Using subgroup discovery to relate odor pleasantness and intensity to peripheral nervous system reactions

From LRDE

Abstract

Activation of the autonomic nervous system is a primary characteristic of human hedonic responses to sensory stimuli. For smells, general tendencies of physiological reactions have been described using classical statistics. However, these physiological variations are generally not quantified precisely; each psychophysiological parameter has very often been studied separately and individual variability was not systematically considered. The current study presents an innovative approach based on data mining, whose goal is to extract knowledge from a dataset. This approach uses a subgroup discovery algorithm which allows extraction of rules that apply to as many olfactory stimuli and individuals as possible. These rules are described by intervals on a set of physiological attributes. Results allowed both quantifying how each physiological parameter relates to odor pleasantness and perceived intensity but also describing the participation of each individual to these rules. This approach can be applied to other fields of affective sciences characterized by complex and heterogeneous datasets.


Bibtex (lrde.bib)

@Article{	  moranges.22.taffc,
  author	= {Maelle Moranges and Marc Plantevit and Moustafa Bensafi},
  journal	= {IEEE Transactions on Affective Computing},
  title		= {Using subgroup discovery to relate odor pleasantness and
		  intensity to peripheral nervous system reactions},
  year		= {2022},
  volume	= {},
  number	= {},
  pages		= {1-16},
  doi		= {10.1109/TAFFC.2022.3173403},
  abstract	= {Activation of the autonomic nervous system is a primary
		  characteristic of human hedonic responses to sensory
		  stimuli. For smells, general tendencies of physiological
		  reactions have been described using classical statistics.
		  However, these physiological variations are generally not
		  quantified precisely; each psychophysiological parameter
		  has very often been studied separately and individual
		  variability was not systematically considered. The current
		  study presents an innovative approach based on data mining,
		  whose goal is to extract knowledge from a dataset. This
		  approach uses a subgroup discovery algorithm which allows
		  extraction of rules that apply to as many olfactory stimuli
		  and individuals as possible. These rules are described by
		  intervals on a set of physiological attributes. Results
		  allowed both quantifying how each physiological parameter
		  relates to odor pleasantness and perceived intensity but
		  also describing the participation of each individual to
		  these rules. This approach can be applied to other fields
		  of affective sciences characterized by complex and
		  heterogeneous datasets.}
}