Automatic and Instructed Attention in Learned Predictiveness

Automatic and Instructed Attention in Learned Predictiveness Lauren T. Shone (lsho0771@uni.sydney.edu.au) Evan J. Livesey (evan.livesey@sydney.edu.au) School of Psychology, Brennan McCallum Building, University of Sydney, NSW 2006 AUS Abstract combinations, are then employed in order to predict different outcomes. Importantly, although the cues are again presented in compound, this time neither component has superior predictive utility. That is, both A and W are perfect predictors as they share the same objective relationship with outcomes O3 and O4 respectively. What differs between the components of the new compounds is their status as a predictive or non-predictive cue in the initial stage of learning. Subsequent tests reveal that more is learned about the relationship between previously predictive cues and the new outcomes compared to previously non-predictive cues. In novel situations, learning is biased towards information that has a degree of prior predictive utility. In human learning, this is termed the learned predictiveness effect and has proved critical in theorising about the role of attention in learning. Two experiments are reported in which the relative contribution of controlled and automatic processes to learned predictiveness are investigated. Experiment 1 showed that while learned predictiveness is susceptible to instructional manipulation, this effect is partial. Experiment 2 manipulated predictive utility and instruction orthogonally in order to test the potential involvement of automatic processes. It was found that even when cues were explicitly instructed as causal, learning was biased in favour of previously predictive over previously non-predictive cues. Interestingly, this was reversed for cues instructed as irrelevant. This suggests that learned predictiveness benefits attentional control, whereby information is both easier to attend and ignore. Table 1. A typical learned predictiveness design. Phase 1 AW – O1 AX – O1 BW – O2 BX – O2 CY – O1 CZ – O1 DY – O2 DY – O2 Keywords: human learning, attention, controlled processing, automatic processing Introduction An important question facing theories of associative learning is the nature of the relationship between learning and attention. Accordingly, many associative theories (e.g., Kruschke, 2001; Mackintosh, 1975; Pearce & Hall, 1980) accept that stimulus selection is influenced by attentional processes. Such theories share the basic assumption that the attention devoted to a stimulus is flexible, and governed by its past utility in predicting events. Importantly, this will subsequently influence the rate at which a stimulus enters into future associations. Evidence in favour of learned attention originates from experiments in which past predictive utility biases learning in a novel situation. A robust example, first reported by Le Pelley and McLaren (2003; see also Lochmann & Wills, 2003), is the learned predictiveness effect. The basic experimental design used to demonstrate the effect is shown in Table 1. Participants are initially exposed to a scenario in which they are required to learn a causal relationship between cues and outcomes. Each trial consists of the presentation of a compound of two cues, leading to one of two outcomes. Critically, each compound consists of one perfectly predictive cue (represented by A – D), and one non-predictive cue (W – Z). For example, A is consistently paired with the outcome O1, and therefore has perfect predictive utility. Alternatively, W has no predictive utility because it is paired equally often with both outcomes O1 and O2. Once these relationships have been learned, a novel scenario is introduced. The same cues, in novel Phase 2 AY – O3 BZ – O4 CW – O4 DX – O3 Test AD XY BC WZ Note. Letters indicate individual cues. O1 – O4 refer to four outcomes. Traditionally, this bias, consistently replicated across various scenarios (see Le Pelley, 2010, for a recent review), has been interpreted to suggest that attention is modulated by the difference in predictive validity during initial stages of learning. According to this logic, attention to A – D will be high following phase 1 and will therefore have an advantage when entering into new associations during the second phase. This effect has proved critical in theorising about the reciprocal nature of the relationship between human learning and attention. The learned predictiveness effect is consistent with models of associative learning that assume attention changes according to mechanisms of associative competition (e.g., Mackintosh, 1975; Le Pelley, 2004; Pearce & Mackintosh, 2010). For example, Mackintosh (1975) proposed that changes in the association between a cue and an outcome are governed by both attention paid to the cue and the discrepancy between the occurrence of the outcome and the extent to which it is already predicted on the basis of that cue, that is, the prediction error for an individual cue. Critically, attention to the cue changes according to a