The Role of Causal Attributions in Observational Conditioning

Our behavior towards a stimulus can change as a result of observing a regularity between that stimulus and someone else’s emotional reaction, a type of social learning referred to as observational conditioning. We explore the idea that causal attributions (i.e., the extent to which the observer attributes the model’s reaction to the stimulus) play an important role in observational conditioning effects. In three experiments (total N = 665), participants watched videos in which one cookie was followed by a positive reaction and another cookie was followed by a negative reaction, after which their own evaluations of each cookie were measured via self-reports and an implicit association test (IAT). Critically, we manipulated whether the observed reactions were high or low in terms of distinctiveness (Experiments 1a and 1b) or consensus and consistency (Experiment 2). These three variables are known to influence stimulus attributions and were therefore predicted to moderate observational conditioning effects. In line with our predictions, high distinctiveness (Experiments 1a and 1b) and high consensus and consistency (Experiment 2) both resulted in larger observational conditioning effects, with one exception: high distinctiveness did not lead to larger changes in automatic evaluations (i.e., IAT effects). Taken together, our findings suggest that causal attributions play an important role in observational conditioning. We outline more elaborate analyses of the attributional processes that are involved and suggest potential future directions for research on observational conditioning.

Learning via instructions about observations: exploring similarities and differences with learning via actual observations

Our behaviour toward stimuli can be influenced by observing how another person (a model) interacts with those stimuli. We investigated whether mere instructions about a model's interactions with stimuli (i.e. instructions about observations) are sufficient to alter evaluative and fear responses and whether these changes are similar in magnitude to those resulting from actually observing the interactions. In Experiments 1 (n = 268) and 2 (n = 260), participants either observed or read about a model reacting positively or negatively to stimuli. Evaluations of those stimuli were then assessed via ratings and a personalized implicit association test. In Experiments 3 (n = 60) and 4 (n = 190), we assessed participants' fear toward stimuli after observing or reading about a model displaying distress in the presence of those stimuli. While the results consistently indicated that instructions about observations induced behavioural changes, they were mixed with regard to whether instructions were as powerful in changing behaviour as observations. We discuss whether learning via observations and via instructions may be mediated by similar or different processes, how they might differ in their suitability for conveying certain types of information, and how their relative effectiveness may depend on the information to be transmitted.

EXPRESS: Observational evaluative conditioning is sensitive to relational information

Social learning represents an important avenue via which evaluations can be formed or changed. Rather than learn slowly through trial and error, we can instead observe how another person (a "model") interacts with stimuli and quickly adjust our own behaviour. We report five studies (n = 912) that focused on one subtype of social learning, observational evaluative conditioning (OEC), and how it is moderated by relational information (i.e., information indicating how a stimulus and a model's reactions are related). Participants observed a model reacting positively to one stimulus and negatively to another, and were either told that these reactions were genuine, faked, or opposite to the model's actual feelings. Stimulus evaluations were then indexed using ratings and a personalised Implicit Association Test (pIAT). When the model's reactions were said to be genuine, OEC effects emerged in the expected direction. When the model's reactions were said to be faked, the magnitude of self-reported, but not pIAT, effects was reduced. Finally, stating that the model's reactions were opposite to his actual feelings eliminated or reversed self-reported effects and eliminated pIAT effects. We consider how these findings relate to previous work as well as mental-process theories.

Reflecting on 25 Years of Research Using Implicit Measures: Recommendations for Their Future Use

For more than 25 years, implicit measures have shaped research, theorizing, and intervention in psychological science. During this period, the development and deployment of implicit measures have been predicated on a number of theoretical, methodological, and applied assumptions. Yet these assumptions are frequently violated and rarely met. As a result, the merit of research using implicit measures has increasingly been cast into doubt. In this article, we argue that future implicit measures research could benefit from adherence to four guidelines based on a functional approach wherein performance on implicit measures is described and analyzed as behavior emitted under specific conditions and captured in a specific measurement context. We unpack this approach and highlight recent work illustrating both its theoretical and practical value.