For practical reasons, we decided to examine both the effect of texting (Experiment 1) and of phone talking (Experiment 2) on cognition, using the same sample of participants, with these experimental blocks counterbalanced across participants. Each experiment contained a single-task condition (mental arithmetic was performed alone), an expected-load condition (participants anticipated an interruption by an incoming text message or phone call, respectively), and a performed-load condition (mental arithmetic was performed in combination with a memory load or active talking, respectively). Crucially, the text message (Experiment 1) was presented prior to task processing (in order to measure expectancy unconfounded with real task processing), and the expected-load condition (phone call, Experiment 2) occurred shortly after the experimental block. Notably, due to the difficulty to randomize the expected-load condition, we decided to present the three critical experimental conditions in a fixed order (single task, expected load, performed load), which means that differences between the conditions are confounded with potential task order effects. This has important consequences as research hypotheses can only be tested in one direction. That is, we are allowed to ask questions about expected and performed dual-task interference but not about potential benefits (and consequently, the same applies to the interpretation of potential effects). We averaged the single-task condition in order to cushion the impact of all kinds of test-taker effects (i.e., fatigue, practice effects, etc.). Apart from that, one half of the sample was first administered with Experiment 1 (texting: single, expected, and performed) followed by Experiment 2 (phone talking: single, expected, and performed), and the other half of the sample was administered in the counterbalanced order. They were introduced with the experimental paradigm and were instructed to concentrate throughout the experimental session, that is, to respond with maximum speed and accuracy (cf. Ulrich and Miller, 1994).

# Implementation of Phone-Call Expectancy (Experiment 1)

Whether the sole expectation to receive a call from a student colleague affects cognitive performance is an empirical question, and although most people would agree with such a hypothesis from everyday experience, it is difficult to experimentally manipulate aspects of pure expectation such that it mimics the naturalistic aspects of phone calls in real life, regarding relevance and time pressure to answer the impending phone call. Therefore, the nature of this aspect of our study is

exploratory, and only serves to obtain a first impression from the detailed analysis of cognitive processes derived from automatic and controlled processing components of continuous mental addition. The procedure was such that the experimenter informed the participant that he/she will get a phone call during the processing of the task and instructed the participant to answer the call as quickly as possible. The phone lied in front of the participant on the table and called out to him/her to be picked up and used.

# Implementation of Phone Conversation (Experiment 1)

We used the method of story-based natural conversation (cf. Drews et al., 2008), using a scripted semi-structured interview guideline, to mimic the coordinated, joint-activity features of a naturalistic everyday-life small talk conversation among students. The screenplay resembled the method of improvisational theater and contained the following essential elements in the following order (1) become acquainted with each other, telling names, etc. (2) asking about how he/she is doing today, (3) asking about where he/she is living, in which part of the city, etc., (4) asking about what he/she had for lunch, etc., (5) asking about what courses are offered this semester, which courses he/she is currently attending, which his/her favorite lecture and/or professor is, (6) and, for example, what could define a possible motto for the "psychoparty" (an annual party arranged by third-semester students). Further, the interview contained optional elements to ensure the conversation to flow appropriately in the eventual case of the participant being either extremely talkative or taciturn (i.e., to regulate turn-taking). Our aim hereby was to establish a conversation with balanced conversation's proportion, regulating each partner's contribution to the talk toward a value of about 50% (maximal tolerable deviation about 40–60 or 60–40%. These questions were also chosen among a sample of standard questions to retain smalltalk, for example, asking whether he/she like animals, whether he/she is doing sports, whether he/she likes hot wetter, whether he/she knows certain proverbial sayings, etc.

# Implementation of Text Message Expectancy

In a similar way, another exploratory aspect of our study contained the question of whether the sole expectation to receive a text message from a student colleague in some way affects cognition. For example, everyday experience would imply that expecting a text message from a student colleague during a lecture (where the phone is not allowed to be used actively) potentially distracts individuals such that attention is directed away from the content of the lecture toward the potential content of the message. The procedure was such that the experimenter informed the participant that he/she will get text a message during the processing of the task and instructed the participant not to answer the message until the experimental block is finished. Again, the phone lied before the participant on the table and called out to him/her to be picked up and used.

