Top-Down Processing Examples in Psychology

What is Top-Down Processing?

Top-down processing in psychology refers to perception guided by prior knowledge, experiences, and expectations, influencing the interpretation of sensory information.

Top-down processing involves the brain “sending down” stored information to the sensory system as it receives information from the stimulus, enabling a plausible hypothesis to be made without the need to analyze every feature of the stimulus.

Thus, top-down processing uses the contextual information of things that we already know or have already experienced in combination with our senses to perceive new information.

In top-down information processing, perceptions are interpreted from individual frameworks that help us perceive and interpret information.

These frameworks, also known as schemas, are constructed from past experiences, prior knowledge, emotions, and expectations (Piaget, 1953).

Why we use Top-Down Processing

British psychologist Richard Gregory (1970) proposed that perception is constructive: it depends on top-down processing to interpret new information.

He argued that sensory information alone is not enough. A great deal of what reaches the eye is lost before it reaches the brain (over 90%, on Gregory’s own estimate). Perception must draw on prior knowledge and experience to fill that gap.

Rather than processing every sensation from scratch, the brain combines the senses with existing knowledge to form a hypothesis about what the new information means.

Influences on Top-Down Processing

According to Gregory (1970), different factors can influence top-down processing, such as expectations, emotion, motivation, and culture. This is known as perceptual set theory.

You can understand how top-down processing works by considering examples of this phenomenon in action.

Typos

The human mind does not read every letter individually but rather words collectively. As long as the first and last letters of the word are in the same spot, we can identify the correct word, despite the typo.

Goldstein (2018) argues that our ability to make sense of typos and misspellings is another example of top-down processing. We actively apply our previous experiences, knowledge, and expectations to identify misspelled words correctly.

Stroop Effect

The Stroop effect, named after psychologist John Ridley Stroop (1935), shows how interference slows reaction time. Try it yourself.

Picture a list of color names, each printed in a different, mismatched ink color. Naming the ink color, while ignoring the word itself, is the task.

Stroop (1935) found that participants named the ink color quickly when it matched the word’s meaning, but needed far more attention when it did not.

Visual Illusions

The Necker cube is a visual illusion, an ambiguous wireframe drawing created by Louis Albert Necker (1832). Either of its two front squares can appear closer to the viewer.

Viewers can easily flip between the two orientations because the brain forms two equally probable hypotheses about the cube’s structure. Neither wins outright.

The sensory input never changes, yet the percept does: proof that perception flows from prior knowledge downward, not just from the eyes upward.

The Hollow-Face Illusion

The hollow-face illusion is Gregory’s most striking demonstration of top-down processing. When a realistic mask is rotated to show its hollow, concave side, viewers still see a normal, convex face bulging outward.

Depth cues such as shading and motion correctly signal a hollow surface, but a lifetime of seeing convex faces overrides them. The stored expectation wins.

• Aim: To test whether the hollow-face illusion captures all visual processing equally, or only conscious perception.
• Method: Króliczak, Heard, Goodale, and Gregory (2006) had observers point at a target on the illusory face, either with a fast, flicking finger movement or with slow, deliberate pointing.
• Results: The fast movements were guided by the true hollow geometry and escaped the illusion, but slow pointing and conscious report followed the convex illusion.
• Conclusion: A fast, memory-light action route can bypass a stored prior that still dominates conscious perception, so top-down knowledge does not penetrate all vision equally.

This dissociation shows up in clinical populations too. People with schizophrenia are markedly less susceptible to the hollow-face illusion: their perception follows the true, hollow geometry rather than the convex face-prior. Brain-imaging analyses trace this to weaker top-down signalling from frontoparietal regions to visual cortex (Dima et al., 2009).

Auditory Illusions

Phonemic restoration shows that top-down processing shapes hearing as well as vision: listeners perceive speech sounds that are not physically present in the sound signal.

• Aim: To test whether listeners use top-down linguistic knowledge to restore speech sounds missing from the acoustic signal.
• Method: Warren (1970) recorded the sentence “The state governors met with their respective legislatures convening in the capital city” and replaced the first s in legislatures with a cough.
• Results: Listeners reported hearing the complete word, and struggled to say exactly where the cough had occurred.
• Conclusion: The auditory system fills the gap using word and sentence context, delivering what listeners expect to hear, not just what arrives at the ear.

Prior linguistic knowledge, not the raw sound alone, decides what we hear.

Bayesian Approach

Human perception rarely relies on the senses alone. Kersten, Mamassian, and Yuille (2004) argue that the brain combines current sensory input with prior knowledge to interpret ambiguous stimuli, a process known as the Bayesian approach.

The Bayesian approach treats perception as a trade-off: how well an interpretation fits the current data, weighed against how probable it is given past experience.

This trade-off has two parts.

• Likelihood: how well each possible interpretation matches the current sensory evidence.
• Prior: how probable that interpretation is, based on everything perceived before.

When the sensory data are clear, the likelihood dominates and perception is data-driven. When the data are ambiguous, such as a shape defined only by shading, the prior takes over. The brain interprets it as whatever shape it has seen before (Kersten et al., 2004).

This is Gregory’s hypothesis, formalised as a mathematical trade-off.

Critical Evaluation

Gregory’s theory is usually tested against James Gibson’s (1979) bottom-up account. Gibson argued that the senses pick up enough information from the environment directly, with no need for guesswork. The truth lies somewhere between the two.

