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  \parbox{.5\columnwidth}{\raggedright Robert Felty\\University of Michigan}%
  \parbox{.5\columnwidth}{\raggedleft robfelty@umich.edu\\ \@date}%
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\date{March 9th, 2007}
\title{Cross-linguistic differences in lexical access and spoken word
recognition}
\author{Robert Felty\\
University of Michigan\\robfelty@umich.edu} 
\begin{document}
%%% adjust hyphenation - we want less hyphenation, so we increase tolerance
\pretolerance=-1
\tolerance=4500
\adjdemerits=6400
\doublehyphendemerits=90000
\finalhyphendemerits=14400
\bibliographystyle{../../bib/felty3}     
%\raggedright
\maketitle
\begin{slide}[toc=]{Acknowledgments}
I would like to thank the members of my committee for their support and input
\begin{itemize}
  \item Jos\'e Benk\'i (co-chair, Communications Sciences and Disorders,
  Michigan State University)
  \item Patrice Beddor (co-chair, Linguistics)
  \item Andries Coetzee (Linguistics)
  \item Robert Kyes (German)
\end{itemize}
{\small I would also like to thank those who helped me prepare and pilot the
stimuli for the experiments and the Linguistics department at the
University of Konstanz for letting me use their facilities, and for their
hospitality.}
\end{slide}
\section{Background}
\begin{slide}[toc=Goals]{Research Goals}
  \begin{itemize}
  \item Learn more about the role of morphology in the mental lexicon 
  \item  That is, are morphemes stored separately in the lexicon and then
  combined to form words during lexical access, or are words stored whole in
  the lexicon?
  \item Extend previous research using open response spoken word recognition
  to bisyllabic words
  \item Compare context effects across two phonologically similar, yet
  morphologically diverse languages
  \end{itemize}
\end{slide}
\begin{slide}{Morphology}
\onslide*{1}{
\begin{itemize}
  \item The study of Lexical Access seeks to determine how the mental lexicon
  affects language processing.
  \item The role of morphology in the lexicon is studied widely in lexical
  access research
  \item Results from cross-linguistic research suggest that morphology plays
  different roles in lexical access based on the type of morphological system
  of the language
\end{itemize}
}
\onslide*{2}{
%\end{slide}
%\begin{slide}{Models}
\begin{itemize}
\item Two classes of models differ in their predictions of how morphologically complex words are stored in the lexicon and accessed.
  \item Associative Models
  \begin{itemize}
    \item Claim that words are stored whole in the lexicon
    \item Examples: TRACE, MERGE
  \end{itemize}
  \item Combinatorial Models
  \begin{itemize}
    \item Claim that morphemes are stored separately and combined during lexical access 
    \item Also known as morphological decomposition models
    \item Examples: \citet{Taft1988, Taft1975} 
  \end{itemize}
\end{itemize}
}
\end{slide}

%\begin{slide}{Context Effects}
%Previous research has found several different context effects which play a role in word recognition\\
%I will be focusing on the following context effects:
%\begin{itemize}
%  \item Lexical status (word or nonword)
%  \item Morphology (bimorpheme or monomorpheme)
%  \item Lexical frequency (how often a word occurs)
%  \item Neighborhood Density (how similar a word is to other words)
%\end{itemize}
%\end{slide}

%\overlays{3}{
\begin{slide}{Previous Research}
\begin{itemize}
  \item Using a Lexical Decision task, and a Cross-modal Priming task, \citet{Clahsen2001} found a difference in processing of German inflected adjectives.
\end{itemize}
\small
\begin{table}[!htb]
\centering
\tablecaption{Example from \citet{Clahsen2001}}
\label{ClahsenExample}
\begin{tabular}{*{8}{l}}
\hline\hline
    \multicolumn{4}{c}{-m dominant adjectives} & \multicolumn{4}{c}{-s dominant adjectives} \\
    &Stem form  &-m &-s  &   &Stem form &-m  & -s    \\
\ifnum\Slides=1
   ruhig&838% 
  &\onslide*{2}{\psframebox[linecolor=pdcolor4]{51}}%  
   \onslide*{3}{\psframebox[linecolor=pdcolor3]{51}}%  
   \onslide*{1}{\psframebox[linecolor=pdcolor3]{51}}%
  &\onslide*{2}{\psframebox[linecolor=pdcolor3]{13}}%  
   \onslide*{1}{\psframebox[linecolor=pdcolor3]{13}}%
   \onslide*{3}{\psframebox[linecolor=pdcolor5]{13}}%
  &rein&783  &14
  &\onslide*{2}{\psframebox[linecolor=pdcolor4]{38}}%  
   \onslide*{3}{\psframebox[linecolor=pdcolor5]{38}}%
   \onslide*{1}{\psframebox[linecolor=pdcolor3]{38}}\\
\else
  ruhig&838 &51 & 13 &rein&783  &14&38\\
\fi
\hline
\end{tabular}
\end{table}
\end{slide}
%}

