effect of prediction in silencing sensory signals. Cereb. Cortex 22, 2197–2206 (2012).
Jiang, J., Summerfield, C. & Egner, T. Attention sharpens the distinction between expected and unexpected percepts in the visual brain. J. Neurosci. 33, 18438–18447 (2013).
Dong, D. & Atick, J. Statistics of natural time-varying images. Comput. Neural Sys. 6, 345–358 (1995).
Grill-Spector, K., Henson, R. & Martin, A. Repetition and the brain: neural models of stimulus-specific effects. Trends Cogn. Sci. 10, 14–23 (2006).
Carandini, M. & Ferster, D. A tonic hyperpolarization underlying contrast adaptation in cat visual cortex. Science 276, 949–952 (1997).
den Ouden, H. E., Kok, P. & de Lange, F. P. How prediction errors shape perception, attention, and motivation. Front. Psychol. 3, 548 (2012).
Kovacs, G., Iffland, L., Vidnyanszky, Z. & Greenlee, M. W. Stimulus repetition probability effects on repetition suppression are position invariant for faces. Neuroimage 60, 2128–2135 (2012).
Larsson, J. & Smith, A. T. FMRI repetition suppression: neuronal adaptation or stimulus expectation? Cereb. Cortex 22, 567–576 (2012).
Summerfield, C., Wyart, V., Johnen, V. M. & de Gardelle, V. Human scalp electroencephalography reveals that repetition suppression varies with expectation. Front. Hum. Neurosci. 5, 67 (2011).
Stefanics, G., Kimura, M. & Czigler, I. Visual mismatch negativity reveals automatic detection of sequential regularity violation. Front. Hum. Neurosci. 5, 46 (2011).
Todorovic, A., van Ede, F., Maris, E. & de Lange, F. P. Prior expectation mediates neural adaptation to repeated sounds in the auditory cortex: an MEG study. J. Neurosci. 31, 9118–9123 (2011).
Andics, A., Gal, V., Vicsi, K., Rudas, G. & Vidnyanszky, Z. FMRI repetition suppression for voices is modulated by stimulus expectations. Neuroimage 69, 277–283 (2013).
Valentini, E., Torta, D. M., Mouraux, A. & Iannetti, G. D. Dishabituation of laser-evoked EEG responses: dissecting the effect of certain and uncertain changes in stimulus modality. J. Cogn. Neurosci. 23, 2822–2837 (2011).
Kaliukhovich, D. A. & Vogels, R. Stimulus repetition probability does not affect repetition suppression in macaque inferior temporal cortex. Cereb. Cortex 21, 1547–1558 (2011).
Kovacs, G., Kaiser, D., Kaliukhovich, D. A., Vidnyanszky, Z. & Vogels, R. Repetition probability does not affect fMRI repetition suppression for objects. J. Neurosci. 33, 9805–9812 (2013).
Mayrhauser, L., Bergmann, J., Crone, J., & Kronbichler, M. Neural repetition suppression: evidence for perceptual expectation in object-selective regions. Front. Hum. Neurosci. 8, 225 (2014).
de Gardelle, V., Waszczuk, M., Egner, T. & Summerfield, C. Concurrent repetition enhancement and suppression responses in extrastriate visual cortex. Cereb. Cortex 23, 2235–2244 (2012).
de Gardelle, V., Stokes, M., Johnen, V. M., Wyart, V. & Summerfield, C. Overlapping multivoxel patterns for two levels of visual expectation. Front. Hum. Neurosci. 7, 158 (2013).
Miller, E. K. & Desimone, R. Parallel neuronal mechanisms for short-term memory. Science 263, 520–522 (1994).
Friston, K. A theory of cortical responses. Phil. Trans. R. Soc. 360, 815–836 (2005).
Wacongne, C., Changeux, J. P. & Dehaene, S. A neuronal model of predictive coding accounting for the mismatch negativity. J. Neurosci. 32, 3665–3678 (2012).
Bitzer, S., Park, H., Blankenburg, F. & Kiebel, S. J. Perceptual decision making: drift-diffusion model is equivalent to a Bayesian model. Front. Hum. Neurosci. 8, 102 (2014).
Usher, M. & McClelland, J. L. The time course of perceptual choice: the leaky, competing accumulator model. Psychol. Rev. 108, 550–592 (2001).
