Prediction and uncertainty in associative learning: examining controlled and automatic components of learned attentional biases

ABSTRACT

It has been suggested that attention is guided by two factors that operate during associative learning: a predictiveness principle, by which attention is allocated to the best predictors of outcomes, and an uncertainty principle, by which attention is allocated to learn about the less known features of the environment. Recent studies have shown that predictiveness-driven attention can operate rapidly and in an automatic way to exploit known relationships. The corresponding characteristics of uncertainty-driven attention, on the other hand, remain unexplored. In two experiments we examined whether both predictiveness and uncertainty modulate attentional processing in an adaptation of the dot probe task. This task provides a measure of automatic orientation to cues during associative learning. The stimulus onset asynchrony of the probe display was manipulated in order to explore temporal characteristics of predictiveness- and uncertainty-driven attentional effects. Results showed that the predictive status of cues determined selective attention, with faster attentional capture to predictive than to non-predictive cues. In contrast, the level of uncertainty slowed down responses to the probe regardless of the predictive status of the cues. Both predictiveness- and uncertainty-driven attentional effects were very rapid (at 250 ms from cue onset) and were automatically activated.

KEYWORDS:

• Attention
• Associative learning
• Dot probe
• Predictiveness
• Uncertainty

Acknowledgements

The authors would like to thank Janelle Fernandes for her assistance in running the experiments.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1. Here, we follow recent literature (e.g., Feldmann-Wüstefeld et al., 2015; Shone, Harris, & Livesey, 2015) in taking automatic as a synonym for “independent of ongoing task goals” (see also Awh et al., 2012; Moors & De Houwer, 2006). We also use automatic to indicate the rapid nature of the effects (e.g., Evans, 2008). The results that we report in this article are compatible with these two characteristics of automaticity. However, we note that stricter definitions of automaticity do exist. For instance, other characterizations of automaticity require that the effect should be not only independent of the actual goals, but also counterproductive (Perlman & Tzelgov, 2006). The procedures used in the current experiments do not allow us to determine whether our effects would still be considered automatic under such stricter criteria.

2. Notably, studies with non-human animals provide stronger evidence for an influence of uncertainty on learning rate (e.g., Haselgrove et al., 2010; Kaye & Pearce, 1984a). Indeed, it has been proposed that attentional mechanisms relating to uncertainty could differ fundamentally across species (Haselgrove et al., 2010).

Additional information

Funding

This work was supported by Australian Research Council (ARC) Discovery Projects [grant number DP140103268], [grant number DP160103063]. Mike Le Pelley was supported by an ARC Future Fellowship [grant number FT100100260].