Evidence from a task conflict-free, item-specific Stroop ...

Acta Psychologica 164 (2016) 39–45

Contents lists available at ScienceDirect

Acta Psychologica journal homepage: www.elsevier.com/locate/actpsy

Contingency learning is not affected by conflict experience: Evidence from a task conflict-free, item-specific Stroop paradigm Yulia Levin a,⁎, Joseph Tzelgov b a b

Department of Psychology and Zlotowski Center for Neuroscience, Ben-Gurion University of the Negev, Israel Department of Psychology and Zlotowski Center for Neuroscience, Department of Brain and Cognitive Sciences, Ben-Gurion University of the Negev, and Achva Academic College, Israel

a r t i c l e

i n f o

Article history: Received 12 February 2015 Received in revised form 14 December 2015 Accepted 15 December 2015 Available online xxxx Keywords: Automaticity of reading Item-specific proportion congruent effect Informational conflict Task conflict Contingency learning Cognitive control

a b s t r a c t A contingency learning account of the item-specific proportion congruent effect has been described as an associative stimulus–response learning process that has nothing to do with controlling the Stroop conflict. As supportive evidence, contingency learning has been demonstrated with response conflict-free stimuli, such as neutral words. However, what gives rise to response conflict and to Stroop interference in general is task conflict. The present study investigated whether task conflict can constitute a trigger or, alternatively, a booster to the contingency learning process. This was done by employing a “task conflict-free” condition (i.e., geometric shapes) and comparing it with a “task conflict” condition (i.e., neutral words). The results showed a significant contingency learning effect in both conditions, refuting the possibility that contingency learning is triggered by the presence of a task conflict. Contingency learning was also not enhanced by the task conflict experience, indicating its complete insensitivity to Stroop conflict(s). Thus, the results showed no evidence that performance optimization as a result of contingency learning is greater under conflict, implying that contingency learning is not recruited to assist the control system to overcome conflict. © 2015 Elsevier B.V. All rights reserved.

1. Introduction Reading is an acquired human ability to decode and interpret visual lexical symbols. In adults, this ability is known to be automatic, that is, it occurs whenever a lexical stimulus is encountered. The most dramatic demonstration of the automaticity of the reading process is an interference effect obtained in the Stroop task (Stroop, 1935). In this task, participants have to name the color of visually presented words (e.g., blue for the stimulus RED presented in blue ink) while ignoring their meaning (e.g., the word RED). There is no need to read the words to accomplish the task and yet reading occurs, as evidenced by slower response times for incongruent stimuli (e.g., RED in blue ink) than for neutral letter strings (e.g., XXXX in blue ink). The fact that reading takes place in spite of the fact that it is not required, and even interferes with performance, demonstrates its automaticity (Perlman & Tzelgov, 2006). The interference, or conflict, produced by the automatic performance of the irrelevant reading task has been shown to be a target of cognitive control. That is, when a conflict becomes too strong, cognitive control is able to reduce it. Much evidence for that ability of the cognitive system came from a bulk of studies that manipulated the proportion of congruent vs. incongruent stimuli to control the Stroop effect

⁎ Corresponding author at: Department of Psychology, Ben-Gurion University of the Negev, P.O.B. 653, Beer Sheva 84105, Israel. E-mail address: [email protected] (Y. Levin).

http://dx.doi.org/10.1016/j.actpsy.2015.12.009 0001-6918/© 2015 Elsevier B.V. All rights reserved.

(e.g. Logan, 1985; Logan & Zbrodoff, 1979). The main finding of these studies, or what is known as the “list-wide proportion-congruent effect”, was that the magnitude of the observed interference effect was smaller when the experienced conflict was too strong (i.e., large proportion of incongruent trials in the list). Several models have been proposed to explain the mechanism by which conflict is reduced in the Stroop task (a conflict-monitoring framework; Blais, Robidoux, Risko, & Besner, 2007; Botvinick, Braver, Barch, Carter, & Cohen, 2001; Botvinick, Cohen, & Carter, 2004; De Pisapia & Braver, 2006). According to Botvinick et al.'s (2001); Botvinick et al.'s (2004) conflict-monitoring architecture, increasing the proportion of incongruent trials raises the amount of (response) conflict (i.e., stronger competition between the response activated by the color-naming process and the irrelevant response activated by reading). The elevation in conflict is detected by the conflict-monitoring unit, which in turn signals units responsible for control exertion. The control is achieved through focusing attention on the relevant task. This way the irrelevant reading task does not get much attention and the conflict it produces is considerably reduced. It has also been proposed that the control system is not just able to reduce the conflict accumulated at the list level, but is also flexible enough to reduce the conflict produced by specific items in the list (Bugg, Jacoby, & Chanani, 2011; Bugg, Jacoby, & Toth, 2008; see also Blais et al., 2007). The “item-specific proportion-congruent” effect (Jacoby, Lindsay, & Hessels, 2003; Jacoby, McElree, & Trainham, 1999) demonstrates that when the proportion of incongruent stimuli is

