We explore the development of habitual responding within the colour-word contingency learning paradigm, in which participants respond to the colour of neutral words. Each word is most often presented in one colour. Learning is indicated by faster responses to the colour when the word is presented in the expected rather than in the unexpected colour. In Experiment 1, participants took part in two sessions, separated by one day. Critically, one set of words was trained across both days, and other new sets of words were introduced at various time points. Overall performance was faster on trials with overtrained words. Additionally, contingency effects were larger for overtrained words than for words introduced on Day 2. Removing the contingency had a similar impact on the learning effect for overtrained and new words. However, during a counterconditioning phase, where the words were made predictive of new colours, the previous contingency continued to influence performance for overtrained words but not for more recently introduced words. Relatedly, the new contingency was not acquired for the overtrained words. The reverse pattern was observed for recently-introduced words, with the newly-introduced contingency rapidly acquired and the influence of the old contingency quickly extinguished. In Experiments 2 and 3, however, both new and old learning effects were observed for both overtrained and recently-acquired contingencies. The net results suggest that while contingency learning effects are highly pliable during initial and subsequent learning, early-acquired contingency knowledge is maintained after removal of the contingency. Implications for models of learning are discussed.

## Introduction

For any intelligent organism to be able to understand and interact effectively with its environment, it is necessary to learn the regularities between events and outcomes (Allan, 2005; Beckers, De Houwer, & Matute, 2007; Shanks, 2010). Knowledge of the meaning of words, the tastes of foods, and the likely results of our actions are all built on this contingency learning backbone. These regularities in the environment shape the behavioural repertoire of the organism. The present paper explores how contingency learning also helps to shape automatized or default responding to a stimulus. In particular, we ask how quickly this responding becomes stable and resistant to changes in contingencies, and we discuss potential relations to habit formation.

One useful paradigm for studying contingency learning is the colour-word contingency learning paradigm (Schmidt, Crump, Cheesman, & Besner, 2007; for related paradigms, see Carlson & Flowers, 1996; Levin & Tzelgov, 2016; Lewicki, 1985, 1986; Miller, 1987; Musen & Squire, 1993; Schmidt & De Houwer, 2012b; for a review, see MacLeod, 2019). In the typical version of this paradigm, participants respond to the print colour of words (or the reverse; Forrin & MacLeod, 2017) with a key press (for verbal variants, see Atalay & Misirlisoy, 2012; Forrin & MacLeod, 2017). Each word is presented most often in one colour (e.g., “find” most often in purple, “help” most often in orange, etc.). Learning of the word-response contingencies is indicated by faster and more accurate responses tohigh contingency trials, where the word is presented in its most frequent colour (e.g., “find” in purple), relative to low contingency trials, where the word is presented in an infrequent colour (e.g., “find” in orange). One useful feature of this paradigm is the robustness of the effect, with nearly 100% of participants showing a positive contingency effect with very short experiments (e.g., 5–10 minutes).

In previous work, it has been observed that the colour-word contingency learning effect appears almost instantly after the start of the task (Schmidt et al., 2007; Schmidt & De Houwer, 2012c, 2016b;Schmidt, De Houwer, & Besner, 2010;O. Y.-H. Lin & MacLeod, 2018). Within the very first block of trials, the contingency effect is typically already robust, even with blocks as small as 18 trials. Similarly quick acquisition is observed in other, related implicit learning paradigms (e.g., Lewicki, 1985; Nissen & Bullemer, 1987; Schmidt & De Houwer, 2012a). There is, in addition, a small but significant gradual increase in the contingency effect with increasing practice (O. Y.-H. Lin & MacLeod, 2018; Schmidt & De Houwer, 2016b), indicating that there is a cumulative learning effect (i.e., continued strengthening of learning over time).

Interestingly, work has also indicated that when the contingency is removed from the experiment (i.e., after some blocks of experiencing the contingency), the contingency effect very rapidly diminishes (O. Y.-H. Lin & MacLeod, 2018; Schmidt & De Houwer, 2016b; Schmidt et al., 2010). Though not necessarily eliminated entirely (and even persisting in much reduced form for hundreds of trials; Schmidt & De Houwer, 2016b), the effect does approach zero almost as quickly as the initial contingency learning effect appeared. This suggests that the contingency effect is heavily influenced by very recently-encountered events. The fact that contingency learning effects are so pliable in the colour-word contingency learning paradigm is intriguing. Though persistent effects of previously experienced contingencies are still observed after the contingencies are removed, and the contingency effect cannot be exclusively explained by word-colour conjunctions one to five trials back (Schmidt et al., 2010; but see, Giesen, Schmidt, & Rothermund, 2020; Schmidt, Giesen, & Rothermund, in press), the results so far suggest that, for the most part, recent experience is what matters most.

Contingency learning effects like this are interesting in that they resemble a habit (for a review, see Wood & Rünger, 2016). Organisms tend to repeat behaviours in response to given stimuli, especially when these responses are rewarded (Thorndike, 1911). In the colour-word contingency learning paradigm this is reflected by the bias to repeat the frequently paired response to the (task-irrelevant) word stimulus. The best way to define a habit is not consistently agreed upon (De Houwer, 2019). On the one hand, a habit might be defined in a broad sense as roughly synonymous with “automaticity.” In this sense, habitual responding is observed when a learned response is automatically evoked by a stimulus as the so-called “default” response (Evans & Stanovich, 2013). This definition focuses on the conditions under which responding occurs and does not specify the underlying mental mechanisms (whether the response is caused by stimulus representations, attitudes, or goals). A narrower definition of a habit, however, refers exclusively to the automatic priming of a specific response, and specifically excludes the operation of goals or attitudes (Wood & Rünger, 2016). Even more restrictively, Dickinson (1985; Heyes & Dickinson, 1990) defines habits as stimulus-driven responses (mediated by mental S-R associations) that have been installed via overtraining, and contrasts them with goal-directed actions that are driven by representations of the values and expectancies of the outcomes of these responses. By these more restrictive criteria, it can be quite difficult to determine when a behaviour is truly habitual, even in common metrics of habits like stimulus revaluation (e.g., De Houwer, Tanaka, Moors, & Tibboel, 2018; Moors, Boddez, & De Houwer, 2017).

Another way of thinking about contingency learning and habits (which is not necessarily incompatible with the view of Dickinson, 1985) is in terms of memory traces. According to an episodic memory perspective (Schmidt et al., 2010; Schmidt, De Houwer, & Rothermund, 2016), the colour-word contingency learning effect results from the storage and retrieval of episodic memories or exemplars (Logan, 1988). As more and more episodes linking a stimulus to a response are stored, presentation of said stimulus will more strongly bias retrieval in favour of the high contingency response.

In either case, the automatic biasing of the high contingency response to a word in the colour-word contingency learning paradigm might be regarded as a habit, though this might depend both on how one defines a habit and what assumptions are made about the mechanisms producing the effect (De Houwer, 2019). In the present manuscript, our primary interest is in exploring the automatic or default biasing of the high contingency response in the colour-word contingency learning paradigm, though we will return to potential implications for habit formation later.

The fact that contingency effects can be so pliable is interesting, as this suggests that recently-encoded events are particularly potent in their influence on performance, whereas older encoded events have minimal impact or rapidly become less potent (e.g., weakly retrievable). If so, then automatic responding might not be nearly as stable as previously thought. That is, it could be that a large chunk of what we regard as default responding (whether habitual in the restrictive sense or goal-mediated) is actually due to retrieval of only a very limited number of recently-encoded event memories (Giesen et al., 2020; Schmidt et al., in press). Note that persistence of an automatic response over time is not necessarily inconsistent with this notion: Repeatedly responding in a similar way to a stimulus as you did on your recent experiences with the same stimulus will tend to preserve the same behaviours long term.

It should be noted, on the other hand, that previous colour-word contingency learning experiments were relatively short in duration. They may therefore not reflect cases in which, after a considerable amount of practice, a stable representation of the meaningful connection between the stimulus and response may eventually emerge. For instance, over a lifetime one learns that the word “blue” is pronounced “blue.” Even after considerable practice in a Stroop task where “blue” is presented equally often in all colours, the word “blue” will continue to interfere with naming of incongruent colours (Ellis & Dulaney, 1991; Gul & Humphreys, 2015; MacLeod, 1998). That is, the relation between the word “blue” and its verbalisation is not quickly forgotten simply because the word is no longer predictive of the “blue” verbal response within the context of the experiment. It could be argued, however, that training studies with Stroop stimuli like this should be interpreted differently. Because the (repeatedly-reinforced) goal is to name the print colour congruently throughout the task (e.g., saying “blue” to the print colour blue), the habitual links between colour word stimuli and verbal responses may be indirectly reinforced (e.g., the link between the concept “blue” and the verbalization “blue” is reinforced during colour naming, which indirectly keeps the word “blue” linked to a “blue” verbalisation). The colour-word contingency learning paradigm is more neutral in this regard, given the lack of a meaningful, pre-existing relation between the predictive (non-colour word) and target (colour) dimensions.

Also potentially informative in this regard is a series of experiments by MacLeod and Dunbar (1988). In these experiments, participants were trained to name novel shapes as colours (e.g., naming one polygon as “blue,” another as “pink,” etc.). After practice in shape naming, the task was reversed and participants named the (actual) print colours of the shapes. This allowed assessment of both “congruent” (or high contingency) trials, in which the (now task-irrelevant) shape is presented in the high contingency colour (i.e., the colour the shape was named as during training), and “incongruent” (or low contingency) trials, in which the shape is presented in a low contingency colour. In contrast to the colour-word contingency learning paradigm, a congruency/contingency effect was not observed immediately in this training paradigm. Instead, it took multiple days of training before the shape-colour contingency began to influence performance when naming the colour of the shapes. This may have been due to the change in the task context (i.e., swapping the task-relevant and -irrelevant features) or some other factor. In any case, in their Experiment 2, a congruency/contingency effect was still observed three months after the end of training. Although it is impressive that this contingency effect persisted in the absence of continued training, they did not test to see how this contingency effect was influenced by direct changes in the contingencies. It is possible, for instance, that the contingency effect would be immediately obliterated after introduction of a new contingency (counterconditioning) in which the shapes were briefly retrained with new colour names.

Given the above considerations, it is not clear whether the automatic response tendencies developed during implicit learning procedures, such as the colour-word contingency learning paradigm, are stable. More generally, if such a training procedure does produce stable responding, then how long might one need to practice before the contingency effect is stable enough to, for instance, be resistant tounlearning (i.e., removal of the contingency) orcounterconditioning (i.e., introduction of a new, conflicting contingency)? That is, how much training is needed for acquired contingency knowledge to form a stable automatic response that is strong enough to override thecurrently-experienced stimulus-response contingencies?

In our previous research, we already studied unlearning with the colour-word contingency learning paradigm, but the contingency was always removed after a relatively short training period (O. Y.-H. Lin & MacLeod, 2018; Schmidt & De Houwer, 2016b; Schmidt et al., 2010). In the present series of three experiments, we investigated whether a contingency effect for heavily overtrained stimuli diminished rapidly after the contingency is removed (unlearning) and whether the effect of the original contingency was either further maintained or rapidly changed when a new, different contingency was introduced (counterconditioning). These three types of procedures (implemented in different phases of the experiments) are illustrated in Figure 1. We also investigated to what extent a new contingency was quickly acquired for stimuli that were previously associated with other colours. In particular, one set of words was trained first for an extended period of time (e.g., for two days in Experiments 1 and 3 or in one long session in Experiment 2). After this training, the contingency was removed for the second to last phase, and finally an alternative contingency was introduced in the final phase. These effects for heavily overtrained stimuli were compared to the same effects for recently-introduced contingencies (i.e., with stimuli that were introduced only briefly before the unlearning and counterconditioning phases).

Figure 1

Three types of testing phases in the experiments with the stimuli that correspond to each condition. In the initial learning phase, a contingency was introduced. During the unlearning phase, the contingency was removed. During the counterconditioning phase, a different contingency for the same words was introduced. For overtrained words, the original learning phase comprised many blocks spread over either two days (Experiments 1 and 3) or one long session (Experiment 2). For other words, the original learning phase comprised only five small sub-blocks.

Figure 1

Three types of testing phases in the experiments with the stimuli that correspond to each condition. In the initial learning phase, a contingency was introduced. During the unlearning phase, the contingency was removed. During the counterconditioning phase, a different contingency for the same words was introduced. For overtrained words, the original learning phase comprised many blocks spread over either two days (Experiments 1 and 3) or one long session (Experiment 2). For other words, the original learning phase comprised only five small sub-blocks.

