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Data · dataset · 2017

Symptom Self-Reports Are Susceptible to Misinformation

Listed in IISH Dataverse and ODISSEI Portal — shown once because both records carry DOI 10.34894/ecov4q

We examined whether self-reported symptoms are affected by explicit and implicit misinformation.

Description

In Experiment 1, undergraduates (N = 60) rated how often they experienced common somatic and psychological symptoms. During a subsequent interview, they were exposed to misinformation about two of their ratings: one was inflated (upgraded misinformation), while another was deflated (downgraded misinformation).

Close to 82% of the participants accepted the upward symptom misinformation, while 67% accepted the downward manipulation. Also, 27% confabulated reasons for upgraded symptom ratings, while 8% confabulated reasons for downgraded ratings. At a follow-up test, some days later, participants (n = 55) tended to escalate their symptom ratings in accordance with the upgraded misinformation.

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Such internalization was less clear for downgraded misinformation. There was no statistically significant relation between dissociativity and acceptance or internalization of symptom misinformation. In Experiment 2, a more subtle and implicit form of misinformation was employed.

Undergraduates (N = 50) completed a checklist of common symptoms and were provided with feedback for some symptoms (targets) misleadingly suggesting that a slight majority of their peers experienced these targets on a regular basis. Next, participants rated the checklist again. Overall, symptom ratings went down for control but not for targets symptoms.

Participants with escalated symptom ratings for targets had higher dissociativity scores than those who did not exhibit escalation. Taken together, our results suggest that upward symptom manipulations affect symptom self-reports more than downward manipulations and that dissociativity may play a role in symptom escalation when misinformation is subtle.

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Social Sciences · Social Sciences
Provenance · 2 source records, 12 field assertions
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IISH Dataversedoi:10.34894/ECOV4Q12 d agoJSON v1
ODISSEI Portaldoi:10.34894/ECOV4Q8 d agoJSON v1
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