\begin{slide}[toc=predictions]{Qualitative Predictions}
\begin{itemize}
  \item A highly inflectional language (German) will show a greater effect of morphological complexity than a language with little inflectional morphology (English) 
   \item Other context effects such as lexical frequency and neighborhood density will have a smaller effect on non-native listeners than native listeners, given that their lexicons are not as developed
\end{itemize}
\end{slide}
\section{Method}
\begin{slide}{Task / Subjects}
\begin{itemize}
  \item Open Response Speech-In-Noise Task 
  \begin{itemize}
  \item Participants respond via keyboard input
  \end{itemize}
  \item 2 different Signal to Noise Ratios (SNRs) used for each experiment
  \item signal dependent (but uncorrelated) noise (\nocite{Schroeder1968} see Schroeder, 1968) 
  \item Two separate experiments 
  \begin{itemize}
  \item Experiment 1 \dash 30 native speakers of English
  \item Experiment 2 \dash 32 native speakers of German
  \end{itemize}
\end{itemize}
\end{slide}

\begin{slide}[method=direct]{English Materials}
%\onslide*{1}{
%\subslide{\bf English Stimuli (exp. 1 \& 4)}
\begin{itemize}
  \item 150 CVCCVC words
    \begin{itemize}
      \item 74 monomorphemic% 
       \slideonly{
       \includemovie[label=basket]{}{}{sound/basket3.wav}\\%
       \movieref[play]{basket}%
       }
       {\it basket} 
       \ipa{/b\ae skIt/}
       {\it compass} \ipa{/k@mp@s/}   
       {\it random} \ipa{/\textturnr \ae nd@m/} 
      \item 76 bimorphemic%
       \slideonly{
       \includemovie[label=mending]{}{}{sound/mending3.wav}\\%
       \movieref[play]{mending}%
       }
       {\it mending} 
      \ipa{/mEndI\ng/}
      {\it painted} \ipa{/peIntId/}
      {\it senses} \ipa{/sEnsIz/}
    \end{itemize}
      \item 150 CVCCVC nonwords
       \slideonly{
       \includemovie[label=nutvit]{}{}{sound/nutvit2.wav}\\%
       \movieref[play]{nutvit}%
       }
       {\it nutvit}
     \ipa{/nUtvIt/}
     {\it nisren} \ipa{/nIsrIn/} 
     {\it tulsid} \ipa{/tUlsId/}
  \item single male talker
\end{itemize}
\end{slide}
\begin{slide}[method=direct]{German Materials}
\begin{itemize}
  \item 150 CVCCVC words
    \begin{itemize}
      \item 75 monomorphemic%
       \slideonly{
       \includemovie[label=dunkel]{}{}{sound/dunkel.wav}\\%
       \movieref[play]{dunkel}%
       }
       {\it dunkel}
       \ipa{/dUNk@l/}   
       {\it selten} \ipa{/zElt@n/}
       {\it hektik} \ipa{/hEktIk/} 
      \item 75 bimorphemic%
       \slideonly{
       \includemovie[label=feindes]{}{}{sound/Feindes.wav}\\%
       \movieref[play]{feindes}%
       }
      {\it Feindes} 
      \ipa{/faInd@s/}
      {\it bestem} \ipa{/bEst@m/}
      {\it derber} \ipa{/dErb@r/}
    \end{itemize}
  \item 150 CVCCVC nonwords
       \slideonly{
       \includemovie[label=nemschen]{}{}{sound/nemschen.wav}\\%
       \movieref[play]{nemschen}%
       }
     {\it nemschen} 
     \ipa{/nEmS@n/}
     {\it mofkem} \ipa{/mOfk@m/} 
     {\it bomgech} \ipa{/bOmg@x/}
  \item single male talker
\end{itemize}
\end{slide}
\section{Analysis}
\begin{slide}{Confusion}
\begin{enumerate}
\item Convert spelling to phonemes
\item For each SNR, Block (word or nonword), and position (C1, C2 etc.) make a confusion matrix
\item For each subject, calculate the mean word score ($p_w$) and phoneme score ($p_p$)
\end{enumerate}
\end{slide}