40

Y. Levin, J. Tzelgov / Acta Psychologica 164 (2016) 39–45

manipulated at the level of specific words, the words mostly presented as incongruent stimuli tend to produce less interference than those mostly presented as congruent stimuli. 1.1. Cognitive control, learning and what is in between The process of cognitive control is assumed to proceed in a way that can be described as “automatic”, that is, without assuming any hidden agency deciding when, where and how to intervene. As such, for the last couple of decades there has been some tension in this research field to differentiate between the “automatic control” and simple learning mechanisms, or to define how much the former may be relying on the latter. According to the proposal of Verguts and Notebaert (2008, 2009), a simple learning process may be in fact “in service of control”. Specifically, their proposal holds that the goal of control (i.e., conflict reduction) can be achieved through associative (Hebbian) learning that binds together all currently active (i.e., task-relevant) representations. That is, according to this account the control is actually based on a learning process. It is important for the present discussion to note that except for extending the general conflict-monitoring theory (Botvinick et al., 2001, 2004) by explaining how the system knows “where” to intervene, it shares most of its other features. Thus, when the conflict is sufficiently reduced, less learning occurs, which means learning in this situation is dependent on and guided by the magnitude of the experienced conflict. In contrast, there are suggestions that learning does not represent a mechanism “in-service-of control”, but separate cognitive phenomena that sometimes might “mimic” the effects of control. That is, learning is assumed to produce an independent (confounding) effect on reaction time (RT) that happens to look like the effect of conflict reduction attributed to cognitive control. In the context of the Stroop task, Schmidt, Crump, Cheesman, and Besner (2007) proposed that the item-specific proportion-congruent effect might be driven by such learning that has nothing to do with (controlling the) conflict. This contingency learning account of the item-specific proportion-congruent effect is based on the fact that in the original study of Jacoby et al.'s (2003), as well as in the recent replications (Hazeltine & Mordkoff, 2014), faster responses were also observed for congruent items in a mostly congruent condition, as compared to the condition where the probability of an item appearing in a congruent or incongruent color was equal.1 This result cannot be accounted for by assuming the intervention of cognitive control, since congruent items do not produce response conflict, and therefore are not able to engage control (see also Levin & Tzelgov, 2014). According to the contingency learning account (Schmidt, 2013a, 2013b; Schmidt & Besner, 2008; Schmidt et al., 2007; for contrasting views see Bugg & Hutchison, 2013; Bugg et al., 2011; Hutchison, 2011; see also Abrahamse, Duthoo, Notebaert, & Risko, 2013; Atalay & Misirlisoy, 2012; Bugg, 2014), the item-specific proportion-congruent effect is better described as a speeding-up observed for the words frequently appearing in a specific (be it congruent or incongruent) color, and is due to the fact that manipulation of proportions at the item level creates contingencies between specific words and responses. These contingencies are learned and subsequently used to predict responses. For example, if RED frequently appears in blue ink, the learned association would be “if the word is RED then push the ‘blue’ button”. Note, in contrast to the learning-based control view (Verguts & Notebaert, 2008, 2009), contingency learning is not assumed to be aided by response conflict, but rather to represent a general ability to bind stimuli and responses on the basis of their existing correlations. The mechanism of contingency learning as implemented in the parallel-episodic processing model (Schmidt, 2013a) has no feature that is able to measure the response conflict, nor has it a property allowing for allocation of attention

1

A 50/50 condition in Jacoby et al.'s (2003) study.

in an adaptive manner, and yet it successfully simulates the pattern of the item-specific proportion-congruent effect. However, according to recently reported data, which will be discussed shortly, there might be a third type of control–learning relationship that comes right in between the two aforementioned proposals and which is at the focus of the present study. Recent studies showed that implicit learning processes might not be completely independent of conflict as suggested by Schmidt et al. (2007) for contingency learning. However, the way they depend on conflict does not fit the learning-based control put forward by Verguts and Notebaert (2008, 2009) (i.e., conflict-monitoring framework) either. Deroost, Vandenbossche, Zeischka, Coomans, and Soetens (2012) presented a probabilistic sequence of the colors in the Stroop task, which was implicitly learned by the participants. They found that sequence learning did not help to reduce the conflict (i.e., the Stroop effect). Stroop conflict however, was shown to enhance the expression2 of learning: the acquired sequence knowledge was used more under conflict (i.e., incongruent) conditions than under conflict-free (i.e., congruent and neutral) conditions. Boosting effects of conflict on implicit learning have also been reported in other studies. Deroost and Soetens (2006) observed a larger sequence learning effect for participants who were trained with incompatible (i.e., conflicting) than compatible stimulus– response mappings. Similarly, Zhao, Ngo, McKendrick, and TurkBrowne (2011) found that engaging in a secondary (i.e., interfering) task during the training phase, as opposed to passive viewing, improved statistical learning. Finally, Vandenbossche, Coomans, Homble, and Deroost (2014) reported a larger sequence learning effect for aged adults under high-interference (i.e., a dual task performed in the same modality) than under low-interference (i.e., a cross-modal dual task) training condition. To summarize, the implicit sequence learning was not found “to serve control” by reducing the conflict (Deroost et al., 2012) as assumed for learning-based control (Verguts & Notebaert, 2008, 2009). Yet, the observed enhancement and stronger reliance on implicit learning in conflict environments (Vandenbossche et al., 2014; Zhao et al., 2011; see also Deroost & Soetens, 2006; Koch, 2007, Exp. 1) speaks for the possibility that implicit learning processes might nevertheless contribute to cognitive control. However, this may happen not by reducing the conflict but according to Deroost et al. (2012), through optimization of the task performance: “Optimization of task performance was accomplished by an increased reliance on implicit sequence knowledge under high conflict. This indicates that implicit learning processes can be flexibly recruited to support cognitive control” (p. 15). This idea might seem novel in the domain of cognitive control, since the latter is traditionally described as the process that is sensitive to the amount of conflict and that aims to minimize this conflict when it gets too strong. However, considering the control process more broadly makes it is perfectly clear that the reduction of the conflict is only needed to ensure a good level of performance in the ongoing task. Stated otherwise, the final goal of the control process is to protect the performance from the conflict-related decline. Bugg's (2014) study provides empirical support for such a view of cognitive control. It was shown in a series of experiments that in a high-conflict context, cognitive control was only engaged as a “last resource”, when stimulus–response associations did not allow maintaining a sufficient level of performance. Thus, the magnitude of the conflict seems only to matter when it has a detrimental effect on performance. This emphasizes the importance of the performance rather than conflict per se in the context of control engagement. One way to preserve the required performance when conflict arises, as suggested by the conflict-monitoring theory, is by reducing the

2 As opposed to acquisition of learning that was not affected by the amount of conflict (manipulated by the proportion of congruent trials) at the training phase.


conflict. This is obviously the most efficient way that solves the problem from the root—if you want to protect the performance from interference, eliminate the cause of the interference. However, as the results of the aforementioned studies suggest, there might be another way to “assist” maintaining good performance in the face of conflict, namely, recruitment of the implicit learning processes and stronger reliance on what was learned by these processes. For example, learning what button should be pushed when the word that appears is “red” (i.e., contingency learning) allows shortening the time that is needed to respond, thus promoting better performance. Such performanceboosting learning seems to be especially useful when performance has been damaged by conflict. That is, it is conceivable that in conflict situations, the control system, along with its attempts to reduce the conflict, might also engage/enhance such an implicit learning process that may help to maintain performance in a faster and resource-saving way.

1.2. Possible contribution of the contingency learning to cognitive control Regarding Stroop contingency learning, it has been claimed that this learning process does not represent learning-based control (as proposed by Verguts & Notebaert, 2008, 2009). This claim was supported by empirical data demonstrating a contingency learning effect with neutral color-unrelated words (Schmidt et al., 2007). By showing that the effect of contingency learning was observable even when the congruency variable was not manipulated, the authors claimed that it had to be regarded as an independent process that had nothing to do with control. One of the aims of the current study is to complete the attempt of Schmidt et al. to disregard contingency learning as a mechanism contributing to cognitive control. Whereas Schmidt et al. only considered one way in which learning might contribute to control—the way suggested by Verguts and Notebaert—we suggest that there is another option that should be considered as well. Specifically, we investigated whether contingency learning in the Stroop task might represent the “assisting” type of the control–learning relationship, where the contingency learning is recruited not to reduce the conflict, but to help the control system optimize performance (Deroost et al., 2012) in some of the trials. What gives rise to the latter possibility is a notion of the multipleconflict nature of Stroop interference. Using neutral words in Schmidt et al.'s (2007) and Schmidt and Besner's (2008) experiments did not eliminate the Stroop conflict completely. This is because response conflict is not the only conflict known to contribute to Stroop interference (Goldfarb & Henik, 2007; MacLeod & MacDonald, 2000; for contrasting views see Melara & Algom, 2003). In fact, response conflict is a direct outcome of the parallel activation of two color concepts in the semantic network, which is frequently referred to as the informational conflict (Goldfarb & Henik, 2007; MacLeod & MacDonald, 2000).3 The activation of one color concept represents the retrieval of the word's meaning as a result of the reading process, whereas the activation of the second color concept represents processing of the color of the ink the word is written in. Note, however, that even when the reading does not result in activation of the irrelevant color concept, for example, when color-unrelated stimuli such as neutral words (e.g., DOG) or letter strings (e.g., LGFD/ XXXX) are used, the interference effect, though of smaller magnitude, is still observed (Brown, 2011; Klein, 1964; Sharma & McKenna, 1998). This fact indicates that the main origin of Stroop interference is not an informational incompatibility that leads eventually to the

3 Note that in a typical Stroop experiment, each color concept requires a different vocal response, so in such experiments, response conflict and informational conflict are confounded (but for a manual response design allowing decoupling of this confound see De Houwer, 2003). Thus, in the present article we do not distinguish between these two types of conflict and when mentioning “response conflict” we refer to both the informational and response conflicts.

41

response conflict, but a competition between two possible tasks that one can perform when the stimulus has lexical properties—reading, which is automatically activated while not being a part of the task requirement (Tzelgov, 1997), and the color-naming task.4 The competition between the two tasks has been conceptualized by MacLeod and MacDonald (2000) as well as by Goldfarb and Henik (2007) to be the task conflict, the existence of which has been well-documented by behavioral (Entel, Tzelgov, Bereby-Meyer, & Shahar, 2014; Kalanthroff, Goldfarb, & Henik, 2013) as well as neuroimaging data (Bench et al., 1993; see also Aarts, Roelofs, & van Turennout, 2009; Steinhauser & Hübner, 2009). As evident from this discussion, interference due to task conflict can be obtained as long as the stimulus can be read, regardless of whether it is related to color or not (for an analysis further delineating the contribution of each type of conflict to the Stroop effect see Levin & Tzelgov, 2015). Hence, using color-unrelated words in Schmidt and Besner's (2008) and Schmidt et al.'s (2007) studies eliminated the response conflict but not the main component of the Stroop interference—the task conflict that was experienced by the participants in every trial. Therefore, in this experiment the contingency learning effect was only observed under a conflict condition. This raises a question whether constant presence of the task conflict in Schmidt and Besner's and Schmidt et al.'s experiments could enhance (or even trigger) the contingency learning process in order to optimize performance by facilitating the RTs on some of the trials. Answering this question and refuting the possibility that contingency learning may be involved in “assisting” control by optimizing performance requires evidence that it can also be observed in a Stroop situation where no task conflict is produced by the stimuli. To that end, in one condition we employed an item-specific paradigm with stimuli that could not be read (i.e., geometric shapes), and the second condition was identical to that used by Schmidt et al. (2007) and used neutral words as stimuli. Comparing the performance in these conditions would allow first, to test whether contingency learning is triggered5 by conflict experience. If that is the case, we should not expect to observe the contingency learning effect in the shapes condition, but only in the words condition. Second, if the contingency learning effect is observed with shapes stimuli, it is possible to test whether its effect, as has been shown for other implicit learning processes, is boosted by the conflict experience. The latter would express itself in a larger contingency learning effect under a task conflict (i.e., neutral words) condition. Results supportive of at least one of these hypotheses would indicate that contingency learning is recruited as a performance-optimizing tool assisting the control system to overcome conflict.

2. Method 2.1. Participants Forty-two undergraduate students at Ben-Gurion University of the Negev (25 females and 17 males, mean age = 25 years old, SD = 2.3), who were native speakers of Hebrew, participated in the experiment and were paid 25 NIS. All participants reported having normal or corrected-to-normal vision acuity, as well as normal color vision. No

4 Note, by “main origin” we do not mean the task conflict contributes most of the Stroop effect in a classic color-word task, but that it has a key role in initiation of the interference. Simply put, without engaging in the irrelevant reading task, there would be no Stroop interference no matter what stimulus type (incongruent/neutral word) is used. 5 Based on the results of Deroost et al. (2012) showing that conflict only affects the expression but not the acquisition of learning, we believe that the “triggering” is a less likely scenario than the “enhancement”. However, since we use another paradigm than the one used originally by Deroost et al., it would not be experimentally correct not to consider this possibility as well.

42


one reported having a learning disability or attention deficit/hyperactivity disorder.

2.2. Materials For half of the participants the stimuli were four-letter neutral words (plane, cake, doll, cat) in Hebrew, and for the other half the stimuli were geometric shapes (circle, triangle, rhombus, rectangle). All word stimuli were printed in 42-point David font and subtended a visual angle of 1.3 degrees vertically and 2.9 degrees horizontally from a viewing distance of 60 cm. The shape stimuli subtended a visual angle of 1.7 degrees vertically and 1.6 degrees horizontally. Importantly, both stimulus conditions were matched with respect to the amount of perceptual information. Thus, each shape was made up of relatively the same number of pixels as the mean number of pixels within the words (1152.75 and 1117.25 pixels for shapes and words, respectively). Stimuli could appear in one of the four colors: red, blue, green, and yellow. Color was not allowed to repeat in consecutive trials. There were 384 trials in the experiment. In the high-contingency condition, each of four words/shapes was paired with a specific (randomly selected for each item) color in 72 trials, resulting in a total of 288 high-contingency trials (75%). In the low-contingency condition, each of four words/shapes was presented 8 times in each of the three remaining colors, resulting in a total of 96 low-contingency trials (25%).

2.3. Procedure The stimuli were presented on a Dell 19-inch monitor with a resolution of 1280 × 1024 pixels. Stimulus presentation was controlled by OpenSesame 2.8.0 software (Mathôt, Schreij, & Theeuwes, 2012). Participants sat approximately 60 cm from the computer screen. They were told to name the color of the stimulus appearing at the center of the screen as accurately and as fast as possible. Responses were made manually by pressing a key on a keyboard: “a” for blue, “z” for green, “k” for red, and “m” for yellow. The response buttons were marked with appropriate colored stickers. The experiment started with a 32-trial practice session. During the practice, each stimulus appeared equally often in each color. Following the practice, participants performed 384 experimental trials. Participants were given two 2-second breaks during the experiment session. The experimental trial started with a fixation white cross that remained at the center of the screen until the participant pressed the SPACE bar with their thumb. The fixation was followed by a blank screen for 500 ms. After that the target appeared and remained visible until a response was made or for 2000 ms. For every trial in which the participant was not fast enough to respond, a feedback message was presented on screen for 1000 ms: “You did not respond! Please focus!” A trial ended with a blank black display that was presented for 300 ms. In addition, feedback was given to the participants each time they made a mistake: “You made a mistake! Please focus!” The feedback message appeared on the screen for 1000 ms.

2.4. Design The design of the experiment included two variables. Stimulus category (neutral words/shapes) was treated as a between participant factor. Participants were assigned to each level of the stimulus category in a counterbalanced manner. The second variable was contingency level (high contingency/low contingency) and was manipulated within participants.

