The Automated Template

The results from Experiment 1 indicate that repeating the target probe trial-by-trial (Olivers, 2009) does reduce search time and error rates. The

purpose of repeating the probe was to ‘offload’ a search template ‘partially [from the WM] to other systems’ (Olivers et al., 2011). Instead of testing the

competition of WM resources between an AMI and a search template in a dual (search and memory) paradigm (Olivers, 2009), here I tested the ‘offload’ idea in a search-only paradigm, comparing the consistent-mapping (fixed cue) to the varied-mapping conditions (varied cue). The ‘offload’, following Olivers et al.(2011), refers to a process that a consolidated template is removed from WM or becomes weak in terms of WM strength. I focused specifically on how the WM strength of a template affects search performance. Specifically, I asked which part of a decision-making process associates with offloading a template.

The results from the varied cue condition in Experiment 1 suggest that a successful guided search is viable even without an active WM template, presuming that the template no longer resides in the WM system. The offloaded template appears to render search more efficient (Figure 5-2) than when a template is updated in every trial. The offloading advantage however was observed only in Experiment 1 when the timing of search display

appearance was highly predictable, a condition leading to a decrease in the decision threshold. This result is consistent with the view of the activation-state hypothesis (Olivers, 2009). The automated template, been achieved via

repeating an identical target probe in every trial, lowers only the response threshold. The null effect in the decision rate, on the other hand, suggests that the perceptual sensitivities of matching a target to a WM template are similar in the two cue conditions. The explanation of similar perceptual sensitivities in an identical template and an updated template is likely only when an observer

needs not raise vigilance to prepare an upcoming a search display, which either appear immediately (50-ms ISI) or after a short while (400-ms ISI). High

certainty about a target’s identity (i.e., fixed cue condition) and a display’s timing (i.e., a single 50-ms ISI in Experiment 1) can cause an observer to adjust

his/her response threshold when an identical template renders similar perceptual quality as an updated template. That is, both forms of memory representation – an automated template and an active WM template – are capable of permitting a successful search with a similar decision rate when a search task is highly predictable. As a consequence, the difference lies in the decision threshold, as shown in Experiment 1.

However, when a 400-ms ISI was randomly inserted into a testing block originally containing only 50-ms ISI, the relation between the fixed and varied cue conditions was reversed, as shown in Experiment 2. This reverse pattern is robust, as Experiment 3 replicated the pattern with more per-condition

observations. One explanation of the reversed relation is the certainty of a target’s appearance timing, thereby affecting an observer’s decision threshold.

This is suggested by a significant ISI contribution on the decision threshold in the AIC model, although the BIC model indicates that the ISI factor contributes only to non-decision time.

Two critical differences were introduced in Experiment 2 (& Experiment 3), because of to the ISI factor. Firstly, the timing of a search display

appearance became uncertain. Observers could not predict explicitly the when a display might appear neither automate implicitly their key-press actions with respect to the display time. The uncertain timing likely results in increasing in

cautiousness. Secondly, because Experiment 2 used two ISIs and allocated them randomly, observers might respond to several 50-ms (or 400-ms) ISI trials and encounter unexpectedly a 400-ms (or 50-ms) ISI trial, and vice versa.

Apparently, the latter sequence (i.e., several 400-ms trial and then 50-ms trial) hinders search more than the former sequence. Further, comparing to the per-condition observation numbers in other studies (Olivers, 2009; Woodman et al., 2007), Experiment 2 and Experiment 3 used relatively large number of trials, so the observers should experience both ISI sequences. The mix of the two ISI sequences, as suggested by the model fitting, may result in an adjustment of response strategy (as AIC model suggested) and this associated with the cue factor. Admittedly, the current data suggest also that the ISI factor affected also the non-decision time (see the discussion in Section 5.4.4).

The fixed cue condition, if as the activation-state hypothesis (Olivers, 2009) presumed, offloads a WM template to other systems. The result

associated with the fixed cue condition in Experiment 2 then implies either (1) that to reload an automated template back to the WM takes up additional time when a search display comes up unpredictably early (i.e., 50 ms) and this is a consequence of decision threshold (as well as non-decision time) adjustment or (2) that the uncertain ISI pattern causes an undecided state of WM template offloading. The second possibility might result in an increase in the decision threshold when in some trials observers kept a dissipating WM template.

The result in Experiment 3 further support for the argument of cue-related threshold adjustment, because when an additional route (i.e., double trial number) to automate template is introduced, the accuracy rate in 50-ms ISI

condition becomes the only traditional statistics differentiating the fixed and varied cue condition. This result supports Olivers’s (2009; also Olivers et al., 2011) account that whether the template is automated plays a critical role in the search performance. When observers become highly familiar with the task via performing a large number of trials, the advantage of the mean RT and drift rate for the varied cue condition disappears, but its accuracy disadvantage still exists in the 50-ms ISI condition. Apparently, the automated template, though may not alter the drift rate, helps to maintain a strong decision confidence when a hard-to-predict upcoming search task is displayed immediately. This

hypothesis however remains to be verified, because only the main effect of the ISI, rather than the cue × ISI interaction, contribution in the AIC model and top-level model variations was observed.

More concretely, I suggest that the varied cue condition enforced a process of template rehearsal in WM. When given a long ISI, observers were given the opportunity to go over the template, thereby maintaining an accuracy rate as the fixed cue condition. The observation of the similar accuracy rate in 400-ms ISI but not in 50-ms ISI in Experiment 2 and 3 suggests that the

template strength in the varied cue condition might reach similar level as that in the fixed cue condition. This is in line with the BIC model, showing that Q factor contribute to the decision threshold, but not the drift rate variations. That is, although the varied cue manipulation rendered the target identity less certain, the long ISI strengthened the template and this resolved the accuracy rate, but not mean RT, difference.

When the two uncertain factors - display timing and target identity – were

introduced, observers might become less confident to commit a response. The data for accuracy suggest an increase in the response criterion, but cannot rule out a decrease in the decision rate. This ambiguity is made clear by the drift-diffusion and LBA models, showing in Experiment 2 the cue factor depends only on the decision threshold, with a lower threshold for the varied cue condition (0.83 & 0.54) than for the fixed cue condition (0.92 & 0.55). This is consistent with the argument of an enhanced activation for the memory template in the varied cue condition (Olivers, 2009). In other words, the enhanced WM template results in a decrease in the decision threshold. However, this is in contrast to an insignificant difference of the decision threshold in Experiment 1 (fixed cue = 0.701, varied cue = 0.727), when there was no temporal

uncertainty and in Experiment 3 (fixed cue = 0.55, varied cue = 0.54), when there was a drastic increase in trial number. A further evidence for the certainty-related threshold change is the general magnitude of the decision threshold. The decision thresholds in Experiment 2 are generally higher than those in Experiment 1, which then are higher than those found in Experiment 3.

The changes in decision thresholds across the three experiments and the null effect of decision rate support the interpretation of task certainty and decision threshold.

In summary, comparing to updating a target probe in every trial,

repeating a target probe does influence the decision threshold, reflecting on the patterns of mean latency and accuracy. The random ISI patterns cause

changes of cue effect in the decision threshold and manifest as a pattern of speed-accuracy trade-off in the average data.