\begin{slide}{J-factor}
\onslide*{1}{
\begin{itemize}
\item The j-factor model provides a measure of context effects.  
\item The j-factor model assumes that phonemes are the basic unit of speech, and that phonemes are perceived independently (which has been shown to hold true most of the time% 
\ifnum\Slides=0 \citealp[; see ][]{Fletcher1953, Allen1994}\fi).  
\item The probability of correctly identifying a given word (or nonword) can be calculated as the product of the probabilities of its constituent phonemes%
\ifnum\Slides=0 , as shown in equation \ref{j-factor1}\fi.
\end{itemize}
}
\onslide*{2}{
\begin{equation}
\label{j-factor1}
p_{w}=p_{C1}p_{V1}p_{C2}p_{C3}p_{V2}p_{C4}
\end{equation}
\ifnum\Slides=0 
where $p_w$ is the probability of correctly identifying a word (or nonword). Assuming that phonemes are perceived independently, (\ref{j-factor1}) can be rewritten as:
\fi
\begin{equation}
\label{j-factor2} 
 p_{w}=p^{j}_{p}
\end{equation}
\ifnum\Slides=0  where $j$ is the number of phonemes, and $p_p$ is the geometric
mean of the probabilities of each constituent phoneme.  Rewriting
(\ref{j-factor2}), the quantity j can be empirically determined from confusion
matrices by:\fi
\begin{equation}
j =\frac{log(p_{w})}{log(p_{p})}
\end{equation}
}
\onslide*{3}{
  \subslide{\bf Previous J-factor results}
  \begin{itemize}
    \item 3 studies have used the j-factor model with CVC English stimuli
    \citep{Boothroyd1988, Olsen1997, BenkiJASA} 
    \item All have found $j_{nonword}\approx3$ and $j_{word}\approx2.5$
    \item 1 study using CVC Mandarin stimuli \citep{BenkiMandarin} did not
    find a difference between words and nonwords
  \end{itemize}

} %end onslide
\end{slide}
\begin{slide}[toc=predictions]{Quantitative Predictions}
\onslide*{1}{
\begin{itemize}
  \item Nonwords  \dash $j = 6$; interpretation is that phonemes are being predicted independently of one another
  \item Words \dash  $j < 6$; interpretation is that lexical status is affecting perception.  
  \item Morphology \dash $j_{bi}>j_{mono}$; interpretation is that monomorphemes have more
  context than bimorphemes
  \item Frequency \dash $j_{word} \propto \frac{1}{\text{frequency}}$;
  interpretation is that frequency provides a facilitatory effect
  \item Neighborhood density \dash $j_{word} \propto \text{density}$;
  interpretation is that density provides an inhibitory effect
\end{itemize}
}
\onslide*{2}{
  \slideonly{
  \subslide{Hypothetical Results}
  \begin{figure}
  \ifnum\Dark=1
    \includegraphics[width=3in]{fig/exp2/sampleDark}
  \else
    \includegraphics[width=3in]{fig/exp2/sampleLight}
  \fi
  \end{figure}
  }
}
\end{slide}
\section[toc=English Results]{Experiment One Results \subtitle{English
listeners}}
\begin{slide}[toc=lexical status]{English \dash Lexical Status}
  \begin{itemize}
    \item As expected, there is a significant difference in $j$ between words
    and nonwords \ifnum\Slides=0 (see Figure \ref{F:exp1JFacSubPlot}, page
    \pageref{F:exp1JFacSubPlot}) \fi
    \item $j$ for nonwords is slightly smaller than expected 
  \end{itemize}
\slideonly{
\begin{figure}[htbp]
  \ifnum\Dark=1
    \includegraphics[width=3in]{fig/exp1/blockDark}
  \else
    \includegraphics[width=3in]{fig/exp1/blockLight}
  \fi
\end{figure}
}
\end{slide}

\begin{slide}[toc=morphology]{English \dash Morphology}
  \begin{itemize}
  \item After removing confounds with lexical frequency and neighborhood
  density, no significant
  difference was found between monomorphemes and bimorphemes 
  %\item This indicates a greater context effect for monomorphemes than bimorphemes 
  \end{itemize}
  \slideonly{
  \begin{figure}[h]
  \ifnum\Dark=1
    \includegraphics[width=3in]{fig/exp1/typeDark}
  \else
    \includegraphics[width=3in]{fig/exp1/typeLight}
  \fi
  \end{figure}
  }
\end{slide}
\begin{slide}[toc=frequency]{English \dash Lexical Frequency}
  \begin{itemize}
   \item Words were grouped into low and high frequency groups via median
   splits 
    \item As predicted, high frequency words have a lower $j$, indicating a
    facilitatory effect of frequency
  \end{itemize}
  \slideonly{
    \begin{figure}[htbp]
    \ifnum\Dark=1
      \includegraphics[width=3in]{fig/exp1/freqDark}
    \else
      \includegraphics[width=3in]{fig/exp1/freqLight}
    \fi
    \end{figure}
  }
\end{slide}

\begin{slide}[toc=density]{English \dash Neighborhood Density}
\begin{itemize}
  \item Words were also grouped into sparse and dense neighborhoods via median
  splits
  \item As predicted, an increase in density causes an inhibitory effect
\end{itemize}
  \slideonly{
    \begin{figure}[htbp]
    \ifnum\Dark=1
      \includegraphics[width=3in]{fig/exp1/fwnpDark}
    \else
      \includegraphics[width=3in]{fig/exp1/fwnpLight}
    \fi
    \end{figure}
  }
\end{slide}