Two possible results could occur in this setup. The first possibility we term therecent-events-matter-most scenario. In this scenario, older events have minimal impact on performance, and performance is primarily determined by the stimulus-response bindings in the recently encountered events. That is, the learning mechanism is strongly “myopic” to events that werejust experienced. This scenario is radically different from that predicted by traditional views of automatic responding discussed above, which assume that associations progressively strengthen over time, implying that frequency is more important than recency instead (of course, no one would argue that recent events do not influence behaviour at all). If the recent-events-matter most scenario obtains, then the contingency effect will disappear rapidly when the contingency is removed, even for the heavily trained stimuli. For instance, participants will stop responding faster to “find” in purple very shortly after “find” is changed to be presented equally often in all colours. It should similarly be expected that a newly introduced contingency for the same stimuli (i.e., counterconditioning) is rapidly learned. For instance, if “find” is now presented most often in orange (instead of purple), then participants should rapidly begin responding faster to “find” in orange, even after considerable training with “find” in purple. At the same time, the “old” high contingency (e.g., “find” in purple) should no longer influence performance after an extended unlearning phase and subsequent introduction of a “new” high contingency (e.g., “find” in orange).

The second possibility we term the eventually-stable-habit scenario. In this scenario, while recently acquired contingency knowledge may be more pliable early on (i.e., as the recent-events-matter-most scenario suggests), the memory bias for an overtrained contingency is more stable after sufficient training. In other words, sufficiently repeated encoding of a stimulus-response binding into memory eventually makes it difficult for new bindings to “break” the overtrained habit. Thus, for the overtrained stimulus set, unlearning should be less rapid. In other words, we might expect the original contingency to “stubbornly persist” after the contingency is removed for overtrained stimuli (i.e., no unlearning). Similarly, acquisition of a new contingency during counterconditioning should be reduced when the original contingency was overtrained. For instance, if “find” is changed to be presented most often in orange (rather than purple), then speeded responses to “find” in orange should not emerge quickly (perhaps not at all). This would indicate that the find-purple habit is too strongly ingrained to be quickly overcome. At the same time, the “old” high contingency should continue to influence performance, that is, participants should continue to respond quickly to “find” in purple long after the find-purple contingency has been replaced by the find-orange contingency.

It should be noted in advance that the recent-events-matter-most and eventually-stable-habit scenarios are deliberately presented as extremes, the former proposing very myopic learning and the latter proposing stubborn habit persistence. The truth may equally well lie somewhere in between these two extremes. That is, there might be both some continued influence of older experiences (e.g., from overtraining) in addition to influences of recently-acquired information. This would imply that we adapt quickly to newly-experienced events, but do not “catastrophically forget” everything that came before. To foreshadow our results, exactly this sort of mixed influence of both the new and old experiences was observed.

## Experiment 1

### Method

#### Participants

Fifty Ghent University undergraduates participated in the study on two separate 30 minute sessions one day apart in exchange for €10. Our sample size was determined a priori, but partially subjectively. In particular, as we had never previously studied counterconditioning with this procedure, we did not know how large of an effect to expect (i.e., for a priori power calculation). The current sample size seemed more than reasonable based on our prior experiences with the procedure on related topics. Two participants, however, did not show up for the second session and were therefore removed from the sample. Another participant had 16% incorrect responses in the main part of the experiment (i.e., excluding the practice block). This was over 2.5 standard deviations above the mean sample error rate. This participant was also removed from the sample. This participant contributed some notable noise to the sample, but inclusion of this participant did not influence the most critical results. Another participant had an empty cell (i.e., no correct response times) and was also removed.

#### Apparatus

The experiment utilized a standard PC. Stimulus and response timing were controlled with E-Prime 2 software (Psychology Software Tools, Pittsburgh, PA). The experiment files, along with the raw data, participant averaged data, and R scripts are available on the Open Science Framework (https://osf.io/7fwae/). Participants responded to purple, orange, and grey stimuli with the “J,” “K,” and “L” keys, respectively, on an AZERTY keyboard. Although we did not enforce specific fingers for the three keys, all participants in this and related studies defaulted to the standard keyboard resting position (i.e., right index on the J-key, right middle finger for the K-key, and right ring finger for the L-key).

#### Design

The structure of the experiment is presented in Figure 2. In each of the two testing days, participants were exposed to four larger mega-blocks, each with 5 sub-blocks of 36 trials (180 trials in each mega-block; 1440 trials total). In addition, on Day 1 participants also began with a practice block of 36 trials to familiarize participants with the colour-to-key mapping. To this end, trials in the practice block consisted of the stimulus “@@@@” presented in each of three colours (purple, orange, and grey) 12 times each and participants were instructed to respond as quickly and accurately as possible. For the four experimental mega-blocks of each day, four sets of three words (Sets A–D) were created from a list of 12 four letter, first person Dutch verbs. The verbs are presented in Table 1. Note that which words were a part of which set and which words were presented most often in which colour were counterbalanced by assigning words, in the order listed in Table 1, to lists offset by participant number (e.g., “vind,” “help,” and “weet” were the Set A words for Participants 1, 13, 25, etc., “help,” “weet,” and “denk” were the Set A words for Participants 2, 14, 26, etc.). The first three experimental mega-blocks of Day 1 constituted the training phase in which Set A (e.g., “vind,” “help,” and “weet”) and Set B words (e.g., “denk,” “roep,” and “geef”) were presented. In each of five sub-blocks of each mega-block, one word from each set was presented most often (i.e., four of six times: 67%) in purple (e.g., “vind” and “denk”), a second most often in orange (e.g., “help” and “roep”), and a third most often in grey (e.g., “weet” and “geef”). Each word was presented once (17%) in each of the remaining two colours per sub-block. Note that Set A stimuli were the “overtrained” words that were used throughout the entire experiment. Set B words were filler words included in the first training phase to keep the task similar throughout (i.e., with six words in three colours). Because they were not of interest for the main analyses, we do not report the analyses on Set B stimuli, though we note that comparisons with Set A stimuli revealed nothing problematic. Sets C and D served as the non-overtrained stimuli that appeared later in the experiment. In the forth and final mega-block of Day 1, Set A stimuli remained, but Set B words were replaced by Set C words, which had a word-colour contingency manipulation identical to that for Sets A and B. In this final mega-block, it is possible to compare the magnitude of the learning effect of the heavily-overtrained Set A words with the newly-experienced Set C words.

Table 1

Dutch stimuli and English translations with example set assignments.

Set ASet BSet CSet D

Dutch English Dutch English Dutch English Dutch English
vind find denk think zend send kies choose
help help roep call lach laugh maak make
weet know geef give neem take trek pull
Set ASet BSet CSet D

Dutch English Dutch English Dutch English Dutch English
vind find denk think zend send kies choose
help help roep call lach laugh maak make
weet know geef give neem take trek pull

Note: This is only one example counterbalancing of words to sets.

Figure 2

Composition of the phases, mega-blocks, and sub-blocks in the two testing days, with the sub-blocks of each mega-block indicated as separate squares. The two stimulus sets (A–D) in a given mega-block are indicated along with the contingency (in percentage) for the high contingency response. Changes in stimulus sets and contingency percentages from the prior mega-block are presented in bold. Note that in the final mega-block the “old” contingency is weakened and a “new” contingency is introduced.

Figure 2

Composition of the phases, mega-blocks, and sub-blocks in the two testing days, with the sub-blocks of each mega-block indicated as separate squares. The two stimulus sets (A–D) in a given mega-block are indicated along with the contingency (in percentage) for the high contingency response. Changes in stimulus sets and contingency percentages from the prior mega-block are presented in bold. Note that in the final mega-block the “old” contingency is weakened and a “new” contingency is introduced.

On Day 2, the first mega-block was identical to the last one experienced on Day 1 (i.e., with Set A and Set C words), allowing a comparison of Set A and Set C again, but after a night of consolidation. The next mega-block again maintained the Set A words, but replaced the Set C words with Set D words (i.e., another newly trained set). In the third mega-block to follow, the same Set A and Set D words were again presented, but the words were no longer predictive of the colour response. In particular, each of the words was presented two out of six times (33%) in each of the three colours. Thus, this mega-block constitutes the unlearning phase as it allows comparing the rate of unlearning for the heavily-overtrained Set A words with the newly-learned Set D words. In a final mega-block, which constitutes the counterconditioning phase, the same Set A and Set D words were again presented, but the contingencies were now changed. In particular, the word that used to predict purple was now presented most often in orange, the word that used to predict orange was presented most often in grey, and the word that used to be presented most often in grey was presented most often in purple. Thus, this mega-block allowed us to assess to what extent the new contingency is learned and whether the heavily-overtrained Set A contingencies are more resistant to a change in contingency than the newly-experienced Set D contingencies. For each of these sets (A and D), we can compare old high contingency items, which are pairings that previously had a high contingency but not anymore, to new high contingency items, which are pairings that currently have a high contingency but did not before. Both of these can further be compared to low contingency items, which are pairings that never had high contingency.

Note that for most of the above-mentioned contrasts we might not only expect larger contingency effects for overtrained stimuli, but also potential main effects of word type. This is because both the high and the low contingency pairings with overtrained stimuli have been experienced more frequently than the high and low contingency pairings with recently-acquired contingencies (for further discussion of the frequency versus proportion distinction, seeSchmidt & De Houwer, 2016a). In other words, we might expect that high contingency Set A items will be responded to faster than high contingency Set D items and, similarly, that low contingency Set A items will be responded to faster than low contingency Set D items.

#### Procedure

Stimuli were presented in the center of a black (0,0,0) screen, and presented in bold, 18 pt. Courier New font. On each trial, the participant was first presented a white (255,255,255) fixation “+” for 150 ms. This was followed by the word (or @’s during practice) in a neutral brown (255,183,113) for 150 ms, which was then colourized in one of the three target colours: purple (128,0,128), orange (255,165,0), or a light grey (192,192,192), which correspond to “purple,” “orange,” and “silver” in the standard E-Prime/HTML colour palette. This word preview was used because it is known to boost contingency effects (Schmidt & De Houwer, 2016b), likely because the word has more time to influence colour identification. The stimulus remained on the screen until either a response was made or 1500 ms elapsed. Following correct responses, the next trial began immediately. Following an incorrect response or 1500 ms without a response “XXX” was presented in white for 1000 ms before the next trial. Participants were instructed to try to respond as quickly and accurately as possible.

### Results

Figure 3

Experiment 1 contingency effect (low minus high contingency) as a function of block for the overtrained stimuli (top) and other stimuli (bottom) with standard errors. The sub-block numbers are indicated on the x-axis, but data are aggregated over the larger mega-blocks for presentation purposes. Light grey indicates the unlearning phase and dark grey indicates the counterconditioning phase. The single squares in the last block represent the low minus new high contingency contrast.

Figure 3

Experiment 1 contingency effect (low minus high contingency) as a function of block for the overtrained stimuli (top) and other stimuli (bottom) with standard errors. The sub-block numbers are indicated on the x-axis, but data are aggregated over the larger mega-blocks for presentation purposes. Light grey indicates the unlearning phase and dark grey indicates the counterconditioning phase. The single squares in the last block represent the low minus new high contingency contrast.

#### Day 1, Sets A and C

First, we compared Set A (which had already been trained for 15 sub-blocks) with the newly added Set C using a sub-block (16–20) by contingency (high vs. low) by set (A vs. C) ANOVA to test for potential differences between the more heavily trained Set A stimuli over the new Set C stimuli. The contingency effect was significant, F(1,45) = 10.233,MSE = 5520, p = .002, $ηp2=.19$

${{\eta}_p^2} = .19$
⁠, and this contingency effect increased across blocks, F(1,45) = 10.645, MSE = 4321, p = .002, $ηp2=.19$
${{\eta}_p^2} = .19$
. Interestingly, there was a main effect of set, F(1,45) = 8.987, MSE = 3331,p = .004, $ηp2=.17$
${{\eta}_p^2} = .17$
, indicating faster overall performance for the overtrained Set A stimuli. In particular, Set A high contingency trials (mean = 612 ms, SE = 11) were marginally faster than Set C high contingency trials (mean = 622 ms, SE = 11),F(1,45) = 3.493, MSE = 2986,p = .068, $ηp2=.07$
${{\eta}_p^2} = .07$
, and Set A low contingency trials (mean = 626 ms, SE = 12) were significantly faster than Set C low contingency trials (mean = 639 ms, SE = 13),F(1,45) = 4.852, MSE = 4189,p = .033, $ηp2=.10$
${{\eta}_p^2} = .10$
. Thus, an overall advantage was evident for the overtrained stimuli, albeit only significantly so for the infrequent pairings. Globally, this is consistent with the notion that the frequency of co-occurrences between a stimulus and response is important, not just the proportion (Schmidt & De Houwer, 2016a). However, the contingency effect (i.e., the difference between high and low contingency trials) was not significantly different between Set A and C stimuli, F(1,45) = 0.213,MSE = 3843, p = .545, $ηp2<.01$
${{\eta}_p^2} < .01$
, BF01 = 8.95.

