Psychology 2003

Psychology 4940, Section 995 – Neurophysiology for Cognitive Researchers
Syllabus Spring 2014
What: An online seminar to cultivate your understanding of current and classic neurophysiological studies of
attention, priming, memory, affect, and decision-making.
Instructor: Teresa Hawkes, Ph.D.
Email: [email protected]
Office: Skype: teresa.hawkes1
Where: Required Course Websites:
Online at D2L
OU Psychology Seminar 4940 995 Facebook Page:
When: January 13 – May 2, 2014
“THESE days it is easy to get irritated with the exaggerated interpretations of brain imaging — for example, that
a single fMRI scan can reveal our innermost feelings — and with inflated claims about our understanding of the
biological basis of our higher mental processes.”
--Eric Kandel, NY Times, 9-06-2013
We will learn how to interpret current evidence for the neural correlates of cognitive behaviors.
Course Overview:
Uses D2L system to deliver lecture material, assignments, discussion board activities, and exams.
Instructor will post two example slide presentations. The instructor creates and actively guides the Discussion
Board, and creates a Facebook page of anatomical, physiological, and cognitive information drawn from peerreviewed sources.
Students will learn to think critically about current studies on the neurophysiological basis of cognition. They
will become familiar with some of the methods and statistical tools used at the molecular, cellular, systems, and
cognitive levels.
Course Materials:
Original research and review papers supplied by the Instructor.
TEXT: Cognitive Neuroscience, 3rd Edition. Marie T. Banich and Rebecca J. Compton. Wadsworth Cengage
Students select a primary paper from a list of papers in the five key areas of Attention, Priming,
Memory, Affect, and Decision-making to present to the class. Students may present a research or modeling
paper. All students will read the methods and review papers. Students then create a powerpoint presentation
with substantive presentation notes. Powerpoints and notes are posted to D2L Discussion Board. All students
must respond substantively to each presentation. A rubric for the powerpoint, presentation notes, and student
responses will be provided the first week of class.
There will be two midterm exams covering Chapters from the text and the papers being presented.
Final exam is essay. Students select four of eight possible long answer and data analysis questions
covering primary paper presentations.
Grade Components:
1) Powerpoint presentation (100 points)
2) Presentation notes (includes annotation of five of the research paper’s and review paper’s
references). (50 points)
3) Discussion Board Participation (100 points)
4) 2 midterm exams (multiple choice questions) covering neurophysiology of Attention,
Priming, Memory, Affect, and Decision-making. (75 points each-150 points total)
5) Final essay exam (100 points)
Grading Scale:
A is 90-100
B is 80-89
C is 70-79
D is 60-69
F is below 60
Academic Misconduct. Two simple academic misconduct rules to remember:
1. All work must be your own; otherwise it is a violation of the academic conduct code. This means that
working with another student or students on your homeworks, quizzes, Excel worksheets, exams or
discussion contributions is prohibited. Furthermore, using resources from past sections is also
prohibited. All written material will be processed by turnitin plagiarism-checking software.
2. See the OU student academic conduct code for all other rules and regulations at
Other. When contributing to D2L, be respectful of your fellow students. Maintain a professional demeanor in
discussions or chats.
Work Schedule
Week of
Methods Papers
Midterm 1
Midterm 2
Final Exam
Tentative Papers
Logothetis, N.K., Pauls, J., Augath, M., Trinath, T., and Oeltermann, A . (2001). Neurophysiological
investigation of the basis of the fMRI signal. Nature 412, 150-157.
Luck, S.J. (2004). Ten simple rules for designing and interpreting ERP experiments. In Handy, T.C., Eventrelated potentials: a methods handbook. Boston: MIT Press.
He, B., Yang, L., Wilke, C., and Yuan, H. (2011). Electrophysiological imaging of brain activity and
connectivity – challenges and opportunities. IEEE Trans Biomed Eng, 58, 1918-1931.
