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IV.Brain Function II: Evidence from Neuroanatomy and Perception Brain, Mind, and Belief: The Quest for Truth “Our neural pathways establish reruns of what.

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Presentation on theme: "IV.Brain Function II: Evidence from Neuroanatomy and Perception Brain, Mind, and Belief: The Quest for Truth “Our neural pathways establish reruns of what."— Presentation transcript:

1 IV.Brain Function II: Evidence from Neuroanatomy and Perception Brain, Mind, and Belief: The Quest for Truth “Our neural pathways establish reruns of what has gone on before. Like the three-year-old who insists on watching The Little Mermaid over and over again, we cling to our warped illusions with a tenacious grip. Get your bloody hands off my illusion! Even though it makes us miserable, we prefer to place our faith in the disaster we have made.” Pam Grout

2 Where we have been  Figuring out how the brain works  Methods in general use Lesion studies Functional brain imaging  Guiding principles in current use Tool-driven inquiry  Functional brain imaging Misapplied metaphor  The brain is a computer  A better way Think harder Use evidence from linguistics 2

3 Thinking harder  Avoid metaphorical thinking The brain is not a computer Not like a human being with paper & pencil & books In fact it is not like anything else  It is itself: the brain 3 REVIEW

4 Where we are  The mind is a relational network system As revealed by evidence from linguistics  We can study relational networks at different levels of precision Abstract network notation Narrow network notation  Hypothesis: Relational networks are implemented in neural structures 4

5 Levels of precision  Abstract relational network notation  Narrow relational network notation  Neural structures A node of narrow RN notation is implemented physically as a bundle of neurons

6 Where we are headed  Consider further evidence Neuroanatomy Perceptual neuroscience  Examine further findings on perception 6

7 Functional bundles of neurons: Cortical columns Vernon Mountcastle, Perceptual Neuroscience (1998), p. 192 “[T]he effective unit of operation…is not the single neuron and its axon, but bundles or groups of cells and their axons with similar functional properties and anatomical connections.”

8 The cortical (mini)column Compare: atom and molecule :: neuron and column of neurons Molecule: a bundle of atoms that function together as a unit C ortical Column: a bundle of neurons that function together as a unit

9 Gray matter and white matter Gray matter White matter

10 Coronal section magnified From top to bottom, About 3 mm Has 6 layers

11 Microscopic views 11 Different stains show different features

12 The node of narrow RN notation vis-à-vis neural structures  The node corresponds not to a single neuron but to a bundle of neurons  The cortical column  A column consists of neurons stacked on top of one another  All neurons within a column act together When a column is activated, all of its neurons are activated

13 Large-scale cortical anatomy  The cortex in each hemisphere Appears to be a three-dimensional structure But it is actually very thin and very broad  The grooves – sulci – are there because the cortex is “crumpled” in order to fit inside the skull 13

14 Topologically, the cortex of each hemisphere (not including white matter) is..  Like a thick napkin, with Area of about 1300 square centimeters  200 sq. in.  2600 sq cm for whole cortex Thickness varying from 3 to 5 mm Subdivided into six layers  Just looks 3-dimensional because it is “crumpled” in order to fit inside the skull 14

15 Topological essence of cortical structure  Each column represents a node  The network is thus a large two-dimensional array of nodes  Third dimension for Internal structure of the nodes (columns) Cortico-cortical connections (white matter) 15

16 Neurons, Columns, Cortex  At the small scale.. Each column contains around 80 neurons  At a larger scale.. Each column acts as a node of the cortical network  The cerebral cortex as an array* of columns: Grey matter — columns of neurons White matter — inter-column connections 16 *Array: two dimensional (a lot simpler than 3-dimensional)

17 Composition of a typical minicolumn  Contains about 80 neurons Range: 70 to 110 Mostly pyramidal neurons  Cell bodies of these neurons are “stacked” vertically (i.e., in a column – hence the name)  Fibers extending from the cell bodies Many are vertical (especially those of pyramidal cells) Some are horizontal  They connect to neighboring columns 17

18 Evidence for columns  Microelectrode penetrations of cortex Electrode is small enough to detect activation in a single neuron  If perpendicular to cortical surface Neurons all of same response properties  If not perpendicular Neurons of different response properties 18

19 Column in a cat’s cortex for a point on the cat’s paw

20 Columns as functional units: Orientation of lines (visual cortex) Microelectrode penetrations K. Obermayer & G.G. Blasdell,

21 Bundles of columns  Minicolumn – microns diameter  Maxicolumn – a contiguous bundle of minicolumns (typically around 100) microns diameter Dimensions vary from one part of cortex to another In some areas at least, they are roughly hexagonal  (There are also larger bundles) 21

22 Columns of different sizes View: looking downward from top of column. So each circle represents a column 22 MinicolumnLarger column

