{"id":212,"date":"2009-07-02T22:15:55","date_gmt":"2009-07-02T22:15:55","guid":{"rendered":"http:\/\/www.physiologicalcomputing.net\/wordpress\/?p=212"},"modified":"2021-12-22T20:22:10","modified_gmt":"2021-12-22T20:22:10","slug":"emotional-hci","status":"publish","type":"post","link":"http:\/\/www.physiologicalcomputing.net\/?p=212","title":{"rendered":"Emotional HCI"},"content":{"rendered":"

Just read a very interesting and provocative paper entitled “How emotion is made and measured<\/a>” by Kirsten Boehner and colleagues.\u00a0 The paper provides a counter-argument to the perspective that emotion should be measured\/quantified\/objectified in HCI and used as part of an input to an affective computing system or evaluation methodology.\u00a0 Instead they propose that emotion is a dynamic interaction that is socially constructed and culturally mediated.\u00a0 In other words, the experience of anger is not a score of 7 on a 10-point scale that is fixed in time, but an unfolding iterative process based upon beliefs, social norms, expectations etc.<\/p>\n

This argument seems fine in theory (to me) but difficult in practice.\u00a0 I get the distinct impression the authors are addressing the way emotion may be captured as part of a HCI evaluation methodology.\u00a0 But they go on to question the empirical approach in affective computing.\u00a0 In this part of the paper, they choose their examples carefully.\u00a0 Specifically, they focus on the category of ‘mirroring’ (see earlier post) technology wherein representations of affective states are conveyed to other humans via technology.\u00a0 The really interesting idea here is that emotional categories are not given by a machine intelligence (e.g. happy vs. sad vs. angry) but generated via an interactive process.\u00a0 For example, friends and colleagues provide the semantic categories used to classify the emotional state of the person.\u00a0 Or literal representations of facial expression (a web-cam shot for instance) are provided alongside a text or email to give the receiver an emotional context that can be freely interpreted.\u00a0 This is a very interesting approach to how an affective computing system may provide feedback to the users.\u00a0 Furthermore, I think once affective computing systems are widely available, the interpretive element of the software may be adapted or adjusted via an interactive process of personalisation.<\/p>\n

So, the system provides an affective diagnosis as a first step, which is refined and developed by the person – or even by others as time goes by.\u00a0 Much like the way Amazon makes a series of recommendations based on your buying patterns that you can edit and tweak (if you have the time).<\/p>\n

My big problem with this paper was that a very interesting debate was framed in terms of either\/or position.\u00a0 So, if you use psychophysiology to index emotion, you’re disregarding the experience of the individual by using objective conceptualisations of that state.\u00a0 If you use self-report scales to quantify emotion, you’re rationalising an unruly process by imposing a bespoke scheme of categorisation etc.\u00a0\u00a0 The perspective of the paper reminded me of the tiresome debate in psychology between objective\/quantitative data and subjective\/qualitative data about which method delivers “the truth.”\u00a0 I say ‘tiresome’ because I tend towards the perspectivist <\/a>view that both approaches provide ‘windows’ on a phenomenon, both of which have advantages and disadvantages.<\/p>\n

But it’s an interesting and provocative paper that gave me plenty to chew over.<\/p>\n","protected":false},"excerpt":{"rendered":"

Just read a very interesting and provocative paper entitled “How emotion is made and measured” by Kirsten Boehner and colleagues.\u00a0 The paper provides a counter-argument to the perspective that emotion should be measured\/quantified\/objectified in HCI and used as part of an input to an affective computing system or evaluation methodology.\u00a0 Instead they propose that emotion […]<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"spay_email":""},"categories":[5,7],"tags":[9,28,79],"jetpack_featured_media_url":"","jetpack_shortlink":"https:\/\/wp.me\/pY315-3q","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"http:\/\/www.physiologicalcomputing.net\/index.php?rest_route=\/wp\/v2\/posts\/212"}],"collection":[{"href":"http:\/\/www.physiologicalcomputing.net\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/www.physiologicalcomputing.net\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/www.physiologicalcomputing.net\/index.php?rest_route=\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"http:\/\/www.physiologicalcomputing.net\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=212"}],"version-history":[{"count":1,"href":"http:\/\/www.physiologicalcomputing.net\/index.php?rest_route=\/wp\/v2\/posts\/212\/revisions"}],"predecessor-version":[{"id":4749,"href":"http:\/\/www.physiologicalcomputing.net\/index.php?rest_route=\/wp\/v2\/posts\/212\/revisions\/4749"}],"wp:attachment":[{"href":"http:\/\/www.physiologicalcomputing.net\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=212"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/www.physiologicalcomputing.net\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=212"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/www.physiologicalcomputing.net\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=212"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}