Wong, K. F. & Wang, X. J. A recurrent network mechanism of time integration in perceptual decisions. J. Neurosci. 26, 1314–1328 (2006).
Tsetsos, K., Usher, M. & McClelland, J. L. Testing multi-alternative decision models with non-stationary evidence. Front. Neurosci. 5, 63 (2011).
Bollimunta, A., Totten, D. & Ditterich, J. Neural dynamics of choice: single-trial analysis of decision-related activity in parietal cortex. J. Neurosci. 32, 12684–12701 (2012).
Ress, D. & Heeger, D. J. Neuronal correlates of perception in early visual cortex. Nature Neurosci. 6, 414–420 (2003).
Summerfield, C., Egner, T., Mangels, J. & Hirsch, J. Mistaking a house for a face: neural correlates of misperception in healthy humans. Cereb. Cortex 16, 500–508 (2006).
Serences, J. T. & Boynton, G. M. The representation of behavioral choice for motion in human visual cortex. J. Neurosci. 27, 12893–12899 (2007).
Nienborg, H. & Cumming, B. G. Decision-related activity in sensory neurons reflects more than a neuron's causal effect. Nature 459, 89–92 (2009). This is the first study to show that sensory circuits do not passively relay stimulus information but increasingly reflect the perceptual belief of the agent over the time course of a trial, suggesting a decision-related, top-down alteration of sensory evidence.
Newsome, W. T., Britten, K. H. & Movshon, J. A. Neuronal correlates of a perceptual decision. Nature 341, 52–54 (1989).
Britten, K. H., Shadlen, M. N., Newsome, W. T. & Movshon, J. A. The analysis of visual motion: a comparison of neuronal and psychophysical performance. J. Neurosci. 12, 4745–4765 (1992).
Shadlen, M. N., Britten, K. H., Newsome, W. T. & Movshon, J. A. A computational analysis of the relationship between neuronal and behavioral responses to visual motion. J. Neurosci. 16, 1486–1510 (1996).
Mante, V., Sussillo, D., Shenoy, K. V. & Newsome, W. T. Context-dependent computation by recurrent dynamics in prefrontal cortex. Nature 503, 78–84 (2013).
Berkes, P., Orban, G., Lengyel, M. & Fiser, J. Spontaneous cortical activity reveals hallmarks of an optimal internal model of the environment. Science 331, 83–87 (2011). By sampling spontaneous neural activity in the visual cortex of awake ferrets during different stages of development, the authors observed increasing similarity between spontaneous activity and activity evoked by natural images with increasing age. They suggest that spontaneous activity might reflect a prior world model.
Fiser, J., Berkes, P., Orban, G. & Lengyel, M. Statistically optimal perception and learning: from behavior to neural representations. Trends Cogn. Sci. 14, 119–130 (2010).
Carandini, M. & Heeger, D. J. Normalization as a canonical neural computation. Nature Rev. Neurosci. 13, 51–62 (2012).
Bartlett, N. R. in Vision and Visual Perception. (ed. Graham, C. H.) 185–207 (John Wiley and Sons, 1965).
Hubel, D. H. & Wiesel, T. N. Receptive fields of single neurones in the cat's striate cortex. J. Physiol. 148, 574–591 (1959).
Padoa-Schioppa, C. Range-adapting representation of economic value in the orbitofrontal cortex. J. Neurosci. 29, 14004–14014 (2009).
Louie, K. & Glimcher, P. W. Efficient coding and the neural representation of value. Ann. NY Acad. Sci. 1251, 13–32 (2012).
Soltani, A., De Martino, B. & Camerer, C. A range-normalization model of context-dependent choice: a new model and evidence. PLoS Comput. Biol. 8, e1002607 (2012).
Cheadle, S. et al. Adaptive gain control during human perceptual choice. Neuron 81, 1429–1441 (2014).
de Gee, J. W., Knapen, T. & Donner, T. H. Decision-related pupil dilation reflects upcoming choice and individual bias. Proc. Natl Acad. Sci. USA 111, E618–625 (2014).
Treue, S. & Martinez Trujillo, J. C. Feature-based attention influences motion processing gain in macaque visual cortex. Nature 399, 575–579 (1999).