#### Day 2, Sets A and C

Sets A and C were again compared on Day 2 with a sub-block (21–25) by contingency (high vs. low) by set (A vs. C) ANOVA. The contingency effect was significant, F(1,45) = 30.091, MSE = 1824, p < .001, $ηp2=.40$

${{\eta}_p^2} = .40$
⁠. There was again a main effect of block, F(1,45) = 13.312, MSE = 6216,p < .001, $ηp2=.23$
${{\eta}_p^2} = .23$
, perhaps again hinting at fatigue. As with the previous block, the contingency effect was not significantly different between the two sets, F(1,45) = 2.277,MSE = 2713, p = .138, $ηp2=.05$
${{\eta}_p^2} = .05$
, BF01 = 2.68, though the trend was for a larger effect for the overtrained Set A stimuli (mean = 550 ms, SE = 8 and mean = 571 ms,SE = 8, respectively, for high and low contingency) than for Set D stimuli (mean = 560 ms, SE = 8 and mean = 570 ms, SE = 10, respectively).

#### Day 2, Sets A and D

Next, we compared Set A with a newly-introduced Set D using a sub-block (26–30) by contingency (high vs. low) by set (A vs. D) ANOVA, again to test for any potential advantages for the overlearned Set A stimuli over the new Set D stimuli. The main effect of contingency was significant,F(1,45) = 21.608, MSE = 2915,p < .001, $ηp2=.32$

${{\eta}_p^2} = .32$
⁠. This contingency effect interacted with set, F(1,45) = 8.817, MSE = 3996,p = .004, $ηp2=.16$
${{\eta}_p^2} = .16$
, indicating larger contingency effects for the overtrained Set A stimuli than for the new Set D stimuli. Indeed, the contingency effect was significant for Set A, F(1,45) = 24.466, MSE = 3932, p < .001, $ηp2=.35$
${{\eta}_p^2} = .35$
, but not for Set D,F(1,45) = 0.672, MSE = 2978,p = .417, $ηp2=.01$
${{\eta}_p^2} = .01$
. Again, there was a main effect of set,F(1,45) = 5.295, MSE = 1589,p = .026, $ηp2=.11$
${{\eta}_p^2} = .11$
, indicating faster responses to the overtrained Set A stimuli. In particular, Set A high contingency trials (mean = 563 ms, SE = 9) were significantly faster than Set D high contingency trials (mean = 582 ms, SE = 10),F(1,45) = 19.160, MSE = 2037,p < .001, $ηp2=.30$
${{\eta}_p^2} = .30$
, but there was no difference between Set A (mean = 592 ms, SE = 11) and D (mean = 586 ms,SE = 8) low contingency trials,F(1,45) = 1.299, MSE = 3547,p = .260, $ηp2=.03$
${{\eta}_p^2} = .03$
.

#### Day 2, unlearning

Next, we compared Sets A and D during unlearning using a sub-block (31–35) by contingency (high vs. low) by set (A vs. D) ANOVA (i.e., where the contingency factor codes for the previously-applicable regularity). The contingency effect across sets was still significant during unlearning, F(1,45) = 26.748, MSE = 2178,p < .001, $ηp2=.37$

${{\eta}_p^2} = .37$
⁠. However, there was no difference in the contingency effect between sets, F(1,45) = 0.034,MSE = 2382, p = .855, $ηp2<.01$
${{\eta}_p^2} < .01$
, BF01 = 9.48, with the difference between high (mean = 579 ms, SE = 11) and low contingency trials (mean = 594 ms, SE = 11) in Set A being similar to that in Set D (mean = 578 ms, SE = 11 and mean = 595 ms, SE = 10, respectively). This finding is not as the eventually-stable-habit hypothesis would predict. In addition, the contingency effect was marginally smaller for Set A stimuli in the unlearning phase relative to the preceding acquisition phase,F(1,45) = 4.029, MSE = 528,p = 0.051, $ηp2=0.08$
${{\eta}_p^2} = 0.08$
. The fact that the contingency effect was still robust in the absence of a contingency is also not consistent with the recent-events-matter-most hypothesis. There was also a marginal main effect of sub-block, F(1,45) = 3.483, MSE = 3324, p = .069, $ηp2=.07$
${{\eta}_p^2} = .07$
, again hinting at fatigue.

#### Day 2, counterconditioning

Finally and most critically, we considered the counterconditioning blocks where we directly pitted an old (recent or overtrained) high contingency against a newly-introduced inconsistent high contingency. Most importantly, we began by considering whether reduction in the effect of the old high contingency and learning of the new high contingency was faster with the new Set D stimuli than with the overtrained Set A stimuli. For this, we began by comparing the trials in which the word was presented with the colour that was high contingency during initial training (old high contingency) with trials in which the word was presented with the colour that is currently high contingency (new high contingency). Trials in which the word was presented in a colour that was low contingency in all phases will be considered afterwards. Thus, we first conducted a sub-block (36–40) by contingency (old high vs. new high) by set (A vs. D) ANOVA, and followed this with the relevant contrasts. There was no main effect of contingency,F(1,45) = 0.002, MSE = 3438,p = .969, $ηp2<.19$

${{\eta}_p^2} < .01$
⁠. Note, of course, that this is not a test of the contingency effect per se, but of the comparison between the new versus old contingency. Thus, this should not be interpreted as no evidence of learning. There was a robust crossover interaction between contingency and set, F(1,45) = 14.334, MSE = 4009,p < .001, $ηp2=.24$
${{\eta}_p^2} = .24$
, as illustrated in Figure 4. Exploring this interaction further, old high contingency items (mean = 579 ms, SE = 10) were responded to significantly faster than new high contingency items (mean = 593 ms, SE = 11) in Set A, F(1,45) = 8.780, MSE = 3336, p = .005, $ηp2=.16$
${{\eta}_p^2} = .16$
, but significantlyslower (mean = 595 ms, SE = 11 vs. mean = 582 ms, SE = 10, respectively) in Set D,F(1,45) = 6.856, MSE = 4111,p = .012, $ηp2=.13$
${{\eta}_p^2} = .13$
. Furthermore, in ANOVAs comparing high to low contingency, responses were significantly faster on old high contingency relative to low contingency trials (mean = 598 ms,SE = 12) for Set A, F(1,45) = 6.890,MSE = 7019, p = .012, $ηp2=.13$
${{\eta}_p^2} = .13$
, indicating a preservation of the original learning, but there was no significant difference between new high contingency and low contingency trials, F(1,45) = 0.351,MSE = 6774, p = .557, $ηp2<.01$
${{\eta}_p^2} < .01$
, BF01 = 7.96, suggesting a failure to acquire the new contingency (albeit with a numerical trend in the correct direction). In contrast, for Set D stimuli there was no difference between old high and low contingency trials (mean = 599 ms, SE = 11), F(1,45) = 0.356,MSE = 10570, p = .553, $ηp2<.01$
${{\eta}_p^2} < .01$
, BF01 = 8.84, suggesting abolition of the original contingency learning effect (though, again the means were in the correct direction), but a significant difference between new high and low contingency items,F(1,45) = 8.742, MSE = 6013,p = .005, $ηp2=.16$
${{\eta}_p^2} = .16$
, indicating acquisition of the new contingency.

Figure 4

Experiment 1 mean response times with standard errors for old, new, and low contingency items (collapsed across sub-block) for the overtrained (Set A) and recently-introduced (Set D) stimuli during counterconditioning.

Figure 4

Experiment 1 mean response times with standard errors for old, new, and low contingency items (collapsed across sub-block) for the overtrained (Set A) and recently-introduced (Set D) stimuli during counterconditioning.

### Discussion

Experiment 1 produced results that did not overwhelmingly support either of the two strong views mentioned in the Introduction. For some contrasts, there was no robust evidence of a difference between overtrained and newly-acquired contingencies. For instance, no differences were observed between Set A and Set D stimuli in the unlearning phase, where the contingency effect for overtrained Set A stimuli marginally decreased (i.e., did not “stubbornly persist” in an unchanged magnitude). Findings such as this could be considered as evidence for the recent-events-matter-most scenario, which predicts no differences between heavily-overtrained and newly-learned contingencies. On the other hand, overall response times were faster to the overtrained Set A stimuli relative to newly-added stimuli during the acquisition phases for Sets C and D (but only on Day 1 for the former). That is, there was a general speedup of responses to the overtrained stimuli, both high and low contingency, relative to recently-introduced stimuli. This is consistent with the notion that practice with the co-occurrence of stimuli (even if task irrelevant) and responses benefits performance (Lemercier, 2009; Schmidt & De Houwer, 2016a; Schmidt et al., 2016). That is, even though the proportions of high versus low contingency pairings are equivalent for overtrained and recently-introduced contingencies (i.e., 4/6 high contingency pairings, and 1/6 for each low contingency pairing), participants have more frequently observed each compound stimulus for the overtrained Set A stimuli (Y.-H. Lin, 2015). For instance, by the end of the acquisition phase for Set D, participants had seen each high contingency pairing 120 times for Set A, but only 20 times for Set D. Similarly, they had seen each low contingency pairing 30 times for Set A, but only 5 times for Set D. However, the contingency effect (i.e., difference between high and low contingency trials) was only (robustly) larger in the Set A versus Set D comparison. For Set C, it is possible that the overall advantage (i.e., main effect) for the overtrained stimuli worked against the contingency effect, in line with previous findings that effects tend to scale up with mean response time (Stevens et al., 2002; Urry, Burns, & Baetu, 2015; Schmidt & De Houwer, 2016b; Schmidt et al., 2016). That is, even though contingencyknowledge may be stronger for the overlearned stimuli, there is less time for this contingency knowledge to beexpressed (i.e., to influence colour decisions) as overall responding was quicker to overlearned stimuli. Stated differently, even though the contingency knowledge might be stronger for Set A, the difference between high and low contingency trials might not be notably larger in this set because the fast overall response speed to Set A stimuli precluded the influence of the contingency knowledge on the response times.

During counterconditioning, there was a trend for faster responses to both the old and new high contingency trials relative to low contingency trials, though only the old contingency effect was robust for the overtrained Set A and only the new contingency effect was robust for newly added Set D items. The interaction between set (A vs. D) and contingency (new vs. old) was robust. This suggests that the old contingency persists to a greater extent with overtraining, and the new contingency is learned more quickly with newly-acquired contingencies. For all comparisons between overtrained and recently-acquired contingencies, no results pointed in the reverse direction than the eventually-stable-habits account would suggest (e.g., smaller effects of the old contingency for Set A), though an overwhelming difference between overtrained and recently-acquired contingencies does not seem apparent (i.e., for many contrasts there were no differences, and for others only small differences). Together, the results might suggest some carryover influence of overtraining, but a remaining potent influence of recent events (i.e., even with overtraining).

## Experiment 2

Results from Experiment 1 suggest that neither of the two extreme views we discussed in the Introduction are correct. That is, it was not the case that only recent events had an influence on performance (which would have predicted no differences at all between Set A and Sets C and D), and it was also not the case that overlearning completely prevented any new learning (which would have predicted, for instance, no unlearning at all for Set A). Instead, the results supported an intermediate view, with some findings suggesting a lasting (though perhaps subtle) influence of overtraining, but with a robust influence of very recent experiences. Experiment 2 aimed to provide a conceptual replication of Experiment 1. The most important change was that we dropped the two-day design and instead used a longer single day design (with 6 mega-blocks in one day, instead of 4 per day). We also dropped the Set C stimuli. Put differently, Experiment 2 was identical to Experiment 1 in all respects except that sub-blocks 16–25 (the fourth and fifth mega-blocks in Figure 2) were dropped and the remaining 30 sub-blocks (6 mega-blocks) were tested in one day. This does imply, however, that the initial training duration for Set A is shorter in Experiment 2 and does not include a night of sleep consolidation. Also note that we continue to label the last-introduced words as “Set D” for consistency with the prior experiment even though there was no longer a Set C.

