The relationship between anxious traits and learning about changes in stochasticity and volatility
Fig 5
Experiment 2 – Behavioural results.
Accuracy, win-stay, and lose-shift responses per condition in (A) the full sample (N = 152) and (C) low (N = 78) and high (N = 74) ANX groups. Differences in these measures for low vs. high noise and low vs. high volatility are shown in (B) the full sample and (D) low and high ANX groups. (A, B) Main effects of noise on behaviour seen in Experiment 1 (see Fig 2) were replicated, with greater task performance, more win-stay behaviour, and less lose-shift behaviour under low compared to high noise conditions (all p < .01 in full sample).There was a volatility*noise interaction on accuracy (p = .001), with higher accuracy in low compared to high volatility under low noise, but similar levels under high noise across the volatility conditions. There was also a volatility*noise interaction on win-stay behaviour (p = .001), with more win-stay for high compared to low volatility under low noise but less win-stay for high compared to low volatility under high noise, replicating Experiment 1. There were more lose-shift responses under high compared to low volatility (p < .001). (C, D) A three-way interaction between volatility, noise and ANX group on win-stay behaviour was found (p = .022). Although both ANX groups show more win-stay responses for low compared to high noise conditions (also captured across the full sample), this effect is heightened in the low ANX group, whereas the high ANX group show elevated win-stay behaviour for low vs. high volatility, compared to the low ANX group. Note that volatility-induced change in win-stay behaviour is minor (also seen in the full sample) compared to noise-induced change, also seen in Experiment 1. Error bars represent ± 1 SEM.