[Depmix-commits] r244 - / papers/individual trunk

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

Author: ingmarvisser
Date: 2008-11-26 21:05:09 +0100 (Wed, 26 Nov 2008)
New Revision: 244

Modified:
   papers/individual/individual.tex
   todo
   trunk/DESCRIPTION
Log:
updated individual paper

Modified: papers/individual/individual.tex
===================================================================
--- papers/individual/individual.tex	2008-11-17 10:29:04 UTC (rev 243)
+++ papers/individual/individual.tex	2008-11-26 20:05:09 UTC (rev 244)
@@ -47,16 +47,16 @@
 Traditionally, these models were restricted to categorical, mostly
 binary, observed variables, placing severe restrictions on possible
 measurement models.  In this paper, the basic model is extended to
-include arbitrary distributions for the observed variables, including
-multi-variate distributions.  Moreover, there is optional support to
-include time-varying predictors.  In effect, this model consists of
-mixtures of generalized linear models with Markovian dependencies over time
-to model the change process.  In addition, transition parameters can
-be made to depend on covariates as well, such that the switching
-regime between states depends on characteristics of the individual or
-the experimental situation.  The model is illustrated with an example
-of participants' learning in the Iowa Gambling Task and in the Weather 
-Prediction Task.}
+include various distributions for the observed variables, including
+mixed multi-variate distributions.  Moreover, there is an option to
+include time-varying predictors on the observed distributions.  In
+effect, this model consists of mixtures of generalized linear models
+with Markovian dependencies over time to model the change process.  In
+addition, the transition parameters can be estimated with covariates,
+such that the switching regime between states depends on
+characteristics of the individual or the experimental situation.  The
+model is illustrated with examples of participants' learning in the
+Iowa Gambling Task and in the Weather Prediction Task.}
 
 
 \begin{document}
@@ -64,33 +64,42 @@
 
 % intro
 Discrete change frequently occurs in learning and development: in
-learning concepts, in performance on Piagetian tasks, in
-discrimination learning and in conditioning.  This chapter is
-concerned with detecting the nature and time points of change in (individual)
-time series.  We present a framework of dependent mixture models that can
-be used to differentiate between gradual and discrete learning events
-in individual time series data.  Before presenting the model in formal
-terms and providing some illustrations, we first review some examples
-in which discrete change is found.
+learning concepts and discrimination learning, in performance on
+Piagetian tasks such as the conservation of liquid task and the
+balance scale task, and in conditioning.  This chapter is concerned
+with detecting and characterizing such discrete changes in
+(individual) time series data.  We present a framework of dependent
+mixture models that can be used to test whether discrete learning
+events are present and, if so, to test at which point in time those
+changes are taking place.  Before presenting the model in formal terms
+and providing some illustrations, we first review some examples from
+the developmental psychology literature in which discrete change is
+typically found.
 
 % phenomena of discrete change in development/learning
-Piagetian developmental theory assumes step-wise changes in the
-strategies that children apply in all kinds of tasks such as the
-conservation learning and the balance scale task (REFERENCE??).
+Piagetian developmental theory assumes step-wise changes in the rules
+that children apply in all kinds of tasks such as in conservation
+learning and in the balance scale task (SieglerAlibali19??).
 \citet{Maas1992} developed a catastrophe model to describe step-wise
-changes or phase transitions in learning and developmental processes.
-They applied the catastrophe model to learning in the conservation of
-liquid task (REFERENCE??)  in which children have to judge relative
-volumes of liquid in glasses of different heights and widths.  Young
-children tend to ignore the width dimension and hence always choose
-the glass with the highest level of liquid (REFERENCE??).
-\citet{Maas1992} showed that there is a sudden transition to a new
-strategy in which the children also take the width of the glasses into
-account when judging the volume of liquids.
+changes predicted to occur by Piagetian theory; importantly, they
+derived precise statistical criteria for testing whether two
+discretely different stages exist.  They applied the catastrophe model
+to development on the conservation of liquid task (InhelderPiaget19??)
+in which children have to judge relative volumes of liquid in glasses
+of different heights and widths.  Young children tend to ignore the
+width dimension and hence always choose the glass with the highest
+level of liquid (InhelderPiaget19??).  It is assumed that there is a
+sudden transition to a new rule, for example, a rule in which the
+children also take the width of the glasses into account when judging
+the volume of liquids.
 
