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by: Watson Stamm Jr.

Neuroergonomics PSYC 768

Watson Stamm Jr.
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This 55 page Class Notes was uploaded by Watson Stamm Jr. on Monday September 28, 2015. The Class Notes belongs to PSYC 768 at George Mason University taught by Staff in Fall. Since its upload, it has received 27 views. For similar materials see /class/215173/psyc-768-george-mason-university in Psychlogy at George Mason University.


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Date Created: 09/28/15
Cognitive Neuroscience PSYC 768 Cognitive Neuroscience Methods 11 Raja Parasuraman Overview Information Processing Neuropsychology Computational Modeling all at the 101 level Information Processing Psychology 0 Features of the Cognitive Approach Mental Representations Transformations of Representations Information Processing Stages 0 Mental representations can be symbolic or more veridicul A dog The Information Processing View 0 Behavior is a function of the execution of different stages of information processing Sensory memory iconicechoic memory Shortterm memory Perceptual processing Symbolic or semantic processing Decision making Response selection Response execution 0 Each stage passes on its product to the next stage 0 Stages make differential demands on a central processing store attentional resources working memory Mental Chronometry 0 Study of the time course of information processing Posner 1978 Chronometric Explorations of Mind 0 Measures reaction time RT event related potentials ERPs see later Does 4 1 5 Yes Fast response Does 4 3 7 Yes Slower response Does 4 8 12 Yes Even slower response Additive Factors Method Sternberg Memory set eg A K L Z Single test item presented in or out of set eg L 39 Response Yes in set or No out of set Measure Response Time Manipulation Memory set size s R Sternberg Task Results 0 RTs for both Yes and No responses increase with memory set size 0 Increase is linear 40 msitem memory search rate 0 Slope is same for Yes and No responses exhaustive rather than selfterminating search Yes Reactlon NO Tlme Set Size Logic of the Additive Factors Method If task factors A and B add independently to RT and do not interact then they affect di erent stages of processing eg stimulus degradation affects the encoding stage response force affects the response execution stage However if two factors C and D S Encode 39 have nonadditive effects on RT and interact then they affect the same stage eg memory set size and word frequency have interactive effects on RT therefore affecting search stage seaFCh Decide espon R memory A B Additive effects on RT C D NonAdditive effects on RT Additive and NonAdditive Effects High Force Low Force RT Low High Stimulus Degradation 1 Stimulus degradation and response force do not interact affect different stages encoding and response execution respectively Low Frequency High Frequency Low High Memory Set Size 1 Memory set size and word frequency interact affect same stage memory search Logic of the Additive Factors Method With the additive factors method stages of processing can be inferred and sometimes timed However Reaction Time re ects the sum of all information processing stages hence absolute measure of processing stage time is dif cult t1 t2 t3 tn RTt1t2t 11 Brain Imaging and Stage Models of Cognition fMRI paradigms subtraction Donder s method Additive factors manipulate 2 factors and observe pattern of effects of activation 39 additive different brain regions interactive same or overlapping brain regions ERPs can provide absolute timing of processing ERPs can also provide converging evidence on timing that can be better than RT see later lecture eg P300 latency sensitive only to encoding and memory search manipulation not response selection and response execution manipulations Hence P300 latency can be a purer measure of cognitive processing time than RT ERPs and Timing of Information Processing Stages s memor P300 Latency S Encode P1 100 ms Search Decide rlespontr memoryH P3 or P300 VI 300 ms Stage Models Serial vs parallel vs cascaded processing 5quot R seria1 S R Parallel Cascaded Information Flow Top down VS Bottom up TE CT Percepts without concepts are blind concepts without percepts are empty Immanuel Kant Critique of Pure Reason 1781 Information Flow Linear vs interactive cyclic information processing 8quot R Inf rm i process1ng approach g go perception c D aetion Ecological Psychology approach Neuropsychological Methods 0 Cognitive neuroscience is not