Sequential Dependence and Non-linearity in Affective Responses: a Skin Conductance Example

Jennifer Healey
Proceedings of IJCAI 2019 3rd Workshop on Artificial Intelligence in Affective Computing, PMLR 122:1-8, 2020.

Abstract

Individual affective responses frequently vary from the mean and often exhibit non-linear and time and sequence dependent properties. This paper examines the extent to which commonly made assumptions of linearity and sequential independence are valid using skin conductance responses to an acoustic stimulus as an example. We present 19 sessions of skin conductance traces where participants respond to five 50 millisecond acoustic bursts designed to elicit a startle. We show the data from the perspective of an online algorithm: individual responses, non-linear and dependent on prior events. We show that the coefficient of variation depends on sequence position and that these are large at 65%, 97%, 110%, and 100%. We discuss the risk of making inferences on single impressions.

Cite this Paper

BibTeX
@InProceedings{pmlr-v122-healey20a, title = {Sequential Dependence and Non-linearity in Affective Responses: a Skin Conductance Example}, author = {Healey, Jennifer}, booktitle = {Proceedings of IJCAI 2019 3rd Workshop on Artificial Intelligence in Affective Computing}, pages = {1--8}, year = {2020}, editor = {Hsu, William}, volume = {122}, series = {Proceedings of Machine Learning Research}, month = {10 Aug}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v122/healey20a/healey20a.pdf}, url = {https://proceedings.mlr.press/v122/healey20a.html}, abstract = {Individual affective responses frequently vary from the mean and often exhibit non-linear and time and sequence dependent properties. This paper examines the extent to which commonly made assumptions of linearity and sequential independence are valid using skin conductance responses to an acoustic stimulus as an example. We present 19 sessions of skin conductance traces where participants respond to five 50 millisecond acoustic bursts designed to elicit a startle. We show the data from the perspective of an online algorithm: individual responses, non-linear and dependent on prior events. We show that the coefficient of variation depends on sequence position and that these are large at 65%, 97%, 110%, and 100%. We discuss the risk of making inferences on single impressions.} }
Endnote
%0 Conference Paper %T Sequential Dependence and Non-linearity in Affective Responses: a Skin Conductance Example %A Jennifer Healey %B Proceedings of IJCAI 2019 3rd Workshop on Artificial Intelligence in Affective Computing %C Proceedings of Machine Learning Research %D 2020 %E William Hsu %F pmlr-v122-healey20a %I PMLR %P 1--8 %U https://proceedings.mlr.press/v122/healey20a.html %V 122 %X Individual affective responses frequently vary from the mean and often exhibit non-linear and time and sequence dependent properties. This paper examines the extent to which commonly made assumptions of linearity and sequential independence are valid using skin conductance responses to an acoustic stimulus as an example. We present 19 sessions of skin conductance traces where participants respond to five 50 millisecond acoustic bursts designed to elicit a startle. We show the data from the perspective of an online algorithm: individual responses, non-linear and dependent on prior events. We show that the coefficient of variation depends on sequence position and that these are large at 65%, 97%, 110%, and 100%. We discuss the risk of making inferences on single impressions.
APA
Healey, J.. (2020). Sequential Dependence and Non-linearity in Affective Responses: a Skin Conductance Example. Proceedings of IJCAI 2019 3rd Workshop on Artificial Intelligence in Affective Computing, in Proceedings of Machine Learning Research 122:1-8 Available from https://proceedings.mlr.press/v122/healey20a.html.

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