### Method

#### Participants

Sixty-one Ghent University undergraduates participated in the study in one 30 minute session in exchange for €5. A slightly larger sample was collected because we supposed that any potential differences between overtrained and recently-learned contingencies might be smaller with a shorter training period. Using the same exclusion criteria as the prior experiment, only one participant was excluded due to an empty cell.

#### Apparatus, Design, and Procedure

The apparatus, design, and procedure of the current experiment were identical to Experiment 1 with the following exceptions. The two mega-blocks with Set C stimuli from Experiment 1 (see Figure 2) were dropped and the remaining six mega-blocks were run in one day (i.e., 3 A+B learning mega-blocks, followed by A+D learning, unlearning, and counterconditioning blocks; 1080 trials in total).

### Results

Data were analysed in the same manner as in the prior experiment. The contingency effect as a function of the mega-blocks is presented in Figure 5. The means and standard errors for the response times and errors are presented in Table A2 in the Appendix (see also the R scripts).

Figure 5

Experiment 2 contingency effect (low minus high contingency) as a function of mega-block (collapsed across sub-blocks, indicated inx-axis) with standard errors. Data are aggregated over the larger blocks. Light grey indicates the unlearning phase and dark grey indicates the counterconditioning phase. The single squares in the last block represent the low minus new contingency contrast.

Figure 5

Experiment 2 contingency effect (low minus high contingency) as a function of mega-block (collapsed across sub-blocks, indicated inx-axis) with standard errors. Data are aggregated over the larger blocks. Light grey indicates the unlearning phase and dark grey indicates the counterconditioning phase. The single squares in the last block represent the low minus new contingency contrast.

#### Sets A and D

First, we compared Set A (which had already been trained for 15 sub-blocks) with a newly-introduced Set D using a sub-block (16–20) by contingency (high vs. low) by set (A vs. D) ANOVA. The main effect of contingency was significant, F(1,59) = 28.503, MSE = 5422,p < .001, $ηp2=.33$

${{\eta}_p^2} = .33$
⁠. This contingency effect interacted with set, F(1,59) = 11.529, MSE = 3099,p = .001, $ηp2=.16$
${{\eta}_p^2} = .16$
, indicating larger contingency effects for the overtrained Set A stimuli than for the new Set D stimuli. The contingency effect was significant for Set A (mean = 595 ms,SE = 8 and mean = 629 ms, SE = 8 for high and long contingency trials, respectively), F(1,59) = 39.518, MSE = 4288, p < .001, $ηp2=.40$
${{\eta}_p^2} = .40$
, and for Set D (mean = 610 ms,SE = 8 and mean = 622 ms, SE = 8, respectively), F(1,59) = 4.919, MSE = 4233, p = .030, $ηp2=.08$
${{\eta}_p^2} = .08$
. There was also a marginal main effect of sub-block, F(1,59) = 3.256, MSE = 6562,p = .076, $ηp2=.05$
${{\eta}_p^2} = .05$
.

#### Unlearning

Next, we compared Sets A and D during unlearning using a sub-block (21–25) by contingency (high vs. low) by set (A vs. D) ANOVA. The contingency effect was only marginal during unlearning,F(1,59) = 3.103, MSE = 4003,p = .083, $ηp2=.05$

${{\eta}_p^2} = .05$
⁠. There was a main effect of sub-block,F(1,59) = 6.113, MSE = 6745,p = .016, $ηp2=.09$
${{\eta}_p^2} = .09$
, hinting at fatigue. Critically, there was no difference in the contingency effect between sets,F(1,59) = 2.432, MSE = 2350,p = .124, $ηp2=.04$
${{\eta}_p^2} = .04$
, BF01 = 4.72, with the difference between high (mean = 610 ms, SE = 8) and low contingency trials (mean = 612 ms, SE = 7) in Set A similar to that in Set D (mean = 606 ms, SE = 9 and mean = 617 ms, SE = 8, respectively). In addition, the contingency effect was robustly smaller for Set A stimuli during unlearning relative to the preceding acquisition phase, F(1,59) = 22.060, MSE = 676, p < .001, $ηp2=.27$
${{\eta}_p^2} = .27$
. These latter two findings are again inconsistent with the eventually-stable-habit view.

#### Counterconditioning

For the counterconditioning phase, we again begin with a block (26–30) by contingency (old vs. new) by set (A vs. D) ANOVA. Again, there was no main effect of contingency, F(1,59) = 0.780,MSE = 5259, p = .375, $ηp2=.01$

${{\eta}_p^2} = .01$
(note again that this is not a test of learning, but rather a comparison of old vs. new learning). In contrast to Experiment 1, however, the contingency by set interaction was not significant, F(1,59) = 0.347, MSE = 3756,p = .558, $ηp2<.01$
${{\eta}_p^2} < .01$
, BF01 = 9.64, as illustrated in Figure 6. Relative to low contingency trials (mean = 629, SE = 10 for Set A and mean = 623, SE = 10 for Set D), the contingency effect (across sets) was significant for the old contingency (mean = 619,SE = 10 for Set A and mean = 610, SE = 10 for Set D) trials (i.e., old high vs. low), F(1,59) = 4.501, MSE = 8898, p = .038, $ηp2=.07$
${{\eta}_p^2} = .07$
, and for the new contingency (mean = 612, SE = 8 for Set A and mean = 607, SE = 8 for Set D) trials (i.e., new high vs. low), F(1,59) = 12.257, MSE = 5729, p < .001, $ηp2=.17$
${{\eta}_p^2} = .17$
, indicating both some preservation of the old contingency and acquisition of the new one.

Figure 6

Experiment 2 mean response times (collapsed across sub-block) with standard errors for old, new, and low contingency items for the overtrained (Set A) and recently-introduced (Set D) stimuli during counterconditioning.

Figure 6

Experiment 2 mean response times (collapsed across sub-block) with standard errors for old, new, and low contingency items for the overtrained (Set A) and recently-introduced (Set D) stimuli during counterconditioning.

### Discussion

Like Experiment 1, Experiment 2 produced some results indicating an overtraining advantage for Set A stimuli, but this time only in the comparison of the contingency effect for the just-introduced Set D stimuli relative to the overtrained Set A stimuli. In particular, there was a larger contingency effect for Set A. However, there was again no difference between the two sets during unlearning. Further, there was evidence of both a preservation of the old contingency and acquisition of the new contingency during the counterconditioning phase. Unlike Experiment 1, however, this did not seem to be influenced markedly by set. The old contingency effect does, on its own, indicate a persisting influence of older experiences: Even after 180 trials of unlearning and a subsequent introduction of an opposing contingency during counterconditioning, the originally-trained regularity continued to influence behaviour. However, even extensive overtraining (i.e., for Set A) did not seem to altogether prevent acquisition of a new contingency and this is not consistent with the strong view that heavy overtraining prevents acquisition of new knowledge.

## Experiment 3

Results from Experiments 1 and 2 both provide some hints of an effect of overtraining, but not evidence for an overwhelming effect. For instance, the original contingency for Set A stimuli did not “stubbornly persist” through unlearning, instead reducing substantially as with the Set D stimuli. Further, Experiment 2 did not replicate the interaction between set (A vs. D) and contingency (old high vs. new high), instead showing significant effects for both new and old high contingencies (relative to low contingency) across sets, indicating both persistence of old contingency knowledge (inconsistent with the recent-events-matter-most scenario) and acquisition of new contingency knowledge (inconsistent with the eventually-stable-habits scenario). This might indicate that the amount of initial training is less important for persistence of the old contingency through counterconditioning than Experiment 1 suggested. Alternatively, it might be that the shorter overtraining phase was responsible for the lack of an interaction in Experiment 2. We therefore decided to run a third experiment as a conceptual replication of Experiment 1, that is, with a session of two separate days, but with an even longer training phase.

### Method

#### Participants

Fifty Ghent University undergraduates participated in the study in two sessions in exchange for €10, as in Experiment 1. Using the same exclusion criteria as the prior experiments, no participants were excluded.

#### Apparatus, Design, and Procedure

The apparatus, design, and procedure of the current experiment were identical to Experiment 1 with the following exceptions. There were six mega-blocks per day instead of four, but we again excluded Set C stimuli and ran nine mega-blocks of A+B learning (i.e., 6 on Day 1 and 3 on Day 2), followed by the same A+D learning, unlearning, and counterconditioning mega-blocks as in the prior two experiments (2160 trials total).

### Results

Data were analysed in the same manner as in the prior experiments. The contingency effect as a function of the larger blocks is presented in Figure7. The means and standard errors for response times and errors are presented in Table A3 in the Appendix (see also the R scripts).

Figure 7

Experiment 3 contingency effect (low minus high contingency) as a function of block (sub-blocks indicated in x-axis) with standard errors. Data are aggregated over the mega-blocks. Light grey indicates the unlearning phase and dark grey indicates the counterconditioning phase. The single squares in the last sub-block represent the low minus new high contingency contrast.

Figure 7

Experiment 3 contingency effect (low minus high contingency) as a function of block (sub-blocks indicated in x-axis) with standard errors. Data are aggregated over the mega-blocks. Light grey indicates the unlearning phase and dark grey indicates the counterconditioning phase. The single squares in the last sub-block represent the low minus new high contingency contrast.

#### Sets A and D

First, we compared the overtrained Set A with a newly-introduced Set D using a sub-block (46–50) by contingency (high vs. low) by set (A vs. D) ANOVA. The main effect of contingency was significant,F(1,49) = 28.982, MSE = 4675,p < .001, $ηp2=.37$

${{\eta}_p^2} = .37$
⁠. This contingency effect interacted with set, F(1,49) = 7.061, MSE = 3667,p = .011, $ηp2=.13$
${{\eta}_p^2} = .13$
, again indicating larger contingency effects for the overtrained Set A stimuli than for the new Set D stimuli. The contingency effect was significant for Set A (mean = 560 ms,SE = 8 and mean = 593 ms, SE = 9 for high and low contingency, respectively), F(1,49) = 33.698,MSE = 4152, p < .001, $ηp2=.41$
${{\eta}_p^2} = .41$
, and for Set D (mean = 574 ms,SE = 8 and mean = 587 ms, SE = 8, respectively), F(1,49) = 5.121, MSE = 4190, p = .028, $ηp2=.09$
${{\eta}_p^2} = .09$
.

#### Unlearning

Next, we compared Sets A and D during unlearning using a block (51–55) by contingency (high vs. low) by set (A vs. D) ANOVA. The contingency effect was significant, F(1,49) = 19.746, MSE = 4235, p < .001, $ηp2=.29$

${{\eta}_p^2} = .29$
⁠, indicating persistence of the initial contingency during unlearning. There was a marginal main effect of set,F(1,49) = 3.925, MSE = 2862,p = .053, $ηp2=.07$
${{\eta}_p^2} = .07$
, indicating overall faster responses for Set A stimuli, consistent with an overall benefit for overtrained stimuli. However, there was no difference in the contingency effect between sets, F(1,49) = 0.846, MSE = 3177,p = .362, $ηp2=.02$
${{\eta}_p^2} = .02$
, BF01 = 7.27, though with a numerical trend in the direction that the eventually-stable-habit account would predict (mean = 565 ms,SE = 8 and mean = 587 ms, SE = 7 for high and low contingency in Set A, and mean = 575 ms, SE = 8 and mean = 590 ms, SE = 7 in Set D). The contingency effect for Set A was only non-significantly reduced in the unlearning phase relative to the preceding acquisition phase, F(1,49) = 1.983, MSE = 891, p = .165, $ηp2=.04$
${{\eta}_p^2} = .04$
.

#### Counterconditioning

For the counterconditioning phase, we again begin with a sub-block (56–60) by contingency (old high vs. new high) by set (A vs. D) ANOVA. Again, there was no main effect of contingency, F(1,49) = 0.329, MSE = 5955, p = .569, $ηp2<.01$

${{\eta}_p^2} < .01$
(note again that this is not a test of the contingency effect proper, but the new versus old contingency). Again, the contingency by set interaction was not significant,F(1,49) = 0.322, MSE = 3860,p = .573, $ηp2<.01$
${{\eta}_p^2} < .01$
, as illustrated in Figure 8, BF01 = 9.30. There was a significant sub-block by contingency interaction,F(1,49) = 6.134, MSE = 6842,p = .017, $ηp2=.11$
${{\eta}_p^2} = .11$
, indicating a tendency for the old contingency effect to decrease and the new contingency effect to increase across sub-blocks. Relative to low contingency trials (mean = 591 ms,SE = 9 in Set A and mean = 589 ms, SE = 9 in Set D), the contingency effect (across sets) was significant for the old contingency (mean = 572 ms, SE = 9 in Set A and mean = 573 ms, SE = 9 in Set D) trials (i.e., old vs. low),F(1,49) = 6.087, MSE = 10300,p = .017, $ηp2=.11$
${{\eta}_p^2} = .11$
, and for the new contingency (mean = 576 ms, SE = 8 in Set A and mean = 579 ms,SE = 7 in Set D) trials (i.e., new vs. low),F(1,49) = 8.001, MSE = 5310,p = .007, $ηp2=.14$
${{\eta}_p^2} = .14$
.

