Some books listed below are relatively unique to the UCSD way of doing Cognitive Science, and you should probably read them if you haven't. A few of the books are more advanced, and their material overlaps quite a bit with graduate courses in this department.
If this is all very new to you, then we recommend one book on cognitive psychology, one on language, one on experimental design, one on the brain, on programming and/modeling, one on Human-computer interaction.
Design and Analysis of Experiments (Cog Sci 14A and B) If you have taken any undergraduate courses in statistics and logic, you probably have this covered. Course textbooks:
(Cogs 14A) Introduction to Research Methods
(Cogs 14B) Introduction to Statistical Analysis
Fundamental Cognitive Phenomena (Cog Sci 101A-B) Course textbooks:
(Cogs 101A) Sensation and Perception
(Cogs 101B) Learning, Memory, and Attention
(Cogs 101C) Language
(Cogs 151) Analogy and Conceptual Systems
(Cogs 152) Cognitive Foundations of Mathematics
(Cogs 156) Language Development
(Cogs 157) Music and the Mind
Cognitive Development (Cog Sci 110, 115) Course textbooks:
(Cogs 110) The Developing Mind
(Cogs 115) Neurological Development and Cognitive Change
Distributed Cognition, Everyday Cognition, Cognitive Engineering (Cog Sci 100, 102A-B-C, Design 1) You should be somewhat familiar with these books, especially the first, obviously. Happily, they are all interesting reading. Course textbooks:
(Dsgn 1) Design of Everyday Things
(Cogs 102A) Distributed Cognition
(Cogs 102C) Cognitive Design Studio
Churchland, P. S. (1986). Neurophilosophy. Cambridge: MIT Press.
Clark, Andy. (1997). Being There: Putting Brain, Body, and World Together Again. Cambridge, MA: MIT Press.
Gibson, J. J. (1979). The ecological approach to visual perception. Boston: Houghton Mifflin.
Haugeland, J. (1981). Mind design. Cambridge: MIT Press.
Lakoff, G., & Johnson, M. (1980). Metaphors we live by. Chicago: University of Chicago Press.
Lakoff, G. & Nunez, R. (2000). Where Mathematics Comes From: How the Embodied Mind Brings Mathematics into Being. New York: Basic Books.
Maturana, H. & Varela, F. (1987). The Tree of Knowledge: The Biololgical Roots of Human Understanding. Boston: Shambhala.
Newell, A. (1990). Unified theories of cognition. Cambridge: Harvard University Press.
Varela, F., Thompson, E. & Rosch, E. (1991). The Embodied Mind: Cognitive Science and Human Experience.Cambridge: MIT Press.
Nunez, R. & Freeman, W. (Eds.) (1999). Reclaiming Cognition: The Primacy of Action, Intention, and Emotion.Throverton, UK: Imprint Academic.
Neurobiology and Neurophysiological Bases of Cognition (Cog Sci 17, 107A-B-C) This requirement is covered by any reasonable undergraduate course in neurobiology. Course textbook:
(Cogs 17) Neurobiology of Cognition
(Cogs 107A) Neuroanatomy and Physiology, (Cogs 107B) Systems Neuroscience
The following link is to a thorough set of tutorials on basic neuroscience from Columbia University. A good primer course for those without experience in neuroscience:
The following is a list of journal publications that either defined a conceptual field in neuroscience, represent important recent advances in a field, or review a field. These articles concern issues that are likely to be addressed at some point during COGS-201 and can serve as a 'jumping-off' point for understanding major issues in neuroscience. They also represent the depth of understanding of neuroscience issues that we would hope all graduate students in Cognitive Science reach. The full articles can be accessed through UCSD's library, by request from the Cognitive Science Graduate Office, or from Professor Nitz - firstname.lastname@example.org
1. In 1988, Zipser and Andersen published a Nature article entitled 'Back-propogation programmed network that simulates properties of a subset of posterior paritetal neurons'. This article was an early contribution to the concept of 'gain' fields whereby neuronal networks may code several combinations of variables via firing rates. A more recent application of this idea to hippocampal neurons is given by Leutgeb et al. in the 2005 Science article entitled 'Independent codes for spatial and episodic memory in hippocampal neuronal ensembles'.
2. In 2004, Patricia Goldman-Rakic published an article wherein correlates of working memory in the prefrontal cortex of monkeys were studied as they relate to the dopamine neuromodulatory system of the brain. This Science article entitled 'Selective D2 receptor actions on the functional circuitry of working memory' is relevant both to more complex ideas as to the functioning of neuromodulatory systems in the brain and to the application of neurophysiological correlates of working memory to the study of schizophrenia.
3. The function of one of the brain's largest structures, the basal ganglia, remains unknown despite its dysfunction in Parkinsons disease and Huntintins disease. Hikosaka's group has produced much work that yields insight into basal ganglia function. The 2002 Nature paper entitled 'A neural correlate of response bias in monkey caudate nucleus' represents the beginning of a powerful new concept regarding the function of the basal ganglia.
4. fMRI is one of the most powerful ways to examine brain function in humans. However, interpreting fMRI findings as they apply to the neurophysiology of the brain is no simple task. Logothetis' 2001 Nature paper entitled 'Neurophysiological investigation of the basis of the fMRI signal' begins to address this problem through direct measurement of neurophysiological signals and fMRI signals simultaneously.
5. There is an extensive role of sub-cortical neuromodulatory systems in guiding learning-dependent changes in cortical connectivity. The 1998 article from the Merzenich group entitled 'Cortical map reorganization enabled by nucleus basalis activity' demonstrates this. Note that the same group has been able to apply principles of this work to their work with children having learning disabilities.