Frank, M.J. and Fossella, J.A. (2011). Neurogenetics and pharmacology of learning, motivation, and
cognition. Neuropsychopharmacology Reviews, 36, 133-152.
Thayer, J.F., Ahs, F., Fredrikson, M., Sollers III, J.J. and Wager, T.D. (2012). A meta-analysis of heart rate
variability and neuroimaging studies: implications for heart rate variability as a marker of stress and health.
Neuroscience and Biobehavioral Reviews 36, 747-756.
Corbetta, M., and Shulman, G.L. (2002). Control of goal-directed and stimulus-driven attention in the
brain. Nature Reviews Neuroscience, 201-215.
Posner, M.I. (2012). Imaging Attention Networks. NeuroImage, 61, 450-456.
Verhaeghen, P. and Cerella, J. (2002). Aging, executive control, and attention: a review of meta-analyses.
Neuroscience and Biobehavioral Reviews, 849-857.
Dux, P.E. and Marois, R. (2009). The attentional blink: A review of data and theory. Attention,
Perception, & Psychophysics, 1683-1700.
Best, J.R. and Miller, P.H. (2010). A developmental perspective on executive function. Child
Development, 81, 1641-1660.
Luck, S.J., Woodman, G.F. and Vogel, E.K. (2000). Event-related potential studies of attention. Trends in
Cognitive Sciences, 4, 432-440.
Dehaene, S., Changeux, J-P. (2005). Ongoing spontaneous activity controls access to consciousness: a
neuronal model for inattentional blindness. PLOS Biology, 910-927.
Frith, C. (2001). A framework for studying the neural basis of attention. Neuropsychologia, 1367-1371.
Marcovitch, S. and Zelazo, P.D. (2009). A hierarchical competing systems model of the emergence and
early development of executive function. Developmental Science, 12, 1-18.
Hopfinger, J.B., Woldorff, M.G., Fletcher, E.M., and Mangun, G.R. (2001). Dissociating top-down
attentional control from selective perception and action. Neuropsychologia, 1277-1291.
Green, J.J. and McDonald, J.J. (2008). Electrical neuroimaging reveals timing of attentional control
activity in human brain. PLOS Biology, 1-9.
Grent-‘t-Jong, T. and Woldorff, M.G. (2007). Timing and sequence of brain activity in top-down control
of visual-spatial attention. PLOS Biology, 5, 114-1126.
Milham, M.P., Banich, M.T., Claus, E.D., and Cohen, N.J. (2003). Practice-related effects demonstrate
complementary roles of anterior cingulate and prefrontal cortices in attentional control. NeuroImage, 18, 483493.
Chennu, S., Craston, P., Wyble, B., and Bowman, H. (2009). Attention increases the temporal precision of
conscious perception: verifying the neural-ST2 model. PlOS Computational Biology, 5, 1-13.
Fan, J., Byrne, J., Worden, M.S., Guise, K.G., McCandliss, B.D., Fossela, J. and Posner, M.I. (2007). The
relation of brain oscillations to attentional networks. Journal of Neuroscience, 27, 6197-6206.
Hopf, J.-M. and Mangun, G.R. (2000). Shifting visual attention in space: an electrophysiological analysis
using high spatial resolution mapping. Clinical Neurophysiology, 111, 1241-1257.
Dien, J., Spencer, K.M. and Donchin, E. (2004). Parsing the late positive complex: mental chronometry
and the ERP components that inhabit the neighborhood of the P300. Psychophysiology, 41, 665-678.
Theeuwes, J. (2013). Feature-based attention: it is all bottom-up priming. Philosophical Transactions of
the Royal Society B: Biological Sciences; 368, 1-11.
Stevens, W.D., Wig, G.S., and Schacter, D.L. (2008). Implicit memory and priming. Learning and memory:
a comprehensive reference, volume 2, 623-644.
