Patrick Cavanagh Department of Psychology Harvard University Attention Routines

Visual Routines: Ullman Perception Routines - Automatic, no introspection - Grouping, light constancy, shape, pictorial cues Attention Routines - Voluntary initiation, reportable output - No intermediate, accessible states - Set selection criteria, spatial & temporal relations, track Cognition Routines - Multiple steps - Intermediate steps are accessible to introspection

Early Work on Attention Single focus. Aristotle, -4c Active, improves sensitivity. Lucretius, 1c Involuntary shifts. Augustine of Hippo, 4c Pop-out vs scrutiny. Alhazen, 11c Feature Integration. Wolff, 18c That’s what so nice about cognitive science, You can drop out for a couple of centuries and not miss a thing, Fodor, 21c

1960s = 100 Decade Relative Number of Publications MedLine Keywords awareness learning perception cognition memory attention

Selection Space, time limits Reposition scrutiny

Crowding is selection acuity While looking at the central dot, you can clearly see the lines below but you cannot individuate or count through them below as you can above. The lower groups of lines are within resolution limit of vision but beyond limit of selection

Crowding is selection acuity D A E GN R Region of selection is scalable but has a minimum size that is surprisingly coarse

Crowding is selection acuity D A E GN R F R K LQ F S N P F H L A GX O P A G E B N A S Previously legible letters no longer accessible when smallest selection area inclused other letters

S E E SN D F R K LQ F S N P F H L A GX O P A G E B N A S S E E SN D Crowding is selection acuity Unique feature provides feature based selection when location based selection is blocked

Crowding is selection acuity Items are blocked from awareness but registered at cortical level. (He, Cavanagh, & Intriligator, 1996) Loss of access to location. (Intriligator, & Cavanagh, 2001) Fixed extent, independent of size of items. (Tripathy & Cavanagh, 2002)

Resolution at different eccentricities

Selection Spotlight of Attention? Selecting a single item. “2”

Selection Not a spotlight! Only one label per selection “some lines”

Selection Selecting a single unfamiliar item: Cannot scrutinize or integrate. “some lines”

Scrutiny Based on Multiple Independent Selections Pylyshyn & Storm, 1988; Yantis, 1992; Intriligator & Cavanagh, Object tracking experiments suggest 4 to 5 independent selections

Selections Multiple selection operators. Integrate relative position and content. “bottom right corner” “top right corner” “top left corner” “bottom left corner” “ ”

Temporal Resolution individuating events in time sequence of individual events vs a rush of undifferentiable flicker 7 Hz (14 frames/sec) limit for a single stream of events ( Verstraten, Cavanagh, & Labianca, 2000)

2 frames/second8 frames/second Compare two streams for color vs orientation (Holcombe & Cavanagh, 2001) Comparing across streams?

Multiple Streams Track 4 streams for identity But comparison or integration across selections is much slower

True for all spatial relations of compound items Inside - outside: Treisman & Gormican, 1988 Above-below; left- right: Logan, 1994; Moore et al, 2001 Front-back: Moore et al, 2001

Finding target, red left of green, is slow

Analyze multiple selections familiar things red bargreen bar Determine relation between two selections Slow analysis Left-of Use two concurrent attention selections to get both halves Centrally represented description “Red-left-of-green”

Temporal Relations Temporal relations require multiple selections -Detecting an object’s motion e.g. bouncing, walking -Detecting temporal order e.g. light then dark

Temporal relations: Sprites Flexible descriptions which embody object constraints Online animation to fit changing image data Cavanagh, Labianca, & Thornton, 2001

Search for direction is slow Visual search task: Find rightward walker among leftward 4 normal Ss

Search for walker among non walkers is slow Find walker among scrambled figures Identifying walker requires attention. Parietal patients have difficulty seeing biological motion.

Tracking Keeping tabs during occlusion, motion, change High-level motion Tracking models

Example: Multiple Object Visual Tracking Pylyshyn & Storm, 1988; Yantis, 1992; Intriligator & Cavanagh, 2001.

Low-level motion useless for tracking Have to know where object is to read out its low-level motion signals But if you know where the object is on a moment by moment basis, no need to read out low-level motion.

Salience map Retina Figure/ground and selection Fixed array of Reichardt detectors Select detectors within object Read out motion Reichardt detectors operate on salience map Lu & Sperling 3rd-Order Motion

Salience vs Tracking Salience can drive this motion because items have different features in each frame Attentively track either direction here, but items all identical. Salience cannot act, requires internal model of motion trajectory — sprite.

Tracking Mechanism Move window  s Retina Object shifts in window Feature tracking (Del Viva & Morrone, 1998) Snakes (Terzopoulos et al, 1987) Balloons (Cohen, 1999) Active contours (Blake & Isard, 1999)

Computer Vision Examples of Tracking 2D snake3D shrink wrap Tracked features direct model animation

Attention Routines Selection -surprisingly coarse in both space and time -no access to details of single selection Spatial and temporal relations -computing descriptions based on two or more selections in space or time is slow Tracking -mechanism remains to be identified