"The strategy of 'Strong Inference': the scientist must construct two or more falsifiable hypotheses that might explain a phenomenon; devise crucial experiments to eliminate one or more of them; carry out the experiments to obtain decisive results; then “recycle” the process to eliminate subsidiary hypotheses."
"If investigators test multiple hypotheses prevailing in their field with disconfirmatory tests rather than simply defending their own views, science becomes more a game than a war"
"Platt has also influenced the field of psychology, another integrative field. One of many studies influenced by Platt was Dixon and Moore’s (2000) examination of “developmental ordering” in infants. Methodologically similar to the study of Huey et al. (1999), it focused on such questions as whether skill A appears before skill B or vice versa; whether skills develop continuously or in saltatory fashion; whether the onset of separate skills overlap; and whether measurements of continuous phenomena using categorical scales is legitimate. Dixon and Moore credited Platt as a guiding light in suggesting disconfirmatory tests and multiple hypotheses. Similarly, Carpenter et al. (1993) strongly advocated Platt’s recommendations in resolving schizophrenia into its subcategories and correlating them with anatomical derangements of the brain with a strong-inference rationale."
Dixon, J. A., and C. F. Moore. 2000.The logic of interpreting evidence of developmental ordering: Strong inference and categorical measures. Dev Psychol 36:826–34.
Carpenter,W.T., et al. 1993. Strong inference, theory testing, and the neuroanatomy of schizophrenia. Arch Gen Psychiatry 50:825–31.
Monday, October 29, 2007
Friday, October 05, 2007
Self discipline in academic success
Self discipline is key to academic success. How to achieve self discipline?
First, avoid situations that need self control: unplug your internet cable, put away your favorite newspaper, and the list goes on...
Second, have self-reflection over what you are doing, what you have done and then you know what you should do: don't check email too often, don't look for deals on the internet too often, and the list goes on too...
Third, have a schedule and stick to it. Um, sounds easier said than done. But HAVE THE SCHEDULE! You know you can do it with the first two notions in your mind...
First, avoid situations that need self control: unplug your internet cable, put away your favorite newspaper, and the list goes on...
Second, have self-reflection over what you are doing, what you have done and then you know what you should do: don't check email too often, don't look for deals on the internet too often, and the list goes on too...
Third, have a schedule and stick to it. Um, sounds easier said than done. But HAVE THE SCHEDULE! You know you can do it with the first two notions in your mind...
Thursday, October 04, 2007
Some lessons
Inspired by Robert Sternberg and others:
1)Research project: Before you do a project, ask yourself what the best and worst results will be? So you know whether it is worth the time and effort. Have the guts to give up certain projects. Also, remember to avoid all-or-none projects, those either will get published or get trashed. Simply add more reasonable dependent variables and use more manipulation methods. Be aware that there should be a balance between this and fishing around. But what counts a good project? I guess ideas from which you can see big pictures will survive in the long run (e.g. have multiple implications, have impact across several fields). See my previous post--Eight Standards for Evaluation Psychology Papers
2)Research program: You research is not just some papers or projects; rather, it is a coherent, systematic program.
3)Research attitude: Not try to compete with others but try to compete with yourself. Ask yourself how you can do better next time.
1)Research project: Before you do a project, ask yourself what the best and worst results will be? So you know whether it is worth the time and effort. Have the guts to give up certain projects. Also, remember to avoid all-or-none projects, those either will get published or get trashed. Simply add more reasonable dependent variables and use more manipulation methods. Be aware that there should be a balance between this and fishing around. But what counts a good project? I guess ideas from which you can see big pictures will survive in the long run (e.g. have multiple implications, have impact across several fields). See my previous post--Eight Standards for Evaluation Psychology Papers
2)Research program: You research is not just some papers or projects; rather, it is a coherent, systematic program.
3)Research attitude: Not try to compete with others but try to compete with yourself. Ask yourself how you can do better next time.
Thursday, August 23, 2007
Matlab fundamentals
From: Matlab for psychologists: A tutorial
[]: To enter most data into matlab, you need to use square brackets [ ]
(): Use round brackets ( ) to get things out of a matrix or to refer to just part of a matrix
: In round brackets, a colon (:) means everything in a row or column and is normally used to extract data from a matrix; between two numbers, the colon means count from A to B one integer at a time; a set of three numbers with a colon specifies the step to use for counting, e.g. G = 3:4:20 means G counts from 3 to 20 in steps of 4
+ means plus - means minus ' means transpose (turn rows into columns and vice versa) ( ) round brackets can be used to specify the order of operations according to BODMAS
* means matrix multiplication / means matrix division.