3. Results and discussion One participant was replaced because he mistakenly quit the experiment before the end. The data collected from each participant was trimmed so that no RT outliers (i.e., RTs b 250 ms and RTs = 2000 ms6) were included in the analysis of variance (0.06% of the data). RTs of error trials were also excluded from the analysis of variance. Due to a very low error rate (0.8% of the data) in the present experiment, no separate analysis was conducted for accuracy. All effects were tested at the significance level (α) of .05. The two-way contingency X stimulus category analysis of variance revealed a significant main effect for contingency, showing that across stimulus category, RTs for high-contingency items were faster than for low-contingency items, F (1, 40) = 50.6, MSE = 215, η2p = .56, p b .001. No main effect was obtained for stimulus category, F (1, 40) = 2, MSE = 17,494, η2p = .05, p = .164.7 The interaction between the two variables was significant, F (1, 40) = 6.3, MSE = 215, η2p = .13, p = .016, demonstrating a twice as large contingency learning effect for shapes, F (1, 40) = 46.2, MSE = 215, η2p = .54, p b .001, than for word stimuli, F (1, 40) = 10.6, MSE = 215, η2p = .21, p = .002 (see Fig. 1). First of all, the present data provide an additional replication of previous findings (Schmidt & Besner, 2008; Schmidt et al., 2007), demonstrating that contingency learning does not depend on the presence of response conflict caused by incongruent color words, but is also evident with response conflict-free neutral words. That is, our data support the notion that contingency learning does not function as a learningbased control mechanism that aims to reduce Stroop interference. As for the critical question raised by the current study, namely, whether constant experience of the task conflict caused by neutral words in Schmidt and Besner's (2008) and Schmidt et al.'s (2007) studies could enhance or even trigger the contingency learning, the results do not supportive either of these possibilities. The contingency learning effect was observed with unreadable task conflict-free geometric shapes. Hence, the present study shows that although contingency learning in the Stroop task was originally demonstrated under a condition that included task conflict, it was likely not dependent on (i.e., triggered by) this conflict experience. As for the less extreme possibility, according to which contingency learning, similar to other implicit learning processes (Deroost & Soetens, 2006; Deroost et al., 2012; Vandenbossche et al., 2014; Zhao et al., 2011), can be enhanced by conflict experience, our data proved to be inconsistent as well. Contrary to the findings of the previous studies on implicit learning, we found a smaller rather than larger contingency learning effect in a conflicting (i.e., words) than in a conflict-free (i.e., shapes) condition. Finding a contingency learning effect in both a conflict and a conflict-free condition with no indication of the enlargement of the learning effect as a consequence of the constant conflict experience, refutes the possibility that contingency learning is involved in what we called an “assisting” type of control–learning relationship. A demonstrated, “immunity” of the contingency learning process to the presence of the (task) conflict implies that it is not recruited by the control system as a tool allowing improvement of performance in the face of conflict. In that sense, our study completes the attempt of contingency learning proponents (Schmidt & Besner, 2008; Schmidt et al., 2007) to disregard contingency learning as a conflict-dependent process, by showing that contingency learning not only does not represent learning-based control, but it also does not contribute to the control process by its ability to facilitate performance, and thus counteract the effect of conflict. As already mentioned, in contrast to the studies on other implicit learning processes that showed a larger learning effect under conflict

6

RTs slower than 2000 ms were not allowed by the experiment program. Note, the insignificance of this effect is attributed to a smaller sensitivity of the between-participant design. 7


Fig. 1. Response latencies for contingency and stimulus category. Vertical bars represent standard error of the mean.

conditions, in our study a larger contingency learning effect was observed under a conflict-free condition. One possible reason for that inconsistency might be that in contrast to other implicit learning processes that are reported as not being related to such high-order cognitions such as working memory (Siegelman & Frost, 2015) and attention resources (Jiménez & Méndez, 1999; Turk-Browne, Junge, & Scholl, 2005), contingency learning might depend on them. It has been shown, for example, that working memory load weakens the contingency learning effect (Schmidt, De Houwer, & Besner, 2010). Loading working memory, however, means reducing the amount of attention resources devoted to the task/process of interest. Therefore, contingency learning might be sensitive to the amount of available attention resources. This is consistent with the smaller contingency learning effect found in our study for word stimuli. Words automatically trigger another task—reading—whereas shapes do not. Performing an additional task consumes attentional resources that otherwise would be devoted to the learning of contingencies. Such sensitivity of the contingency learning with respect to available attention resources, which of course should further be investigated in future studies, seems to be consistent with demonstrated “immunity” of the contingency learning to conflict experience. That is, since the cognitive system ideally maintains good performance at minimal cost (for a discussion see Goldfarb & Henik, 2014), it might be profitable for the cognitive system to enhance only those implicit learning processes that do not require much cognitive resources. If contingency learning indeed consumes attention resources then enhancing it in conflict situations may provide no advantage or may even cause more harm to performance, which explains why the contingency learning effect was not enlarged under a conflict condition in the present study. Another issue that has already been partially acknowledged in the introduction section is the apparent similarities existing between contingency learning and what is known as “implicit learning”. The latter includes various learning processes such as probabilistic sequence learning (Nissen & Bullemer, 1987), artificial grammar learning (Reber, 1967) and statistical learning (Saffran, Aslin, & Newport, 1996) that refer to the basic ability of the cognitive system to pick up the regularities existing in a continuous environment. Implicit learning is traditionally described as an associative, unintentional process that “proceeds automatically, as by product of mere exposure” (Saffran, Johnson, Aslin, & Newport, 1999) “for the purpose of generating expectations” (Siegelman & Frost, 2015; for review see Cleeremans, Destrebecqz, & Boyer, 1998). Thus, for example, in a serial-response

