Sensitivity of implicit evaluations to accurate and erroneous propositional inferences

• Main Authors: Dunham, Y. (Author), Kurdi, B. (Author)
• Format: Article
• Language: English
• Published: Elsevier B.V. 2021
• Subjects: adult; Affect Misattribution Procedure; Article; Associative theories; Bayes theorem; Bayes Theorem; conditioning; construct validity; controlled study; deductive reasoning; discriminant validity; evaluation study; experimental error; human; human experiment; Humans; hypothesis; Implicit Association Test; Implicit evaluations; Inferential reasoning; information; internal consistency; knowledge; learning; Learning; mental representation; normal human; prediction; priority journal; Propositional theories; reaction time; reasoning; self report; Self Report; sensitivity and specificity; social aspect; social cognition; social interaction
• Online Access: View Fulltext in Publisher

 Explicit (directly measured) evaluations are widely assumed to be sensitive to logical structure. However, whether implicit (indirectly measured) evaluations are uniquely sensitive to co-occurrence information or can also reflect logical structure has been a matter of theoretical debate. To test these competing ideas, participants (N = 3928) completed a learning phase consisting of a series of two-step trials. In step 1, one or more conditional statements (A → B) containing novel targets co-occurring with valenced adjectives (e.g., “if you see a blue square, Ibbonif is sincere”) were presented. In step 2, a disambiguating stimulus, e.g., blue square (A) or gray blob (¬A) was revealed. Co-occurrence information, disambiguating stimuli, or both were varied between conditions to enable investigating the unique and joint effects of each. Across studies, the combination of conditional statements and disambiguating stimuli licensed different normatively accurate inferences. In Study 1, participants were prompted to use modus ponens (inferring B from A → B and A). In Studies 2–4, the information did not license accurate inferences, but some participants made inferential errors: affirming the consequent (inferring A from A → B and B; Study 2) or denying the antecedent (inferring ¬B from A → B and ¬A; Studies 3A, 3B, and 4). Bayesian modeling using ordinal constraints on condition means yielded consistent evidence for the sensitivity of both explicit (self-report) and implicit (IAT and AMP) evaluations to the (correctly or erroneously) inferred truth value of propositions. Together, these data suggest that implicit evaluations, similar to their explicit counterparts, can reflect logical structure. © 2021 Elsevier B.V.