.* means element-wise multiplication ./ means element-wise division .^ means power (i.e. A.^2 means A squared)
If a function has just one output, the output can be placed straight into a variable, but if a function has several outputs, square brackets are needed to group the output variables.
All functions have the form:
[output1, output2, ...] = function (arg1, arg2, ...)
All the mathematical functions you need to operate on single values are available, e.g sin, cos, tan, sqrt, log and pi. Matlab has a variety of useful functions which you can apply to the rows or columns of a matrix, including sum, mean, std, max and min. By default, these work on columns, but you can change the dimension to work on rows (e.g. To sum the rows of K, you must tell the sum function to operate in the 2nd dimension (1st = columns, 2nd = rows)).
In Matlab, you can apply logical operators to whole arrays as well as to individual numbers, and then you obtain a logical array which can be used to index another array. Logical indexing is most useful when one variable is used to categorise another. You can convert a logical index into a subscript index using find, which tells you the non-zeros values of the logical.
NaN stands for Not A Number and can be used in any vector or matrix in place of missing data. Matlab provides alternate versions, nansum, nanmean and nanstd which let you ignore NaNs and find the sum, mean and standard deviation of your data.
It is good practice to start every script with the line clear all (unless you specifically want to keep previous variables), and a comment line saying what the script does. The semicolon ; is a very useful character for scripts. Some other useful commands when scripting include pause which pauses the script if you do want to see the output on the screen (hit any key to start the script running again), and Crtl-C (that is, press the Control key and the letter C at the same time) which stops a crashed script and gives you back your command prompt.
if can also have an else, which executes if the first part is false or equal to zero, and must have an end to finish the command. If statements are often useful for catching errors.
A for loop must always have an end to tell it where to stop, and can also be stopped prematurely with break. It has a counter which tells it how many times to run through the loop. The counter can be used within the loop as an index or as a variable but should NOT be changed inside the loop.
A switch loop is like testing lots of ifs one after the other. Put the value you want to match in brackets after the switch statement. It is useful if you want to match a value to several alternatives. If none of the cases matches, the lines after otherwise will execute. A switch must have an end to tell it to stop. (disp means display text). Switch loops can also determine if one text string matches another text string, and are useful for classifying text strings.
While is like a continuous for loop, which keeps going until it gets a false value. Again, there must be an end to stop the loop. While loops are most useful for reading in text files of an unknown length.
Try ... Catch: This is not really a control element, it is used for catching errors and preventing them from crashing your script.
Functions take inputs and return outputs, but everything that happens within a function is hidden or encapsulated from the main workspace. This means that if variables within a function have the same name as variables in the workspace, they won't interfere with each other or over-write each other’s value. The exception to this rule is global variables. If a variable is declared as global in a function (i.e. the line global var is included at the top of the function) AND is declared as global in another function or script, the variables are no longer hidden and share values. A function is written like a script, but it must begin with the word function and then have the form [outputs] = function_name(inputs). It is good practice to put some helpful information after the function name. All lines immediately after the function name which start with a % act as the help file for the function, so it is useful to record the format of the inputs and outputs which the function needs.
The general rule is that Matlab looks first in the current directory, and then in every directory in the path in order, until it finds a command with the name you asked for. To see what is in the Matlab path, type path, and to edit the path, select the option Set path on the file menu. If you want to know quickly which command Matlab is running, type which followed by the command name.
The save, load and clear commands work in lists separated by spaces, not with brackets. You just type the command, followed by a list of stuff to save, clear or load. You can also save stuff in different formats, for example to load into SPSS or Excel. The command dlmwrite writes a matrix as a text file with the numbers separated by tabs.
Text (i.e. strings) can be assigned to a variable just like a number, as long as the text is put within single quotes (it looks nicer to put a space between using ' '). And you can use num2str to turn numbers into text to display them. It is generally inconvenient to put text strings into arrays because Matlab will complain if they aren't all the same length, but a cell array behaves like a matrix, without minding what size each entry is. Cells can be referred to in just the same way as matrices, but they use curly brackets {} instead of round ones ().