\ifnum\Slides=0
\begin{figure}[p]
  \hspace{-.3in}\includegraphics{fig/exp1/JFactorSubPlotPres}
  \caption{Experiment 1 (English listeners) J-factor Analysis by
  subject \dash Each plot compares two subsets of results from the subject
  analysis. Curves represent $y=x^{j}$. Frequency and neighborhood density
  plots show only word results.
  P-values given are from 2-sample t-tests; before computing the statistics,
  all points lying in the floor or ceiling ranges ($>.95$ or $<.05$) were
  removed, but are still shown on the plot.}
  \label{F:exp1JFacSubPlot}
  \end{figure}
\fi

\section[toc=German Results]{Experiment Two Results \subtitle{German
listeners}}
\begin{slide}[toc=]{Item Exclusion}
  \begin{itemize}
    \item Initial results for German had much lower than expected j-scores
    \item Additional analysis revealed that this was due to stimuli containing
      post-vocalic /\textipa{\;R}/ which frequently does not behave as an
      independent phoneme
    \item Results for lexical status and morphology shown here have excluded
     words containing post-vocalic /\textipa{\;R}/ \\94
     nonwords and 79 words (36 monomorphemic and 43 bimorphemic) 
    \item Lexical frequency and neighborhood density effects did not seem to be
    affected by this, so they are shown with the full set of stimuli
  \end{itemize}
\end{slide}
\begin{slide}[toc=lexical status]{German \dash Lexical Status}
  \begin{itemize}
    \item As predicted, $j_{word}$ is significantly lower than $j_{nonword}$
    \ifnum\Slides=0 (see Figure \ref{F:exp2JFacSubPlot}, page
    \pageref{F:exp2JFacSubPlot}) \fi
    \item $j$ for nonwords is slightly smaller than expected 
  \end{itemize}
\slideonly{
\begin{figure}[htbp]
  \ifnum\Dark=1
    \includegraphics[width=3in]{fig/exp2/blockDark}
  \else
    \includegraphics[width=3in]{fig/exp2/blockLight}
  \fi
\end{figure}
}
\end{slide}

\begin{slide}[toc=morphology]{German \dash Morphology}
  \begin{itemize}
  \item As predicted, $j_{mono}$ was significantly lower than $j_{bi}$ 
  \item This indicates a greater context effect for monomorphemes than bimorphemes 
  \end{itemize}
  \slideonly{
  \begin{figure}[h]
  \ifnum\Dark=1
    \includegraphics[width=3in]{fig/exp2/typeDark}
  \else
    \includegraphics[width=3in]{fig/exp2/typeLight}
  \fi
  \end{figure}
  }
\end{slide}
\begin{slide}[toc=frequency]{German \dash Lexical Frequency}
  \begin{itemize}
    \item Effects of lexical frequency were also significant
    \item However, the effect is opposite of that predicted \dash we find an
    {\color{pdcolor5} inhibitory} effect
  \end{itemize}
  \slideonly{
    \begin{figure}[htbp]
    \ifnum\Dark=1
      \includegraphics[width=3in]{fig/exp2/freqDark}
    \else
      \includegraphics[width=3in]{fig/exp2/freqLight}
    \fi
    \end{figure}
  }
\end{slide}

\begin{slide}[toc=density]{German \dash Neighborhood Density}
\begin{itemize}
  \item Neighborhood density is also significant
  \item As predicted, an increase in density causes an inhibitory effect
\end{itemize}
  \slideonly{
    \begin{figure}[htbp]
    \ifnum\Dark=1
      \includegraphics[width=3in]{fig/exp2/fwnpDark}
    \else
      \includegraphics[width=3in]{fig/exp2/fwnpLight}
    \fi
    \end{figure}
  }
\end{slide}

