[Depmix-commits] r231 - /

[noreply at r-forge.r-project.org]

Author: maarten
Date: 2008-11-03 00:33:18 +0100 (Mon, 03 Nov 2008)
New Revision: 231

Modified:
   individual.tex
Log:


Modified: individual.tex
===================================================================
--- individual.tex	2008-10-31 21:57:34 UTC (rev 230)
+++ individual.tex	2008-11-02 23:33:18 UTC (rev 231)
@@ -344,12 +344,12 @@
 membership. 
 
 
-\subsection{Reponse distributions and parameters}
+\subsection{Response distributions and parameters}
 
 % this needs some more on the design, interface to glm and similar
 % other models etc. 
 The package is built using S4 classes (object oriented classes in R)
-to allow easy extensibility (REFERENCE to S4 methodology). 
+to allow easy extensibility (Chambers, 1998). 
 
 Each row of the transition matrix and the initial state probabilities:
 \begin{itemize}
@@ -488,9 +488,67 @@
 
 \subsection{Weather prediction task}
 
-Still to add models and data. 
+%Still to add models and data. 
 
+The Weather Prediction Task (WPT, Knowlton, Squire \& Gluck, 1994) 
+is a probabilistic categorization task, in which participants learn to 
+predict the state of the weather (sunny, or rainy) on the basis 
+of four ``tarot'' cards (cards with abstract geometrical patterns). 
+Each cue pattern is associated with a particular probability distribution 
+over the states of the weather. In order to perform in the task, 
+participants must predict the weather in accordance with these
+conditional probabilities.
 
+%The WPT has been popular in neuropsychological research, 
+%particularly because amnesic patients perform this task rather
+%well, despite not being able to remember actually many aspects
+%of the task (or in some cases, even performing the task at all).
+%This has led to the conclusion that probabilistic category learning
+%depends on implicit memory, which is separate from explicit
+%memory. While this conclusion is debatable (Speekenbrink, Channon 
+%\& Shanks, 2008), the finding of relatively unimpaired performance
+%by amnesic individuals remains striking.
+
+There are different accounts of probabilistic category learning. 
+According to instance or exemplar learning theories, participants
+learn by storing each encountered cue-outcome pairing. When presented
+with a cue pattern, these exemplars are retrieved from memory, and weighted
+according to their similarity to the probe cue pattern, to form a 
+classification. According to associative theories, participants gradually 
+learn by gradually associating the individual cues (or cue patterns in 
+configural learning) to the outcomes. In rule-learning, participants are 
+taken to extract rules by which to categorize the different cue patterns. 
+Gluck, Shohamy and Myers (2002) proposed a number of such rules (or strategies). A main
+difference between these is whether responses are based on the 
+presence/absence of a single cue, or whether responses are based on 
+cue patterns. Gluck et al. formulated all strategies in a deterministic and
+optimal manner (e.g., the multi-cue strategy corresponded to giving the optimal
+response to each cue pattern). Meeter et al. allowed for probabilistic 
+responding (a small probability of giving the non-optimal response). 
+
+Alternative non-strategy based analyses of the WPT (Lagnado et al, Speekenbrink et al) 
+have estimated response strategies by variations of logistic regression. 
+
+%associative, rule-based.  
+Here, we analyze the behavior of a single individual performing the
+WPT for 200 trials. We let each state be characterized by a Generalized
+Linear Model with a Bernoulli response and logistic link function. We are
+interested in whether a DMM can recover a strategy model in line with Gluck et al. 
+As we fit the data to a single subject, we must place some constraints.
+Specifically, we constrain the state transitions to be in a ``left-right'' format
+(states can only proceed to the immediately adjacent state and never back) and 
+the initial state 
+
+A single state model (usual GLM) 
+We started with a 3-state model, 
+
+\begin{itemize}
+	\item N=200
+	\item 4 choice data, displayed in blocks of 20 trials
+	\item optimal strategy is choosing C or D
+\end{itemize}
+
+
 \section{Discussion}
 	
 \begin{itemize}
@@ -516,6 +574,9 @@
 Insensitivity to future consequences following damage to human
 prefrontal cortex.  Cognition, 50(1Ð3), 7Ð15.
 
+Chambers, J. M. (1998). Programming with Data: A Guide to the S Lan-
+guage. New York: Springer-Verlag.
+
 Crone, E. A.,  \& van der Molen, M. W. (2004).  Developmental changes in
 real life decision making: Performance on a gambling task previously
 shown to depend on the ventromedial prefrontal cortex.  Developmental
@@ -525,11 +586,19 @@
 marker hypothesis: A critical evaluation.  Neuroscience and
 Biobehavioral Reviews, 30(2), 239-271.
 
+Gluck, M. A., Shohamy, D., \& Myers, C. (2002). How do people solve the 
+weather prediction task?: Individual variability in strategies for 
+probabilistic category learning. Learning \& Memory, 9, 408-418.
+
 Huizenga, H. M., Crone, E. A., \& Jansen, B. R. J. (2007).
 Decision-making in healthy children, adolescents and adults explained
 by the use of increasingly complex proportional reasoning rules.
 Developmental Science, 10(6), 814-825.
 
+Knowlton, B. J., Squire, L. R., \& Gluck, M. A. (1994). 
+Probabilistic classification learning in amnesia. 
+Learning \& Memory, 1 , 106-120.
+
 Siegler, R. S. (1981).  Developmental sequences within and between
 concepts.  Monographs of the Society for Research in Child
 Development, 46(2, Serial No.  189).
@@ -542,7 +611,8 @@
 (NEST).  Maarten Speekenbrink was supported by the ESRC Centre for
 Economic Learning and Social Evolution (ELSE).  Thanks to Hilde
 Huizenga, Brenda Jansen and Anna van Duijvenvoorde for the Iowa
-Gambling task data.
+Gambling task data and David Lagnado for the Weather Prediction Task
+data.
 
 
 \bibliography{all,ingmar}