Matlab has a set of string manipulation functions which can be used to do things like find a particular string in a cell array (strmatch). To turn numbers from a cell array into a numeric array, use cat.
Use a structure to organize your variables. Structures are a hierarchical way to organize your data, where a single variable can have many fields, each of which has a name starting with a dot (full stop). This could be useful if you want to keep all the data from all your subjects in the same format, in order to apply the same process to all of it.
From the Matlab command prompt, you can control your computer just like at the command line. You can also use the command system (for windows) or unix (for unix) to send a command to system.
num2str and str2num - convert between numbers and strings. isspace - find white space characters, isletter - finds letters, strmatch, strfind, strcmp and findstr are all variants on finding a string within a line. Type help iofun and help strfun for more information on file input and text functions.
[]: To enter most data into matlab, you need to use square brackets [ ]
(): Use round brackets ( ) to get things out of a matrix or to refer to just part of a matrix
: In round brackets, a colon (:) means everything in a row or column and is normally used to extract data from a matrix; between two numbers, the colon means count from A to B one integer at a time; a set of three numbers with a colon specifies the step to use for counting, e.g. G = 3:4:20 means G counts from 3 to 20 in steps of 4
+ means plus - means minus ' means transpose (turn rows into columns and vice versa) ( ) round brackets can be used to specify the order of operations according to BODMAS
* means matrix multiplication / means matrix division.
.* means element-wise multiplication ./ means element-wise division .^ means power (i.e. A.^2 means A squared)
If a function has just one output, the output can be placed straight into a variable, but if a function has several outputs, square brackets are needed to group the output variables.
All functions have the form:
[output1, output2, ...] = function (arg1, arg2, ...)
All the mathematical functions you need to operate on single values are available, e.g sin, cos, tan, sqrt, log and pi. Matlab has a variety of useful functions which you can apply to the rows or columns of a matrix, including sum, mean, std, max and min. By default, these work on columns, but you can change the dimension to work on rows (e.g. To sum the rows of K, you must tell the sum function to operate in the 2nd dimension (1st = columns, 2nd = rows)).
In Matlab, you can apply logical operators to whole arrays as well as to individual numbers, and then you obtain a logical array which can be used to index another array. Logical indexing is most useful when one variable is used to categorise another. You can convert a logical index into a subscript index using find, which tells you the non-zeros values of the logical.
NaN stands for Not A Number and can be used in any vector or matrix in place of missing data. Matlab provides alternate versions, nansum, nanmean and nanstd which let you ignore NaNs and find the sum, mean and standard deviation of your data.
It is good practice to start every script with the line clear all (unless you specifically want to keep previous variables), and a comment line saying what the script does. The semicolon ; is a very useful character for scripts. Some other useful commands when scripting include pause which pauses the script if you do want to see the output on the screen (hit any key to start the script running again), and Crtl-C (that is, press the Control key and the letter C at the same time) which stops a crashed script and gives you back your command prompt.
if can also have an else, which executes if the first part is false or equal to zero, and must have an end to finish the command. If statements are often useful for catching errors.
A for loop must always have an end to tell it where to stop, and can also be stopped prematurely with break. It has a counter which tells it how many times to run through the loop. The counter can be used within the loop as an index or as a variable but should NOT be changed inside the loop.
A switch loop is like testing lots of ifs one after the other. Put the value you want to match in brackets after the switch statement. It is useful if you want to match a value to several alternatives. If none of the cases matches, the lines after otherwise will execute. A switch must have an end to tell it to stop. (disp means display text). Switch loops can also determine if one text string matches another text string, and are useful for classifying text strings.
While is like a continuous for loop, which keeps going until it gets a false value. Again, there must be an end to stop the loop. While loops are most useful for reading in text files of an unknown length.
Try ... Catch: This is not really a control element, it is used for catching errors and preventing them from crashing your script.
Functions take inputs and return outputs, but everything that happens within a function is hidden or encapsulated from the main workspace. This means that if variables within a function have the same name as variables in the workspace, they won't interfere with each other or over-write each other’s value. The exception to this rule is global variables. If a variable is declared as global in a function (i.e. the line global var is included at the top of the function) AND is declared as global in another function or script, the variables are no longer hidden and share values. A function is written like a script, but it must begin with the word function and then have the form [outputs] = function_name(inputs). It is good practice to put some helpful information after the function name. All lines immediately after the function name which start with a % act as the help file for the function, so it is useful to record the format of the inputs and outputs which the function needs.