Figure 8

Experiment 3 mean response times (aggregated across sub-blocks) with standard errors for old high, new high, and low contingency items for the overtrained (Set A) and recently-introduced (Set D) stimuli during counterconditioning.

Figure 8

Experiment 3 mean response times (aggregated across sub-blocks) with standard errors for old high, new high, and low contingency items for the overtrained (Set A) and recently-introduced (Set D) stimuli during counterconditioning.

### Discussion

As in the prior two experiments, Experiment 3 produced some results consistent with a benefit from overtraining. In particular, contingency effects were larger for Set A stimuli relative to Set D during initial acquisition of Set D contingencies. Similarly, response times were overall faster to Set A stimuli during unlearning. However, learning of the new contingency was observed for both sets of stimuli during counterconditioning. There was, nevertheless, again a persistence of the old contingency in the counterconditioning phase, which did not differ notably between the overtrained and recently-acquired stimuli. Together, the results again suggest a very strong influence of recent experiences, including for overtrained stimuli, as the recent-events-matter-most hypothesis would suggest. However, the results again suggest that learning is not completely “myopic” to only very recent experiences.

## General Discussion

In the present series of experiments, we asked whether overlearning of contingencies either over two days (Experiments 1 and 3) or a single longer session (Experiment 2) would lead to a more stable learning effect, resistant to unlearning or counterconditioning, which we referred to as the eventually-stable-habit view. Alternatively, we considered the idea that learning might be rather “myopic” to recent events, whereby “habits” are maintained merely due to the continued repetition of recently-executed behaviours. This alternative view would suggest no observable differences between overtrained and recently-acquired contingencies. Our data are consistent with a more intermediate view: both lasting influences of older experiences and marked sensitivity to recent ones.

Some influences of older events were clearly observed. For instance, in all three experiments there was a larger contingency effect and/or an overall main effect speeding for Set A (overtrained) stimuli relative to newly-introduced Set B or C stimuli. This is also consistent with past findings that contingency effects, while acquired quite quickly, do tend to slowly increase with further training (Schmidt & De Houwer, 2016b). Similarly, contingency effects remained significant within the 180-trial unlearning phase, indicating persistence of a contingency when the regularity no longer applied. On the other hand, the contingency was not “stubbornly resistant” to the extent that no reductions in the learning effect were observed.

Perhaps most interesting was the newly-introduced counterconditioning phase. In all three experiments, the trend was for persistence of the old contingency effect, albeit in a reduced form, despite the preceding 180-trial unlearning phase and the introduction of a new, competing contingency. There was also a trend for acquisition of the new contingency effect in the counterconditioning phase, however. In Experiment 1, there was a significant interaction between the old and new contingency effects. In particular, while the new contingency effect was not robust (though numerically trending) and the new contingency effect was significant for the recently-introduced stimuli (Set D), the reverse was true for overlearned stimuli: The old contingency effect persisted and the newly-changed contingency was non-significant (though again trending in the correct direction). This pattern did not replicate in Experiments 2 and 3, however, where both the old and new contingencies influenced performance regardless of set. The reason for this discrepancy is uncertain. The significant interaction in Experiment 1 could have been a Type 1 error. Alternatively, a true but very small effect might exist that was detected only in one experiment. Indeed, as a general trend “hints” in favour of some overtraining effect were rather systematically observed in our experiments, but only some findings were significant and no overtraining effects were overwhelmingly large. Globally, then, the results are again not consistent with the strong idea that overtrained stimuli are inflexibly resistant to new learning: Some lasting influence of older events is observed, but new contingencies are picked up even for overtrained stimuli.

It is further noteworthy that there were a number of contrasts between overtrained and recently-acquired stimuli that were performed (i.e., two main ones per phase if we consider both the main effect of set and the interaction between set and contingency). Only some of these came out as significant and most of the observed differences were not substantial. We also note that we did not make corrections for multiple comparisons and there was clearly some noise and inconsistencies across the many comparisons, so more targeted replications of specific findings seem warranted. However, we do note that all significant cross-set comparisons were in the direction predicted by the eventually-stable-habit view. Still, any lasting influences from overtraining seem rather underwhelming. It is perhaps important to stress that these findings were not, however, unclear, as the results revealed significant effects that are inconsistent with each of the “extreme” positions we discussed in the Introduction. For instance, the eventually-stable-habits view clearly should have predicted, for overtrained stimuli: (a) no reduction in contingency effects with unlearning (but this was observed), (b) no acquisition of new contingencies during counterconditioning (but this was also observed). Similarly, the results also are not consistent with the extreme view that learning is myopic to only very recently occurring events, as the persistence of the old contingency through unlearning and counterconditioning clearly demonstrates. Thus, robust effects argue against both extreme views and therefore suggest that a more moderate view is necessary.

Collectively, the results of the present three experiments are consistent with the idea that both frequency and recency influence the quality of representations (in this case, representations of contingency; see Moors, 2016). Our results might be coherently explained by one high learning rate memory mechanism (e.g., Logan, 1988; Schmidt et al., 2016). According to this view, the individual impact of a given experience on current behaviour is related to how long ago the past experience occurred. Recently-experienced events have a particularly potent influence on behaviour, whereas older and older memory traces have increasingly smaller (but non-zero) influences on current behaviour. This notion has often been referred to as thepower law of practice (Logan, 1988; Newell & Rosenbloom, 1981). The actual form of acquisition may be exponential (Heathcote, Brown, & Mewhort, 2000; Myung, Kim, & Pitt, 2000), which, averaged across participants, appears more like a power function. In any case, the rough notion is illustrated in Figure 9. If the participant is, for instance, currently partway through a counterconditioning phase, then learning of a “new” contingency can be explained by the potent influence of recent events. However, there will also be continued influence of older events from the prior unlearning and acquisition phases, which can explain the persistence of an old contingency. Differences between overtrained (Set A) and recently-acquired (Set D) stimuli can be explained by the extra traces encountered for the former stimuli. The reason for relatively weak overtraining effects can similarly be explained by the decay of much older memory traces. Similar notions have been forwarded to explain skill acquisition (Logan, 1988) and repetition priming (Grant & Logan, 1993; Logan, 1990).

Figure 9

An illustration of a power law influence on behaviour. Recently-encoded events have a particularly strong influence on behaviour, whereas older and older memories have ever diminishing influences. In this example, the counterconditioning phase is marked in dark grey, the unlearning phase in light grey, and acquisition in white.

Figure 9

An illustration of a power law influence on behaviour. Recently-encoded events have a particularly strong influence on behaviour, whereas older and older memories have ever diminishing influences. In this example, the counterconditioning phase is marked in dark grey, the unlearning phase in light grey, and acquisition in white.

This theoretical account may also explain why more notable differences were observed between Set A and Set D stimuli during acquisition, but less clearly during unlearning and counterconditioning. In particular, the extra learning experiences for Set A stimuli were more recent during acquisition, but more distant in time when the unlearning and subsequent counterconditioning phases eventually began. In that vein, somewhat larger effects of overlearning might be observable during counterconditioning if the counterconditioning directly follows acquisition.

Related to the above discussion, the present results might prove informative in constraining conceptual and modelling accounts of learning. For instance, the Parallel Episodic Processing (PEP) model of Schmidt and colleagues (2016) has been used to simulate a range of findings from the colour-word contingency learning paradigm, along with a number of other binding, timing, attentional, and control phenomena. Similar to what we described above, the PEP stores traces of individual events and similarity-based retrieval of these “exemplars” produces learning effects. It is already the case in the PEP model that recently-encoded events are more strongly retrieved from memory that older events. This allows the model to learn quickly and simulate (with the same mechanism) more transitory influences on behaviour, such as distracter-response binding effects (Frings, Rothermund, & Wentura, 2007). Future modelling work, however, could aim to see whether the PEP (or other models) is able to simulate the both the lasting and dynamic adaptations to contingencies as observed in the present report.

The current research introduces interesting new avenues for future research. For instance, we observed that an originally-trained contingency that was acquired over two sessions on two days persisted through 180 unlearning trials and even through another 180 counterconditioning trials. But how much longer would this effect persist? Various permutations of the learning and counterconditioning phase lengths might clarify this issue further. Presumably, the new contingency will overwhelm the old contingency eventually, and the 180-trial counterconditioning phase in the current report may have simply been too short. Future work with longer counterconditioning phases might therefore explore whether an old contingency does eventually extinguish entirely. Similarly, it may be the case that the old contingency eventually does become stable and completely prevents acquisition of a new contingency. Future research might aim to extend the initial training over much longer durations (e.g., weeks) before introducing counterconditioning.

More globally, the present results might suggest that much of what we consider to be a habit is driven by recent experiences. If recent experiences are, indeed, more influential on the maintenance of an automatic behaviour than is typically assumed, then this might have interesting implications. Although certainly speculative, to change automatic behaviour (e.g., undesirable habits), it might suffice to force a change in a limited number of recent experiences. That is, the default response to a stimulus could change even if only the most recent experiences are different from many old ones. At the same time, our data show that old memory traces will persist to some degree, which could explain relapse of old automatic behaviours, including undesirable habits. Such relapse might be particularly likely as time elapses. In this case, events inconsistent with the original default response (e.g., counter-habitual behaviours) will lose their advantage of recency so that traces of old automatic behaviours can resurface. This is related to explanations for spontaneous recovery, that is, the re-emergence of a previously-extinguished behaviour (Briggs, 1954). It would thus be interesting to rerun our studies but add a delay between counterconditioning and a subsequent unlearning test phase. It might be the case that the old contingency resurfaces more strongly with delay (Pavlov, 1927).

Relatedly, two of our three experiments included two days of training. This was largely done with the aim of breaking up the lengthy training. However, the two-day training did involve an intermediate night of sleep reconsolidation. Future work might explore the role of consolidation more directly. Some existing work already suggests that consolidation does play some role in the strength of learning. For instance, in Geukes, Gaskell, and Zwitserlood (2015) trained participants with novel words and colour words as pairs and observed Stroop-like (or learning) effects when intermixed with colour word distracters. Without colour word distracters, however, the Stroop effect was only observed on Day 2 (i.e., after consolidation). Similarly, the role of sleep or simply time in consolidation could be explored (Lindsay & Gaskell, 2013).

Another factor of interest that might be interesting to explore in the current design is context dependency. It is likely that, in real life, automatic (e.g., habitual) behaviours have been emitted in many contexts whereas recent (e.g., counter-habitual) behaviours inconsistent with the original learning have been emitted in more restricted contexts (e.g., therapy). Also, from learning psychology we know that first learning is less context dependent than new learning (for a review, see Bouton, 2004). Hence, old memory traces might have more impact in new contexts than in those contexts specifically used for unlearning or counterconditioning. To explore this notion with the present materials, one might therefore imagine introducing contextual changes, for instance, after counterconditioning to see whether this leads to a re-emergence of the old contingency.

We also remind the reader that “habit” is inconsistently defined in the literature (see the Introduction). Thus, depending on how one defines a habit, our lengthy acquisition phase may or may not be considered sufficient to establish a habit. It is similarly not clear whether the incidental learning effects explored in the present report are due to goal-free stimulus-response learning or whether the learning also includes goal-directed actions. Independently of whether the absence of goals should be considered a defining feature of a habit (see De Houwer, 2019, for concerns with this perspective), it remains an interesting open question to explore in future research (e.g., by conducting similar studies with or without deliberate learning goals).

## Data Accessibility Statement

The experiment files, along with the raw data, participant averaged data, and R scripts are available on the Open Science Framework (https://osf.io/7fwae/).

### Appendix

Table A1

Experiment 1 response time and error means and standard errors.

