Cognition Through Imagination and Affect Murray Shanahan Imperial College London Department of Computing

1 Overview Brain-inspired architectures Cognitively mediated action An internal sensorimotor loop

2 Brain-inspired Architectures Progress towards the vision of human-level AI has been slow Classical AI has not yet succeeded in devising systems that can match the common sense reasoning skills of a young child Biologically-inspired AI has been very slow to move beyond trivial motor tasks and tackle the difficult questions of cognition Some researchers are now turning to the human brain for inspiration, especially to architecture- level theories of its functioning

3 A New Vocabulary We have a whole new set of concepts to explore But all should be in scare quotes Many are alien to both top-down classical AI and bottom-up biologically-inspired AI “Imagination” “Emotion” “Consciousness” Or, more technically Internally closed sensorimotor loops Affect-based mechanisms of selection Global workspaces

4 Imagination and Affect Here we have an internally-closed sensorimotor loop that can simulate interaction with the environment It rehearses trajectories through sensorimotor space without having to traverse those trajectories for real The outcome of various potential trajectories can be evaluated. This where affect comes in The result impacts on action selection

5 Why an Internal Loop? The inner sensorimotor loop implements a form of analogical representation The medium of representation has the same structure as what is being represented – eg: a map We get spatial properties for free, and complex shapes can be represented The dynamics of the inner loop has a close relationship to the dynamics of the outer loop It can realise inner speech as well as mental imagery Categories become attractors in a state space having same structure as that of sensory input This addresses the symbol grounding problem

6 A Cognitively-mediated Action On sight of green, turn-right is action has highest “salience” But this reactive response is held on veto while turning right is rehearsed Sight of red of predicted But red is aversive So salience of turn-right is modulated down, resulting in turn-left becoming the action with highest salience Again this response is held on veto Now sight of blue is predicted, and blue is associated with reward So salience of turn-left is modulated up Eventually it reaches a threshold, veto is released, and robot acts

7 The Core Circuit VC / IT = visual cortex / inferior temporal cortex AC = association cortex GW / BG = global workspace / basal ganglia Am = amygdala This “core circuit” combines an internal sensorimotor loop with mechanisms for broadcast and competition, and thereby marries the simulation hypothesis with global workspace theory GW / BG AC1a AC2a AC3a AC1b AC2b AC3b VC / IT Am

8 Affect Circuitry (Am) Reward Punish Salience1 Salience2 Salience3 VC / IT GW BG VC / IT = visual cortex / infero-temporal cortex, BG = basal ganglia, GW = global workspace

9 Motor Circuitry MC = motor cortex BG = basal ganglia Am = amygdala MC1 MC2 MC3 BG Veto Selected action buffer Motor command VC / IT Am Robot motor controllers New action detector VC / IT Urgency

10 Concluding Remarks The brain-inspired approach to building cognitive systems is promising But it is still relatively unexplored Affect plays a vital role in the proposed architecture It is currently a simple scalar value. A vector of “basic emotions” would be interesting to investigate The relationship to consciousness is very interesting Too bad there’s no time to talk about it Shanahan, M.P. (2006). A Cognitive Architecture that Combines Inner Rehearsal with a Global Workspace. Consciousness and Cognition 15, 433–449.