The general rule is that Matlab looks first in the current directory, and then in every directory in the path in order, until it finds a command with the name you asked for. To see what is in the Matlab path, type path, and to edit the path, select the option Set path on the file menu. If you want to know quickly which command Matlab is running, type which followed by the command name.
The save, load and clear commands work in lists separated by spaces, not with brackets. You just type the command, followed by a list of stuff to save, clear or load. You can also save stuff in different formats, for example to load into SPSS or Excel. The command dlmwrite writes a matrix as a text file with the numbers separated by tabs.
Text (i.e. strings) can be assigned to a variable just like a number, as long as the text is put within single quotes (it looks nicer to put a space between using ' '). And you can use num2str to turn numbers into text to display them. It is generally inconvenient to put text strings into arrays because Matlab will complain if they aren't all the same length, but a cell array behaves like a matrix, without minding what size each entry is. Cells can be referred to in just the same way as matrices, but they use curly brackets {} instead of round ones ().
Matlab has a set of string manipulation functions which can be used to do things like find a particular string in a cell array (strmatch). To turn numbers from a cell array into a numeric array, use cat.
Use a structure to organize your variables. Structures are a hierarchical way to organize your data, where a single variable can have many fields, each of which has a name starting with a dot (full stop). This could be useful if you want to keep all the data from all your subjects in the same format, in order to apply the same process to all of it.
From the Matlab command prompt, you can control your computer just like at the command line. You can also use the command system (for windows) or unix (for unix) to send a command to system.
num2str and str2num - convert between numbers and strings. isspace - find white space characters, isletter - finds letters, strmatch, strfind, strcmp and findstr are all variants on finding a string within a line. Type help iofun and help strfun for more information on file input and text functions.
Wednesday, August 22, 2007
Twenty-night tips for better writting
By Robert J. Sternberg
• What you say:
1State clearly the problem you are addressing and then organize the article around the problem
2 Start Strong
3Make clear up front what the new and valuable contribution of your article is, and make sure you are right
4 Tell readers why they should be interested
5 Make sure article does what it says it will do
6 Make sure literature review is focused, reasonably complete, and balanced
7Make sure how your work builds on that of others
8Check your data analysis and interpretations
9 Always explain what your results mean
10Make sure that your conclusions follow your data
11Make clear what the limitations of your work are
12 Be sure to consider alternative interpretations of the data
13 End strong and state a clear take-home message
• How you say it:
14 Write sentences that are readable, clear, and concise
15 Emphasize logical flow and organization
16Explain what you are going to say, say it, and then restate what you have said
17 Be creative, give concrete examples
18Don’t assume people will “know what you mean”, or be familiar with abbreviations or jargon
19 Write to be interesting
20Write for a somewhat more broad and less technically skilled audience than you expect
21 Avoid an autobiography of the study
• What to do with what you say:
22 Proofread
23 Check for fit with journal guidelines and subject matter
24 Read your paper at least once while imaging yourself to be a critical reviewer or, even better, ask a college to do the same
25Cite likely referees (who conceivably merit citation)
26Write for your likely referees and readers
• What to do with what others say:
27 Take journal reviews seriously, but remember reviewers are not gods (a fact that has escaped some reviewers)
28 Don’t take reviewers’ comments personally
29 Perseverance pays, to a point
• What you say:
1State clearly the problem you are addressing and then organize the article around the problem
2 Start Strong
3Make clear up front what the new and valuable contribution of your article is, and make sure you are right
4 Tell readers why they should be interested
5 Make sure article does what it says it will do
6 Make sure literature review is focused, reasonably complete, and balanced
7Make sure how your work builds on that of others
8Check your data analysis and interpretations
9 Always explain what your results mean
10Make sure that your conclusions follow your data
11Make clear what the limitations of your work are
