These tools are available for download.
The IASLab face set was developed in 2006 and includes 50 unique identities. The face exemplars are posed expressions in which posers were provided with an emotion label, but were not directed to move specific muscles in a pre-determined configuration. The set includes poses of neutral, sad, happy, angry, fearful, surprised, calm, excited, and disgusted portrayals by all identities. All poses have direct gaze and averted gaze variants. All poses also have open mouth and closed mouth variants. All images have been edited so that the eyes are centered in the same location. While the set does contain some racial/ethnic diversity, we do not have self-reported demographic information of the posers. A subset of expressions (forward gaze, closed mouth exemplars) of the IASLab Face Set have been normed by ratings of emotion category, attractiveness, and intensity. These norms will be shared along with the set.
This set is available for academic/non-profit research only. For questions about use of the face set, please contact Aidan Mahoney at [email protected]
Complete the IASLab Face Set Agreement Form and email it to the address on the form. You will then receive an email reply with a link to download the set.
The defensive verbal behavior rating scale is a coding procedure for interview material that adopts a dimensional strategy for the assessment of defensive functioning. Each response is coded using a 4-point scale (0-3) that allows coders to conceptualize defensive functioning in terms of 2 dimensions:
(1) the level of awareness of a perceived threat to the self, and
(2) the degree of distortion in the conscious content of thought.
The scale also distinguishes between defenses enacted in the interaction with the interviewer (defensive process) and defenses described by the participant but not enacted in the course of the interview (defensive content).
These mood induction clips consist of instructions and three clips for a participant to view and listen to music while immersing themselves in each clip.
Here are the IASLab’s GitHub repositories: