Jochen Triesch, UC San Diego, 1 Attention Outline: Overview bottom-up attention top-down attention physiology of attention and awareness inattention and change blindness

Jochen Triesch, UC San Diego, 2 Credits: major sources of material, including figures and slides were: Itti and Koch. Computational Modeling of Visual Attention. Nature Reviews Neuroscience, Sprague, Ballard, and Robinson. Modeling Attention with Embodied Visual Behaviors, Fred Hamker. A dynamic model of how feature cues guide spatial attention. Vision Research, Frank Tong. Primary Visual Cortex and Visual Awareness. Nature Reviews Neuroscience, and various resources on the WWW

Jochen Triesch, UC San Diego, 3 How to think about attention? William James: “Everyone knows what attention is” overt vs. covert attention attention as a filter attention as enhancing the signal produced by a stimulus tuning system to a specific stimulus attribute attention as a spotlight location-, feature-, object-, modality-, task- based attention as binding together features attention as something that speeds up processing attention as distributed competition

Jochen Triesch, UC San Diego, 4 Important Questions what is affected by attention? where in the brain do we see differences between attended/unattended conditions? what controls attention? how many things can you attend to? is attention a useful notion at all? Or is it too blunt and unspecific?

Jochen Triesch, UC San Diego, 5 Bottom-up Attention

Jochen Triesch, UC San Diego, 6 Points to note: saliency of location depends on its surround integration into single saliency map (where?) inhibition of return is important how are things updated across eye movements purely bottom-up models provide very poor fit to most experimental data

Jochen Triesch, UC San Diego, 7 Looking to maximize visual reward infants may be primarily driven by visual saliency at about a year of age they start with gaze-following: “looking where sombody else is looking” foundational skill important for learning language,... does not emerge normally in certain developmental disorders theory: they learn to exploit the caregiver’s direction of gaze as a cue to where interesting things are G. Deák, R. Flom, and A. Pick (18 and 12 month-olds)

Jochen Triesch, UC San Diego, 8 Infant: can look at CG or any region of space only sees what is in the region it looks at decides when and where to shift gaze discrete regions of space (N=10) interesting object/event in one location, some- times moving randomly caregiver (CG) looks at object with probability p valid Carlson & Triesch (2003):

Jochen Triesch, UC San Diego, 9 Overview of Infant Model Infant model is simple two agent system (Findlay & Walker, 1999): “when agent” decides when to shift gaze “where agent” decides where to look “when” agent: shift gaze? “where” agent: where to? yes/no new location fixation time object in view inst. reward CG in view CG head pose instantaneous reward (habituating)

Jochen Triesch, UC San Diego, 10 Infant Model Details Habituation: reward for looking at an object decreases over time: Softmax action selection balances exploration/exploitation (τ >0: temperature) β: habituation rate, h fix (0) habituation level at beginning of fixation, t: time since start of fixation Q(s t,a t ) = Q(s t,a t ) + α[r t+1 + γQ(s t+1,a t+1 ) - Q(s t,a t )] TD error Agents learn with tabular SARSA algorithm: Q: state action value, α: learning rate

Jochen Triesch, UC San Diego, 11 Simulation Results basic set indeed sufficient for gaze following to emerge model first learns to look at CG, then learns gaze following Caregiver Index (CGI): ratio of gaze shifts to CG Gaze Following Index (GFI): ratio of gaze shifts following CG’s line of regard learning time (error bars are standard deviations of 10 runs)

Jochen Triesch, UC San Diego, 12 Variation of Reward Structure no learning if things that CG looks at are not rewarding learning poor if CG too rewarding (Williams syndrome?) no learning if CG aversive (Autism?) time until GFI>0.3

Jochen Triesch, UC San Diego, 13 Scheduling Visual Routines Sprague, Ballard, and Robinson (2005): VR platform to study visual attention in complex behaviors where several goals have to be negotiated (“Walter”) rewards are coupled to successful completion of behaviors

Jochen Triesch, UC San Diego, 14 Abstraction hierarchy:

Jochen Triesch, UC San Diego, 15 Behaviors modeled as RL agents:

Jochen Triesch, UC San Diego, 16 Maximum Q Values and best actions: obstacle avoidancesidewalk followinglitter pickup

Jochen Triesch, UC San Diego, 17 Growing uncertainty about state unless you look: control of eye gaze by behavior that experiences biggest loss due to uncertain state information

Jochen Triesch, UC San Diego, 18 switching contexts with a state machine:

Jochen Triesch, UC San Diego, 19 comparing Walter to Human subjects in same task: how often does a behavior control gaze in the “on sidewalk” context?

Jochen Triesch, UC San Diego, 20 comparing Walter to Human subjects in same task: which behavior controls the eye gaze across different contexts?

Jochen Triesch, UC San Diego, 21 Modulation of V4 activity Motter (1994)

Jochen Triesch, UC San Diego, 22 Model Hamker (2004)

Jochen Triesch, UC San Diego, 23 Feedback from higher level exerting input gain control:

Jochen Triesch, UC San Diego, 24 a. switching from red to green. b. spatial effects due to feedback from premotor areas

Jochen Triesch, UC San Diego, 25 Model vs. Experiment: Experiment Model

Jochen Triesch, UC San Diego, 26 Detection of stimuli and V1 activity Super, Spekreijse, and Lamme (2001): monkey’s task: detect texture defined region and saccade to it record from orientation selective cell in V1 how is cell’s response correlated with monkey’s percept?

Jochen Triesch, UC San Diego, 27 enhancement of late (80-100ms) response only if target is actually detected by the monkey “seen”“not seen”