chapter 5 part 2 Flashcards
aude oliva and antonio torralba - global image features
features that can be perceived rapidly and are associated with specific types of scenes
- degree of:
- naturalness
- openness
- roughness
- expansion
- color
global image features are holistic and rapidly perceived - properties of a scene as a whole
what else enable us to perceive the gist of a scene so rapidly besides global image features
- past experiences in perceiving properties of the environment - for example, we associate blue with open sky
which two types of regularities in the environment influence perception?
physical regularities and semantic regularities
physical regularities
regularly occurring physical properties of the environment
examples of physical regularities
more vertical and horizontal orientations in the environment than oblique - in both human made and natural environments
when one object partially covers another one, the contour of the partially covered object “comes out the other side”
light from above assumption - we assume light is coming from above because that is how it is in most of our environments - indentations vs mounds?
semantic regularities
characteristics associated with activities that are common in different types of scenes
knowledge of what a given scene typically contains is called a scene schema - easier to identify things that fit into the schema
helmohotz’s theory of unconscious inference
the image we see on our retina is ambiguous meaning our perceptions are the result of unconscious assumptions or inferences that we make about the environment
for example, in order to decided what stimuli created an image on the retina, we employ the likelihood principle
likelihood principle
we perceive the object that is most likely to have caused the pattern of stimuli we have received
bayesian inference
a statistical approach to perception in which perception is determined by taking probabilities into account - probabilties are based on past experiences in perceiving properties of objects and scenes
according to bayesian inference, which two factors determine our estimate of the probabilities of an outcome?
- the prior probability (or prior), which is our intial estimate of the probability of an outcome
- the likelihood of the outcome, which is the extent to which the available evidence is consistent with the outcome
give examples of priors and likelihood in bayesian inference theory
maria - priors for three healthproblems
- heartburn, cold, lung disease
likelihood - heartburn and cold are more likely
knows that cold = cough heatburn doesn’t = cough
if someone if coughing she will choose cold
persistence of vision
the perception of a visual stimulus continues for about 250ms (1/4) second after the stimulus is extinguished - eliminated by presenting a visual masking stimulus
this way we control exactly how long we want the stimulus to be visible for
the multiple personalities of a blob
a bloc is a shape that is perceived as different objects depending on its orientation and the context within which it is seen - demonstrates the effect of semantic regularities
predictive coding
describes how the brain uses past experiences to predict what we will perceive
- when new visual input reaches the receptors and is sent upwards in the visual system, the signal is compared to predictions flowing downward from higher levels - if the signal matches, nothing happens. But if it doesn’t match, then a prediction error signal is generated, which is sent back up to the higher levels so that the prediction can be modified
lateral occipital complex
area in the ventral pathway of the brain that is involved in recognizing any type of object
