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CogLab: Signal Detection:
[ How do we detect stimuli in our environment? ]
Task: Detect Target (10 dots in a diagonal row) among Noise (star field)
Two Choice Decision: Target Present or Absent
Independent Variable: Amount of noise (144,400 or 1,000 dots)
(presence (50%) or absence (50%) of target)
Dependent Variable: response of “present” or “absent”
Hit Rate :
Hits / Hits + Misses
False Alarm Rate:
False Alarms / False Alarms + Correct Rejections
Sensitivity: (0= no discrimination, larger value = better discrimination)
Ability to discriminate signal from noise
Depends on stimulus, sensory system, attention, etc….
[d’ = Z(hit rate) - Z(false alarm rate) ]
![<ul><li><p><span style="background-color: transparent; font-family: "Times New Roman", serif;">Ability to discriminate signal from noise </span></p></li><li><p><span style="background-color: transparent; font-family: "Times New Roman", serif;">Depends on stimulus, sensory system, attention, etc….</span></p></li></ul><p><span style="background-color: transparent; font-family: "Times New Roman", serif;">[<em>d’ </em>= Z(hit rate) - Z(false alarm rate) ]</span></p>](https://knowt-user-attachments.s3.amazonaws.com/59eeb935-c3b4-4207-99a6-04a1bad4354a.png)
Bias: (<0=bias for “present”, 0=no bias, >0 = bias for “absent”)
Tendency to provide one answer over the other
A feature of decision making, can depend on relative cost of misses versus false alarms
[C = Z(hit rate) + Z(false alarm rate) / 2]
![<ul><li><p><span style="background-color: transparent; font-family: "Times New Roman", serif;">Tendency to provide one answer over the other </span></p></li><li><p><span style="background-color: transparent; font-family: "Times New Roman", serif;">A feature of decision making, can depend on relative cost of misses versus false alarms</span></p></li></ul><p><span style="background-color: transparent; font-family: "Times New Roman", serif;">[<em>C = </em>Z(hit rate) + Z(false alarm rate) / 2]</span></p>](https://knowt-user-attachments.s3.amazonaws.com/d1ab394f-290a-4ce2-b71c-e8466fe6bbdb.png)
Direct Perception Theories : Bottom-up processing
Perception comes from stimuli in the environment
Parts are identified and put together, and then recognition occurs
Constructive Perception Theories: Top-Down processing
People actively construct perceptions using information based on expectations
Bottom-Up Processing: Recognition by-components theory (RBC) - Irving Biederman (1987)
We perceive objects by perceiving elementary features
Geons: three-dimentional volumes
Objects are recognized when enough information is available to identify objects geons
[ Parts are identified and put together, and then recognition occurs ]
Geons: Three-dimentional volumes
Discriminability: geons can be distinguished from other geons from almost all viewpoints
Resistance to Visual noise: geons can be perceived in “noisy” conditions
Invariance: recognizable no matter the illumination direction, surface markings, and texture
Distinctiveness: 36 different geons have been identified
[ objects are recognized by identifying geons and their relationships ]
Geons & RBC:
we can recognize objects from a subset of geons
Airplane composed of 9 geons
[ Recognized correctly: 96% of the time from 6 geons // 78% of the time from 3 geons ]
Principal of Componential Recovery:
The key to object recognition is not the amount of information, but the ability to identify its components (geons)
Top-Down Processing: Perception Involves:
Inferences based on context (surrounding elements of the visual scene)
Guessing from experience (knowledge and expectations based on the past)
Perceiving Size: bottom-up processing
the size of the image on the retina
Perceiving Size: top-down processing
the perceived distance of the object // the size of the object relative to other objects in the environment
Job of Perception:
Infer the distal stimulus given only the proximal stimulus
Helmholtz’s Theory of Unconscious Inference (~1860) : Top-Down Theory
Our perceptions result from unconscious assumptions we make about the environment (we use knowledge to inform our perceptions)
We infer much of what we know about the world
Likelihood Principle….
Likelihood Principle:
we perceive the world in the way that is “most likely” based on our past experiences
Gestalt Laws: Gestalt Psychology (~1900)
The mind groups patterns according to laws of perceptual organization
These “laws” are actually heuristics based on what usually happens in the environment
[ Heuristic = “rule of thumb” ]
Provides best-guess solution to a problem
Fast
Often correct
“The whole is greater than the sum of its parts”
Gestalt: Law of Good continuation:
lines tend to be seen as following the smoothest path
Gestalt: Laws of good figure
(simplicity or pragnanz) : every stimulus is seen so the resulting structure is as simple as possible
Gestalt: Law of similarity:
similar things appear grouped together
Gestalt: Law of Proximity:
nearby objects appear grouped together
Gestalt: Law of Closure:
separate elements will tend to be grouped to form closed figures
Gestalt: Law of familiarity:
things are more likely to form groups if the groups appear familiar or meaningful