# Implementation of Text Message Communication

The participant obtained a text message and was asked the following questions: "Was wäre ein optimales Geburtstagsgeschenk für Dich, wenn der Preis egal wäre?" ("What would you want as a birthday present? What would you choose, if money is no object?"). The participant was instructed to think about an answer during the processing of the task and to answer this questions after finishing the experimental block.

# RESULTS (EXPERIMENT 1: TEXTING)

# Data Treatment

Responses faster than 100 ms were regarded outliers and removed from RT analysis. To effectively take advantage of the full scope of distributional analysis, we only used a minimal-trimming method by removing the three slowest reactions for each of the conditions, according to Ulrich and Miller (1994), and in accordance with our previous use of this method (e.g., Steinborn and Huestegge, 2016). Incorrect responses were regarded response errors and used to compute an index of error rate.

# Standard Performance Indices

For each of the experimental conditions, we computed the reaction time mean (RTM) to index average response speed and the RTCV to index relative response-speed variability, according to the suggestion of Flehmig et al. (2007) and Flehmig et al. (2010), and according to our previous use of this method. RTCV is obtained by computing the standard deviation of the RTs (separately for each individual and experimental condition) divided by the individual mean of RTs (for each individual and experimental conditions). Error percentage (EP) indicated the rate of incorrect responses, and served as measure of response accuracy.

# Distributional Analysis

To analyze the distribution of responses, we computed the interpolated vincentized CDF of responses with 19 percentiles for each of the experimental conditions according to the suggestion of Ulrich et al. (2007). By means of this analysis, we were to know whether the hypothesized effect of phone conversation is due to a generic slow-down of all responses or alternatively due to a selective slow-down of the long percentiles of the CDF. To more directly account for experimentally induced effects of distributional shape (right-tail density accumulation effects, further referred to as skewness), we additionally adopted an ex-Gaussian model approach but only as a descriptive model of reaction times to analyzing its three parameters mean, dispersion, and shape (µ, σ, and τ). We computed ex-Gaussian model parameters for each participant according to the methodical rules provided by Lacouture and Cousineau (2008). Within the context of a chained mental-arithmetic task, parameters µ and σ can readily be interpreted as localization and dispersion (around µ) indicators while τ is sensitive to experimental effects

TABLE 1 | Mean reaction time (RT) and standard error of the mean (SE) as a function of the factors context and demand, separately for Experiments 1 and 2 (texting vs. talking).

N = 39; RT, reaction time mean; EP, error rate (%); M and SE are population parameters, and SE is transformed for within-subject designs according to Cousineau (2005), shown in brackets.

on right-tail density accumulation (Steinborn and Huestegge, 2016).

#### Standard Analysis

The design contained the experimental factors context (single task vs. expecting text message vs. handling text message) and demand (easy vs. hard mental arithmetic) and contained RT and error rate as dependent measures. Complete statistical results are referred to in **Table 1** and visually displayed in **Figure 1**. Responses became not slower in the experimental blocks as compared to the single-task condition. As expected, responses were faster in easy than in hard mental-arithmetic trials, as indicated by a main effect of demand on RTM [F(1,38) = 653.7, p < 0.01]. Finally, multitasking did not differentially impose negative effects on automatic and controlled components of mental arithmetic. Finally, it should be mentioned that there was no speed-accuracy trade-off that could compromise the interpretation of effects on RT. In fact, errors were low overall and therefore not further considered (cf. Steinborn et al., 2017).

## Distributional Analysis

Besides effects on average response speed, we hypothesized that multitasking increased performance variability in the primary task, which should perhaps be more pronounced for hard than for easy mental arithmetic. However, a visual inspection of the CDFs (**Figure 2**) indicates that neither expected nor performed text message communication severely affected aspects of distributional skewness in the mental-arithmetic task. There was no significant effect on any parameter of performance variability, with respect to the global GLM effect (**Tables 1–3**).

