1. Read this article for Friday [1]Chelazzi L, Miller EK, Duncan J, Desimone R. A neural basis for visual search in inferior temporal cortex. Nature 1993; 363: 345-347.

2. “My theory is that …” Be able to complete this sentence by Nov 1 –This means you’ve completed some background reading including some primary literature –You’ve put lots of thought into crafting a testable, focused theory and predictions that follow from that theory

3. Midterm 1 Grade Distribution

4. Visual Neuron Responses LGN cells converge on “simple” cells in V1 imparting orientation (and location) specificity

5. The Feed-Forward Sweep Hierarchy can be defined more functionaly The feed-forward sweep is the initial response of each visual area “in turn” as information is passed to it from a “lower” area Consider the latencies of the first responses in various areas

6. After the Forward Sweep By 150 ms, virtually every visual brain area has responded to the onset of a visual stimulus But visual cortex neurons continue to fire for hundreds of milliseconds! What are they doing?

7. After the Forward Sweep By 150 ms, virtually every visual brain area has responded to the onset of a visual stimulus But visual cortex neurons continue to fire for hundreds of milliseconds! What are they doing? with sufficient time (a few tens of ms) neurons begin to reflect aspects of cognition other than “detection”

8. Extra-RF Influences One thing they seem to be doing is helping each other figure out what aspects of the entire scene each RF contains –That is, the responses of visual neurons begin to change to reflect global rather than local features of the scene –recurrent signals sent via feedback projections are thought to mediate these later properties

9. 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.

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

11. 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

12. 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

13. 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?

14. 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

15. 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

16. 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

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

18. 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

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

20. 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

21. Feedback Signals and Attention Instruction cues (for example 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 What does this accomplish?

22. Feedback Signals and Attention What does this accomplish? Preface to attention: Two ways to think about attention –Attention improves perception, acts as a gateway to memory and consciousness –Attention is a mechanism that routes information through the brain It is the brain actively reconfiguring itself by changing the way signals propagate through networks It is a form of very fast, very transient plasticity

23. Feedback Signals and Attention Put another way: –It may strike you as remarkable that a single visual stimulus should “activate” so many brain areas so rapidly –In fact it should be puzzling that a visual input doesn’t create a runaway “chain reaction” The brain is massively interconnected Why shouldn’t every neuron respond to a visual stimulus

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

25. Attention as Information Selection –consider a simple visual scene:

26. Attention as Information Selection –What if the scene and task gets more complex: “Point to the red vertical line”? –What has to happen in order for this task to be accomplished?

27. Point to Waldo

28. Attention as Information Selection One conceptualization of attention is that it is the process by which irrelevant neural representations are disregarded (deemphasized? suppressed?) Another subtly different conceptualization is that attention is a process by which the neural representations of relevant stimuli are enhanced (emphasized? biased?)

29. Attention as Information Selection These ideas apply to other modalities –auditory “Cocktail Party” problem –somatosensory “I don’t feel my socks” problem

30. Early Selection Early Selection model postulated that attention acted as a strict gate at the lowest levels of sensory processing Based on concept of a limited capacity bottleneck

31. Late Selection Late Selection models postulated that attention acted on later processing stages (not sensory)

32. Early Selection Early Selection model was intuitive and explained most data but failed to explain some findings Shadowing studies found that certain information could “intrude” into the attended stream –Subject’s name, loud noises, etc.

33. Late vs. Early Various hybrid models have been proposed –Early attenuation of non-attended input –Late enhancement of attended input

34. Electrophysiological Investigations of Attention

35. Modulation of Auditory Pathways Hillyard et al. (1960s) showed attention effects in human auditory pathway using ERP Selective listening task using headphones –Every few minutes the attended side was reversed –Thus they could measure the brain response to identical stimuli when attended or unattended beep beep beep beep boop beep beep beep beep boop beep beep attending LEFT Ignoring RIGHT

36. Modulation of Auditory Pathways Result: ERP elicited by attended and unattended stimuli diverges by about 90ms post stimulus –Long before response is made –Probably in primary or nearby auditory cortex

37. Modulation of Auditory Pathways Other groups have found ERP modulation even earlier – as early as Brainstem Auditory Response Probably no robust modulation as low as cochlea by ~40 ms, feed forward sweep is already well into auditory and associated cortex –Thus ERP effects may reflect recurrent rather than feed forward processes