43

task (Nissen & Bullemer, 1987) a target (e.g., an asterisk) appears in a serial fashion in one of four (or more) possible locations on the screen. Participants are asked to indicate the location where the target appeared by pushing a key representing that location. Unbeknownst to the participants, the target follows a predictive (deterministic or probabilistic) sequence. This sequence is unintentionally learned by the participants, as indicated by the gradual improvement of performance with practice.8 However, when the sequence is unexpectedly changed to a random sequence, the last two or three blocks of the response RTs are elevated. The RT elevation demonstrates, therefore, the effect of mismatch between the expected location of the target, based on the learned sequence, and the real location. The former effect is usually considered to be the effect of learning acquisition, whereas the effect of changing to a random sequence demonstrates the retrieval/expression of the acquired (sequence) knowledge. As one can note, learning the sequence and using the acquired knowledge is highly profitable in that it maximizes performance. Using the terminology proposed by Perlman and Tzelgov (2006), most of the examples of implicit learning represent incidental learning. Incidental learning is learning that is acquired unintentionally, when no explicit instructions are given, but it is beneficial to the task intentionally performed. The usefulness of incidental learning to task performance is the main feature allowing differentiating it from automatic learning, which is also acquired unintentionally but does not offer any performance advantage. In fact, as the results of Perlman and Tzelgov show, automatic learning acquisition and expression may occur even when they interfere with ongoing performance (see also Poznanski & Tzelgov, 2010 for artificial grammar learning paradigm). Within this terminology, it is clear that contingency learning in the Stroop task represents another exemplar of incidental implicit learning. There are obviously no instructions given to participants to attend to regularities in the co-appearance of specific words and colors in the Stroop task, and yet those are noticed and very quickly learned. According to Schmidt et al. (2010), it only takes 18 trials to acquire such knowledge. The acquired contingency knowledge is then used to predict responses, and therefore to optimize performance, which is consistent with the definition of incidental learning. However, despite the aforementioned “theoretical-level” similarities, there are fundamental differences between the Stroop contingency learning and implicit learning paradigms. The most obvious difference is the nature of a developed association. In the implicit learning literature, it has been shown that even when the possibility of creation of a pure stimulus–response association is eliminated by mapping multiple responses to the same response key, the stimulus–stimulus association can underlie the learning effect (e.g., Deroost et al., 2012). Whether the same is true for the Stroop contingency learning paradigm is still to be shown in future studies. Schmidt et al. (2007, Experiment 4) showed that contingency learning proceeds exclusively through association between a word and a key response, but not between a word and a color-naming response. According to these findings, no contingency learning should be evident with vocal responses, for example, where the association can only be made with a color-naming response. If no contingency learning is observed with vocal responses, such a finding would make an interesting departure from the “implicit learning view” of the contingency learning process, according to which any regularities in the environment can be automatically picked up by the cognitive system. Of course, such an investigation requires a design that would allow disentangling the process of learning acquisition and the process of the retrieval/expression of what was learned.

8 Another variation of this paradigm employs a between-subject design in which the effect of sequence learning is estimated by comparing the performance of the group that is exposed to the sequenced target appearance with the performance of the group exposed to a random target appearance.

44


Another difference between the contingency learning paradigm and sequence learning paradigm relates to the nature of the generated predictions in temporal terms. Hence, what underlies the sequence learning effect, for example, is the development of an association between the identity of the target in the current trial and its identity in the subsequent one. Hence, the predictions made based on the learned sequence are relevant to the next trial. This is not the case, however, in the color-word contingency learning as discussed here, where the learned stimulus–response association is relevant to the response made in the current trial. Importantly, the latter is allowed by the fact that in this paradigm, the employed stimuli are two-dimensional and possess a task-irrelevant (word) dimension, which by itself constitutes another aspect of dissimilarity. In contrast to the sequence learning paradigm, it is the irrelevant (distracting) dimension of the stimulus that becomes associated with a specific response in the Stroop contingency learning paradigm. It is still to be understood whether these dissimilarities might or might not be significant when relating and possibly equating contingency learning with implicit learning phenomena. For now, it seems that it cannot be expected that findings from one paradigm would be necessarily replicated in the other. In this context, the present study can be viewed as providing new information that adds to the similarities between the contingency learning and other implicit learning processes. As our data show, contingency learning does not require special conditions, such as conflict experience or a presence of an additional task — it can develop with unreadable, conflict-free stimuli, such as geometric shapes. This finding is consistent with the definition of implicit learning according to which it is a basic human ability to pick up the regularities from the environment through mere exposure to them. In this sense, contingency learning is not different from, for example, statistical learning found in infants who manage to learn what sequences in continuous speech represent words. There is no conflict experience that can push the newborns to engage in such a process; obviously no instructions or other extrinsic motivators are given, and yet the learning occurs. According to the present results, contingency learning shares this feature of acquisition by mere exposure as well. However, as we mentioned previously, other possible moderators such as response modality should be investigated as well to support the “automaticity” of colorword contingency learning. 4. Conclusions Schmidt and Besner (2008) and Schmidt et al. (2007) explicitly claimed that the mechanism underlying contingency learning does not depend on the presence of Stroop conflict, meaning that contingency learning is not a mechanism recruited to help control, but is an independent process that functions in parallel to it (Schmidt, 2013a, 2013b). However, until now, it has only been demonstrated that contingency learning is independent of response conflict, meaning that it does not represent learning-based control (Verguts & Notebaert, 2008, 2009). In the present study, we investigated whether the constant presence of a task conflict in the reported experiments (i.e., Schmidt & Besner, and Schmidt et al.) could enhance or even trigger contingency learning. Such findings would mean that contingency learning does depend on conflict experience but not in the sense proposed by the conflict-monitoring theory (e.g., a learning-based control). According to Deroost et al. (2012), instead of regulating the magnitude of the experienced conflict, some implicit learning mechanisms might be recruited by the control system to counteract detrimental effects of conflict on performance (i.e., an “assisting” form of control–learning relationship). The results showed that contingency learning was neither triggered nor boosted by the presence of the task conflict. The present results imply that contingency learning is completely independent of any conflict experience, and therefore does not represent learningbased control nor the “assisting” form of the control–learning relationship.

