Your Test

1. Your Test • Email crystal.ehresman@uleth.ca to go over your test in person • 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