primarily interested in diagnosis management or rehabilitation of people with brain lesions disorders this is the province of clinical neuropsychology However neuropsychological data can inform information processing computational and neural models of cognition also neural reality test Two Traditions in Neuropsychology 0 Classical neuropsychology Group studies Standardized neuropsychological tests Effects of lesion of particular brain area on pattern of standardized tests Emphasis on brain localization 0 Cognitive neuropsychology Single case studies Both standardized neuropsychological and informationprocessing tests Emphasis on developing informationprocessing models of cognitive function without respect to brain localization The Lesion Method Localization of function eg Broca s area and speech production Holism eg Lashley s Views on memory storage Power of lesion method in animals direct link between brain area damaged and behavior Human no control over area damaged extent of damage etc However lesion method may be superior to neuroimaging in establishing causality of brain cognition associations Fellows et al 2005 Interpreting Lesion Results Single association Area X 9 De cit in Function A Not very powerful and potentially limited interpretation Single dissociation Area Y 9 No De cit in Function B Of greater interest particularly if teamed with single association Many neuropsychological studies are of this type eg lesion of hippocampus in animals and in patients with temporal lobe epilepsy impairs formation of long term memory whereas damage to other areas eg occipital lobe does not so much Hence hippocampus is necessary for establishment of memory Clinical example Wada sodium amytal test in neurosurgery Interpreting Lesion Results Problem with single associationdissociation method is that effects could be due to differential taskresource or task demand artifacts Shalllice 1988 task dif culty nonspeci c arousal task strategy Double dissociation Area X 9 De cit in Function A Area X 9 No De cit in Function B Area Y 9 De cit in Function B Area Y 9 No De cit in Function A Combining the Lesion Method with Cognitive Stage Models Compare performance of patient group with speci c lesion on Tasks A and B where Task A involves processing stages p1 p2 p3 pn Task B involves all of A s stages a unique stage pX Normal controls can do both tasks Patients can do Task A well but not Task B single dissociation Then can associate lesion with processing stage pX Even better with double dissociation and processing stages pX and py J l l X 4 lesion Problems with the Lesion Method Determining extent of lesion less so now with availability of MRI eg Broca39s patient s brain found in Paris museum Variability in lesion site size cf animal studies Is the brain area necessary and critical for performance Or merely associated with it Cortical tissue essential for performance or bers of passage Disconnection syndrome Virtual lesions with TMS can help resolve some problems Computational Modeling Neural Networks Some Key Historial Figures in Computing and Brain Science 0 Von Neumann 1941 0 Norbert Wiener 1945 0 Warren McCullough 1943 0 Frank Rosenblatt 1957 0 Oliver Selfridge 1958 0 David Hubel amp Torsten Wiesel 1959 0 Marvin Minsky amp Seymour Papert 1969 Von Neumann Computing Architecture 0 Permament and transient memory CPU programs and data 0 CPU for dim W d mathematicaloand loglcal operatlons Control unit to steer IIEI IOR 1 program ow Von Neumann vs Arti cal Neural Networks Von Neumann 0 Follows rules 0 Solution canmust be formally speci ed 0 Cannot generalize Not error tolerant ANN Learns from data 0 Rules on data are not Visible Able to generalize 0 Copes well With noise More biologically plausible With limits Warren McCullough McCullough W amp W Pitts 1943 A logical calculus of the ideas immanent in nervous activity Bulletin of Mathematical Biophysics 5115133 McCullough W 1949 Embodiments ofMiml MIT Press Brain as computing machine Networks of neurons hardwired analog summation switches Mind emerges from nonhierarchical net of neurons F 5 35quot 1 WI 1 mamas 11mm McCullough amp Pitts 1943 Neurons are connected by directed weighted paths positive excitatory or negative inhibitory Each neuron has a fixed threshold If the net input into the neuron is greater than the threshold the neuron fires The threshold is set such that any nonzero inhibitory input will prevent the neuron from firing 69 Threshold of Y gt4 Feature Detectors Hubel amp Wiesel 1959 Singleunit recordings from cat striate cortex 0 Selective response