6. In 2005, the Moser group published a paper entitled 'Microstructure of a spatial map in the entorhinal cortex'. The work describes 'grid' cells, one of the most important discoveries in neuroscience in the last decade and the likely basis for the understanding of how our perception of abstract spaces (e.g., position in the environment) is realized based on self-motion and the egocentric positioning of environmental stimuli such as sounds or objects.
7. The firing patterns of posterior parietal, MST, and MT neurons of the monkey have been studied extensively with respect to perception and decision-making processes in the brain. A recent (2009) article in Science by the Shadlen group represents a particularly good example of current ideas concerning how the brain mediates decision-making and how the study of decision-making can lead to insights concerning concepts such as confidence play out in brain activity. It is entitled 'Representation of confidence associated with a decision by neurons in the parietal cortex'. Recent work by the Newsome group published in Neuron (2008) touches on 'noise correlation' among neurons as it relates to application of task rules and decision-making. This article ('Context-dependent changes in functional circuitry in visual area MT') in particular demonstrates that brain signals relevant to perception and task performance are sometimes revealed only through development of non-classical analyses of brain activity.
8. Does the brain operate by a temporal code or a rate code? Both. This fundamental question has been argued among neuroscientists for decades now. The 2003 Nature article entitled 'Independent rate and temporal coding in hippocampal pyramidal cells' touches on important advances in understanding the means by which brain networks communicate information. Notably, phase precession is probably the most robust example of temporal coding in the brain of an awake, behaving animal. Temporal coding as a mechanism for transmitting information in the brain also received a major boost from the 1989 Nature paper by the Singer group entitled 'Oscillatory responses in cat visual cortex exhibit inter-columnar synchronization which reflects global stimulus properties'.
9. What is the function of sleep? Many have tried to answer this question and many have failed. Today, much work suggests that sleep may have something to do with processing of learned material. A recent paper by the Csicsvari group in Nature Neuroscience ('Reactivatino of experience-dependent cell assembly patterns in the hippocampus') examines the role of the hippocampus in this process.
10. Poo's work examining the strength of connectivity between neurons according to the co-activity patterns of those neurons revolutionized current work on brain plasticity. The 1998 Journal of Neuroscience publication 'Synaptic modifications in cultured hippocampal neurons: dependenden on spike timing, synaptic strength, and postsynaptic cell type' speaks to today's understanding of how Donald Hebb's learning rules relate to brain activity patterns.
11. Reinforcement learning, of course, has a very long history. Major advances have been made over the last decade through study of dopamine neuron activity as it relates to reward probabilities in very simple associative tasks. A 2003 Science article from the Schulz group entitled 'Discrete coding of reward probability and uncertainty by dopamine neurons' represents the outcome of work that nicely bridges the gap between models for reinforcment learning and brain dynamics related to reinforcement learning.
12. In our lifetime, quadripeligics will use robot bodies to walk. Work in this field takes great advantage of the 'population vector', a conceptualization concerning the units of information existent in the brain. A recent (2004) Science article by the Schwartz group examines how population vectors relate to both perception and action. Check out other work from this laboratory to see how population vectors are applied in the field of neuroprosthetics.
13. The brain's mediator of episodic memory, the hippocampus, must process memories in such a way that both distinctions and generalizations can be made between and among experiences. A 2008 Neuron article by Shohamy and Wagner entitlted 'Integrating memories in the human brain: hippocampal-midbrain encoding of overlapping events' touches on this process as it occurs in the human brain.
14. Why do we not act out our dreams? The Chase lab's 1991 Journal fo Neuroscience article entitled 'The postsynaptic ihibitory control of lumbar motoneurons during the atonia of active sleep: effect of strychnine on motoneuron properties' explains why. The article also reflects on the control of sleep/wake states by relatively small numbers of neurons concentrated in the pons and hypothalamus.
15. Mircea Steriade spent a career helping us to understand the basic mechanisms by which activity between the thalamus and cortex is coordinated. The results also have implications for brain mechanisms of consciousness. Check out the 2002 Journal of Neuroscience paper 'Model of thalamocortical slow-wave sleep oscillations and transitions to activated states' to get an idea as to how that part of your brain required for perception actually operates.
Programming for Cognitive Science (Cog Sci 18)
Students entering our Ph.D. program should be able to program in a higher language, e.g. Java. Programming languages frequently used in research and teaching include but are not limited to Java, Matlab, C++, and Python.
Computational Modeling and Artificial Intelligence (Cog Sci 109, 118A, 118B, 118C, 118D, 181, 185, 188, 189)
An important early book:
McClelland, J., & Rumelhart, D. (1988). Parallel distributed processing (Vols. 1 & 2). Cambridge: MIT Press.
(Cogs 109) Modeling and Data Analysis
(Cogs 118A) Introduction to Machine Learning I
(Cogs 118B) Introduction to Machine Learning II
(Cogs 118C) Neural Signal Processing
(Cogs 118D) Mathematical Statistics for Behavioral Data Analysis
Ballard, D.H. (1997). Pattern Recognition and Machine Learning. Cambridge, MA : MIT Press.
Dayan, P. and Abbot, L.F.(2001). Theoretical Neuroscience. Cambridge, MA: MIT Press.
Duda, Hart & Stork (2001). Pattern Classification. (2nd Ed.). Wiley.
Forsyth, David A. and Ponce, Jean (2003). Computer Vision: A Modern Approach. Prentice Hall.
Haykin, S. (1999). Neural Networks: A Comprehensive Foundation. Prentice Hall.
MacKay, David J.C. (2003). Information Theory, Inference, and Learning Algorithms. Cambridge University Press.
Recommended for students not specializing in computation:
O'Reilly, Randall C. and Munakata, Yuko (2000). Computational Explorations in Cognitive Neuroscience. A Bradford Book, The MIT Press.