Isaacson, J. S. & Scanziani, M. How inhibition shapes cortical activity. Neuron 72, 231–243 (2011).
Kok, P., Jehee, J. F. & de Lange, F. P. Less is more: expectation sharpens representations in the primary visual cortex. Neuron 75, 265–270 (2012). This human neuroimaging study shows that visual prior expectation about stimulus orientation reduces neural activity in V1, but concurrently increases a classifier's ability to read out the stimulus' orientation from BOLD activity patterns. This suggests that expectations might lead to a sharper neural representation of the stimulus.
Bastos, A. M. et al. Canonical microcircuits for predictive coding. Neuron 76, 695–711 (2012).
Koopmans, P. J., Barth, M., Orzada, S. & Norris, D. G. Multi-echo fMRI of the cortical laminae in humans at 7 T. Neuroimage 56, 1276–1285 (2011).
Polimeni, J. R., Fischl, B., Greve, D. N. & Wald, L. L. Laminar analysis of 7T BOLD using an imposed spatial activation pattern in human V1. Neuroimage 52, 1334–1346 (2010).
Murray, E. A., Gaffan, D. & Mishkin, M. Neural substrates of visual stimulus-stimulus association in rhesus monkeys. J. Neurosci. 13, 4549–4561 (1993).
Schapiro, A. C., Gregory, E., Landau, B., McCloskey, M. & Turk-Browne, N. B. The necessity of the medial temporal lobe for statistical learning. J. Cogn. Neurosci. 26, 1736–1747 (2014).
Schapiro, A. C., Kustner, L. V. & Turk-Browne, N. B. Shaping of object representations in the human medial temporal lobe based on temporal regularities. Curr. Biol. 22, 1622–1627 (2012).
den Ouden, H. E., Daunizeau, J., Roiser, J., Friston, K. J. & Stephan, K. E. Striatal prediction error modulates cortical coupling. J. Neurosci. 30, 3210–3219 (2010).
Bar, M. & Aminoff, E. Cortical analysis of visual context. Neuron 38, 347–358 (2003).
Bar, M. et al. Top-down facilitation of visual recognition. Proc. Natl Acad. Sci. USA 103, 449–454 (2006).
Summerfield, C. et al. Predictive codes for forthcoming perception in the frontal cortex. Science 314, 1311–1314 (2006).
The authors are grateful to M. Usher, T. Egner, T. Donner, S. van Gaal and P. Kok for comments on the manuscript. This work was supported by a European Research Council Starter Award to C.S., and a James S. McDonnell Fund award to F.d.L.
The authors declare no competing financial interests.
- Bayes' rule
Bayes' rule describes how the relative probability (or odds) of two possible hypotheses changes (from prior odds ratio to posterior odds ratio) as new evidence is acquired. Formally, the posterior odds ratio is calculated by multiplying the prior odds ratio by the likelihood ratio (also known as the Bayes factor), which is the probability of observing the new evidence, given the two hypotheses.
A negative evoked potential (measured by electro-encephalography or magneto-encephalography) that peaks between 80 ms and 120 ms after the onset of a sensory stimulus (which can be visual, auditory or somatosensory). This potential is sensitive to manipulations of stimulus predictability.
A large positive evoked potential that peaks between 250 ms and 500 ms after the presentation of a stimulus. The P3b is associated with decision processes and strongly reacts to rare, surprising events.
In the oddball paradigm, participants are subjected to frequent 'standard' stimuli interspersed with rare 'oddball' stimuli that require a response. Oddball stimuli elicit enhanced sensory and decision-related neural responses.
- Selective attention
The cognitive function by which information is selected for further processing on the basis of its salience or relevance to a current task or goal.
About this article
Cite this article
Summerfield, C., de Lange, F. Expectation in perceptual decision making: neural and computational mechanisms. Nat Rev Neurosci 15, 745–756 (2014) doi:10.1038/nrn3838
Nature Communications (2019)
Frontiers in Psychology (2019)
Journal of Cognitive Neuroscience (2019)
Intracranial Recordings and Computational Modeling of Music Reveal the Time Course of Prediction Error Signaling in Frontal and Temporal Cortices
Journal of Cognitive Neuroscience (2019)
Journal of Vision (2019)