to orientation 0 Selective response to other Visual features Hubel amp Weisel featural hierarchy topographical mapping hy er complex ce Is complex cells Simple cells quot MCP Neural Networks for Complex Feature Detection and Action for a Bird Wishing to Eat Object Purple Round Eat fBlueberry Yes Yes Yes Golf ball No Yes No Violet Yes No No Hamburger No No No Networks that Learn i 2 p M u 0 1949 Donald Hebb The Organization of Behavior When an axon of cell A is near enough to excite cell B and repeatedly 0r persistently takes part in ring it some growth process or metabolic change takes place in one or both cells such thatA s efficiency as one of the cells ring B is increased Arti cial Neural Networks or Connectionist Models 1982 Hop eld Neural network model of associative memory 1984 Development of 3layer and multilayer models 1985 Rumelhart et al The Back Propagation Learning Rule 1986 Sejnowski s NETTalk Phonetics system that quickly in 12 hours learned to read and translate text patterns into sounds with a 95 success rate 0 Demonstrated on the Johnny Carson s The Today Show where the machine sounded uncannily like a child learning to read aloud while it was training Nodes Neurons 0 Each node in an ANN has an input summation function an activation threshold function and an output aj Activation value of unit j Wij Weight on the link from unit j to unit i ini Weighted sum of inputs to unit i mi j aj ai Activation value of unit i also known as the output value g Activation function ai gi i 2ng j w Activation Function Learning Rules mi i al 3136 111 Simple Delta Rule Awij nTj ai aj Where n learning rate Tj Training output Back Propagation Rule Simple delta rule modi ed by squashing function eg sigmoid and computed recursively from out put back through output layer hidden layers to input layer A Pattern Recognition Problem for Red Riding Hood Approach kindly grandmother or handsome woodcutter Run away from the Big bad wolf Training Red Riding Hood Big ears Run away Big eyes Scream l L k f Bl teeth v 339 v v 00 or g woodcutter Kindly Kiss on cheek Wrinkled Approach Handsome Offer food to Flirt with The Big Bad Wolf Big ears 1 1 Run away Big eyes 1 1 Scream Bi teeth v v r 1 Look for g 1 O v woodcutter Kindly 0 0 Kiss on cheek Wrinkled 1 0 Approach Handsome 0 0 Offer food to 0 Flirt with Grandma Big ears 1 0 Run away Big eyes 1 0 Scream v v 9 39 9 r e 439o39x Big teeth 0 v V 0 Look for 39 i woodcutter Kindly 1 1 Kiss on cheek Wrinkled 1 1 Approach Handsome 0 1 Offer food to 0 Flirt with Big ears Big eyes Big teeth Kindly Wrinkled Handsome The Handsome Woodcutter 0 0 0 0 0 0 1 01 0 1 1 0 Run away Scream Look for woodcutter Kiss on cheek Approach Offer food to Flirt with Training for Big Bad Wolf Detection Connection Weight Matrix Outijfi igtor 1 4 2 4 2 3 3 1 1 0 2 3 2 1 2 1 1 5 2 3 1 2 5 1 0 3 3 O 4 4 2 0 1 0 4 2 0 5 4 0 0 1 3 2 3 1 2 0 1 4 O 5 0 3 0 Error Correction Output Vector Desired Error Vector iteration 139 Output Vector iteration i 8 1 2 1 1 9 1 Apply backprop to 3 0 change weight matrix 7 0 3 0 4 0 7 o I Validating the Trained Network New data sets New examplars different big bad wolves grandmothers woodcutters etc Generalization to ambiguous stimuli handsome wolves evil grandmas nerdy woodcutters etc Incomplete or degraded stimuli Network lesioning Current Status of Connectionist Models 39 Multilayer networks can account for many effects in diverse domains of cognition letter and word recognition bottomup factors in visual attention speech recognition 0 Models are becoming more biologically plausible Connectivity patterns eg two parallel systems of vision dorsal and ventral pathways see later lecture Learning rules eg not just back propagation which would be dif cult to instantiate in biological systems Largescale models eg 10000 nodes as in models of retinal function Cognitive Neuroscience PSYC 768 in u I Cognitive Neuroscience I Neuroimaging Rafa Parasummcm Overview Relative Merits of Neuroimaging Methods PET fMRI The role of TMS sum NEUROIMAGING TEC HNIQUES Only pv vide windows 01 telescopes into hm fumim Kcyhnk mm Neuroimaging results can mirror those of the 3 Blind Men and the Elephant 0 It s a trumpet 0 It s a snake 0 etc 0 It s the fear center 0 It s the seat of the soul 0 etc Neuroimaging Alphabet Soup EEG Electroencephalography1930s ERPs EventrRelated Potentials 19605 CT Computed Tomography 19805 MEG Magnetoencepnalograpny 19805 a MRI Magnetic Resonance imaging 19805 PET Positron Emission Tomography 19805 a E SPECT single Photon Emission Computed Tomography 19905 M Functional