1. Your Test Email crystal.ehresman@uleth.ca to go over your test in personcrystal.ehresman@uleth.ca See me if you have to complain about something

2. Extra-RF Influences consider texture-defined boundaries –classical RF tuning properties do not allow neuron to know if RF contains figure or background –At progressively later latencies, the neuron responds differently depending on whether it is encoding boundaries, surfaces, the background, etc.

3. Extra-RF Influences How do these data contradict the notion of a “classical” receptive field?

4. Extra-RF Influences How do these data contradict the notion of a “classical” receptive field? Remember that for a classical receptive field (i.e. feature detector): –If the neuron’s preferred stimulus is present in the receptive field, the neuron should fire a stereotypical burst of APs –If the neuron is firing a burst of APs, its preferred stimulus must be present in the receptive field

5. Extra-RF Influences How do these data contradict the notion of a “classical” receptive field? Remember that for a classical receptive field (i.e. feature detector): –If the neuron’s preferred stimulus is present in the receptive field, the neuron should fire a stereotypical burst of APs –If the neuron is firing a burst of APs, its preferred stimulus must be present in the receptive field

6. Recurrent Signals in Object Perception Can a neuron represent whether or not its receptive field is on part of an attended object? What if attention is initially directed to a different part of the object?

7. Recurrent Signals in Object Perception Can a neuron represent whether or not its receptive field is on part of an attended object? What if attention is initially directed to a different part of the object? Yes, but not during the feed-forward sweep

8. Recurrent Signals in Object Perception curve tracing –monkey indicates whether a particular segment is on a particular curve –requires attention to scan the curve and “select” all segments that belong together –that is: make a representation of the entire curve –takes time

9. Recurrent Signals in Object Perception curve tracing –neuron begins to respond differently at about 200 ms –enhanced firing rate if neuron is on the attended curve

10. Feedback Signals and the binding problem What is the binding problem?

11. Feedback Signals and the binding problem What is the binding problem? curve tracing and the binding problem: –if all neurons with RFs over the attended curve spike faster/at a specific frequency/in synchrony, this might be the binding signal

12. Feedback Signals and the binding problem What is the binding problem? curve tracing and the binding problem: –if all neurons with RFs over the attended curve spike faster/at a specific frequency/in synchrony, this might be the binding signal But attention is supposed to solve the binding problem, right?

13. Feedback Signals and the binding problem So what’s the connection between Attention and Recurrent Signals?

14. Feedback Signals and Attention One theory is that attention (attentive processing) entails the establishing of recurrent “loops” This explains why attentive processing takes time - feed-forward sweep is insufficient

15. Feedback Signals and Attention Instruction cues (for exaple in the Posner Cue-Target paradigm) may cause feedback signal prior to stimulus onset (thus prior to feed-forward sweep) think of this as pre-setting the system for the upcoming stimulus

16. Feedback Signals and Attention We’ll consider the role of feedback signals in attention in more detail as we discuss the neuroscience of attention