\ifnum\Slides=0
\begin{figure}[p]
  \hspace{-.3in}\includegraphics{fig/exp2/JFactorSubPlotPres}
  \caption{Experiment 2 (German listeners) J-factor Analysis by
  subject \dash Each plot compares two subsets of results from the subject
  analysis. Curves represent $y=x^{j}$.  The frequency and density plots
  display only word results.  P-values given are from 2-sample t-tests; before
  computing the statistics, all points lying in the floor or ceiling ranges
  ($>.95$ or $<.05$) were removed, but are still shown on the plot.}
  \label{F:exp2JFacSubPlot}
  \end{figure}
\fi
\section[toc=Discussion]{Discussion}
%\slideonly{
\begin{wideslide}[toc=summary]{Summary of Results }
%}
\begin{table}[htb]
\begin{small}
\centering
\tablecaption{J-factor analysis summary}
\label{T:jFacSum}
  \begin{tabularx}{\columnwidth}{
  >{\setlength{\hsize}{\hsize}\raggedright\arraybackslash}X
  >{\setlength{\hsize}{.8\hsize}\raggedright\arraybackslash}X
  >{\setlength{\hsize}{\hsize}\raggedright\arraybackslash}X
  *{2}{>{\setlength{\hsize}{.9\hsize}\raggedright\arraybackslash}X}
  *{2}{>{\setlength{\hsize}{1.2\hsize}\raggedright\arraybackslash}X}
  %*{2}{>{\setlength{\hsize}{.55\hsize}\raggedright\arraybackslash}X}
  %*{1}{>{\setlength{\hsize}{.65\hsize}\raggedright\arraybackslash}X}
  %*{6}{>{\setlength{\hsize}{.55\hsize}\raggedright\arraybackslash}X}
  %>{\setlength{\hsize}{4.25\hsize}\raggedright\arraybackslash}X}
  }
  \hline\hline
  \rule{0pt}{1.1em}& {\footnotesize Lexical Status} & {\footnotesize
  Morphology} & {\footnotesize Log wordform frequency} & {\footnotesize Log
  lemma frequency}
  & {\footnotesize phonological neighborhood density} & {\footnotesize
  phonetic neighborhood density} \\[.3em]
  \cline{2-7}
  \multicolumn{7}{c}{\vspace*{-.6em}}\\
  \input{table/jDiffs}
  \hline\hline
  \end{tabularx}
***$p<.001$, **$p<.01$, *$p<.05$
\end{small}
\end{table}
%\slideonly{
\end{wideslide}
%}
\begin{slide}[toc=cross-language]{Cross-linguistic effects}
  \onslide*{1}{
    \begin{itemize}
    \item One of the major differences found between the English and German
    results is the effect of morphology
    \item The interpretation for this is that German has a much richer
    inflectional morphology, and therefore morphology plays a larger role in
    the structure of the lexicon
    \item Similar cross-linguistic differences have been reported by
    \citet{Marslen-Wilson2001}. 
    \item  In comparing Polish, Arabic, English, and Chinese they have
    obtained different results in terms of how morphology is processed and
    represented in the lexicon.  
     \end{itemize}
  }
  \onslide*{2}{
    \citet{Marslen-Wilson2001} find that:
    \begin{itemize}
    \item In English, complex words such as {\it darkness}
    are represented by their constituent morphemes, and are combined during
    lexical access. English also exhibits stem-priming, e.g. the
    stem in {\it darkness} and {\it darkly} prime {\it dark}.  This is not the
    case for semantically opaque words such as {\it department}, which does
    not prime {\it depart}. 
    \item Polish also exhibits affix
    priming,  e.g. {\it kotek/ogr\'odek} `a little cat' / `a little garden' --
    the diminutive affix in the prime facilitates perception of the target
    and suffix interference (e.g. {\it pis-anie/pis-arz} `writing'/`writer'
    -- no facilitation is found in such pairs, despite facilitation of
    inflectional endings).  
    \item Morphology seems to play an even larger role in Arabic, which
    has root priming even for semantically opaque words. 
    \end{itemize}
  }
  \onslide*{3}{
    \begin{itemize}
    \item Chinese has virtually no inflectional or derivational morphology
    \item Compounding is very active in Mandarin Chinese, and
    bimorphemic compounds account for up to 70\% of all word forms in the
    language. 
    \item However Marslen-Wilson and colleagues find no evidence for
    morphological decomposition in Mandarin compounds. 
    \end{itemize}
  }
  \onslide*{4}{
    \begin{itemize}
    \item \citet{Vannest2002} also find similarly various results in a
    comparison of English and Finnish derivational morphology.  
    \item Research on Finnish inflectional morphology has shown support for
    combinatorial-like processing \citep[e.g.][]{Laine1999}, Vannest et al.
    \item But they find less evidence for morphological
    decomposition with derivational morphology than for English.  
    \item They hypothesize that words with derivational affixes are stored
    separately in Finnish in order to decrease the amount of morphological
    processing that the Finnish speaker must perform.
  \end{itemize}
  }
\end{slide}
\begin{slide}[toc=interaction]{Interaction of Phonetics and Morphology}
\onslide*{1}{
  \begin{itemize}
  \item It is possible that differences in mono- and bimorphemic stimuli could
  be partially due to acoustics or response bias.
  \item The final consonants in
the bimorphemic stimuli were restricted to the phonemes /\ipa{\;R~s~m~n}/,
which, along with /\ipa{@}/ constitute all of the possible inflectional
endings for nouns and adjectives in German. 
