Visual search mimics configural processing in human causal learning
Perez, O. D.; Narasiwodeyar, S.; Soto, F. A.
Show abstract
Theories of learning distinguish between elemental and configural stimulus processing depending on whether stimuli are processed independently or as whole configurations. Evidence for elemental processing comes from findings of summation in animals where a compound of two dissimilar stimuli is deemed to be more predictive than each stimulus alone, whereas configural processing is supported by experiments employing similar stimuli in which summation is not found. However, in humans the summation effect is robust and impervious to similarity manipulations. In three experiments in human predictive learning, we show that summation can be obliterated when partially reinforced cues are added to the summands in training and test. This lack of summation only holds when the partially reinforced cues are similar to the reinforced cues (Experiment 1) and seems to depend on participants sampling only the most salient cue in each trial (Experiments 2a and 2b) in a sequential visual search process. Instead of attributing our and others instances of lack of summation to the customary idea of configural processing, we offer a formal sub-sampling rule that might be applied to situations in which the stimuli are hard to parse from each other.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- Urgency, Leakage, and the Relative Nature of Information Processing in Decision Making 96%
- A theory of actions and habits: The interaction of rate correlation and contiguity systems in free-operant behavior 95%
- The Temporal Dynamics of Opportunity Costs: A Normative Account of Cognitive Fatigue and Boredom 95%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.