Saccadic latencies are commonly used to study decision mechanisms. For instance, in a random sequence, saccadic latency to a target depends on how frequently it has recently appeared. However, frequency is not the only factor that determines probability. Here we presented targets to the left or right, either in random sequences or in repeating patterns. Although the frequency of appearing on a given side was identical in each case, latencies for the low-frequency side were significantly shorter for repeating patterns than in random sequences, showing that the system can respond to the deterministic probabilities in such patterns. We then disrupted our patterns episodically, recommencing at a random starting position in the sequence. This significantly increased the latency, which remained high until the low-frequency target in the sequence reappeared, implying that the oculomotor system makes strategic use of low-frequency—but high-information—events to determine the phase of repeating sequences. The deterministic sequences of events in our patterns represent a simple model for the habitual sequences of actions commonly performed in daily life, which, when disrupted, require the engagement of a higher level problem-solving strategy to return us to our previous automated sequence as quickly as possible.

^{2}spots, presented on a 24 cd/m

^{2}gray background (CIE 1931,

*x*= 0.281,

*y*= 0.306). Subjects sat 1 m from the monitor, which subtended 23 × 17 deg, and viewed the monitor with their habitual spectacle correction. A chin rest stabilized head position.

*stochastic*runs and

*deterministic*runs (Figure 1). In stochastic runs, left and right targets had a particular frequency (probability of left either 0.25 or 0.75), but each trial was stochastically independent (in control runs, the frequencies were 0.5 and 0.5). In deterministic runs, we used a fixed sequence of targets (either LRRR or LLLR) that continuously repeated. Thus, in both run types, the long-term frequencies to each side were the same (0.25 and 0.75). Therefore, if the oculomotor system forms expectations of what will happen based only on long-term frequencies, latency to the low-probability target should be identical in both the stochastic and deterministic runs. Data were collected in an interleaved and counterbalanced fashion, with a total of 12 stochastic runs (8 runs at

*p*= 0.5, plus two at each of

*p*= 0.25 and

*p*= 0.75) and 4 deterministic runs (2 each of LRRR and RLLL) per subject.

*p*= 0.03) we presented a target randomly to the left or the right, after which the sequence recommenced at a randomly selected starting point (Figure 1, lower set). This served to create transient disruptions in the sequence. If the effects of deterministic presentation are because the oculomotor system learns the entire pattern, disruptions to the sequence should have a protracted effect on saccadic latency. In particular, the effect of the disruption should last at least until the reappearance of the low probability target, as it is this comparatively rare target that provides the most information about the starting point—or phase—of the recommenced sequence.

*p*= 0.03) reversed direction, producing a disruption in the expected pattern similar to that in Experiment 2 above. However, here the target to appear after the disruption is highly predictable. A mechanism sensitive to patterns should therefore only show a change in latency at the moment of the unexpected disruption event, in contrast to the prolonged effect hypothesized in Experiment 2. Each subject performed 14 runs.

^{2}) appeared randomly but with a fixed probability (

*p*(R) = 0.1, 0.5, or 0.9) within a run of 100 presentations. Subjects always made saccades toward targets appearing on the right but, for some runs, were instructed to withhold saccades toward targets appearing on the left. Subjects performed two sequential training runs (200 trials) at each probability and task instruction (withhold/do not withhold) before collecting two further runs for analysis.

_{e}likelihood ratio favoring shift, LLR = 0.25; low appearance frequency data for stochastic versus deterministic conditions, ratios summed across all observers).

*p*(L∣R)) as we would expect saccadic latencies to return to normal shortly after the disruption, irrespective of whether or not the low-frequency target had reappeared.

_{e}units). Data in which the low-frequency stimulus had not yet reappeared four trials after the disruption (Figure 3, abscissa = 4) were not included in the analysis as no subject had more than three latency measurements to define the distribution.

*p*= 0.03) the sequence reversed. A mechanism sensitive to patterns should only show a change in latency at the moment of the unexpected sequence reversal, as the target to appear after this disruption was predictable with a high confidence. Figure 4 shows this is indeed the case: the significant elevation in latencies is confined to the disruption event itself. For each subject, we compared the distribution of saccades remote from a disruption (grouped under the “≤−1 or ≥+6” category in Figure 4) with those at the moment of disruption and found that for the four subjects in which convergent fits were obtained three favored the shift model (one subject weakly favored a swivel, LLR = 0.62). The summed log-likelihood ratios strongly favored the shift model (LLR = 24.1).