Strengths

Top-down processing explains several things a purely bottom-up account cannot:

• Explains Illusions: It accounts for the Necker cube, the hollow-face illusion, and phonemic restoration under one principle: perception as inference.
• Efficient: Guessing the most likely interpretation is faster than analysing every feature of a scene from scratch.
• Testable: The Bayesian and predictive-processing frameworks turned Gregory’s “hypothesis” into a precise, measurable trade-off (Kersten et al., 2004).
• Clinical Relevance: Altered top-down weighting helps explain unusual perception in conditions such as psychosis.

Limitations

The theory also has real weaknesses:

• Understates the Senses: Gibson (1979) argued that a moving observer in good light picks up far richer information than Gregory assumed, leaving less for top-down processing to explain.
• Novel Stimuli: A brand-new object with no matching schema is still often perceived accurately, which the theory struggles to explain.
• Low Ecological Validity: Much of the supporting evidence comes from artificial lab displays and illusions, which may exaggerate top-down processing’s everyday role.
• Not All-or-Nothing: Fast, action-guiding movements can bypass a stored prior that still dominates conscious perception, so the illusion is not total (see the Hollow-Face Illusion above).

Contemporary Research

Recent work extends the theory to explain individual differences in perception, particularly in psychosis.

• Aim: To test whether strengthening prior knowledge improves perception of ambiguous images, and whether heavy reliance on priors relates to psychosis proneness.
• Method: Teufel et al. (2015) showed participants two-tone “Mooney” images, degraded pictures that look like meaningless blotches until the original photo reveals the hidden object. They measured recognition accuracy before and after that reveal, in healthy participants and people with early psychosis.
• Results: Prior knowledge produced a clear, measurable improvement in object detection. People with higher psychosis-proneness, and those with early psychosis, leaned on that prior knowledge even more strongly.
• Conclusion: Top-down priors normally sharpen perception, but over-relying on them, relative to the actual sensory evidence, is a marker of the psychosis-prone perceptual style.

This pattern is not unique to Teufel’s study. Powers, Mathys, and Corlett (2017) conditioned healthy people and voice-hearing patients to associate a tone with a light. Many later “heard” the tone from the light alone.

Sterzer et al. (2018) bring this evidence together into the predictive-coding account of psychosis: mis-set weighting between predictions and prediction errors produces hallucinations and delusions.

Bruner, J. S., & Minturn, A. L. (1955). Perceptual identification and perceptual organization. The Journal of General Psychology, 53(1), 21-28. https://doi.org/10.1080/00221309.1955.9710133

Deregowski, J. B., Muldrow, E. S. & Muldrow, W. F. (1972). Pictorial recognition in a remote Ethiopian population. Perception, 1, 417-425.

Dima, D., Roiser, J. P., Dietrich, D. E., Bonnemann, C., Lanfermann, H., Emrich, H. M., & Dillo, W. (2009). Understanding why patients with schizophrenia do not perceive the hollow-mask illusion using dynamic causal modelling. NeuroImage, 46(4), 1180-1186. https://doi.org/10.1016/j.neuroimage.2009.03.033

Gibson, J. J. (1979). The ecological approach to visual perception. Houghton Mifflin.

Goldstein, E. B. (2018). Cognitive psychology. Mason OH: Cengage.

Gregory, R. (1970). The intelligent eye. London: Weidenfeld and Nicolson.

Kersten, D., Mamassian, P., & Yuille, A. (2004). Object perception as Bayesian inference. Annu. Rev. Psychol., 55, 271-304.

Króliczak, G., Heard, P., Goodale, M. A., & Gregory, R. L. (2006). Dissociation of perception and action unmasked by the hollow-face illusion. Brain Research, 1080(1), 9-16. https://doi.org/10.1016/j.brainres.2005.01.107

Necker, L. (1832). LXI. Observations on some remarkable optical phenomena seen in Switzerland; and on an optical phenomenon which occurs on viewing a figure of a crystal or geometrical solid. The London and Edinburgh Philosophical Magazine and Journal of Science, 1 (5), 329-337.

Piaget, J. (1953). The origin of intelligence in the child (International library of psychology, philosophy, and scientific method) . London: Routledge & Paul.

Powers, A. R., Mathys, C., & Corlett, P. R. (2017). Pavlovian conditioning-induced hallucinations result from overweighting of perceptual priors. Science, 357(6351), 596-600. https://doi.org/10.1126/science.aan3458

Sterzer, P., Adams, R. A., Fletcher, P., Frith, C., Lawrie, S. M., Muckli, L., Petrovic, P., Uhlhaas, P., Voss, M., & Corlett, P. R. (2018). The predictive coding account of psychosis. Biological Psychiatry, 84(9), 634-643. https://doi.org/10.1016/j.biopsych.2018.05.015

Stroop, J.R. (1935). Studies of interference in serial verbal reactions. Journal of Experimental Psychology, 18, 643–662.

Swets, J. (1964). Signal detection and recognition by human observers; contemporary readings. New York: Wiley.

Teufel, C., Subramaniam, N., Dobler, V., Perez, J., Finnemann, J., Mehta, P. R., Goodyer, I. M., & Fletcher, P. C. (2015). Shift toward prior knowledge confers a perceptual advantage in early psychosis and psychosis-prone healthy individuals. Proceedings of the National Academy of Sciences, 112(43), 13401-13406. https://doi.org/10.1073/pnas.1503916112

Warren, R. M. (1970). Perceptual Restoration of Missing Speech Sounds. Science, 167(3917), 392-393.