12 Be sure to consider alternative interpretations of the data
13 End strong and state a clear take-home message
• How you say it:
14 Write sentences that are readable, clear, and concise
15 Emphasize logical flow and organization
16Explain what you are going to say, say it, and then restate what you have said
17 Be creative, give concrete examples
18Don’t assume people will “know what you mean”, or be familiar with abbreviations or jargon
19 Write to be interesting
20Write for a somewhat more broad and less technically skilled audience than you expect
21 Avoid an autobiography of the study
• What to do with what you say:
22 Proofread
23 Check for fit with journal guidelines and subject matter
24 Read your paper at least once while imaging yourself to be a critical reviewer or, even better, ask a college to do the same
25Cite likely referees (who conceivably merit citation)
26Write for your likely referees and readers
• What to do with what others say:
27 Take journal reviews seriously, but remember reviewers are not gods (a fact that has escaped some reviewers)
28 Don’t take reviewers’ comments personally
29 Perseverance pays, to a point
Eight Standards for Evaluation Psychology Papers
By Robert J. Sternberg
Standard 1: The paper contains one or more surprising results that nevertheless make sense in some theoretical context
Standard 2: The results in the paper are of major theoretical or practical significance
Standard 3: The ideas in the paper are new and exciting, perhaps representing a new way of looking at an old problem
Standard 4: Interpretation of results is unambiguous
Standard 5: The paper integrates into a new, simpler framework, data that had previously required a complex, possibly unwieldy framework
Standard 6: The paper contains a major debunking of previously held ideas
Standard 7: The paper presents an experiment with a particularly clever paradigms or experimental manipulation
Standard 8: The findings or theory presented in the paper are general ones
Standard 1: The paper contains one or more surprising results that nevertheless make sense in some theoretical context
Standard 2: The results in the paper are of major theoretical or practical significance
Standard 3: The ideas in the paper are new and exciting, perhaps representing a new way of looking at an old problem
Standard 4: Interpretation of results is unambiguous
Standard 5: The paper integrates into a new, simpler framework, data that had previously required a complex, possibly unwieldy framework
Standard 6: The paper contains a major debunking of previously held ideas
Standard 7: The paper presents an experiment with a particularly clever paradigms or experimental manipulation
Standard 8: The findings or theory presented in the paper are general ones
Wednesday, March 28, 2007
Members of NAS in Psychology Set
Edward Adelson
Massachusetts Institute of Technology
Psychology
2006
John Anderson
Carnegie Mellon University
Psychology
1999
Richard Atkinson
University of California, San Diego
Psychology
1974
cognitive science, mathematical pyschology, applied mathematics
Linda Bartoshuk
University of Florida
Psychology
2003
Gordon Bower
Stanford University
Psychology
1973
Jan Bures
Czech Academy of Sciences
Psychology
1995
Susan Carey
Harvard University
Psychology
2002
cognitive development, conceptual change in childhood and in history of science
A. Noam Chomsky
Massachusetts Institute of Technology
Psychology
1972
William Estes
Indiana University
Psychology
1963
John Flavell
Stanford University
Psychology
1994
theory of mind development in children
Robert Galambos
University of California, San Diego
Psychology
1960
visual and auditory physiology
Charles Gallistel
Rutgers, The State University of New Jersey, New Brunswick
Psychology
2002
John Garcia
University of California, Los Angeles
Psychology
1983
Wendell Garner
Yale University
Psychology
1965
Perception
Rochel Gelman
Rutgers, The State University of New Jersey, New Brunswick
Psychology
2006
Lila Gleitman
University of Pennsylvania
Psychology
2000
Frances Graham
University of Delaware
Psychology
1988
Norma Graham
Columbia University
Psychology
1998
visual behavior, visual neuroscience, computational models
David Green
University of Florida
Psychology
1978
psychoacoustics, detection theory
Morris Halle
Massachusetts Institute of Technology
Psychology
1988
Richard Held
Massachusetts Institute of Technology
Psychology
1973
vision, sensorimotor coordination, development
Robert Hinde
University of Cambridge
Psychology
1978
the application of biological, psychological and anthropological data, principles to understanding religion and ethics
Ira Hirsh
Washington University
Psychology
1979
Julian Hochberg
Columbia University
Psychology
1980
Leo Hurvich
University of Pennsylvania
Psychology
1975
Jon Kaas
Vanderbilt University
Psychology
2000
brain evolution, sensory and motor systems in mammals
Daniel Kahneman