# RESULTS (EXPERIMENT 2: PHONE TALKING)

#### Standard Analysis

The design contained the experimental factors context (single task vs. expecting phone call vs. phone talking) and demand (easy vs. hard mental arithmetic) and contained RT and error rate as dependent measures. Complete statistical results are referred to in **Tables 3, 4**. Essentially, responses were significantly faster in single-task blocks as compared to the experimental condition, indicating that multitasking affects RTM [F(2,76) = 79.1, p < 0.01]. As expected, responses were faster in easy than in hard mental-arithmetic trials, as indicated by a main effect of demand on RTM [F(1,38) = 422.0, p < 0.01]. More interesting, multitasking differentially affected automatic and controlled components of mental arithmetic, since hard items were more affected than easy items [F(2,76) = 28.6, p < 0.01]. Pre-planned single-comparison analyses revealed that the global GLM effect is driven by the talking-load condition (single task vs. phone talking), indicating a slowing of responses which was differentially more pronounced for hard than for easy arithmetic (**Table 3**: Panels 7–9). Finally, it should be mentioned that there was no speed-accuracy trade-off that could compromise the interpretation of effects on RT. Errors were in the same direction, thus supporting the conclusion that talking load differentially hampers primary-task processing.

#### Distributional Analysis

Besides effects on average response speed, multitasking increased the performance variability of the primary task. Notably, a visual inspection of the CDFs (**Figure 2**) indicates that multitasking affected primary-task performance by destabilizing performance. This is indicated by a main effect of context on the classic variability parameter, RTCV [F(2,76) = 220.9, p < 0.01], which closely corresponds to the visual pattern of skewness of a particular CDF. The main effect of the factor demand on RTCV indicates greater variability for hard than for easy items [F(1,38) = 23.7, p < 0.01], and the context × demand interaction on RTCV indicates that multitasking evoked performance variability to a larger degree in the controlled than in the automatic component of mental arithmetic [F(2,76) = 6.1, p < 0.01]. Since recommended by several authors (Heathcote et al., 1991; Steinhauser and Huebner, 2009), we additionally obtained parameter of skewness from an ex-Gaussian distributional model. As expected, the destabilizing effect on performance is also (even more sensitively) indicated by a main effect of the factor context on the ex-Gaussian τ parameter [F(2,76) = 97.0, p < 0.01], and by

the context × demand interaction effect on the τ parameter [F(2,76) = 36.0, p < 0.01]. Pre-planned single-comparison analyses revealed that the global GLM effect is driven by the talking-load condition (single task vs. phone talking), indicating a slowing of responses which was differentially more pronounced for hard than for easy arithmetic (**Tables 4, 5**: Panels 7–9). Thus, results indicate that phone talking during continuous primary-task performance affects not simply the speed of information-processing throughput (Humphreys and Revelle, 1984; Thorne, 2006; Steinborn et al., 2010), but crucially, the reliability of these processes, supporting an attentional-failure hypothesis rather than a general slow-down

continuous mental arithmetic, separately displayed for Experiments 1 (texting) and 2 (phone talking).

hypothesis of smartphone-conversation effects on cognitive work.

#### DISCUSSION

#### Summary

The aim of this applied study was to examine the effects of (secondary-task) smartphone communication on (primarytask) performance in chained mental arithmetic. The results can be summarized as follows: (1) Contrary to popular opinion, neither the sole expectation of an impending text

message (a question) nor the mental preoccupation with finding an answer (to the question delivered by the text message) had any detrimental effect on primary-task performance. (2) Further, the expectation of an impending phone call was also not detrimental to primary-task performance. (3) However, active conversation was clearly detrimental to primary-task performance, since responses were slower on average in the talking-load condition as compared to the single-task condition. (4) Importantly, talking did not yield a constant slowing but rather a destabilization of continuous mentalarithmetic performance, since the CDF analysis revealed increased distributional skewness beyond scaling variability. (5) The destabilization effect was more pronounced for hard than for easy items, indicating a differential effect on controlled versus automatic components of mental arithmetic. This result might be important to our understanding of how smartphone communication affects cognitive functioning in general, and might also be of applied importance because it may help to understand better how phone conversation impacts on a driver's ability to allocate attention to the task of driving.