23 Cortical minicolumns: Quantities  Diameter of minicolumn: 30 microns  Neurons per minicolumn: (avg )  Minicolumns/mm 2 of cortical surface: 1460  Minicolumns/cm 2 of cortical surface: 146,000  Neurons under 1 sq mm of cortical surface: 110,000  Approximate number of minicolumns in Wernicke’s area: 2,920,000 (at 20 sq cm for Wernicke’s area) (Wernicke’s area is devoted to speech recognition) Cf. Mountcastle 1998: 96 23

24 Cortical column operation  The linguistic system operates as a network whose nodes are cortical columns  Columns do not store symbols  Their basic function: receive and send activation Integration: A column is activated if it receives enough activation from other columns  Can be activated to varying degrees  Can keep activation alive for a period of time Broadcasting: An activated column transmits activation to other columns  Excitatory – contribution to higher level  Inhibitory – dampens competition at same level 24

25 Integration and Broadcasting Integration Broadcasting Wow, I got activated! Now I’ll tell my friends! 25

26 Operations in neurocognitive networks  Activation moves along lines and through nodes  (along the pathways of the brain) Integration Broadcasting  Connection strengths are variable A connection becomes stronger with repeated successful use A stronger connection can carry greater activation 26

27 Basic answer to the what/how question: What goes on in those nodes of the network?  Broadcasting To multiple locations In parallel  Integration 27 Integration and Broadcasting

28 Part of the network for FORK V C Each node in this diagram represents a cortical column M T 28 C — conceptual M — motor T — tactile V — visual

29 Part of the network for FORK V C Each node in this diagram connects to a supporting subnet. For example, Let’s zoom in on this one M T 29

30 Zooming in on the “V” Node.. FORK Etc. etc. (many layers) A network of visual features V The cardinal node of this subnet 30

31 Some nodes of the cortical net for fork V MC T P Au Ar 31 Ar – Articulation Au – Auditory C – Conceptual M – Motor P – Phonological T – Tactile V – Visual

32 Some nodes of the cortical net for fork V MC T P PA PP 32

33 Perception: the basic process  A bottom-up process From primary perceptual area upwards  E.g. primary auditory, for auditory perception  Multiple steps of integration and broadcasting  Takes place in a perceptual area of cortex E.g. auditory cortex for auditory perception  Works by integrating inputs to the associated sense organ E.g. auditory input for auditory perception Multiple steps of integration  From very simple  To more complex 33

34 Perception: Multiple steps of integration and broadcasting DOG From lower levels up to higher levels V 34

35 These are cortical (network) structures that have to be learned  Experiment by David Hubel and Torsten Wiesel  Kittens kept in dark room during critical period for developing vision  Exposed to vertical lines but not horizontal lines  Later, bumped into strings stretched horizontally in their path Couldn’t see them  Their eyes received the information  But their brains couldn’t integrate it 35

36 Hints of what goes on in visual perception (multiple steps of integration and broadcasting) I: Shapes recognized by different low level columns 36

37 Hints of what goes on in visual perception (multiple steps of integration and broadcasting) II. Relatively higher level (but still quite low) 37

38 Hints of what goes on in visual perception (multiple steps of integration and broadcasting) III. At a somewhat higher level 38

39 Hints of what goes on in visual perception (multiple steps of integration and broadcasting) IV. Somewhat higher level Elementary shapes like these.. 39

40 Hints of what goes on in visual perception (multiple steps of integration and broadcasting) IV. Somewhat higher level..can be integrated into more complex formations 40

41 We see only the past  Perception is a bottom-up process From primary perceptual area upwards  Step by step through multiple levels Using network connections that have been established These connections have been built step by step  From lower levels to higher levels  As a result of previous experience  The whole perceptual structure is built through experience Therefore, it is based upon the past  Hence, we see nothing as it is now 41

42 Returning to work after 30 years Play video

43 Perception: Refining the starting (simple) view  The simple (starting) view: A single perceptual modality  Auditory perception in auditory cortex  using auditory information  Step by step from bottom up  Complications/Refinements 1It is not confined to a single perceptual modality 2Not just bottom-up 3Not even confined to posterior cortex 43

44 The McGurk Effect  Acoustic syllable [ba] presented to subjects  with visual presentation of articulatory gestures for [ga]  Subjects typically heard [da] or [ga]  “Evidence has accumulated that visual speech modifies activity in the auditory cortex, even in the primary auditory cortex.” Mikko Sams (2006) How does it work? (1)Visual input (2)Top-down processing 44

45 Refining the starting (simple-minded) view: I 1. It is not confined to a single perceptual modality Example: The McGurk effect  Auditory perception affected by visual input  i.e., top-down processing from visual to auditory Conceptual structure affects auditory perception  The influence of context on speech perception  She cooked it in the frying  an  I’ll help you if I  an 45

