How people learn about causal influence when there are many possible causes: A model based on informative transitions

Four experiments tested how people learn cause-effect relations when there are many possible causes of an effect. When there are many cues, even if all the cues together strongly predict the effect, the bivariate relation between each individual cue and the effect can be weak, which can make it difficult to detect the influence of each cue. We hypothesized that when detecting the influence of a cue, in addition to learning from the states of the cues and effect (e.g., a cue is present and the effect is present), which is hypothesized by multiple existing theories of learning, participants would also learn from transitions - how the cues and effect change over time (e.g., a cue turns on and the effect turns on). We found that participants were better able to identify positive and negative cues in an environment in which only one cue changed from one trial to the next, compared to multiple cues changing (Experiments 1A, 1B). Within a single learning sequence, participants were also more likely to update their beliefs about causal strength when one cue changed at a time ('one-change transitions') than when multiple cues changed simultaneously (Experiment 2). Furthermore, learning was impaired when the trials were grouped by the state of the effect (Experiment 3) or when the trials were grouped by the state of a cue (Experiment 4), both of which reduce the number of one-change transitions. We developed a modification of the Rescorla-Wagner algorithm to model this 'Informative Transitions' learning processes.