  \item /m/ and /n/ are known to be highly confusable with one another. 
  \item In addition, /n/ occurs as an inflectional ending much more frequently
  than /m/. 
  \item  In order to investigate this further, a Signal Detection
  Theory\index{signal detection theory} (SDT) analysis was carried out. 
  \end{itemize}
  }
\onslide*{2}{
 \begin{itemize}
  \item SDT measures the sensitivity of distinguishing two stimuli, using the
  metric, $d^\prime$.
  \item SDT also provides a measure of bias, $c$, which indicates
  whether one is more or less likely to respond with a particular phoneme. 
   \begin{itemize}
     \item Positive values of $c$ indicate a bias towards a response; 
     \item negative values indicate a bias against a response. 
   \end{itemize}
  \item   To carry out the SDT analysis, the original confusion matrices for
  each S/N were transformed into 2x2 submatrices.  An SDT analysis was then
  applied to each submatrix.
  \end{itemize}
}
\onslide*{3}{
%\slideonly{
  \begin{small}
  \vspace{1em}
  \begin{minipage}{.58\thewidth}
%}
%\begin{multicols}{2}
\handoutonly{
%\caption[Signal Detection Theory analysis of /m/ and /n/ submatrix in
%final]{
%{\bf Table 3} 
\tablecaption{
Signal Detection Theory analysis of /m/ and /n/ submatrix in final
position. For this analysis /m/ is considered to be the target stimulus.
Positive $c^\prime$ indicate a bias towards /n/.%
}
}
\begin{enumerate}
  \item in the absence of lexical context effects (nonword condition), /m/ and
  /n/ are highly confusable, with a small bias towards /n/
  \item /m/ and /n/ are perceived as most distinct in the monomorphemic
  condition,
  \item bias towards /n/ is greatest in the bimorphemic case. 
  %The increase in
%distinction in the monomorphemic can be interpreted as a result of the greater
%ability to distinguish between neighbors based on lexical frequency
%information. The bias towards /n/ in the bimorphemic case can be interpreted
%as evidence that listeners are exploiting the fact that the /n/ ending occurs
%most frequently, and they are therefore guessing /n/ more frequently.
%Listeners seem to be depending on a combination of acoustics, lemma frequency,
%and morphological distribution to make their decisions.
\end{enumerate}
%\clearpage
%\slideonly{
  \end{minipage}\hspace{.02\thewidth}%
  \begin{minipage}{.4\thewidth}
%}
\centering
%\label{T:SDTmn}}
%\begin{table}[htbp]
\begin{tabular}{l l r r }
\hline\hline
\rule{0pt}{1.1em}& &\multicolumn{1}{c}{$d^\prime$} &
\multicolumn{1}{c}{$c$}\\[.3em]
\cline{3-4}
\multicolumn{2}{l}{Nonwords} & \rule{0pt}{1.1em}& \\
  & lower S/N (2 dB) &-0.182 & 0.555 \\
  & higher S/N (7 dB) & 0.664 & 0.743\\
  \multicolumn{2}{l}{Bimorphemes} & & \\
  & lower S/N (2 dB) & 1.616&0.984 \\
  & higher S/N (7 dB) &1.913 &0.556 \\
  \multicolumn{2}{l}{Monomorphemes} & & \\
  & lower S/N (2 dB) & 3.514& 0.239\\
  & higher S/N (7 dB) & 4.733& -0.060\\[.3em]
\hline\hline
\end{tabular}
%\end{table}
%\end{multicols}
%\slideonly{
  \end{minipage}
  \end{small}
%}
}
\end{slide}
\begin{slide}{Conclusions}
\begin{itemize}
  \item The j-factor results for CVCCVC words are mostly consistent with the
  previous results using CVC stimuli \ifnum\Slides=0 \citep{Boothroyd1988,
  Olsen1997, BenkiJASA}\fi
  \item One striking new result is that $j_{word}$ does not scale linearly
  with word length
  \item The influence of morphology on spoken word recognition is language
  dependent
  \item The processing differences between mono- and bimorphemic found in this
  study present a challenge to theories of lexical access which assume whole
  word storage.
  \item Listeners are particularly sensitive to lexico-statistical
  information when presented with highly confusable stimuli
  %\item Evidence was shown in support of a combinatorial model of lexical
  %access
\end{itemize}
\end{slide}
\begin{slide}{Future Research}
\begin{itemize}
  \item Further investigate effects of word length on spoken word
  recognition using stimuli of a variety of lengths
  \item Determine the time course of these effects using speech-in-noise tasks
  which also incorporate a measure of time course (either behavioral or
  neurological)
\end{itemize}
\end{slide}
\slideonly{
\begin{wideslide}[toc=]{}
\vspace{.6in}
\begin{center}
\hspace*{.5in}
{\swabfamily\fraklines
\yinitpar{T}
 %\textinit{T}
 {\huge \raisebox{-.2em}{hanks:}}
}
%{\fontfamily{pzc}\fontsize{50}{60}\selectfont Thanks}
%\includegraphics[width=3in]{thanks3}
\end{center}
\end{wideslide}
\begin{wideslide}[toc=]{\small References}
\vspace*{-1em}
\scriptsize
\bibliography{../../bib/felty}
\end{wideslide}
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%         extra slides here in case people ask          %%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\slideonly{
\section[tocsection=hidden,slide=false]{Extra Slides}
%\begin{slide}[toc=]{Items analysis summary}
%\begin{table}[htb]
%\centering
%\tablecaption{Items analysis summary showing $R^2$ for each context effect}
%\label{T:jFacSum}
%\begin{small}
%\begin{tabular}{l *{6}{l}}
%\hline\hline
%& \multicolumn{2}{c}{frequency} && \multicolumn{2}{c}{density}\\