Princeton University
Psychology
2001
Nancy Kanwisher
Massachusetts Institute of Technology
Psychology
2005
William Labov
University of Pennsylvania
Psychology
1993
linguistics
Willem Levelt
Max Planck Institute for Psycholinguistics
Psychology
2000
cognitive science, psycholinguistics
Gardner Lindzey
Center for Advanced Study in the Behavioral Sciences
Psychology
1989
Elizabeth Loftus
University of California, Irvine
Psychology
2004
memory, human memory, false memories, law and psychology
R. Duncan Luce
University of California, Irvine
Psychology
1972
fundamental measurement, utility theory, global psychophysics, mathematical behavioral sciences
Eleanor Maccoby
Stanford University
Psychology
1993
family interaction, gender development
Peter Marler
University of California, Davis
Psychology
1971
James McClelland
Stanford University
Psychology
2001
Douglas Medin
Northwestern University
Psychology
2005
culture and cognition, decision making, culture and education, categorization
George Miller
Princeton University
Psychology
1962
lexical resources for computational linguistics
Walter Mischel
Columbia University
Psychology
2004
structure, and organization of social behavior, personality processes, delay of gratification and self-control mechanisms
Jacob Nachmias
University of Pennsylvania
Psychology
1984
visual perception, psychophysics
Ulric Neisser
Cornell University
Psychology
1989
Elissa Newport
University of Rochester
Psychology
2004
language acquisition, psycholinguistics, cognitive science
Richard Nisbett
University of Michigan
Psychology
2002
culture and cognition, judgment, inference, honor, consciousness
Barbara Partee
University of Massachusetts at Amherst
Psychology
1989
formal semantics, semantics and syntax, logic and language, integration of compositional and lexical semantics
Michael Posner
University of Oregon
Psychology
1981
neural networks of attention and cognition
Dale Purves
Duke University
Psychology
1989
vision, perception, color, brightness, illusions, audition, music, neurobiology
Robert Rescorla
University of Pennsylvania
Psychology
1985
learning, experimental psychology, Pavlovian conditioning
Mark Rosenzweig
University of California, Berkeley
Psychology
1979
David Rumelhart
Stanford University
Psychology
1991
Roger Shepard
Stanford University
Psychology
1977
mental laws, spatial, scientific and musical cognition, perception and imagery, thought experiments, multidimensional scaling
Richard Shiffrin
Indiana University
Psychology
1995
Edward Smith
Columbia University
Psychology
1996
Eugene Sokolov
Moscow State University
Psychology
1975
Elizabeth Spelke
Harvard University
Psychology
1999
George Sperling
University of California, Irvine
Psychology
1985
Claude Steele
Stanford University
Psychology
2003
Saul Sternberg
University of Pennsylvania
Psychology
1982
human experimental psychology, mathematical psychology
Patrick Suppes
Stanford University
Psychology
1978
John Swets
BBN Corporation
Psychology
1990
diagnostic decision-making, measures of decision accuracy, efficacy sensory functions, cognitive psychology
Philip Teitelbaum
University of Florida
Psychology
1974
Anne Treisman
Princeton University
Psychology
1994
Endel Tulving
Rotman Research Institute of Baycrest Centre
Psychology
1988
experimental, clinical, and conceptual analysis of human memory systems and processes, with special emphasis on episodic memory
Allan Wagner
Yale University
Psychology
1992
learning theory, computational neuroscience
Brian Wandell
Stanford University
Psychology
2003
visual neuroscience, reading, brain development, fmri
Jozef Zwislocki
Syracuse University
Psychology
1990
Massachusetts Institute of Technology
Psychology
2006
John Anderson
Carnegie Mellon University
Psychology
1999
Richard Atkinson
University of California, San Diego
Psychology
1974
cognitive science, mathematical pyschology, applied mathematics
Linda Bartoshuk
University of Florida
Psychology
2003
Gordon Bower
Stanford University
Psychology
1973
Jan Bures
Czech Academy of Sciences
Psychology
1995
Susan Carey
Harvard University
Psychology
2002
cognitive development, conceptual change in childhood and in history of science
A. Noam Chomsky
Massachusetts Institute of Technology
Psychology
1972
William Estes
Indiana University
Psychology
1963
John Flavell
Stanford University
Psychology
1994
theory of mind development in children
Robert Galambos
University of California, San Diego
Psychology
1960
visual and auditory physiology
Charles Gallistel
Rutgers, The State University of New Jersey, New Brunswick
Psychology
2002
John Garcia
University of California, Los Angeles
Psychology
1983
Wendell Garner
Yale University
Psychology
1965
Perception
Rochel Gelman
Rutgers, The State University of New Jersey, New Brunswick
Psychology
2006
Lila Gleitman
University of Pennsylvania
Psychology
2000
Frances Graham
University of Delaware
Psychology
1988
Norma Graham