References Aarts, E., Roelofs, A., & van Turennout, M. (2009). Attentional control of task and response in lateral and medial frontal cortex: Brain activity and reaction time distributions. Neuropsychologia, 47, 2089–2099. Abrahamse, E. L., Duthoo, W., Notebaert, W., & Risko, E. F. (2013). Attention modulation by proportion congruency: The asymmetrical list shifting effect. Journal of Experimental Psychology. Learning, Memory, and Cognition, 39, 1552–1562. Atalay, N. B., & Misirlisoy, M. (2012). Can contingency learning alone account for itemspecific control? Evidence from within- and between-language ISPC effects. Journal of Experimental Psychology. Learning, Memory, and Cognition, 38, 1578–1590. Bench, C. J., Frith, C., Grasby, P., Friston, K., Paulesu, E., Frackowiak, R., & Dolan, R. (1993). Investigations of the functional anatomy of attention using the Stroop test. Neuropsychologia, 31, 907–922. Blais, C., Robidoux, S., Risko, E. F., & Besner, D. (2007). Item-specific adaptation and the conflict-monitoring hypothesis: A computational model. Psychological Review, 114, 1076–1086. Botvinick, M. M., Braver, T. D., Barch, D. M., Carter, C. S., & Cohen, J. D. (2001). Conflict monitoring and cognitive control. Psychological Review, 108, 624–652. Botvinick, M. M., Cohen, J. D., & Carter, C. S. (2004). Conflict monitoring and anterior cingulate cortex: An update. Trends in Cognitive Sciences, 8, 539–546. Brown, T. L. (2011). The relationship between Stroop interference and facilitation effects: Statistical artifacts, baselines and a reassessment. Journal of Experimental Psychology. Human Perception and Performance, 37, 85–99. Bugg, J. M. (2014). Conflict-triggered top-down control: Default mode, last resort, or no such thing? Journal of Experimental Psychology. Learning, Memory, and Cognition, 40, 567–587. Bugg, J. M., & Hutchison, K. A. (2013). Converging evidence for control of color-word Stroop interference at the item level. Journal of Experimental Psychology. Human Perception and Performance, 39, 433–449. Bugg, J. M., Jacoby, L. L., & Toth, J. P. (2008). Multiple levels of control in the Stroop task. Memory and Cognition, 36, 1484–1494. Bugg, J. M., Jacoby, L. L., & Chanani, S. (2011). Why it is too early to lose control in accounts of item-specific proportion congruency effects. Journal of Experimental Psychology. Human Perception and Performance, 37, 844–859. Cleeremans, A., Destrebecqz, A., & Boyer, M. (1998). Implicit learning: News from the front. Trends in Cognitive Sciences, 2, 406–416. De Houwer, J. (2003). On the role of stimulus–response and stimulus–stimulus compatibility in the Stroop effect. Memory and Cognition, 31, 353–359. De Pisapia, N., & Braver, T. S. (2006). A model of dual control mechanisms through anterior cingulate and prefrontal cortex interactions. Neurocomputing, 69, 1322–1326. Deroost, N., & Soetens, E. (2006). The role of response selection in sequence learning. The Quarterly Journal of Experimental Psychology, 59, 449–456. Deroost, N., Vandenbossche, J., Zeischka, P., Coomans, D., & Soetens, E. (2012). Cognitive control: A role for implicit learning? Journal of Experimental Psychology. Learning, Memory, and Cognition, 38, 1243–1258. Entel, O., Tzelgov, J., Bereby-Meyer, Y., & Shahar, N. (2014). Exploring relations between task conflict and informational conflict in the Stroop task. Psychological Research (Epub ahead of print). Goldfarb, L., & Henik, A. (2007). Evidence for task conflict in the Stroop effect. Journal of Experimental Psychology. Human Perception and Performance, 33, 1170–1176. Goldfarb, L., & Henik, A. (2014). Is the brain a resource-cheapskate? Frontiers in Human Neuroscience, 8, 857. http://dx.doi.org/10.3389/fnhum.2014.00857. Hazeltine, E., & Mordkoff, J. T. (2014). Resolved but not forgotten: Stroop effect dredges up the past. Frontiers in Psychology, 5, 1327. http://dx.doi.org/10.3389/fpsyg.2014.01327. Hutchison, K. A. (2011). The interactive effects of listwide control, item-based control, and working memory capacity on Stroop performance. Journal of Experimental Psychology. Learning, Memory, and Cognition, 37, 851–860. Jacoby, L. L., McElree, B., & Trainham, T. N. (1999). Automatic influences as accessibility bias in memory and Stroop tasks: Toward a formal model. In D. Gopher, & A. Koriat (Eds.), Attention and performance XVII: Cognitive regulation of performance: Interaction of theory and application (pp. 461–486). Cambridge, MA: MIT Press. Jacoby, L. L., Lindsay, D. S., & Hessels, S. (2003). Item-specific control of automatic processes: Stroop process dissociation. Psychonomic Bulletin & Review, 10, 638–644. Jiménez, L., & Méndez, C. (1999). Which attention is needed for implicit sequence learning? Journal of Experimental Psychology. Learning, Memory, and Cognition, 25, 236–259. Kalanthroff, E., Goldfarb, L., & Henik, A. (2013). Evidence for interaction between the stop signal and the Stroop task conflict. Journal of Experimental Psychology. Human Perception and Performance, 39, 579–592. Klein, G. S. (1964). Semantic power measurement through the interference of words with color-naming. The American Journal of Psychology, 77, 576–588. Koch, I. (2007). Anticipatory response control in motor sequence learning: Evidence from stimulus–response compatibility. Human Movement Science, 26, 257–274. Levin, Y., & Tzelgov, J. (2014). Conflict components of the Stroop effect and their “control”. Frontiers in Psychology, 5, 463. http://dx.doi.org/10.3389/fpsyg.2014.00463. Levin, Y., & Tzelgov, J. (2015). What Klein's “semantic gradient” does and does not really show: Decomposing Stroop interference into task and informational conflict components. (Manuscript in preparation). Logan, G. (1985). Skill and automaticity: Relations, implications and future directions. Canadian Journal of Psychology, 39, 367–386. Logan, G., & Zbrodoff, N. J. (1979). When it helps to be misled: Facilitative effects of increasing the frequency of conflicting stimuli in a Stroop-like task. Memory and Cognition, 7, 166–174. MacLeod, C. M., & MacDonald, P. A. (2000). Interdimensional interference in the Stroop effect: Uncovering the cognitive and neural anatomy of attention. Trends in Cognitive Sciences, 4, 383–391.