%\cline{2-3} \cline{5-6}
%&\rule{0pt}{1.1em} word & lemma &&phonological & phonetic \\
%\rule{0pt}{1.1em}English& .10$^{**}$ & .13$^{**}$ && .14$^{***}$ & .04\\
%German& .12$^*$ & .29$^{***}$ && .02 & .13$^{*}$ \\ 
%English non-native& .09$^{**}$ & .14$^{***}$ && .02 & .00 \\
%German non-native& .00 & .07$^{**}$ && .03 & .09$^{**}$ \\
%\hline\hline
%\end{tabular}
%$^*p<.05, ^{**}p<.01, ^{***}p<.001$ 
%\end{small}
%\end{table}
%\end{slide}
\begin{slide}{Signal-dependent Noise}
  \onslide*{1}{
  \begin{equation}
  \label{noise1}
  s_{noisy} = s + \alpha \cdot \pm 1 \cdot s
  \end{equation}
  where $\pm 1$ is determined randomly on a sample per sample basis, and $\alpha$ is defined as:
  \begin{equation}
  \label{noise2}
  \alpha = \sqrt{\frac{1}{10^{\frac{SNR_{dB}}{10}}}}
  \end{equation}
  }
  \onslide*{2}{
  \begin{itemize}
  \item This method has the advantage that S/N is constant for the entire
  utterance, rather than using an average as with additive (broadband) noise. 
  \item Noise is generated on the fly.
  \item The resulting noise sounds very similar to broadband noise, and
  previous experiments using signal-dependent noise find very similar results
  to broadband noise \ifnum\Slides=0 \citep{BenkiJASA}\fi .
  \includemovie[label=noise]{}{}{sound/noise.wav}
  \includemovie[label=clean]{}{}{sound/basketClean.wav}
  \includemovie[label=basket]{}{}{sound/basket3.wav}
  \item \movieref[play]{noise}{just noise} |
  \movieref[play]{basket}{signal plus noise} |
  \movieref[play]{clean}{just signal}
\end{itemize}
  }
\end{slide}
\begin{slide}[toc=open response]{Open Response Data: Model}
   How does one deal with open response data?
  \begin{itemize}
  \item give as much credit as possible
  \item be consistent
  \end{itemize}
\end{slide}
\begin{slide}[toc=]{Open Response Data: Examples}
     \begin{itemize}
       \item typos
	 \begin{itemize}
	   \item metathesis typo {\it biulded} -- scored as \ipa{bIld@d}
	   \item letters next to each other on keyboard 
	 \end{itemize}
	\item real words in non words {\it bahbone} -- scored as \ipa{babwUn}
       \item misspellings {\it concious} for {\it conscious} 
      \end{itemize}
  \end{slide}
%\begin{slide}[toc=position]{$p_p$ by position}
%\begin{figure}[htbp]
%  \setlength{\columnsep}{25pt}
%  \begin{multicols}{2}
%  \includegraphics[width=2.5in]{fig/exp1/barPpColor.eps}
%  \clearpage
%  \includegraphics[width=2.5in]{fig/exp1/scatterPpColor.eps}
%  \end{multicols}
%\end{figure}
%\end{slide}
\begin{slide}[toc=large j]{Can $j$ be larger than n?}
  \onslide*{1}{
  \begin{itemize}
    \item Consider the words {\it hot} and {\it hut}
    \item The raw CELEX frequency of {\it hot} is 2498 and {\it hut} has a
    frequency of 396
    \item Consider the following hypothetical spoken word recognition results for {\it hot}
    and {\it hut}:\\
    \begin{tabular}{*{7}l}
    \hline\hline
    \rule{0pt}{1.1em}& $p_{C1}$ & $p_V$ & $p_{C2}$ &$p_p$ &  $p_w$ & $j$\\[.3em]
    \cline{2-7}
    \rule{0pt}{1.1em}{\it hot} & .9 & .9 & .9 & .90 & .8 & 2.12\\
    {\it hut} & .9 & .2 & .9 & .54 & .1 & 3.74\\[.3em]
    \hline\hline
    \end{tabular}
    \item The same bias {\color{pdcolor5} for} {\it hot} appears as a bias {\color{pdcolor5} against} {\it
    hut}
    \item a result of $j>n$ does not make sense for subjects
  \end{itemize}
  }
  \onslide*{2}{
  \begin{footnotesize}
  \begin{tabularx}{\textwidth}{
  >{\setlength{\hsize}{1.4\hsize}\raggedright\arraybackslash}X
  *{2}{>{\setlength{\hsize}{.75\hsize}\raggedright\arraybackslash}X}
  *{2}{>{\setlength{\hsize}{.55\hsize}\raggedright\arraybackslash}X}
  *{1}{>{\setlength{\hsize}{.65\hsize}\raggedright\arraybackslash}X}
  *{6}{>{\setlength{\hsize}{.55\hsize}\raggedright\arraybackslash}X}
  >{\setlength{\hsize}{4\hsize}\raggedright\arraybackslash}X}
    \hline\hline
  \rule{0pt}{1.1em}Item &  freq & dens & $p_p$ & $p_w$ & $j$ & $p_{C1}$ &
  $p_{V1}$ & $p_{C2}$ & $p_{C3}$ & $p_{V2}$ & $p_{C4}$ &
  \multicolumn{1}{c}{errors}\\[.3em]
  \hline
  \rule{0pt}{1.1em}hosted & 1 & 1.11 & .71 & .1 & 6.74 & .13 & .97 &
  1 & 1 & 1 & 1 & posted, coasted, hasted, toasted\\ 
  chances & 2.5 & 4.91 & .92 & .8 & 2.67 & .83 & 1 &
  1 & .97 & .87 & .87 & chancing, cancers, cancer, Candice\\[.3em]
    \hline\hline
  \end{tabularx}
%  \begin{tabularx}{\textwidth}{*{12}{l} >{\raggedright\arraybackslash}X}
%  Item &  freq & dens & $p_p$ & $p_w$ & $j$ & C1 & V1 & C2 & C3 & V2 & C4 & errors\\
%  hosted & 1 & 1.11 & .71 & .1 & 6.74 & .13 & .97 &
%  1 & 1 & 1 & 1 & posted, coasted, hasted, toasted\\ 
%  chances & 2.5 & 4.91 & .92 & .8 & 2.67 & .83 & 1 &
%  1 & .97 & .87 & .87 & chancing, cancers, cancer, candice\\
%  \end{tabularx}
  \end{footnotesize}
  }
\end{slide}
} %end slide only for the extra slides
\ifnum\Slides=0 
\setlength{\bibsep}{0em}
{\small
\bibliography{../../bib/felty}}
\onecolumn
\section{Sample Confusion Matrices}
\setlength{\tabcolsep}{2pt}
    \begin{minipage}[t]{0.5\thewidth}
  \centering
    \tablecaption{English \dash V1 nonwords S/N~=~-5~dB.  Numbers
    given are percentages.  Total number of presentations for each phoneme is
    given in the final column. }