Columbia University
Psychology
1998
visual behavior, visual neuroscience, computational models
David Green
University of Florida
Psychology
1978
psychoacoustics, detection theory
Morris Halle
Massachusetts Institute of Technology
Psychology
1988
Richard Held
Massachusetts Institute of Technology
Psychology
1973
vision, sensorimotor coordination, development
Robert Hinde
University of Cambridge
Psychology
1978
the application of biological, psychological and anthropological data, principles to understanding religion and ethics
Ira Hirsh
Washington University
Psychology
1979
Julian Hochberg
Columbia University
Psychology
1980
Leo Hurvich
University of Pennsylvania
Psychology
1975
Jon Kaas
Vanderbilt University
Psychology
2000
brain evolution, sensory and motor systems in mammals
Daniel Kahneman
Princeton University
Psychology
2001
Nancy Kanwisher
Massachusetts Institute of Technology
Psychology
2005
William Labov
University of Pennsylvania
Psychology
1993
linguistics
Willem Levelt
Max Planck Institute for Psycholinguistics
Psychology
2000
cognitive science, psycholinguistics
Gardner Lindzey
Center for Advanced Study in the Behavioral Sciences
Psychology
1989
Elizabeth Loftus
University of California, Irvine
Psychology
2004
memory, human memory, false memories, law and psychology
R. Duncan Luce
University of California, Irvine
Psychology
1972
fundamental measurement, utility theory, global psychophysics, mathematical behavioral sciences
Eleanor Maccoby
Stanford University
Psychology
1993
family interaction, gender development
Peter Marler
University of California, Davis
Psychology
1971
James McClelland
Stanford University
Psychology
2001
Douglas Medin
Northwestern University
Psychology
2005
culture and cognition, decision making, culture and education, categorization
George Miller
Princeton University
Psychology
1962
lexical resources for computational linguistics
Walter Mischel
Columbia University
Psychology
2004
structure, and organization of social behavior, personality processes, delay of gratification and self-control mechanisms
Jacob Nachmias
University of Pennsylvania
Psychology
1984
visual perception, psychophysics
Ulric Neisser
Cornell University
Psychology
1989
Elissa Newport
University of Rochester
Psychology
2004
language acquisition, psycholinguistics, cognitive science
Richard Nisbett
University of Michigan
Psychology
2002
culture and cognition, judgment, inference, honor, consciousness
Barbara Partee
University of Massachusetts at Amherst
Psychology
1989
formal semantics, semantics and syntax, logic and language, integration of compositional and lexical semantics
Michael Posner
University of Oregon
Psychology
1981
neural networks of attention and cognition
Dale Purves
Duke University
Psychology
1989
vision, perception, color, brightness, illusions, audition, music, neurobiology
Robert Rescorla
University of Pennsylvania
Psychology
1985
learning, experimental psychology, Pavlovian conditioning
Mark Rosenzweig
University of California, Berkeley
Psychology
1979
David Rumelhart
Stanford University
Psychology
1991
Roger Shepard
Stanford University
Psychology
1977
mental laws, spatial, scientific and musical cognition, perception and imagery, thought experiments, multidimensional scaling
Richard Shiffrin
Indiana University
Psychology
1995
Edward Smith
Columbia University
Psychology
1996
Eugene Sokolov
Moscow State University
Psychology
1975
Elizabeth Spelke
Harvard University
Psychology
1999
George Sperling
University of California, Irvine
Psychology
1985
Claude Steele
Stanford University
Psychology
2003
Saul Sternberg
University of Pennsylvania
Psychology
1982
human experimental psychology, mathematical psychology
Patrick Suppes
Stanford University
Psychology
1978
John Swets
BBN Corporation
Psychology
1990
diagnostic decision-making, measures of decision accuracy, efficacy sensory functions, cognitive psychology
Philip Teitelbaum
University of Florida
Psychology
1974
Anne Treisman
Princeton University
Psychology
1994
Endel Tulving
Rotman Research Institute of Baycrest Centre
Psychology
1988
experimental, clinical, and conceptual analysis of human memory systems and processes, with special emphasis on episodic memory
Allan Wagner
Yale University
Psychology
1992
learning theory, computational neuroscience
Brian Wandell
Stanford University
Psychology
2003
visual neuroscience, reading, brain development, fmri
Jozef Zwislocki
Syracuse University
Psychology
1990
Monday, February 19, 2007
APA Early Researcher Awards
Early Researcher Awards
The APA Science Student Council is proud to announce its annual competition for early (i.e., pre-doctoral) researchers. The purpose of the program is to recognize outstanding student researchers who are currently early in their graduate training. We are unable to accept submissions from advanced graduate students for research completed earlier in their graduate training.
