Self-report measures for the assessment of trait or state affect are typically biased by social desirability or self-delusion. The present work provides an overview of research using a recently developed measure of automatic activation of cognitive representation of affective experiences, the Implicit Positive and Negative Affect Test (IPANAT). In the IPANAT, participants judge the extent to which nonsense words from an alleged artificial language express a number of affective states or traits. The test demonstrates appropriate factorial validity and reliabilities. We review findings that support criterion validity and, additionally, present novel variants of this procedure for the assessment of the discrete emotions such as happiness, anger, sadness, and fear.
Reconstructing seen image from brain activity by visually-guided cognitive representation and adversarial learning