Y. Levin, J. Tzelgov / Acta Psychologica 164 (2016) 39–45 Mathôt, S., Schreij, D., & Theeuwes, J. (2012). OpenSesame: An open-source, graphical experiment builder for the social sciences. Behavior Research Methods, 44, 314–324. Melara, R. D., & Algom, D. (2003). Driven by information: A tectonic theory of Stroop effects. Psychological Review, 110, 422–471. Nissen, M. L., & Bullemer, P. (1987). Attentional requirements of learning: Evidence from performance measures. Cognitive Psychology, 19, 1–32. Perlman, A., & Tzelgov, J. (2006). Interactions between encoding and retrieval in the domain of sequence-learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 32, 118–130. Poznanski, Y., & Tzelgov, J. (2010). Modes of knowledge acquisition and retrieval in artificial grammar learning. The Quarterly Journal of Experimental Psychology, 63, 1495–1515. Reber, A. S. (1967). Implicit learning of artificial grammars. Journal of Verbal Learning and Verbal Behavior, 6, 855–863. Saffran, J. R., Aslin, R. N., & Newport, E. L. (1996). Statistical learning by 8-month-old infants. Science, 274, 1926–1928. Saffran, J. R., Johnson, E. K., Aslin, R. N., & Newport, E. L. (1999). Statistical learning of tone sequences by human infants and adults. Cognition, 70, 27–52. Schmidt, J. R. (2013a). The Parallel Episodic Processing (PEP) model: Dissociating contingency and conflict adaptation in the item-specific proportion congruent paradigm. Acta Psychologica, 142, 119–126. Schmidt, J. R. (2013b). Questioning conflict adaptation: Proportion congruent and Gratton effects reconsidered. Psychonomic Bulletin & Review, 20, 615–630. Schmidt, J. R., & Besner, D. (2008). The Stroop effect: Why proportion congruent has nothing to do with congruency and everything to do with contingency. Journal of Experimental Psychology. Learning, Memory, and Cognition, 34, 514–523. Schmidt, J. R., Crump, M. J. C., Cheesman, J., & Besner, D. (2007). Contingency learning without awareness: Evidence for implicit control. Consciousness and Cognition, 16, 421–435.

45

Schmidt, J. R., De Houwer, J., & Besner, D. (2010). Contingency learning and unlearning in the blink of an eye: A resource dependent process. Consciousness and Cognition, 19, 235–250. Sharma, D., & McKenna, F. (1998). Differential components of the manual and vocal Stroop tasks. Memory and Cognition, 26, 1033–1040. Siegelman, N., & Frost, R. (2015). Statistical learning as an individual ability: Theoretical perspectives and empirical evidence. Journal of Memory and Language, 81, 105–120. Steinhauser, M., & Hübner, R. (2009). Distinguishing response conflict and task conflict in the Stroop task: Evidence from ex-Gaussian distribution analysis. Journal of Experimental Psychology. Human Perception and Performance, 35, 1398–1412. Stroop, J. R. (1935). Studies of interference in serial verbal reactions. Journal of Experimental Psychology, 18, 643–662. Turk-Browne, N. B., Junge, J. A., & Scholl, B. J. (2005). The automaticity of visual statistical learning. Journal of Experimental Psychology. General, 134, 552–564. Tzelgov, J. (1997). Specifying the relations between automaticity and consciousness: A theoretical note. Consciousness and Cognition, 6, 441–451. Vandenbossche, J., Coomans, D., Homble, K., & Deroost, N. (2014). The effect of cognitive aging on implicit sequence learning and dual tasking. Frontiers in Psychology, 5, 154. http://dx.doi.org/10.3389/fpsyg.2014.00154. Verguts, T., & Notebaert, W. (2008). Hebbian learning of cognitive control: Dealing with specific and nonspecific adaptation. Psychological Review, 115, 518–525. Verguts, T., & Notebaert, W. (2009). Adaptation by binding: A learning account of cognitive control. Trends in Cognitive Sciences, 13, 252–257. Zhao, J., Ngo, N., McKendrick, R., & Turk-Browne, N. B. (2011). Mutual interference between statistical summary perception and statistical learning. Psychological Science, 22, 1212–1219.