\documentclass[10pt]{article}

\usepackage{fullpage}
\usepackage{setspace}
\usepackage{parskip}
\usepackage{titlesec}
\usepackage[section]{placeins}
\usepackage{xcolor}
\usepackage{breakcites}
\usepackage{lineno}
\usepackage{hyphenat}





\PassOptionsToPackage{hyphens}{url}
\usepackage[colorlinks = true,
            linkcolor = blue,
            urlcolor  = blue,
            citecolor = blue,
            anchorcolor = blue]{hyperref}
\usepackage{etoolbox}
\makeatletter
\patchcmd\@combinedblfloats{\box\@outputbox}{\unvbox\@outputbox}{}{%
  \errmessage{\noexpand\@combinedblfloats could not be patched}%
}%
\makeatother


\usepackage{natbib}




\renewenvironment{abstract}
  {{\bfseries\noindent{\abstractname}\par\nobreak}\footnotesize}
  {\bigskip}

\titlespacing{\section}{0pt}{*3}{*1}
\titlespacing{\subsection}{0pt}{*2}{*0.5}
\titlespacing{\subsubsection}{0pt}{*1.5}{0pt}


\usepackage{authblk}


\usepackage{graphicx}
\usepackage[space]{grffile}
\usepackage{latexsym}
\usepackage{textcomp}
\usepackage{longtable}
\usepackage{tabulary}
\usepackage{booktabs,array,multirow}
\usepackage{amsfonts,amsmath,amssymb}
\providecommand\citet{\cite}
\providecommand\citep{\cite}
\providecommand\citealt{\cite}
% You can conditionalize code for latexml or normal latex using this.
\newif\iflatexml\latexmlfalse
\providecommand{\tightlist}{\setlength{\itemsep}{0pt}\setlength{\parskip}{0pt}}%

\AtBeginDocument{\DeclareGraphicsExtensions{.pdf,.PDF,.eps,.EPS,.png,.PNG,.tif,.TIF,.jpg,.JPG,.jpeg,.JPEG}}

\usepackage[utf8]{inputenc}
\usepackage[english]{babel}



\usepackage{float}








\begin{document}

\title{Neural Response Time Analysis: XAI Using Only a Stopwatch}



\author[1]{Eric Taylor}%
\author[2]{Shashank Shekhar}%
\author[2]{Graham  Taylor}%
\affil[1]{Vector Institute}%
\affil[2]{University of Guelph}%


\vspace{-1em}



  
  \date{May 25, 2024}


\begingroup
\let\center\flushleft
\let\endcenter\endflushleft
\maketitle
\endgroup





\selectlanguage{english}
\begin{abstract}
How would you describe the features that a deep learning model composes
if you were restricted to measuring observable behaviours? Explainable
artificial intelligence (XAI) methods rely on privileged access to model
architecture and parameters that is not always feasible for most users,
practitioners, and regulators. Inspired by cognitive psychology research
on humans, we present a case for measuring response times (RTs) of a
forward pass using only the system clock as a technique for XAI. Our
method applies to the growing class of models that use input-adaptive
dynamic inference and we also extend our approach to standard models
that are converted to dynamic inference post hoc. The experimental logic
is simple: If the researcher can contrive a stimulus set where
variability among input features is tightly controlled, differences in
response time for those inputs can be attributed to the way the model
composes those features. First, we show that RT is sensitive to
difficult, complex features by comparing RTs from ObjectNet and
ImageNet. Next, we make specific a priori predictions about RT for
abstract features present in the SCEGRAM dataset, where object
recognition in humans depends on complex intra-scene object-object
relationships. Finally, we show that RT profiles bear specificity for
class identity, and therefore the features that define classes. These
results cast light on the model's feature space without opening the
black box.%
\end{abstract}%



\sloppy




\selectlanguage{english}
\FloatBarrier
\end{document}