#### Effects of Expected Multimedia-Based Communication

Most people would agree, when asked, that impending but temporally uncertain social interaction at the workplace or elsewhere is distracting and can sometimes be even annoying. Further, researchers and practitioners in applied fields would also agree that multimedia-based communication devices represent the biggest distraction at work, despite the methodical difficulties to develop a model (i.e., a micro-case) that exactly mimics the interactive features of multimedia-based communication in natural environments (Ralph et al., 2014, 2015). Therefore, the results presented here are of explorative character, although they

#### TABLE 2 | Results of the experimental effects on standard performance indices (Experiment 1).

Effect size: η 2 p ; Experimental factors: Context (single-task vs. text message expected vs. text message load), Demand (easy vs. hard mental arithmetic); RTM, reaction time mean; EP, error percentage (%); RTCV, reaction time coefficient of variation.

TABLE 3 | Results of the experimental effects on ex-Gaussian parameters (Experiment 1).

Effect size: η 2 p ; Experimental factors: Context (single-task vs. text message expected vs. text message load), Demand (easy vs. hard mental arithmetic).

TABLE 4 | Results of the experimental effects on standard performance indices (Experiment 2).

Effect size: η 2 p ; Experimental factors: Context (single-task vs. text message expected vs. text message load), Demand (easy vs. hard mental arithmetic); RTM, reaction time mean; EP, error percentage (%); RTCV, reaction time coefficient of variation.

might reveal aspects that are relevant for the practical use in future studies. Contrary to our expectations, and to popular beliefs based on everyday experience, expecting an impending text message (containing a question) in our study did not hamper performance in the primary task (Experiment 1). Further, the load imposed with finding an answer (to the question delivered by the text message) did also not detrimentally affect any aspect of primary-task performance (**Figures 1**, **2**). However, it would be premature to conclude that impending text messages are unproblematic with regard to possible distraction effects on primary-task performance, since one cannot definitely exclude that more demanding text messages might affect performance in the primary task.

In Experiment 2, we asked whether the expectation to receive a call from a student colleague affects cognitive performance in the primary task. Likewise as in Experiment 1, expecting an impending phone call was not at all detrimental to primary-task performance. In contrast, actively performed conversation (talking) was clearly detrimental to primary-task performance, since responses were slower on average in the talking-load condition as compared to the single-task condition. Thus, these data would indicate the conclusion that impending phone-call expectancy is not harmful to the individual currently engaged in deliberate information-processing activity, which is counterintuitive to what one would expect from everyday experience. Such findings are often interpreted such that the anticipation of impending distraction could have evocated additional capacity (or enforced a strategy of cognitive shielding) and by this means prevented any impairment of primary-task performance to occur (Fuentes and Campoy, 2008; Bratzke et al., 2009, 2012; Langner et al., 2010, 2011; Szalma and Hancock, 2011; Scheiter et al., 2014). Due to the chosen design features of our study, however, hypotheses could only be formulated in one direction (i.e., toward potential dual-task interference costs, not benefits), and results will thus only be interpreted accordingly. Instead, some critical issues are outlined below.

Critical to a manipulation of expected-load effects (Experiment 1) are two aspects, **(1)** the nature and degree of demand related to processing a text message, **(2)** and the experimental means of performing controls to determine secondary-task engagement (i.e., to determine how long and how intensely the participants were processing the text message). Critical to a manipulation of expectancy for an impending phone call (Experiment 2) are two further aspects, **(3)** the experimental methods of inducing an internal state of hurry (i.e., the problem of getting the participants to act with the required urgency), **(4)** and the methods of controlling for when exactly and how often the participants re-started to preparing for the anticipated event. Within a spare–utilized capacity threading model and related accounts, these intrusions are reflected in aspects of intraindividual performance variability. For example, McDaniel et al. (2004) considered several aspects relevant to study performance costs related to a monitored event. As a general rule, it is important to ascertain whether the phone call can easily be detected perceptually (e.g., phone nearby in sight vs. far-apart, ringing loud vs. muted, etc.), whether the occurrence uncertainty is event-based or time-based (e.g., phone call expected after lunch, or at around 12.00 am), and whether there is time pressure to answer the call pointing on a distinction between immediate-execute vs. delayed-execute secondary-task mode (McDaniel and Einstein, 2000; McDaniel et al., 2004; Einstein and McDaniel, 2005).

