Module 03: Perception Flashcards
distal stimulus
real world objects to be perceived
proximal stimulus
retinal image (upside down and backwards) of object, reception of info and its registration by sense organ
percept
recognition of an object, meaningful interpretation of proximal stimulus
form perception
segregation of while display into objects and background (think of reversible figures like the white vase and black faces)
subjective contours
complex display to simplifying interpretations (think triangle example)
what are the 5 principles of gestalt psychology and explain them
proximity – group things that are close together
similarity – grouping objects that look the same
connectedness/continuation – group objects whose contours form cts straight or curved line
closure – mentally fill gaps to complete picture
common fate – move together, group together
law of pragnanz
out of all ways to interpret displays, tend to select the organization that yields the simplest and most stable shape and form
bottom up processes
data driven
small bits of info from environment, combined to form percept
perception from info in distal stimulus
relatively uninfluenced from previous experience/learning
what are some issues with bottom up processing
context effects: both accuracy and length of time needed to to recognize objects vary with context
expectation effects: similar to context effects but with expectations
bottom up processing can’t really explain this
what are three examples of bottom up processing
template matching, feature analysis, prototype matching
describe template matching
templates: previously stored patterns
an unknown incoming pattern is compared to all templates and identified by template that best matches it
the perceiver does not know what the object is until it is matched to a template
what are some issues with template matching idea
need to have stored an impossibly large amount of templates
humans are capable of recognizing NEW objects
patterns recognized as more or less the same thing even when they differ a lot
describe feature analysis
breaking down into features – using recognition of parts to infer the whole
(whole into parts)
feature not present => detectors do not respond as strongly
what was Neisser’s visual search task and what were the findings
given an array of letters, participants respond if they detect a presence of a certain letter
took longer to find Z than Q in arrays with letters that look like Z and vice versa
(same with auditory things)
what is selfridge’s pandemonium idea
‘demons’ are feature detectors
first level scans input, different feature demons scream louder when the feature is better matched
higher levels then scan output from lower levels