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A computational model of the reviewing of object-files

A computational model of the reviewing of object-files. Michael Liddle Alistair Knott Anthony Robins. Introduction . Selective visual attention. Object-files and the object-specific advantage (OSA). Computational modeling of cortical vision.

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A computational model of the reviewing of object-files

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  1. A computational model of the reviewing of object-files Michael Liddle Alistair Knott Anthony Robins

  2. Introduction • Selective visual attention. • Object-files and the object-specific advantage (OSA). • Computational modeling of cortical vision. • A neural network model of object-file reviewing (first of its kind?)

  3. Selective visual attention

  4. Managing limited resources • Retinal image contains an enormous amount of information. • Processing complexity subject to combinatorial explosion. • Solution: only bother processing information about one object at a time.

  5. Explaining the solution • What actually happens in the brain when we “attend” to an object? • Experiments indicate that attention is the means by which feature “conjunction” and “binding” occurs (Treisman & Gelade, 1980). • What is the medium of this binding? Object-files!

  6. Object-files and the object-specific advantage

  7. Object-files • Kahneman & Treisman (1984) • Provide stable repositories for visual information about four or five objects. • Maintain identity and continuity of objects during a perceptual episode. • Analogy: police files for investigations.

  8. Object-files • When attending to an object for the first time: “open” an object-file. • When reattending to an object: “review” the information in its object-file. • Reviewing involves reconciling old information with new.

  9. The object-specific advantage • Evidence for a object-specific type of priming (Kahneman, Treisman, & Gibbs, 1992), linked to object-file reviewing. • Facilitation for perceptually coherent objects, greater than general priming. • Suggestion is that previous perception of an object allows stored information to speed recognition.

  10. V Q Example: Preview

  11. Example: Linking

  12. V Example: SO condition

  13. Q Example: DO condition

  14. S Example: NM condition

  15. Recognition times NM DO SO

  16. Computational models of cortical vision

  17. Providing a foundation • Object-files must exist at a relatively high level of visual perception. • Important to consider both current thought about neurology of visual attention, as well as existing computational models.

  18. Models of object detection and recognition • Models of detection: • Retinotopic maps of salient regions (saliency maps). • Guide attentional processes. • Models of recognition: • Hierarchical structures (increasing selectivity/receptive field size) • Output encoding of feature conjunctions.

  19. A neural network model of object-file reviewing

  20. Neural network modeling • Connect collection of simple “neuron-like” components via weighted “synapse-like” components. • Basic neuron sums its inputs, and applies an “activation-function” to determine output. • Output is interpreted as a firing rate.

  21. Modeling the object-specific advantage • Need a recognition procedure that can be subject to facilitation (i.e. involves a time course). • Need to store “bottom-up stimulus” information in an object-specific way. • Need to provide “top-down expectation” based on stored information for currently attended object.

  22. Modeling the OSA • Correct expectation should lead to facilitation. • Incorrect expectation should not destroy general priming.

  23. Modeling facilitation • Use type based classification: when type is known, recognition is complete. • Enforce single winning type by lateral competition. • Winner is called the “stimulus type”. • Enhance time factor by using “cascaded activation” neurons.

  24. Type layer Hierarchical feature encoder V V Q S J Modeling facilitation

  25. Type layer Hierarchical feature encoder V V Q S J Modeling facilitation

  26. Type layer Hierarchical feature encoder V V Q S J Modeling facilitation

  27. Type layer Hierarchical feature encoder V V Q S J Recognised Modeling facilitation

  28. Storing stimulus: object specificity • FINSTs (Fingers of INSTantiation) identify “proto-objects” in the scene (Pylyshyn, 1989) • Track their proto-objects as they move and change size/shape. • Set of four or five FINSTs constantly assigned/reassigned from saliency map • Provide candidates for attention.

  29. Storing stimulus: object specificity • Associate a neuron with each FINST. • Selecting a FINST for attention activates its neuron. • Associate stimulus type with current FINST. • Thus a level of indirection is introduced between retinal location and mental representation.

  30. V Q S V J Q Storing stimulus: object specificity Association “stuff”

  31. Association “stuff” V Q S V J Q Storing stimulus: object specificity

  32. V Q S J Association stuff?

  33. Feedforward V Q Type FINST S J Feedback “Object-file” *Excitatory connections shown only Association stuff

  34. Feedforward V Type Q FINST S J V Feedback “Object-file” Q *Excitatory connections shown only Storing stimulus: feedback “stuff”

  35. Feedforward V Q Type FINST S J V Feedback “Object-file” Q *Excitatory connections shown only Storing stimulus: feedback “stuff”

  36. Feedforward V Type Q FINST S J V Feedback “Object-file” Q *Excitatory connections shown only Providing expectations: feedforward “stuff”

  37. Feedforward V Q Type FINST S J V Feedback “Object-file” Q *Excitatory connections shown only Providing expectations: feedforward “stuff”

  38. Storing stimulus:opening an object-file

  39. Feedforward V Q Type FINST S J V Q Feedback “Object-file” *Excitatory connections shown only Storing stimulus

  40. Feedforward V Q Type FINST S J V Q Feedback “Object-file” *Excitatory connections shown only Storing stimulus

  41. Feedforward V Q Type FINST S J V Q Feedback “Object-file” *Excitatory connections shown only Storing stimulus

  42. Feedforward V Q Type FINST S J V Q Feedback “Object-file” *Excitatory connections shown only Storing stimulus

  43. Feedforward V Q Type FINST S J V Q Feedback “Object-file” *Excitatory connections shown only Storing stimulus Recognised

  44. Feedforward V Q Type FINST S J V Q Feedback “Object-file” *Excitatory connections shown only Storing stimulus Recognised

  45. Feedforward V Q Type FINST S J V Q Feedback “Object-file” *Excitatory connections shown only Storing stimulus Recognised Stored

  46. Providing correct expectation:the SO condition

  47. Feedforward V Q Type FINST S J V Feedback “Object-file” *Excitatory connections shown only Providing correct expectation (SO)

  48. Feedforward V Q Type FINST S J V Feedback “Object-file” *Excitatory connections shown only Providing correct expectation (SO)

  49. Feedforward V Q Type FINST S J V Feedback “Object-file” *Excitatory connections shown only Providing correct expectation (SO)

  50. Feedforward V Q Type FINST S J V Feedback “Object-file” *Excitatory connections shown only Providing correct expectation (SO) Recognised

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