1. COMPUTATIONAL MODELING OF INTEGRATED COGNITION AND EMOTION Bob MarinierUniversity of Michigan

2. Introduction  Existing research in cognitive science tends to ignore emotion research  Existing research in emotion tends to ignore cognitive science  Goal is to develop a computational theory of the control of immediate behavior in which emotion has a clear functional role  Claim: Cognitive and emotion theories are actually very complementary 2

3. Newell’s Abstract Functional Operations (NAFO) for immediate behavior PerceiveObtain raw perception EncodeCreate domain-independent representation AttendChoose stimulus to process ComprehendGenerate structures that relate stimulus to tasks and can be used to inform behavior TaskPerform task maintenance IntendChoose an action, create prediction DecodeDecompose action into motor commands MotorExecute motor commands 3

4. NAFO is incomplete PerceiveWhat information is generated? EncodeWhat information is generated? AttendWhat information is required? ComprehendWhat information is required and generated? TaskWhat information is required? IntendWhat information is required? 4

5. Appraisal theories  Idea: Humans evaluate a situation with respect to their goals along a number of innate dimensions  E.g., Novelty, Goal Relevance, Causality, Conduciveness  Appraisals trigger emotional responses  Mapping between appraisal values and emotions is fixed  Problem: Existing process models of appraisal are weak 5

6. Appraisals to emotions 6

7. Integration of cognition and emotion 7  NAFO: process without data  Appraisal theories: Data without process  Claim: Appraisals are the data generated and used by NAFO

8. Integration of NAFO and appraisal 8

9. Extended theory  Implemented in Soar, a cognitive architecture  Provides independently-motivated constraints  Allows for integration with other cognitive mechanisms 9

10. Integration with Soar 10 Long-Term Memories Body Episodic Perception Action ProceduralSemantic Short-Term Memory Decision Procedure Appraisal Detector Soar 9 Chunking Episodic Learning Reinforcement Learning Semantic Learning

11. Extended theory  Distinction between emotion, mood, and feeling  Emotion: Result of appraisals Is about the current situation  Mood: “Average” of recent emotions Provides historical context  Feeling: Emotion “+” Mood What agent actually perceives 11

12. Cognition Emotion Mood Feeling Combination Function Pull Decay Active Appraisals Perceived Feeling Emotion, mood, and feeling 12

13. Extended theory: Learning  Feeling should drive reinforcement learning  Feelings give an indication of how well things are going well  Use feeling intensity and valence as intrinsic reward signal  What is being learned?  Choices related to NAFO What to Attend to When to Intend (vs. Ignore) What to Intend When to create which subtasks, and when to return to supertask 13

14. Learning task 14 Start Goal Optimal Subtasks

15. Results: With and without mood 15

16. Results: With and without mood 16

17. Future work 17  Near future  Explore learning further  Explore new capabilities Giving up Interruptability  Richer domain Continuous time, space  Eventually  Multiple agents: social interaction  Physiology  Interaction between other cognitive mechanisms and emotion

18. NAFO and GOMS 18  In general, these are complementary techniques  GOMS  Focused on HCI  Focused on motor actions (e.g. keypresses)  Less focus on cognitive aspects  NAFO  Focused on required cognitive functions  Allows for a mapping with appraisals Could implement NAFO with GOMS, but would lack the proper labels that allow for the mapping