BlockContingencyResponse TimesError Percentages

Set ASets B–DSet ASets B–D

MeanSEMeanSEMeanSEMeanSE

High 612 12 607 11 5.0 0.9 5.2 1.2
Low 609 16 603 13 7.0 1.6 9.6 1.8
High 601 12 591 11 5.9 1.0 5.0 1.0
Low 614 14 606 14 8.1 1.5 5.5 1.4
High 598 12 601 13 6.9 1.2 4.6 0.9
Low 617 15 614 15 6.5 1.6 5.9 1.5
High 595 10 597 13 4.3 0.8 4.9 1.1
Low 621 15 625 16 6.2 1.4 4.8 1.2
High 598 12 610 12 4.3 0.9 6.5 1.1
Low 617 12 621 12 4.4 1.2 9.5 1.9
High 592 12 611 11 6.1 1.1 6.1 1.0
Low 606 14 640 17 5.7 1.5 6.7 1.6
High 612 12 617 13 6.1 1.0 4.5 0.9
Low 637 13 636 17 7.7 1.5 7.0 1.5
High 615 14 617 14 6.6 1.0 7.6 1.0
Low 632 17 634 15 6.6 1.4 7.4 1.9
High 631 15 622 15 6.9 1.2 3.6 1.0
Low 673 17 647 16 7.0 1.3 10.8 1.8
10 High 609 13 619 14 5.9 1.1 5.7 1.1
Low 648 15 630 14 7.3 1.7 7.4 1.7
11 High 611 14 607 13 7.0 0.9 6.1 1.1
Low 633 15 628 16 7.0 1.6 7.8 1.5
12 High 617 13 609 11 5.7 1.2 5.8 1.0
Low 662 16 635 15 7.8 1.6 8.8 2.1
13 High 607 13 619 14 6.0 1.1 5.7 1.1
Low 631 12 644 14 8.0 1.9 7.4 1.6
14 High 618 13 616 11 6.3 1.1 5.9 1.1
Low 632 16 637 14 7.7 1.5 7.7 1.6
15 High 604 15 607 13 6.0 1.0 4.3 0.8
Low 622 11 640 17 11.6 1.8 8.5 1.8
16 High 622 12 637 13 6.2 1.2 8.3 1.4
Low 609 14 624 15 7.4 1.7 8.1 1.7
17 High 610 12 624 14 6.6 1.2 5.3 1.0
Low 615 15 621 15 6.6 1.5 8.4 1.9
18 High 603 13 620 14 6.3 1.3 5.6 1.0
Low 636 17 663 18 8.9 1.8 6.3 1.3
19 High 610 14 619 15 5.6 1.2 5.9 1.3
Low 633 15 651 16 11.6 2.2 9.1 2.0
20 High 617 13 609 12 6.6 1.3 5.9 1.0
Low 637 18 638 17 7.0 1.7 11.0 1.7
21 High 537 555 10 2.7 0.8 2.5 0.8
Low 553 558 10 2.6 0.9 1.8 0.8
22 High 543 554 3.9 0.8 3.9 0.9
Low 569 12 573 11 5.2 1.2 4.8 1.4
23 High 552 11 552 10 3.7 0.8 2.8 0.7
Low 559 560 12 4.8 1.3 5.5 1.4
24 High 549 561 10 3.4 0.8 5.1 1.1
Low 580 14 576 13 8.0 2.0 8.1 1.9
25 High 571 12 576 11 5.2 1.1 5.8 1.0
Low 594 12 584 13 7.3 1.4 6.6 1.8
26 High 559 574 10 2.7 0.7 5.2 1.0
Low 575 11 580 10 6.7 1.4 5.9 1.2
27 High 558 12 586 11 4.1 0.8 3.9 1.0
Low 592 15 582 12 3.0 1.0 8.4 1.9
28 High 571 12 582 13 5.4 0.9 3.6 0.8
Low 609 17 576 11 7.0 1.5 6.2 1.6
29 High 561 11 586 12 3.6 0.9 4.8 0.9
Low 602 14 592 12 8.4 1.8 6.3 1.2
30 High 567 11 581 12 4.5 1.1 4.8 1.0
Low 583 15 600 13 6.6 1.4 5.2 1.3
31 High 574 15 562 11 5.6 1.2 5.5 1.4
Low 589 12 593 12 5.9 0.8 5.2 1.1
32 High 568 13 582 14 4.8 1.2 5.9 1.3
Low 605 12 594 13 4.2 0.8 6.3 1.0
33 High 594 14 570 13 4.0 1.3 8.1 1.5
Low 589 13 596 11 6.3 1.0 5.4 1.1
34 High 574 12 584 12 4.0 1.4 4.0 1.2
Low 591 12 595 10 6.4 1.0 8.1 1.4
35 High 584 15 593 15 4.4 1.4 5.2 1.3
Low 597 12 596 13 7.0 1.0 7.0 1.4
36 Old 581 16 612 19 7.2 2.3 5.7 1.9
Low 609 17 596 18 11.5 2.6 8.6 2.4
New 604 13 590 11 5.7 1.0 6.2 1.1
37 Old 571 17 586 15 6.5 2.0 5.0 1.8
Low 591 15 611 18 7.2 2.7 9.4 2.7
New 599 14 589 12 6.2 1.3 4.7 1.0
38 Old 575 18 611 20 5.0 1.8 5.8 2.1
Low 590 15 596 18 5.7 1.9 8.6 2.4
New 588 14 574 12 5.8 0.9 8.5 1.3
39 Old 585 16 590 13 3.6 1.5 2.2 1.2
Low 593 18 616 24 7.2 2.3 6.5 2.0
New 588 12 582 10 6.8 1.0 6.3 1.1
40 Old 576 13 588 17 3.6 1.5 9.3 2.5
Low 607 19 597 14 7.2 2.0 4.3 2.0
New 588 11 573 11 6.3 1.2 4.5 0.9
BlockContingencyResponse TimesError Percentages

Set ASets B–DSet ASets B–D

MeanSEMeanSEMeanSEMeanSE

High 612 12 607 11 5.0 0.9 5.2 1.2
Low 609 16 603 13 7.0 1.6 9.6 1.8
High 601 12 591 11 5.9 1.0 5.0 1.0
Low 614 14 606 14 8.1 1.5 5.5 1.4
High 598 12 601 13 6.9 1.2 4.6 0.9
Low 617 15 614 15 6.5 1.6 5.9 1.5
High 595 10 597 13 4.3 0.8 4.9 1.1
Low 621 15 625 16 6.2 1.4 4.8 1.2
High 598 12 610 12 4.3 0.9 6.5 1.1
Low 617 12 621 12 4.4 1.2 9.5 1.9
High 592 12 611 11 6.1 1.1 6.1 1.0
Low 606 14 640 17 5.7 1.5 6.7 1.6
High 612 12 617 13 6.1 1.0 4.5 0.9
Low 637 13 636 17 7.7 1.5 7.0 1.5
High 615 14 617 14 6.6 1.0 7.6 1.0
Low 632 17 634 15 6.6 1.4 7.4 1.9
High 631 15 622 15 6.9 1.2 3.6 1.0
Low 673 17 647 16 7.0 1.3 10.8 1.8
10 High 609 13 619 14 5.9 1.1 5.7 1.1
Low 648 15 630 14 7.3 1.7 7.4 1.7
11 High 611 14 607 13 7.0 0.9 6.1 1.1
Low 633 15 628 16 7.0 1.6 7.8 1.5
12 High 617 13 609 11 5.7 1.2 5.8 1.0
Low 662 16 635 15 7.8 1.6 8.8 2.1
13 High 607 13 619 14 6.0 1.1 5.7 1.1
Low 631 12 644 14 8.0 1.9 7.4 1.6
14 High 618 13 616 11 6.3 1.1 5.9 1.1
Low 632 16 637 14 7.7 1.5 7.7 1.6
15 High 604 15 607 13 6.0 1.0 4.3 0.8
Low 622 11 640 17 11.6 1.8 8.5 1.8
16 High 622 12 637 13 6.2 1.2 8.3 1.4
Low 609 14 624 15 7.4 1.7 8.1 1.7
17 High 610 12 624 14 6.6 1.2 5.3 1.0
Low 615 15 621 15 6.6 1.5 8.4 1.9
18 High 603 13 620 14 6.3 1.3 5.6 1.0
Low 636 17 663 18 8.9 1.8 6.3 1.3
19 High 610 14 619 15 5.6 1.2 5.9 1.3
Low 633 15 651 16 11.6 2.2 9.1 2.0
20 High 617 13 609 12 6.6 1.3 5.9 1.0
Low 637 18 638 17 7.0 1.7 11.0 1.7
21 High 537 555 10 2.7 0.8 2.5 0.8
Low 553 558 10 2.6 0.9 1.8 0.8
22 High 543 554 3.9 0.8 3.9 0.9
Low 569 12 573 11 5.2 1.2 4.8 1.4
23 High 552 11 552 10 3.7 0.8 2.8 0.7
Low 559 560 12 4.8 1.3 5.5 1.4
24 High 549 561 10 3.4 0.8 5.1 1.1
Low 580 14 576 13 8.0 2.0 8.1 1.9
25 High 571 12 576 11 5.2 1.1 5.8 1.0
Low 594 12 584 13 7.3 1.4 6.6 1.8
26 High 559 574 10 2.7 0.7 5.2 1.0
Low 575 11 580 10 6.7 1.4 5.9 1.2
27 High 558 12 586 11 4.1 0.8 3.9 1.0
Low 592 15 582 12 3.0 1.0 8.4 1.9
28 High 571 12 582 13 5.4 0.9 3.6 0.8
Low 609 17 576 11 7.0 1.5 6.2 1.6
29 High 561 11 586 12 3.6 0.9 4.8 0.9
Low 602 14 592 12 8.4 1.8 6.3 1.2
30 High 567 11 581 12 4.5 1.1 4.8 1.0
Low 583 15 600 13 6.6 1.4 5.2 1.3
31 High 574 15 562 11 5.6 1.2 5.5 1.4
Low 589 12 593 12 5.9 0.8 5.2 1.1
32 High 568 13 582 14 4.8 1.2 5.9 1.3
Low 605 12 594 13 4.2 0.8 6.3 1.0
33 High 594 14 570 13 4.0 1.3 8.1 1.5
Low 589 13 596 11 6.3 1.0 5.4 1.1
34 High 574 12 584 12 4.0 1.4 4.0 1.2
Low 591 12 595 10 6.4 1.0 8.1 1.4
35 High 584 15 593 15 4.4 1.4 5.2 1.3
Low 597 12 596 13 7.0 1.0 7.0 1.4
36 Old 581 16 612 19 7.2 2.3 5.7 1.9
Low 609 17 596 18 11.5 2.6 8.6 2.4
New 604 13 590 11 5.7 1.0 6.2 1.1
37 Old 571 17 586 15 6.5 2.0 5.0 1.8
Low 591 15 611 18 7.2 2.7 9.4 2.7
New 599 14 589 12 6.2 1.3 4.7 1.0
38 Old 575 18 611 20 5.0 1.8 5.8 2.1
Low 590 15 596 18 5.7 1.9 8.6 2.4
New 588 14 574 12 5.8 0.9 8.5 1.3
39 Old 585 16 590 13 3.6 1.5 2.2 1.2
Low 593 18 616 24 7.2 2.3 6.5 2.0
New 588 12 582 10 6.8 1.0 6.3 1.1
40 Old 576 13 588 17 3.6 1.5 9.3 2.5
Low 607 19 597 14 7.2 2.0 4.3 2.0
New 588 11 573 11 6.3 1.2 4.5 0.9
Table A2

Experiment 2 response time and error means and standard errors.