46 An important finding from neuroanatomy: Reciprocal connections  An established fact of neuroanatomy: A connection from point A to point B in the cortex is generally accompanied by a connection from point B to point A  Separate fibers (axons): (1) A to B, (2) B to A  In short, cortico-cortical connections are generally bidirectional  Hence, Bidirectional Processing 46 A B

47 Bidirectional processing: reciprocal links excitatory inhibitory 47

48 Perception – Refining a simple-minded view: II 1. Not confined to a single perceptual modality Example: The McGurk effect  Visual input affects auditory perception Conceptual structure affects auditory perception 2. Not just bottom-up Top-down processing fills in unsensed details 3. Not even confined to posterior cortex 48

49 Perception: All these lines represent bi-directional connections DOG Etc. etc. (many layers) V 49

50 A terminological problem  We need to distinguish Perception narrowly conceived  The basic process of recognition  Single perceptual modality  Bottom-up processing  No motor involvement Perception broadly conceived  Two different terms needed Recognition (a.k.a. ‘microperception’)  Bottom-up process in a single perceptual modality Perception (the broad conception) (a.k.a. ‘macroperception’) 50

51 “Micro-perception” and “macro-perception”  Microperception and macroperception  Microperception A.k.a. recognition The local process of integrating features Performed in one perceptual modality Bottom-up  Macroperception The overall process of perception Uses multiple modalities Uses top-down processing 51

52 Perception – Refining a simple-minded view 1. Not just bottom-up Top-down processing fills in unsensed details 2. Not confined to a single perceptual modality The McGurk effect  Visual input affects auditory perception Conceptual structure affects auditory perception 3. Not even confined to posterior cortex Can also use motor neurons (frontal cortex)  Experiment: left hand or right hand?  Mirror neurons 52

53 Left hand or right hand? 53

54 Left hand or right hand? 54

55 Left hand or right hand? 55

56 Left hand or right hand? 56

57 Left or right hand?  How do you do it?  Imaging experiment  Subjects were shown pictures of one hand  Asked to identify: left or right  Functional imaging showed increased CBF in hand area of motor cortex Peter Fox, ca

58 Motor structures in perception  The left-hand vs. right-hand experiment  ‘Mirror neurons’ in motor cortex  Articulation as aid to phonological perception  Articulation in reading  Motor activity in listening to music  Watching an athletic event 58

59 Mirror Neurons  NY Times: “One mystery remains: What makes them so smart?” (Jan. 10, 2006)  Answer: They are not smart in themselves Their apparent smartness is a result of their position: at top of a hierarchy Compare:  The general of an army  The head of a business  Similarly, high-level conceptual nodes The “cardinal node” 59

60 Mirror Neurons  What makes mirror neurons appear to be special? Ans.: They receive input from visual perception The superior longitudinal fasciculus  Connects visual perception to motor areas  How can a motor neuron receive perceptual input? Motor neurons are supposed to operate top-down Answer: bidirectional processing  They also receive perceptual information Bottom-up processing 60

61 Superior Longitudinal Fasciculus From O. D. Creutzfeldt, Cortex Cerebri (1995) 61

62 Are some neurons “smarter” than others?  Alternative: the head of a dedicated net Dedicated nets have hierarchical structure It is the hierarchy as a whole that has those ‘smarts’ Similarly, mirror neurons  They get visual input since they are connected to visual areas Superior longitudinal fasciculus 62

63 Implications of hierarchical organization  Nodes at a high level in a hierarchy may give the appearance of being very “smart”  This appearance is a consequence of their position — at top of hierarchy  As the top node in a hierarchy, a node has the processing power of the whole hierarchy Compare:  The general of an army  The head of a business organization 63

64 Perception of height – Experiment by P. R. Wilson (1968)  Subjects were students in an Australian university Five separate classes A man was introduced as a visitor from Cambridge Univ. ① Class 1: introduced as a student ② Class 2: introduced as a demonstrator ③ Class 3: introduced as a lecturer ④ Class 4: introduced as a senior lecturer ⑤ Class 5: introduced as a professor The man then left the room Students were asked to estimate his height Height estimates increased avg. ½ inch for each step “Professor” was estimated to be 2 ½ inches taller than “student” 64

65 Perception: We see what we expect to see Top-down effects in thinking and perception The mechanism: bidirectional connections Conceptual structure influences perceptual operations Higher-levels of perceptual structure can likewise influence lower levels We see what we expect to see  Where do the expectations come from?  Ans: From information already present in our conceptual/perceptual systems Thus to a large extent we see only the past 65

66 T h a t ‘ s i t f o r t o d a y ! 66


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