Thinking 1

Question 1

what is thinking?

Answer 1

the flexible organisation and manipulation of internal representations

Question 2

what do rationalists emphasise?

Answer 2

• the constructivist nature of the human mind
• emphasise the importance of a priori existing concepts in the understanding of the sensory inputs we receive in order to perceive the world

Question 3

what does empiricism emphasise?

Answer 3

• the importance of sensory observations to build this
• emphasise the sensory data

Question 4

how does the brain think?

Answer 4

uses ‘algorithms’ and assumptions to actively construct an image of the world
- e.g. perceptual illusions, Gestalt laws - laws of continuity, law of closure etc
- brain tries to make meaningful objects from the sensory input

Question 5

why does the brain make meaningful objects from sensory input?

Answer 5

• as we do not have unambiguous information coming through our senses
• incomplete “projection” into the brain, e.g. 3D-2D projections
• even colour can be ambiguous - blue/black, white/gold dress
• shadows looking like different animals

Question 6

give examples of how the brain integrates specific observations (e.g. for visual object recognition: contour lines/ shape, colour) with all kinds of contextual information?

Answer 6

• the probability of the occurrence of a particular ‘object’ - depending on the narrative context, observers either see the young (‘wife’) or the old woman (‘mother in law’)
• other available information, e.g. the sound the object makes

Question 7

what is the ventriloquist effect?

Answer 7

The perceived location of a sound is shifted in space by simultaneously occurring visual stimulus at incongruent location

Question 8

what is the McGurk effect?

Answer 8

The perceived sound of a spoken syllable is altered by incongruent visual input of lip movement

Question 9

who discovered perception as an unconscious inference?

Answer 9

Hermann von Helmholtz and Wilhelm Wundt

Question 10

what is Bayesian cognition?

Answer 10

contradicting camps
- human cognition is based on Bayesian algorithms and this is what brains have evolved to do
- humans fail in taking prior probabilities (=base rate) into account

Question 11

what are the 3 factors of probability?

Answer 11

1. probabilities are non negative (real) numbers between 0 and 1 (0<p<1)
2. The probability of the certain event is 1
3. The probabilities of all separate events that comprise a set add up already and they add up to 1

Question 12

what is conditional probability?

Answer 12

p(A I B) = the probability of B given A

the probability if a particular (hypothetical or real) ‘event’t, e.g. A=2 with in the set of another event

Question 13

what is the Bayes Theorem equation?

Answer 13

p(H1 I O) = p(H1) X p (O I H1) / sum of p(Hi)p(O/Hi)

p(O I H1) = the probability of a certain observation O given that hypothesis H1 was true, also called the likelihood of observation O given H1

p (H1) = the prior probability of hypothesis H1 being true

p (O) = the prior probability of making observation O

p (H1 I O) = the posterior probability of hypothesis H1 being true given observation O being made

Question 14

what is a bayesian inference?

Answer 14

to infer the observations to the (probability of the) hypothesis

Question 15

3 key facts of Bayes theorem

Answer 15

1. inference of ‘ground truth’ (the state of the world) on the basis of (limited) data always comes with uncertainty, since the likelihood, e.g. p (A I B) are virtually never o or 1. The data alone rarely ever tells us with 100% certainty whether Hypothesis 1 or 2 is correct
2. in order to judge the best judgement / inference under uncertainty, the Bayes theorem is crucial
3. For judging which hypothesis is the most likely, consideration of the prior probability is often crucial. individual observations are rarely enough, we need contextual information

Question 16

what is Bayesian perceptual influence?

Answer 16

calculate the ‘posterior
- probability’ of perceptual hypotheses given the sensory evidence and prior probability of the causes
- sensory and prior information weighted according to their ‘precision’ to compute posterior belief (subjective percept)

Question 16

how do we use prior/contextual information?

Answer 16

help influence the perceived level of threat from one and the same person

Question 17

how do we increase the certainty about objects in the real world?

Answer 17

combine signals from different sensory modalities
- The Bayes theorem allows to derive how these ideally combine to come to a common judgement, in the same way as individual observations and prior probability are “combined”

Question 18

what is a probability density function?

Answer 18

something that has a normal distribution also known as Gaussian distribution
- defines the probability function representing the density of a continuous random variable lying between a specific range of value
- SD is the width, wider pdf distribution

Question 19

what is a cumulative density function?

Answer 19

• gives the probability that the random variable X is less than or equal to x
• never reaches one
• SD is the slope, flatter slope in cdf

Question 20

what did Ernst & Banks 2002 do?

Answer 20

• visual and haptic 3D - virtual reality
• task is to compare the height of two ‘bars’ - presented in VR visually and haptically
• can measure the accuracy of judgment in visual domain (presented separately)
  can estimate a person’s accuracy in each modality (separately) for vision also under various levels of noise
• in combined presentations, judge how much their judgement depends on visual, how much on haptic perception

Question 21

The psychometric function Ernst & Banks 2002

Answer 21

• 2 AFC: judge whether the second stimulus was larger than the first (“standard” stimulus)
• The psychometric function is often modelled as a ‘cumulative Gaussian function’
• slope of the psychometric function indicates the ‘performance’ of an observer:
• The better, the steeper
• This is captured by the SD (variance)

Question 22

haptic and visual stimuli Ernst & Banks 2002

Answer 22

2 AFC (serial comparison)
1. “standard stimulus” S (55mm)
2. comparison stimulus ↑↓

measure the psychometric function:
- under haptic stimulation only
- under visual stimulation only
- at different noise levels

get different psychometric functions with different slopes

Question 23

what happens if use combined stimuli

Answer 23

• present both visual and haptic
• when visual stimuli was different from haptic stimulus (kind of a visuo-haptic McGurk effect), allows to estimate the empirical weight with which each sensory modality contributed to the joint estimate
  RESULTS: followed pretty much exactly the optimal weight, as derived from the Bayes Theorem

Question 24

what does Bayesian perception show?

Answer 24

strong evidence that the human brain encodes information probabilistically and applies Bayes-optimal computations for multi-sensory integration to optimally judge stimulus properties

Question 25

Noise characteristics and prior expectations in human visual speed (Stocker and Simoncelli 2006)

Answer 25

• presented moving (‘drifting’) Gabor patches composed of different contrast-levels
• It is known that motion perception of low contrast stimuli is less accurate than at high contrasts
• It is known that there is a general bias to underestimate motion speeds, and this is thought to be due to the fact that lower speeds are more present in natural environments

Question 26

what are the criticisms of these studies (Bowers and Davis 2012)

Answer 26

a) Ernst and Banks did not investigate whether participants integrate sensory signals and prior probability of the causes
b) Stocker and Simoncelli could not manipulate the prior probability distribution - had to assume these / estimate the data. They only show Bayesian model can explain the data very well (and better then other models, may be better models out there