Presentation Articles

Question 1

What are the aspects and advantages of discrete emotion theories?

Answer 1

• There is a limited set of basic emotions that make up the entire spectrum through combination
• Expression of these basic emotions is similar across cultures
• Makes it easy to compute an output behavior from emotional states

Question 2

How do emotions arise according to appraisal theory?

Answer 2

Via the comparison of individual needs and external demands - the person-environment relationship. This is assessed in terms of appraisal variables, which are questions like “Is this event desirable?” etc.

Question 3

Why are appraisal theories sometime criticized in their psychological plausibility?

Answer 3

According to them, there are cognitive processes (evaluating of appraisal variables) that precede emotions. This contradicts the fast and automatic emotional responses we see in the real world.

Question 4

What are the four parts to the EMA Model?

Answer 4

1. Representation of the agent-environment relation
2. Appraisal derivation process
3. Emotion derivation process
4. Affect Consequences

Question 5

The behavioral and cognitive output of the EMA model can affect which steps in the EMA process?

Answer 5

• Overt behavior
• The evaluation of appraisal variables
• The perception of the agent-environment relation

Question 6

What kind of emotions are difficult to model computationally?

Answer 6

Complex social emotions like embarrassment etc.

Question 7

What are the evaluation criteria of a CRUM?

Answer 7

1. Representational Power
2. Computational Power
3. Psychological Plausibility
4. Neurological Plausibility
5. Practical Applicability

Question 8

is backpropagation supervised or unsupervised?

Answer 8

Supervised

Question 9

Describe the neural connection between the Dentate Gyrus and the CA3.

Answer 9

The input from the DG excites relatively few CA3 cells (sparse input), but different patterns of activation of the DG trigger different sets of CA3 neurons

Question 10

What would a computational representation of the hippocampus have to be able to do?

Answer 10

• Store a large number of patterns after a single presentation
• Retrieve information on the basis of partial cues

Question 11

How would learning in the DG be modeled computationally?

Answer 11

With competitive learning

Question 12

How would CA3 be represented computationally?

Answer 12

As an auto-associator

Question 13

Competitive learning would be used to model which parts of the hippocampal system?

Answer 13

Dentate Gyrus and CA1

Question 14

Name 3 rule-based systems

Answer 14

ACT
GPS
SOAR

Question 15

Why do rule based systems have a higher computational power than logic systems?

Answer 15

Because rules don’t have to be interpreted as always true/relevant