Temporal predictability facilitates causal learning.

Abstract

Temporal predictability refers to the regularity or consistency of the time interval separating events. When encountering repeated instances of causes and effects, we also experience multiple cause-effect temporal intervals. Where this interval is constant it becomes possible to predict when the effect will follow from the cause. In contrast, interval variability entails unpredictability. Three experiments investigated the extent to which temporal predictability contributes to the inductive processes of human causal learning. The authors demonstrated that (a) causal relations with fixed temporal intervals are consistently judged as stronger than those with variable temporal intervals, (b) that causal judgments decline as a function of temporal uncertainty, and (c) that this effect remains undiminished with increased learning time. The results therefore clearly indicate that temporal predictability facilitates causal discovery. The authors considered the implications of their findings for various theoretical perspectives, including associative learning theory, the attribution shift hypothesis, and causal structure models.

Two pathways to causal control: Use and availability of information in the environment in people with and without signs of depression

“…Similarly, for experimenters programming no action, cell C and D experience within a free-operant procedure, the temporal frame chosen is arbitrary and may not be consistent with temporal frame used in everyday information processing. Indeed, if a temporal window were adopted that would allow ongoing experience to be 'parsed' into individual events, its size could be dynamic and based on pre-existing knowledge about the situation or many other factors (Buehner, 2005;Greville & Buehner, 2010). However, while in our study participants were able to remove this ambiguity from the causal control problem via their behavior, temporal dynamics and event parsing remain another important known-unknown of causal control learning.…”

Audio-visual speech experience with age influences perceived audio-visual asynchrony in speech

“…That such differences are evident for ALT but not for VLT is in accordance with the prediction that the difference in cognitive processing time between auditory and visual signals constitutes a relatively rigid and conservative natural boundary for which audiolead asynchronies can occur, and that this predictability makes the ALT more susceptible to learning induced finetuning compared to the naturally more variable visual-lead asynchronies. Research shows that temporal predictability is a key element in causal learning (e.g., Hume, 2003;Shanks et al, 1989;Greville and Buehner, 2010) and that perceptual learning entails an increase in the ability to utilize lower level information of visual (Ahissar and Hochstein, 2004) and auditory (Nahum et al, 2010) signals. AV speech signals contain great amounts of information and this information redundancy makes AV speech perception robust in subprime conditions (e.g., Erber, 1969;MacLeod and Summerfield, 1987;Sumby and Pollack, 1954).…”

“…Experience is predicted to affect ALT more than VLT, because ALT is a more stable and ecologically valid boundary. Temporal predictability has been considered a key element in causal learning for some time (Hume, 2003), and more recent research supports this notion (e.g., Shanks et al, 1989;Greville and Buehner, 2010). While naturally occurring visual-leads vary with the distance between the AV event and the perceiver, naturally occurring audio-leads are delimited by the more fixed neural processing time.…”

Structural awareness mitigates the effect of delay in human causal learning

“…The decision is thus simplified to comparing the rate or frequency of outcome occurrences during intervals with and without responses. In other words, the temporal predictability (Greville & Buehner, 2010) of the outcome appears to facilitate the attribution process.…”