    \label{T:exp1lowV1nonwords}
  \input{matrix/exp1/nonword-5V1.tex}
  \end{minipage}
    \begin{minipage}[t]{0.5\thewidth}
  \centering
    \tablecaption{German \dash V1 nonwords S/N~=~2~dB. Numbers
    given are percentages.  Total number of presentations for each phoneme is
    given in the final column. }

    \label{T:exp2lowV1nonwords}
  \input{matrix/exp2/nonword2V1.tex}
  \end{minipage}

\vspace{.7em}

    \begin{minipage}[t]{0.5\thewidth}
  \centering
    \tablecaption{English \dash C4 nonwords S/N~=~-5~dB. Numbers
    given are percentages.  Total number of presentations for each phoneme is
    given in the final column. }
    \label{T:exp1lowC4nonwords}
  \input{matrix/exp1/nonword-5C4.tex}
  \end{minipage}
  \begin{minipage}[t]{0.5\thewidth}
  \centering
    \tablecaption{German \dash C4 nonwords S/N~=~2~dB. Numbers
    given are percentages.  Total number of presentations for each phoneme is
    given in the final column. }
    \label{T:exp2lowC4nonwords}
  \input{matrix/exp2/nonword2C4.tex}
  \end{minipage}
\fi
\end{document}