BlockContingencyResponse TimesError Percentages

Set ASets B–DSet ASets B–D

MeanSEMeanSEMeanSEMeanSE

High 604 11 599 11 2.3 0.7 4.4 0.8
Low 618 12 605 11 4.5 1.2 4.0 1.2
High 590 600 10 2.7 0.5 3.9 0.9
Low 607 12 609 10 3.1 0.9 3.1 0.9
High 598 10 599 11 3.4 0.8 3.0 0.5
Low 617 12 604 11 3.4 1.0 2.3 0.8
High 603 10 597 10 4.0 0.9 3.0 0.7
Low 625 12 592 10 5.1 1.2 5.1 1.2
High 604 10 600 10 4.0 0.8 4.7 0.8
Low 612 10 619 13 4.0 0.9 7.3 1.3
High 584 580 2.6 0.6 4.4 0.7
Low 607 12 609 10 5.1 1.1 5.1 1.1
High 609 11 605 10 4.4 0.9 4.7 0.8
Low 613 12 606 12 7.4 1.2 4.8 1.2
High 611 12 605 10 3.5 0.6 3.7 0.8
Low 631 11 616 10 5.2 1.1 4.8 1.2
High 587 603 10 3.8 0.8 5.5 0.9
Low 609 11 633 11 7.3 1.4 4.8 1.1
10 High 598 10 607 5.4 0.9 4.4 0.9
Low 615 12 626 5.9 1.1 6.4 1.3
11 High 582 10 600 10 4.2 0.7 4.5 0.7
Low 617 13 608 11 3.7 1.0 6.5 1.5
12 High 607 609 11 3.8 0.7 3.3 0.7
Low 647 12 626 13 4.3 1.0 5.7 1.1
13 High 614 11 614 10 4.9 0.9 3.6 0.9
Low 621 11 633 11 4.1 1.1 6.5 1.3
14 High 601 606 11 4.6 0.8 4.3 0.9
Low 618 12 639 12 9.0 1.7 6.4 1.4
15 High 603 608 3.2 0.7 3.6 0.8
Low 633 14 632 11 7.9 1.6 4.5 1.1
16 High 586 606 10 4.4 0.8 4.7 0.8
Low 620 11 612 10 6.6 1.5 5.6 1.2
17 High 598 10 619 4.3 0.9 4.5 0.8
Low 619 12 612 6.4 1.5 6.1 1.5
18 High 598 11 607 10 4.4 0.7 4.3 0.9
Low 633 11 627 13 6.5 1.4 4.6 1.1
19 High 589 606 3.7 0.6 5.9 1.0
Low 636 10 636 13 4.9 1.1 5.9 1.2
20 High 603 612 11 5.4 0.9 5.7 1.0
Low 636 12 622 11 6.2 1.3 7.1 1.3
21 High 586 578 10 2.5 0.9 6.7 1.4
Low 590 602 6.7 0.9 4.5 0.8
22 High 616 11 617 12 5.1 1.2 5.4 1.2
Low 618 10 625 10 5.4 0.9 4.9 0.8
23 High 619 12 622 12 4.8 1.1 5.4 1.2
Low 621 10 627 4.9 0.9 6.0 0.9
24 High 615 11 603 11 4.5 1.3 4.2 1.0
Low 621 610 4.8 0.7 4.6 0.8
25 High 614 12 610 12 4.5 1.1 6.3 1.3
Low 610 10 619 11 6.2 0.9 4.0 0.6
26 Old 623 19 598 13 3.3 1.3 5.0 1.5
Low 611 14 614 13 3.9 1.4 5.0 1.5
New 599 10 604 11 4.5 0.9 3.7 0.7
27 Old 612 15 606 14 6.1 1.7 5.0 1.5
Low 647 17 643 16 6.6 1.7 7.2 1.9
New 623 10 614 5.2 1.0 4.4 0.8
28 Old 626 18 615 18 6.1 1.8 3.9 1.6
Low 624 14 624 16 6.4 1.9 4.4 1.7
New 620 11 608 12 5.8 1.0 5.3 0.9
29 Old 618 14 630 15 2.2 1.1 5.5 1.8
Low 615 14 624 15 9.4 2.2 5.0 1.5
New 610 607 10 4.7 0.8 4.2 0.7
30 Old 609 13 595 14 4.4 1.5 3.9 1.4
Low 643 15 605 15 5.0 1.5 3.9 1.6
New 607 11 604 10 6.5 1.0 4.6 0.7
BlockContingencyResponse TimesError Percentages

Set ASets B–DSet ASets B–D

MeanSEMeanSEMeanSEMeanSE

High 604 11 599 11 2.3 0.7 4.4 0.8
Low 618 12 605 11 4.5 1.2 4.0 1.2
High 590 600 10 2.7 0.5 3.9 0.9
Low 607 12 609 10 3.1 0.9 3.1 0.9
High 598 10 599 11 3.4 0.8 3.0 0.5
Low 617 12 604 11 3.4 1.0 2.3 0.8
High 603 10 597 10 4.0 0.9 3.0 0.7
Low 625 12 592 10 5.1 1.2 5.1 1.2
High 604 10 600 10 4.0 0.8 4.7 0.8
Low 612 10 619 13 4.0 0.9 7.3 1.3
High 584 580 2.6 0.6 4.4 0.7
Low 607 12 609 10 5.1 1.1 5.1 1.1
High 609 11 605 10 4.4 0.9 4.7 0.8
Low 613 12 606 12 7.4 1.2 4.8 1.2
High 611 12 605 10 3.5 0.6 3.7 0.8
Low 631 11 616 10 5.2 1.1 4.8 1.2
High 587 603 10 3.8 0.8 5.5 0.9
Low 609 11 633 11 7.3 1.4 4.8 1.1
10 High 598 10 607 5.4 0.9 4.4 0.9
Low 615 12 626 5.9 1.1 6.4 1.3
11 High 582 10 600 10 4.2 0.7 4.5 0.7
Low 617 13 608 11 3.7 1.0 6.5 1.5
12 High 607 609 11 3.8 0.7 3.3 0.7
Low 647 12 626 13 4.3 1.0 5.7 1.1
13 High 614 11 614 10 4.9 0.9 3.6 0.9
Low 621 11 633 11 4.1 1.1 6.5 1.3
14 High 601 606 11 4.6 0.8 4.3 0.9
Low 618 12 639 12 9.0 1.7 6.4 1.4
15 High 603 608 3.2 0.7 3.6 0.8
Low 633 14 632 11 7.9 1.6 4.5 1.1
16 High 586 606 10 4.4 0.8 4.7 0.8
Low 620 11 612 10 6.6 1.5 5.6 1.2
17 High 598 10 619 4.3 0.9 4.5 0.8
Low 619 12 612 6.4 1.5 6.1 1.5
18 High 598 11 607 10 4.4 0.7 4.3 0.9
Low 633 11 627 13 6.5 1.4 4.6 1.1
19 High 589 606 3.7 0.6 5.9 1.0
Low 636 10 636 13 4.9 1.1 5.9 1.2
20 High 603 612 11 5.4 0.9 5.7 1.0
Low 636 12 622 11 6.2 1.3 7.1 1.3
21 High 586 578 10 2.5 0.9 6.7 1.4
Low 590 602 6.7 0.9 4.5 0.8
22 High 616 11 617 12 5.1 1.2 5.4 1.2
Low 618 10 625 10 5.4 0.9 4.9 0.8
23 High 619 12 622 12 4.8 1.1 5.4 1.2
Low 621 10 627 4.9 0.9 6.0 0.9
24 High 615 11 603 11 4.5 1.3 4.2 1.0
Low 621 610 4.8 0.7 4.6 0.8
25 High 614 12 610 12 4.5 1.1 6.3 1.3
Low 610 10 619 11 6.2 0.9 4.0 0.6
26 Old 623 19 598 13 3.3 1.3 5.0 1.5
Low 611 14 614 13 3.9 1.4 5.0 1.5
New 599 10 604 11 4.5 0.9 3.7 0.7
27 Old 612 15 606 14 6.1 1.7 5.0 1.5
Low 647 17 643 16 6.6 1.7 7.2 1.9
New 623 10 614 5.2 1.0 4.4 0.8
28 Old 626 18 615 18 6.1 1.8 3.9 1.6
Low 624 14 624 16 6.4 1.9 4.4 1.7
New 620 11 608 12 5.8 1.0 5.3 0.9
29 Old 618 14 630 15 2.2 1.1 5.5 1.8
Low 615 14 624 15 9.4 2.2 5.0 1.5
New 610 607 10 4.7 0.8 4.2 0.7
30 Old 609 13 595 14 4.4 1.5 3.9 1.4
Low 643 15 605 15 5.0 1.5 3.9 1.6
New 607 11 604 10 6.5 1.0 4.6 0.7
Table A3

Experiment 3 response time and error means and standard errors.

BlockContingencyResponse TimesError Percentages

Set ASets B–DSet ASets B–D

MeanSEMeanSEMeanSEMeanSE

High 592 11 606 11 2.6 0.7 3.8 0.9
Low 606 13 630 15 4.7 1.2 4.7 1.3
High 590 10 587 10 4.0 0.9 4.3 0.9
Low 608 12 595 11 6.5 1.6 5.9 1.2
High 587 11 590 11 3.3 0.8 4.3 0.8
Low 602 13 608 12 5.1 1.3 6.7 1.4
High 593 11 589 11 4.6 1.0 4.6 1.0
Low 615 13 623 13 6.7 1.4 6.8 1.4
High 596 11 600 11 5.3 0.8 3.3 0.7
Low 633 12 614 11 5.2 1.1 8.1 1.4
High 589 11 577 3.1 0.9 3.5 0.9
Low 607 13 609 13 4.8 1.2 5.1 1.2
High 594 10 591 12 4.8 0.9 4.6 1.0
Low 612 14 621 12 4.8 1.3 6.8 1.5
High 588 11 583 10 5.8 1.2 5.2 1.0
Low 609 13 618 13 5.5 1.3 4.4 1.2
High 586 584 10 4.4 0.9 5.4 0.9
Low 631 14 600 12 6.4 1.5 5.5 1.2
10 High 612 11 598 10 5.2 1.0 5.8 1.0
Low 636 14 633 16 7.4 1.7 5.0 1.2
11 High 584 12 576 12 4.1 0.8 5.8 1.1
Low 600 13 620 12 5.8 1.2 6.7 1.6
12 High 583 595 11 5.0 1.0 4.3 0.9
Low 619 14 631 16 6.5 1.4 8.1 1.6
13 High 590 10 594 11 5.4 0.8 5.7 1.0
Low 643 15 603 12 10.4 1.8 5.1 1.2
14 High 593 11 591 4.7 1.0 3.3 0.7
Low 636 13 649 16 5.8 1.3 7.3 1.4
15 High 593 10 590 11 5.6 1.0 5.5 1.1
Low 613 10 631 14 7.6 1.5 7.5 1.5
16 High 583 589 11 4.5 0.9 4.9 0.9
Low 590 11 602 13 7.2 1.2 7.5 1.5
17 High 601 593 11 3.3 0.6 5.0 0.9
Low 646 16 637 10 5.8 1.4 6.5 1.4
18 High 603 11 597 11 2.9 0.7 5.2 0.8
Low 631 13 626 13 6.1 1.3 8.5 1.3
19 High 589 12 592 5.3 0.9 4.7 1.0
Low 641 13 605 11 5.8 1.5 7.2 1.3
20 High 586 594 4.9 0.9 5.8 1.0
Low 638 13 621 12 4.7 1.2 8.4 1.7
21 High 578 12 581 2.6 0.7 5.5 1.1
Low 605 12 608 13 4.8 1.1 7.1 1.5
22 High 584 11 591 11 4.4 0.8 5.3 0.9
Low 604 16 614 14 5.9 1.3 6.8 1.4
23 High 592 10 589 11 5.0 1.1 5.1 1.0
Low 606 11 621 14 9.1 1.8 6.1 1.3
24 High 590 11 594 3.9 0.9 5.1 1.1
Low 604 12 621 15 6.2 1.3 8.5 1.6
25 High 581 11 588 10 5.4 0.9 4.8 1.0
Low 604 10 612 11 5.8 1.2 5.1 1.3
26 High 568 577 11 4.5 0.9 7.5 1.0
Low 587 12 582 11 3.8 1.0 6.5 1.4
27 High 591 11 583 5.1 1.0 6.3 1.3
Low 601 10 621 15 6.4 1.6 5.1 1.4
28 High 579 11 590 11 5.0 1.0 5.7 1.0
Low 607 12 599 13 7.5 1.5 5.7 1.5
29 High 580 602 11 4.9 0.9 4.8 1.0
Low 629 13 612 13 8.7 1.7 8.0 1.9
30 High 603 11 592 10 5.2 1.1 4.1 0.9
Low 621 15 631 15 9.1 1.6 7.7 1.7
31 High 524 522 4.9 1.0 2.6 0.7
Low 540 544 11 4.8 1.1 5.8 1.2
32 High 520 528 3.2 0.8 3.6 0.8
Low 550 10 556 10 6.7 1.5 5.8 1.4
33 High 545 530 3.9 0.7 4.6 1.0
Low 554 547 4.1 1.0 4.7 1.4
34 High 538 543 11 2.0 0.6 2.8 0.7
Low 552 13 566 11 8.1 1.6 4.1 1.0
35 High 537 540 3.0 0.8 3.1 0.7
Low 567 10 581 12 4.1 1.1 7.4 1.4
36 High 539 10 535 3.7 0.9 3.4 0.8
Low 561 11 558 11 6.1 1.3 5.8 1.3
37 High 542 545 4.6 1.0 4.4 1.2
Low 582 11 563 10 5.4 1.3 3.8 1.4
38 High 543 539 2.8 0.8 3.6 0.8
Low 576 12 566 10 7.0 1.7 7.4 1.7
39 High 539 542 3.7 0.8 4.1 1.0
Low 572 11 570 10 5.7 1.8 5.4 1.3
40 High 543 10 546 10 3.0 0.7 4.8 0.9
Low 576 12 577 12 4.7 1.3 8.8 1.8
41 High 535 520 4.1 0.8 6.0 1.2
Low 566 12 582 12 7.1 1.3 5.4 1.3
42 High 554 557 2.9 0.8 4.1 1.0
Low 582 12 582 12 7.4 1.5 6.4 1.4
43 High 557 555 4.1 0.7 3.9 0.9
Low 589 10 569 13 3.7 1.0 7.2 1.5
44 High 553 545 2.8 0.7 4.3 0.9
Low 590 11 585 11 5.4 1.2 6.1 1.3
45 High 549 10 555 11 3.6 0.8 3.8 0.9
Low 594 13 596 12 9.1 1.9 7.4 1.6
46 High 563 11 574 10 2.6 0.7 5.9 1.0
Low 590 11 585 11 7.8 1.5 8.4 1.5
47 High 543 577 5.3 1.2 5.3 1.1
Low 588 12 588 13 9.1 1.5 2.8 1.0
48 High 564 11 578 10 3.6 0.8 6.1 1.1
Low 601 13 586 12 7.0 1.6 5.7 1.3
49 High 562 10 565 10 4.5 0.8 4.6 0.9
Low 601 15 578 12 8.6 1.6 6.4 1.3
50 High 569 10 577 10 5.1 1.0 4.5 0.9
Low 588 11 598 12 8.8 1.5 7.8 1.5
51 High 562 12 568 14 4.1 1.2 5.5 1.4
Low 584 585 10 7.5 1.4 6.6 1.2
52 High 566 12 577 13 5.1 1.3 4.4 1.2
Low 585 596 10 5.4 0.9 6.0 1.3
53 High 566 10 572 2.7 0.9 7.2 1.9
Low 595 11 593 10 7.5 1.2 5.2 1.0
54 High 564 12 579 14 4.4 1.5 5.1 1.5
Low 586 11 586 10 7.9 1.1 7.5 1.3
55 High 570 12 581 12 4.4 1.1 5.4 1.2
Low 585 10 593 6.5 1.1 4.9 1.1
56 Old 541 13 562 14 3.6 1.6 8.0 2.4
Low 594 14 562 13 6.6 2.1 5.3 2.0
New 573 573 10 6.5 1.0 5.5 1.1
57 Old 574 14 578 15 2.6 1.3 3.3 1.4
Low 593 15 597 18 7.6 2.3 6.6 2.1
New 581 599 10 6.0 1.2 5.8 1.2
58 Old 580 15 566 15 2.7 1.6 7.6 2.1
Low 574 12 617 18 5.3 2.2 4.0 1.5
New 584 11 573 10 6.5 1.0 4.8 0.9
59 Old 592 19 590 14 6.6 1.9 9.3 2.3
Low 590 18 585 15 9.6 2.4 8.0 2.4
New 569 572 5.5 1.1 6.1 1.0
60 Old 588 17 574 15 6.6 1.9 4.6 1.6
Low 608 16 584 13 5.9 1.8 6.0 2.1
New 571 11 578 10 5.3 1.1 7.5 1.2
BlockContingencyResponse TimesError Percentages