Lavigne, F. and Darmon, N. (2008). Dopaminergic neuromodulation of semantic priming in a cortical
network model. Neuropsychologia, 46, 3074-3087.
Wilcox, T., Hirschkowitz, A., Hawkins, L. and Boas, D.A. (2013). The effect of color priming on infant brain
and behavior. NeuroImage, doi: 10.1016/j.neuroimage.2013.08.045.
Rossell, S.L., Price, C.J. and Nobre A.C. (2003). The anatomy and time course of semantic priming
investigated by fMRI and ERPs. Neurpsychologica, 41, 550-564.
Ulrich, M., Hoenig, K., Gron, G., and Kiefer, M. (2013). Brain activation during masked and unmasked
semantic priming: commonalities and differences. Journal of Cognitive Neuroscience,
Gibbons, H., Wiegleb, N. and Stahl, J. (2013). Levels of visuo-spatial selection: an ERP study of negative
priming. Brain and Cognition, 83, 203-217.
Kaiser, D., Walther, C., Schweinberger, S.R., and Kovacs, G. (2013). Dissociating the neural bases of
repetition-priming and adaptation in the human brain for faces. Journal of Neurophysiology,
Hassler, U., Friese, U., Martens, U., Trujillo-Barreto, N. and Gruber, T. (2013). Repetition priming effects
dissociate between miniature eye movements and induced gamma-band responses in the human
encephalogram. European Journal of Neuroscience, 38, 2425-2433.
Instructor: Dan, Y. & Poo, M-M. (2004). Spike timing-dependent plasticity of neural circuits. Neuron, 44,
Lomo, T. (2003). The discovery of long-term potentiation. Philosophical Transactions of the royal Society
London B, 358, 617-620.
Fretham, S.J.B., Carlson, E.S. and Georgieff, M.K. (). The role of iron in learning and memory. Advances
in Nutrition, 2, 112-121.
West, R. (2011). The temporal dynamics of prospective memory: a review of the ERP and prospective
memory literature. Neuropsychologia, 49, 2233-2245.
Gomez-Palacio-Schjetnan, A. & Escobar, M.L. (2013). Neurotrophins and synaptic plasticity. Current
Topics in Behavioral Neuroscience, 15, 117-36.
Baddeley, A. (2012). Working memory: theories, models, and controversies. Annual Reviews of
Psychology, 63, 1-29.
Wallis, G. (2013). Toward a unified model of face and object recognition in the human visual system.
Frontiers in Psychology, doi: 10.3389/fpsyg.2013.00497.
Bliss, T.V.P. & Lomo, T. (1973). Long-lasting potentiation of synaptic transmission in the dentate area of
the anaesthetized rabbit following stimulation of the perforant path. Journal of Physiology, 232, 331-356.
Luu, P., Tucker, D.M. and Stripling, R. (2007). Neural mechanisms for learning actions in context. Brain
Research, 1179, 89-105.
Instructor: Markram, H., Lubke, J., Frotscher, M. & Sakmann, B. (1997). Regulation of synaptic efficacy
by coincidence of postsynaptic APs and EPSPs. Science, 275, 213-215.
Tran, P.V., Dakoji, S., Reise, K.H., Storey, K.K. and Georgieff, M.K. (2013). Fetal iron deficiency alters
proteome of adult rat hippocampal synaptosomes. American Journal of Physiology Regul Integr Comp Physiol,
Klaver, P. & Talsma, D. (2013). Behind the scenes: how visual memory load biases selective attention
during processing of visual streams. Psychophysiology, doi: 10.1111/psyp.12126.
Kragel, J.E. & Polyn, S.M. (2013). Functional interactions between large-scale networks during memory
search. Cerebral Cortex, doi:10.1093/cercor/bht258.
Montalbano, A., Baj, G., Papadia, D., Tongiorgi, E., and Sciancalepore, M. (2013). Blockade of BDNF
signaling turns chemically-induced long-term potentiation into long-term depression. Hippocampus, 23, 879889.