+% \citet{Maas1992} 
+% opzoeken: Siegler & Alibali 19??, intro textbook
+% opzoeken: Inhelder & Piaget, 19??
+
 % balance scale rules
 \citet{Jansen2001} applied the catastrophe model to
-development of strategies on the balance scale task (Siegler, 1981).
+development of reasoning on the balance scale task (Siegler, 1981).
 In the balance scale task participants have to judge which side of a
 balance goes down when the number of weights and their distances to
 the fulcrum are varied over trials.  Younger children tend to ignore
@@ -103,36 +112,40 @@
 the number of weights is equal on both sides of the balance scale.
 This strategy is called Rule 2 \cite{Siegler1981}.
 
+% Jansen 2001: aanpassen in de paper ref ipv het proefschrift
+
 % hysteresis on the balance scale
-\citet{Jansen2001} found clear evidence for stage-wise
-transitions between Rule 1 and Rule 2 by testing criteria that were
-derived from the catastrophe model.  In particular, she found bimodal
-test scores and inaccessibility.  The latter means that there are no
-in-between strategies: children apply either Rule 1 or Rule 2 and
-there is no in-between option.  \citet{Jansen2001} also found
-evidence for hysteresis: the phenomenon that switching between
+\citet{Jansen2001} found evidence for stage-wise transitions, or
+discrete change in our model, between Rule 1 and Rule 2 by testing
+criteria that were derived from the catastrophe model.  In particular,
+she found bimodal test scores and inaccessibility.  The latter means
+that there are no in-between strategies: children apply either Rule 1
+or Rule 2 and there is no in-between option.  \citet{Jansen2001} also
+found evidence for hysteresis: the phenomenon that switching between
 strategies is asymmetric.  Children can switch from Rule 1 to Rule 2
-and back, but this occurs at different trials.  In particular, if the
-distance dimension in the balance scale problems is made more salient
-by increasing the distance difference between weights on either side
-of the balance scale, children may switch from Rule 1 to Rule 2.  If
-subsequently the distance difference is decreased again, children may
-switch back to using Rule 1.  Hysteresis is the phenomenon that this
-switch back occurs at a different value of the control variable, in
-this case the distance difference.
+and back, but this occurs at different values of a continuously
+changing independent variable.  In particular, if the distance
+dimension in the balance scale problems is made increasingly more
+salient by increasing the distance difference between weights on
+either side of the balance scale, children may switch from Rule 1 to
+Rule 2.  If subsequently the distance difference is decreased again,
+children may switch back to using Rule 1.  Hysteresis is the
+phenomenon that this switch back occurs at a different value of the
+control variable, in this case the distance difference.
 
 % conditioning and addiction research
 Also in animal learning and conditioning, evidence is found for sudden
 changes in response behavior \cite{Gallistel2004}.  In particular, in
 their study, evidence was found for sudden onset of learning: at the
-start of the learning experiment, pigeons did not learn anything
-and performance was stable; after a number of trials, learning kicked
-in and there were large increases in performance.  \citeauthor{Gallistel2004} 
-focused on modeling the distribution of onset times: that is, the trials at
-which learning suddenly takes off.  A similar interest in process
-onset times is found in addiction research.  For example,
-\citet{Sher2004} study the age at which children start using alcohol
-and how this related to eventual outcomes in terms of addiction.
+start of the learning experiment, pigeons did not learn anything and
+performance was stable; after a number of trials, learning kicked in
+and there were large increases in performance.
+\citeauthor{Gallistel2004} focused on modeling the distribution of
+onset times: that is, the trials at which learning suddenly takes off.
+A similar interest in process onset times is found in addiction
+research.  For example, \citet{Sher2004} study the age at which
+children start using alcohol and how this related to eventual outcomes
+in terms of addiction.
 
 % discrimination and categorization learning
 Sudden transitions in learning are also observed in simple

Modified: todo
===================================================================
--- todo	2008-11-17 10:29:04 UTC (rev 243)
+++ todo	2008-11-26 20:05:09 UTC (rev 244)
@@ -19,7 +19,11 @@
 
 7) help voor fixed, equal constraints opgeven
 
+8) check bug in posterior versus viterbi bij de modellen van Maartje
 
+9) 
+
+
 
 TODO Medium term
 

Modified: trunk/DESCRIPTION
===================================================================
--- trunk/DESCRIPTION	2008-11-17 10:29:04 UTC (rev 243)
+++ trunk/DESCRIPTION	2008-11-26 20:05:09 UTC (rev 244)
@@ -1,5 +1,5 @@
 Package: depmixS4
-Version: 0.2-0
+Version: 0.2-1
 Date: 2008-06-09
 Title: Dependent Mixture Models
 Author: Ingmar Visser <i.visser at uva.nl>, Maarten Speekenbrink <m.speekenbrink at ucl.ac.uk>