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Naixin Ren, Wenfeng Chen, Xiaolan Fu; The Inversed Affective Learning and Its Cause. Journal of Vision 2016;16(12):922. doi: 10.1167/16.12.922.
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© ARVO (1962-2015); The Authors (2016-present)
People can learn values of objects with only minimal exposure to mildly affective cues, which is known as minimal affective learning (Bliss-Moreau, Barrett, & Wright, 2008). The inclusion/exclusion (I/E) model in evaluative judgment assumes that the instant judgement of an object can be influenced by the enviromental information: information that can be used in forming a representation of the target results in assimilation effects; information that cannot be used in forming a representation of the target would results in contrast effects (Bless & Schwarz, 2010). In previous studies of minimal affective learning, affective stimuli, such as the sentences describing behavior in face-sentence pairs, can always be used to form the representation of the neutral objects. Hence, the I/E model provides an assimilation effect account for minimal affective learning. Following this rationale, if the affective stimulus cannot be used to represent the neutral object during affective learning, there would be a contrast effect. As a result, the neutral object would acquire the opposite value of the paired stimulus, which would lead to an "inversed affective learning", as defined in this study. To test this prediction, face pairs with varied facial attractiveness were adopted in two experiments. After being presented ordinary neutral faces paired with one (Experiment 1) or four (Experiment 2) attractive faces or unattractive faces four times, participants were asked to judge the valence of the isolated neutral faces. Experiment 1 revealed an inversed affective learning. Experiment 2 further demonstrated that there was a stronger effect when the affective stimuli are stronger. This study initially illustrates that neutral objects can acquire the opposite value from affective stimuli through a new minimal affective learning mechanism, and provided a new perspective to affective learning.
Meeting abstract presented at VSS 2016
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