Set ASets B–DSet ASets B–D

MeanSEMeanSEMeanSEMeanSE

High 592 11 606 11 2.6 0.7 3.8 0.9
Low 606 13 630 15 4.7 1.2 4.7 1.3
High 590 10 587 10 4.0 0.9 4.3 0.9
Low 608 12 595 11 6.5 1.6 5.9 1.2
High 587 11 590 11 3.3 0.8 4.3 0.8
Low 602 13 608 12 5.1 1.3 6.7 1.4
High 593 11 589 11 4.6 1.0 4.6 1.0
Low 615 13 623 13 6.7 1.4 6.8 1.4
High 596 11 600 11 5.3 0.8 3.3 0.7
Low 633 12 614 11 5.2 1.1 8.1 1.4
High 589 11 577 3.1 0.9 3.5 0.9
Low 607 13 609 13 4.8 1.2 5.1 1.2
High 594 10 591 12 4.8 0.9 4.6 1.0
Low 612 14 621 12 4.8 1.3 6.8 1.5
High 588 11 583 10 5.8 1.2 5.2 1.0
Low 609 13 618 13 5.5 1.3 4.4 1.2
High 586 584 10 4.4 0.9 5.4 0.9
Low 631 14 600 12 6.4 1.5 5.5 1.2
10 High 612 11 598 10 5.2 1.0 5.8 1.0
Low 636 14 633 16 7.4 1.7 5.0 1.2
11 High 584 12 576 12 4.1 0.8 5.8 1.1
Low 600 13 620 12 5.8 1.2 6.7 1.6
12 High 583 595 11 5.0 1.0 4.3 0.9
Low 619 14 631 16 6.5 1.4 8.1 1.6
13 High 590 10 594 11 5.4 0.8 5.7 1.0
Low 643 15 603 12 10.4 1.8 5.1 1.2
14 High 593 11 591 4.7 1.0 3.3 0.7
Low 636 13 649 16 5.8 1.3 7.3 1.4
15 High 593 10 590 11 5.6 1.0 5.5 1.1
Low 613 10 631 14 7.6 1.5 7.5 1.5
16 High 583 589 11 4.5 0.9 4.9 0.9
Low 590 11 602 13 7.2 1.2 7.5 1.5
17 High 601 593 11 3.3 0.6 5.0 0.9
Low 646 16 637 10 5.8 1.4 6.5 1.4
18 High 603 11 597 11 2.9 0.7 5.2 0.8
Low 631 13 626 13 6.1 1.3 8.5 1.3
19 High 589 12 592 5.3 0.9 4.7 1.0
Low 641 13 605 11 5.8 1.5 7.2 1.3
20 High 586 594 4.9 0.9 5.8 1.0
Low 638 13 621 12 4.7 1.2 8.4 1.7
21 High 578 12 581 2.6 0.7 5.5 1.1
Low 605 12 608 13 4.8 1.1 7.1 1.5
22 High 584 11 591 11 4.4 0.8 5.3 0.9
Low 604 16 614 14 5.9 1.3 6.8 1.4
23 High 592 10 589 11 5.0 1.1 5.1 1.0
Low 606 11 621 14 9.1 1.8 6.1 1.3
24 High 590 11 594 3.9 0.9 5.1 1.1
Low 604 12 621 15 6.2 1.3 8.5 1.6
25 High 581 11 588 10 5.4 0.9 4.8 1.0
Low 604 10 612 11 5.8 1.2 5.1 1.3
26 High 568 577 11 4.5 0.9 7.5 1.0
Low 587 12 582 11 3.8 1.0 6.5 1.4
27 High 591 11 583 5.1 1.0 6.3 1.3
Low 601 10 621 15 6.4 1.6 5.1 1.4
28 High 579 11 590 11 5.0 1.0 5.7 1.0
Low 607 12 599 13 7.5 1.5 5.7 1.5
29 High 580 602 11 4.9 0.9 4.8 1.0
Low 629 13 612 13 8.7 1.7 8.0 1.9
30 High 603 11 592 10 5.2 1.1 4.1 0.9
Low 621 15 631 15 9.1 1.6 7.7 1.7
31 High 524 522 4.9 1.0 2.6 0.7
Low 540 544 11 4.8 1.1 5.8 1.2
32 High 520 528 3.2 0.8 3.6 0.8
Low 550 10 556 10 6.7 1.5 5.8 1.4
33 High 545 530 3.9 0.7 4.6 1.0
Low 554 547 4.1 1.0 4.7 1.4
34 High 538 543 11 2.0 0.6 2.8 0.7
Low 552 13 566 11 8.1 1.6 4.1 1.0
35 High 537 540 3.0 0.8 3.1 0.7
Low 567 10 581 12 4.1 1.1 7.4 1.4
36 High 539 10 535 3.7 0.9 3.4 0.8
Low 561 11 558 11 6.1 1.3 5.8 1.3
37 High 542 545 4.6 1.0 4.4 1.2
Low 582 11 563 10 5.4 1.3 3.8 1.4
38 High 543 539 2.8 0.8 3.6 0.8
Low 576 12 566 10 7.0 1.7 7.4 1.7
39 High 539 542 3.7 0.8 4.1 1.0
Low 572 11 570 10 5.7 1.8 5.4 1.3
40 High 543 10 546 10 3.0 0.7 4.8 0.9
Low 576 12 577 12 4.7 1.3 8.8 1.8
41 High 535 520 4.1 0.8 6.0 1.2
Low 566 12 582 12 7.1 1.3 5.4 1.3
42 High 554 557 2.9 0.8 4.1 1.0
Low 582 12 582 12 7.4 1.5 6.4 1.4
43 High 557 555 4.1 0.7 3.9 0.9
Low 589 10 569 13 3.7 1.0 7.2 1.5
44 High 553 545 2.8 0.7 4.3 0.9
Low 590 11 585 11 5.4 1.2 6.1 1.3
45 High 549 10 555 11 3.6 0.8 3.8 0.9
Low 594 13 596 12 9.1 1.9 7.4 1.6
46 High 563 11 574 10 2.6 0.7 5.9 1.0
Low 590 11 585 11 7.8 1.5 8.4 1.5
47 High 543 577 5.3 1.2 5.3 1.1
Low 588 12 588 13 9.1 1.5 2.8 1.0
48 High 564 11 578 10 3.6 0.8 6.1 1.1
Low 601 13 586 12 7.0 1.6 5.7 1.3
49 High 562 10 565 10 4.5 0.8 4.6 0.9
Low 601 15 578 12 8.6 1.6 6.4 1.3
50 High 569 10 577 10 5.1 1.0 4.5 0.9
Low 588 11 598 12 8.8 1.5 7.8 1.5
51 High 562 12 568 14 4.1 1.2 5.5 1.4
Low 584 585 10 7.5 1.4 6.6 1.2
52 High 566 12 577 13 5.1 1.3 4.4 1.2
Low 585 596 10 5.4 0.9 6.0 1.3
53 High 566 10 572 2.7 0.9 7.2 1.9
Low 595 11 593 10 7.5 1.2 5.2 1.0
54 High 564 12 579 14 4.4 1.5 5.1 1.5
Low 586 11 586 10 7.9 1.1 7.5 1.3
55 High 570 12 581 12 4.4 1.1 5.4 1.2
Low 585 10 593 6.5 1.1 4.9 1.1
56 Old 541 13 562 14 3.6 1.6 8.0 2.4
Low 594 14 562 13 6.6 2.1 5.3 2.0
New 573 573 10 6.5 1.0 5.5 1.1
57 Old 574 14 578 15 2.6 1.3 3.3 1.4
Low 593 15 597 18 7.6 2.3 6.6 2.1
New 581 599 10 6.0 1.2 5.8 1.2
58 Old 580 15 566 15 2.7 1.6 7.6 2.1
Low 574 12 617 18 5.3 2.2 4.0 1.5
New 584 11 573 10 6.5 1.0 4.8 0.9
59 Old 592 19 590 14 6.6 1.9 9.3 2.3
Low 590 18 585 15 9.6 2.4 8.0 2.4
New 569 572 5.5 1.1 6.1 1.0
60 Old 588 17 574 15 6.6 1.9 4.6 1.6
Low 608 16 584 13 5.9 1.8 6.0 2.1
New 571 11 578 10 5.3 1.1 7.5 1.2

## Funding Information

This research was supported by the French “Investissements d’Avenir” program, project ISITE-BFC (contract ANR15-IDEX-0003) to James R. Schmidt, Grant BOF09/01M00209 of Ghent University to Jan De Houwer, and Research Program G073317N of the Research Foundation – Flanders (FWO), and the Research Fund of KU Leuven (C14/17/047) to Agnes Moors.

## Competing Interests

The authors have no competing interests to declare.

## Author Contributions

All authors contributed to conception and design, revised the article, and approved the submitted version for publication. JRS contributed to data acquisition, analyzed and interpreted the data, and drafted the article.

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## Editor Decision Letter

The author(s) of this paper chose the Streamlined Review option, and the Editor's decision letter can be downloaded at: http://doi.org/10.1525/collabra.320.pr

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