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It is well established that statistical learning of visual target locations in relation to constantly positioned visual distractors facilitates visual search. In the present study, we investigated whether such a contextual-cueing effect would also work crossmodally, from touch onto vision. Participants responded to the orientation of a visual target singleton presented among seven homogenous visual distractors. Four tactile stimuli, two to different fingers of each hand, were presented either simultaneously with or prior to the visual stimuli. The identity of the stimulated fingers provided the crossmodal context cue: in half of the trials, a given visual target location was consistently paired with a given tactile configuration. The visual stimuli were presented above the unseen fingers, ensuring spatial correspondence between vision and touch. We found no evidence of crossmodal contextual cueing when the two sets of items (tactile, visual) were presented simultaneously (Experiment 1). However, a reliable crossmodal effect emerged when the tactile distractors preceded the onset of visual stimuli 700 ms (Experiment 2). But crossmodal cueing disappeared again when, after an initial learning phase, participants flipped their hands, making the tactile distractors appear at different positions in external space while their somatotopic positions remained unchanged (Experiment 3). In all experiments, participants were unable to explicitly discriminate learned from novel multisensory arrays. These findings indicate that search-facilitating context memory can be established across vision and touch. However, in order to guide visual search, the (predictive) tactile configurations must be remapped from their initial somatotopic into a common external representational format.

Although humans are well capable of precise time measurement, their duration judgments are nevertheless susceptible to temporal context. Previous research on temporal bisection has shown that duration comparisons are influenced by both stimulus spacing and ensemble statistics. However, theories proposed to account for bisection performance lack a plausible justification of how the effects of stimulus spacing and ensemble statistics are actually combined in temporal judgments. To explain the various contextual effects in temporal bisection, we develop a unified ensemble-distribution account (EDA), which assumes that the mean and variance of the duration set serve as a reference, rather than the short and long standards, in duration comparison. To validate this account, we conducted three experiments that varied the stimulus spacing (Experiment 1), the frequency of the probed durations (Experiment 2), and the variability of the probed durations (Experiment 3). The results revealed significant shifts of the bisection point in Experiments 1 and 2, and a change of the sensitivity of temporal judgments in Experiment 3—which were all well predicted by EDA. In fact, comparison of EDA to the extant prior accounts showed that using ensemble statistics can parsimoniously explain various stimulus set-related factors (e.g., spacing, frequency, variance) that influence temporal judgments.

No abstract

Searching for targets among similar distractors requires more time as the number of items increases, with search efficiency measured by the slope of the reaction-time (RT)/set-size function. Horowitz and Wolfe (Nature, 394(6693), 575-577, 1998) found that the target-present RT slopes were as similar for "dynamic" as for standard static search, even though the items were randomly reshuffled every 110 ms in dynamic search. Somewhat surprisingly, attempts to understand dynamic search have ignored that the target-absent RT slope was as low (or "flat") as the target-present slope-so that the mechanisms driving search performance under dynamic conditions remain unclear. Here, we report three experiments that further explored search in dynamic versus static displays. Experiment 1 confirmed that the targetabsent:target-present slope ratio was close to or smaller than 1 in dynamic search, as compared with being close to or above 2 in static search. This pattern did not change when reward was assigned to either correct target-absent or correct target-present responses (Experiment 2), or when the search difficulty was increased (Experiment 3). Combining analysis of search sensitivity and response criteria, we developed a multiple-decisions model that successfully accounts for the differential slope patterns in dynamic versus static search. Two factors in the model turned out to be critical for generating the 1:1 slope ratio in dynamic search: the "quit-the-search" decision variable accumulated based upon the likelihood of "target absence" within each individual sample in the multiple-decisions process, whilst the stopping threshold was a linear function of the set size and reward manipulation.