Strong preference will be given to students who demonstrate outstanding research abilities earlier in their graduate training (e.g. up to and including master's thesis or equivalent), and who show a greater level of independence in conducting their research.
The application for the 2007 Early Researcher Award will be available later this year.
Questions?
If you have any questions, please email the Science Directorate (or telephone at 202-336-6000).
Early Research Award Selection Criteria
All of the following criteria apply to both the Basic and Applied Science awards.
* The applicant clearly expressed understanding of the field of inquiry and posed meaningful research questions.
* The research design was rigorous and had internal validity, providing meaningful answers to questions posed by the researcher (Note: this criterion will be weighed more heavily for Basic Science award applicants).
* The research findings had external validity, generalizing to broader populations (Note: this criterion will be weighed more heavily for Applied Science award applicants).
* The study made significant theoretical contributions to the respective field of psychology, and/or
* The study made significant application contributions to the respective field of psychology and to society.
* The applicant demonstrated his/her intellectual merit, important in aspects such as conceptualization and design.
* The applicant demonstrated independence and originality in his/her study.
All criteria will be considered within the context of the individual?s overall career path as evidenced by prior research involvement in psychological science.
Preference will be given to students who demonstrate outstanding research abilities earlier in their graduate training (e.g., up to and including master's thesis or equivalent), and to applicants showing a greater level of independence in conducting their research.
BASIC
Basic research in psychology is typically driven by the motivation to gain a fundamental understanding of a phenomenon.
APPLIED
Applied research in psychology is typically motivated by a desire to solve practical problems.
The APA Science Student Council is proud to announce its annual competition for early (i.e., pre-doctoral) researchers. The purpose of the program is to recognize outstanding student researchers who are currently early in their graduate training. We are unable to accept submissions from advanced graduate students for research completed earlier in their graduate training.
Strong preference will be given to students who demonstrate outstanding research abilities earlier in their graduate training (e.g. up to and including master's thesis or equivalent), and who show a greater level of independence in conducting their research.
The application for the 2007 Early Researcher Award will be available later this year.
Questions?
If you have any questions, please email the Science Directorate (or telephone at 202-336-6000).
Early Research Award Selection Criteria
All of the following criteria apply to both the Basic and Applied Science awards.
* The applicant clearly expressed understanding of the field of inquiry and posed meaningful research questions.
* The research design was rigorous and had internal validity, providing meaningful answers to questions posed by the researcher (Note: this criterion will be weighed more heavily for Basic Science award applicants).
* The research findings had external validity, generalizing to broader populations (Note: this criterion will be weighed more heavily for Applied Science award applicants).
* The study made significant theoretical contributions to the respective field of psychology, and/or
* The study made significant application contributions to the respective field of psychology and to society.
* The applicant demonstrated his/her intellectual merit, important in aspects such as conceptualization and design.
* The applicant demonstrated independence and originality in his/her study.
All criteria will be considered within the context of the individual?s overall career path as evidenced by prior research involvement in psychological science.
Preference will be given to students who demonstrate outstanding research abilities earlier in their graduate training (e.g., up to and including master's thesis or equivalent), and to applicants showing a greater level of independence in conducting their research.
BASIC
Basic research in psychology is typically driven by the motivation to gain a fundamental understanding of a phenomenon.
APPLIED
Applied research in psychology is typically motivated by a desire to solve practical problems.
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