Olofsson, J.K., Nordin, S., Sequeira, H., and Polich, J. (2008). Affective picture processing: an integrative
review of ERP findings. Biological Psychology, 77, 247-265.
Schupp, H.T., Flaisch, T., Stockburger, J., and Junghofer, M. (2006). Emotion and attention: event-related
brain potential studies. In Anders, Ende, Junghofer, Kissler & Wildgruber (Eds.), Progress in brain research, vol.
156, Chapter 2. Elsevier.
Thayer, J.F. and Lane, R.D. (2000). A model of neurovisceral integration in emotion regulation and
dysregulation. Journal of Affective Disorders, 61, 201-216.
Thayer, J.F. and Lane, R.D. (2009). Claude Bernard and the heart-brain connection: further elaboration of
a model of neurovisceral integration. Neuroscience and Biobehavioral Reviews, 33, 81-88.
Aarts, K., Houwer, J. and Pourtois, G. (2013). Erroneous and correct actions have a different affective
valence: evidence from ERPs. Emotion, June, epub.
Prochnow, D., Hoing, B., Kleiser, R., Linderberg, R., Wittsack, H.-J., Schafer, R., Franz, M. and Seitz, R.J.
(2013). The neural correlates of affect reading: an fMRI study on faces and gestures. Behavioural Brain Research,
237, 270-277.
Mothes-Lasch, M., Mentzel, H-J., Miltner, W.H.R., and Straube, T. (2013). Amygdala activation to fearful
faces under attentional load. Behavioural Brain Research, 237, 172-175.
Lane, R.D., McRae, K., Reiman, E.M., Chen, K., Ahern, G.L. and Thayer, J.F. (2009).Neural correlates of
heart rate variability during emotion. NeuroImage, 44, 213-222.
Pourtois, G. and Vuilleumier, P. (2006). Dynamics of emotional effects on spatial attention in the human
visual cortex. In Anders, Ende, Junghofer, Kissler & Wildgruber (Eds.) Progress in Brain Research Vol 156, Chapter
4. Elsevier.
Young, J.J. & Shapiro, M.L. (2011). The orbitofrontal cortex and response selection. Annals of the New
York Academy of Sciences, 1239, 25-32.
Heekeren, H.R., Marrett, S. and Ungerleider, L.G. (2008). The neural systems that mediate human
perceptual decision making. Nature Reviews Neuroscience, 9, 467-479.
Van Vugt, M.K. Simen, P., Nystrom, L.E. and Cohen, J.D. (2012). EEG oscillations reveal neural correlates
of evidence accumulation. Frontiers in Neuroscience, doi: 10.3389/fnins.2012.00106.
Mazurek, M.D., Roitman, J.D., Ditterich, J. and Shalden, M.N. (2003). A role for neural integrators in
perceptual decision making. Cerebral Cortex, 13, 1257-1269.
Keiflin, R., Reese, R.M., Woods, C.A. and Janak, P.H. (2013). The orbitofrontal cortext as part of a
hierarchical neural system mediating choice between two good options. The Journal of Neuroscience, 33, 1598915998.
Meister, M.L.R., Hennig, J. and Huk, A.C. (2013). Signal multiplexing and single-neuron computations in
LIP during decision-making. Journal of Neuroscience, 33, 2254-2267.
Vilares, I., Howard, J.D., Fernandes, H., Gottfried, J.A. and Kording, K.P. (2012). Differential
representations of prior and likelihood uncertainty in the human brain. Current Biology, 22, 1641-1648.
Payzan-LeNestour, E., Dunne, S., Bossaerts, P. and O’Doherty, J.P. (2013). The neural representation of
unexpected uncertainty during value-based decision making. Neuron, 79, 191-201.
Chang, L.J. & Sanfey, A.G. (2013). Great expectations: neural computations underlying the use of social
norms in decision-making. Social Cognitive & Affective Neuroscience, 3, 277-284.