Magnetic Resonance imaging 19905 TCDS Transcranial Doppler Sonograpny 19905 a NiRs Near Infrared Spectroscopy 19905 quotquotTMS Transcranial Magnetic Stimulation 19905 leRS Fast Near ln 39ared Spectroscopy 20005 CT Scanner and CT Scan Frillth uI c1 Frillth uI c1 mm mm Wmhuu quot V Prilw39plzsunvml I azukmxnmwzbnkwxuumuu r mm m WWW ammumwu Prilw39plzsunvml mm c1 an MRI in Cug Nemsuulce wr m39www m mmwnmn quotPlum1 on W m 40 um Principls uIFET cunt MRI vs fMRI MRI examines brain anatomy fMRI examines brain function Principles of fMRI Tracks localized changes in oxygen utilization in cerebral microvasculature However unlike PET does not use ionizing radiation Whereas MRI tracks hydrogen protons fMRI images hemoglobin in red blood cells Magnetic properties of oxygenated and deoxygenated hemoglobin differ with the latter being more paramagietic Principles of fMRI c0ntd Increased neural activity leads to increased blood ow Neurons do not use all the oxygenated blood hence there is a net increase in oxygenated to deoxygenated blood leading to an increase in the MR signal the blood oxygenation level depend effect BOLD The rise in blood oxygenation is delayed following the onset of neural activity and prolonged for several seconds fMRI spatial resolution 3 mm better with high eld 4T magIets SlldEZl One of the rst MRI studies Kwong et al 1992 Flickering Checkerboard oFF an s r 0 an 5 OFF an 5 ON ED 5 7 OFF an s nsa Sana BOLD signal w sigma Inaul39y Slide 22 MRI Scanners and fMRI Scans SleE 23 fMRI Methodology Subtraction A Look at the Past Donders and Sternberg I Franciscus Donders 1860 Dutch physiologist I Developed apparatus to measure times to within about 10 milliseconds technical achievement for that period 7 A Reaction Simple Reaction Time to a Stimulus 7 One S One R Respond with R when S comes on r B Reaction Discrimination RT iTwo or more S One R Respond with one R when target S comes on but not the other S C Reaction Choice RT 7 Two or more S Two or more R with corresponding R Frnnviws cumin l39vnd h 3 H4 fMRI Methodology Subtraction A Look at the Past Donders and Sternberg RT A 39 processing motor processing motor execution 7 lofA Dis 39 39 39 time I RT 7 criminath c 7 A A Choice selection time Typically RTAlt R lt I 5 u tiaL tiVe ethod 7 stimulus disuimination identi catinn time RT B 7 RT A 7 Response choice selection time RT 0 7 RT B Stemberg 1966 extended Dondel39s method in hisAddt39tivz Putters Mz md see next Lecture Elli 25 VIRI Methodology Subtraction MRl signal stIength for 7 Fixatinn 7 as 7 Taskrelated artivation MRi Task 7 mm Fixatinn More speci ca y 7 Cnnditinn A Sensnry Mntm39 pence sing 7 Cnnditinn B Sensnry Cognitive Mntm39 mcessing Cn 39tive ac va nn MRI B 7 MRI A only inthe cognitive processing stage Elli 25 fMRI Designs Subtraction Technique 7 To isolate brain activation associated with a cognitive operation 7 BOLD signal for cognitive operation x signal for baseline 7 Similar to Donders39 and ternberg s additive actors method gt 7 Alternate x and baseline 7 Use t test or ANOVA to test for signi mnoe of activation difference between x and baseline xper Cognitive operations in control X b E Su traction yields miva on associated with 55m 27 fMRI Designs c0ntd I Eventrelated Design Similar to ERP designs Measure average BOLD signal to repetition of single events Can be done after data collection and as a function of both stimulus and response eg correct incorrect slowfast responses factors IIII Different discrete events BOLD signal averaged for different repetitions of event or combined event and response like ERPs M 28 What aspect of neural activity does the BOLD signal index Logothetis et al 2001 Local Field Potentials LFP Multi7Unit Activity MUA Logothetis et al 2001 found that D activity is closely related to LFPs This points to a link between BOLD and EEGERPs Slide 29 The Role of TMS Neuroimaging techniques have the following features 7 Use statistical testing 7 ttest ANOVA etc examine significance of increases in blood flow in localized brain regions during a cognitive operation 7 All such tests can be seen as generalization of regression 7 Hence changes in neuroimaging measures eg BOLD signal increase are carrelatiunal 7 eg Task Manipulation X 7 Cognitive Process a Y a Brain Area Z 7 But is brain area Z necessary for cognitive process Y Brain area Z Cognitive process Y Slide 30 10 w nw njmxmmmwh Principls uITMS mm 3953quot awmwm 39 r WWWMquot wmmmma lnilil ms Vanni Sludis m ehb WxAumd l hp ymmpmmmm mm Arraignmean m


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