The time-event correlation effect is due to temporal expectancy, not to partial transition costs.

Abstract

Humans are sensitive to temporal redundancies in their environment. When the identity of a target stimulus is correlated with the duration of the preceding interval, performance is better for frequent than for infrequent combinations of target and interval. This effect has been demonstrated several times in current timing research. However, it can be accounted for by 2 starkly contrasting explanations. The standard account has explained it in terms of learning associations between intervals and stimulus-response events. But, alternatively the effect might be due to partial trial transition costs, because infrequent time-event combinations are proportionally more often partial transitions (i.e., transitions of either interval, or target). We conducted 3 choice response time experiments to distinguish between both explanations. The results clearly show that the time-event correlation effect is due to learning, not to partial transition costs.

When specific action biases meet nonspecific preparation: Event repetition modulates the variable-foreperiod effect.

“…It should be noted, though, that this is not meant to say that timerelated and event-specific information cannot be jointly coded in a shared mental representationit just does not appear to be mandatory: For instance, if a given FP duration indeed is predictive of a particular S-R event, this association is (implicitly) learnt, and joint coding seems to occur (cf. Thomaschke & Dreisbach, 2015;Thomaschke et al, 2016; see also Bouton & Garcia-Gutierrez, 2006). Furthermore, our conclusions hold only for unbiased choice-RT paradigms, that is, situations with response uncertainty, in which a given S-R event is not predictive of subsequent events.…”

“…Nevertheless, it can be asked whether (non-specific) event timing information and (specific) event type information interactively affect RT performance in FP experiments. For instance, it is well known that RT in choice-reaction tasks is affected by probabilistic biases towards particular stimulus-response (S-R) events such that more likely events are more strongly expected and faster responded to in general (Eickhoff, Pomjanski, Jakobs, Zilles, & Langner, 2011;Miller, 1998;Miller & Pachella, 1973) or at certain temporal positions (Thomaschke & Dreisbach, 2015;Thomaschke, Hoffmann, Haering, & Kiesel, 2016;Wagener & Hoffmann, 2010). Using constant-FP paradigms, two early studies tested for an interaction between probabilistic S-R biases and temporal preparation (Bertelson & Barzeele, 1965;Holender & Bertelson, 1975).but produced mixed results, essentially leaving the question open.…”

Different effects of spatial and temporal attention on the integration and segregation of stimuli in time

“…One interpretation is that this apparent disparity arises due to task differences: Making a judgment about the temporal order of stimuli arguably relies more heavily on higher-level decision processes than did the target-search task employed here. Since temporal-orienting effects usually emerge when the primary measure is a metric, like reaction times, that reflects differences in processing stages further up the hierarchy, such as decision or motor processing (Langner et al, 2018;Thomaschke & Dreisbach, 2015;Vangkilde et al, 2012;Volberg & Thomaschke, 2017), this is a pertinent difference between the present study and previous work. Reaction time data were unsuitable for the analysis in the present study, due to the response collection method: unspeeded mouse clicks on a probe grid of 16 locations after a fixed delay from stimulus offset.…”

Item-Specificity and Intention in Episodic Memory

“…Given the structure of the PEP model, there are two further applications that appear fruitful and could lead to novel insights in the respective areas of research. First, time-based expectancy effects are assumed to rely on learned time-event contingencies (e.g., Aufschnaiter et al, 2018 ; Thomaschke & Dreisbach, 2015 ). The PEP model with its incorporated temporal learning mechanism could contribute to an episodic memory-based model of not only temporal, but also time-based expectancy, generate novel assumptions, and ignite further theorizing.…”