Human Inferences about Sequences: A Minimal Transition Probability Model
Fig 4
Tracking of statistical regularities and reaction times.
(A-B) Experimental data redrawn from Huettel et al. (2002) [2]. Subjects were presented with a purely random stream of two items. They had to press a key corresponding to the presented item as fast as possible. Reaction times are sorted depending on whether the local sequence of items followed a local streak of repeated or alternated items, and whether the last item continued or violated the preceding pattern. For instance, in XXXXY, the last item violates a previous streak of four repeated items. (C) Experimental data redrawn from Cho et al. (2002) [20]. The task was similar to Huettel et al. (2002) but reaction times are now sorted based on all possible patterns of repetition (R) or alternation (A) across the five past stimuli. For instance, the pattern AAAR denotes that the current item is a repetition of the previous item, and that the four preceding stimuli all formed alternations (e.g. XYXYY). (D-L) Theoretical surprise levels estimated in purely random sequences by three different local models. These local models differ only in the statistic they estimate. Their single free parameter is the leak of integration, it was fitted to each dataset. We report the regression R2 for these best parameters. Note that regressions include the data for both repetitions and alternations in the case of Huettel et al. Note that only a learning of transition probabilities predicts several aspects of the experimental data.