# Effects of Phone Conversation (Talking)

Researchers usually agree with the allegation that active conversation requires attention for monitoring semantic aspects such as topic and content, for coordinating the time-critical aspect of turn taking (between speaking and listening), and for a rather metacognitive supervision of conversational balance (Kahneman, 1973, pp. 5–12; Salvucci and Taatgen, 2011, pp. 3–24). This means that conversation is a complex matter affected by many factors and therefore difficult to examine in a wholistic fashion (Drews et al., 2008; Bergen et al., 2013). Notably, the problem is actually recognized and is a current point of contention among researchers in basic-research and applied-research domains (cf. Drews et al., 2008, pp. 393–395). In the present study, we decided to examine the effect of phone conversation on cognition by means of the classic continuous dual-task paradigm, where a primary task is performed in streams of continuous action, and where a secondary task is used as a loading or distractor condition, or to "probe" mental focus during primary-task processing (Posner and Boies, 1971, pp. 401–407). Our results indicate that active phone conversation had an enormous impact on mental-arithmetic performance, since the load imposed by conversation yielded slower and somewhat more erroneous responses, as compared to a standard (single-task) condition (**Figure 1**).

Importantly, phone talking not solely slowed but rather destabilized primary-task performance, as indicated by measures of response-speed variability, which was again more pronounced for hard than for easy mental arithmetic (**Tables 4, 5**). Thus, the decrements on RT mean are not interpretable by itself (cf. Miller, 2006, p. 93), since it could be demonstrated that they actually originate from a selective slow-down of responses at long CDF percentiles. It becomes evident from **Figure 2** that the experimental conditions (standard vs. talking load) are not very different at the shorter percentiles of the CDF while the difference increases substantially toward the longest percentiles. **Figure 3** displays a delta plot of the loading effect on mental-arithmetic performance, comparably for the low-demand and the highdemand condition. A delta plot is obtained by calculating the RT difference as induced by an experimental manipulation (e.g., single-task vs. load) against the mean of both experimental conditions for each of the percentiles. By this means, effects of concurrent phone conversation can be evaluated relative to the mean of level of performance, indicating that individuals were not particularly going slower overall but especially became less persistent. In this way, delta plots provide a convenient simplification of the relatively complex information present in the CDFs (cf. De Jong et al., 1994; Ridderinkhof, 2002; Schwarz and Miller, 2012; Ulrich et al., 2015; Steinborn et al., 2016b).

Therefore, the main conclusion our study provides is that phone conversation during mental arithmetic does not globally hamper information-processing speed. Rather, the data indicate

TABLE 5 | Results of the experimental effects on ex-Gaussian parameters (Experiment 2).

Effect size: η 2 p ; Experimental factors: Context (single-task vs. text message expected vs. text message load), Demand (easy vs. hard mental arithmetic).

that load of this kind makes individuals less reliable and less capable to protecting the cognitive system against attention failure. The probability of committing such failures of attention depends on the processing demand of the primary task, being lower for easy than for hard mental arithmetic. This indicates that secondary-task conversation differentially affects automatic and controlled information processing in the primary task. In this way, our study might contribute some important aspects to the understanding of phone-conversation effects on everyday-life tasks such as driving, despite the fact that we employed a continuous mental-arithmetic task to study conversation-related attentional impairments. Thus, particular key characteristics of our study might be those of creating connections between basic and applied research along the concept of a two-state model of attentional failures. For example, Briem and Hedman (1995) concluded that simple phone conversation is in itself not sufficient to adversely affect the ability to maintain road position, but rather increases the risk of traffic accidents by an unfortunate coincidence of a critical traffic event and spontaneous attention failure within the individual (cf. Reason, 1990; Folkard, 1997).

#### Theory and Design Issues

Our theorizing is primarily based upon an intermittent spare–utilized capacity threading model as a general framework, in order to account for two essential findings. The first relates to the empirical fact that loading effects by phone conversation on primary-task performance can primarily be located at a cognitive (not at a peripheral-activity) level. The second refers to the possibility, implied by previous findings, that phonerelated interference does impose a constant amount of costs (of sharing capacity) on primary-task performance, but temporarily blocks information processing in the primary task (by a processing bottleneck) in an all-or-none fashion. In this way, our study diverges from the majority of applied multitasking research where the theorizing usually occurs within the multipleresource model framework. For example, Wickens (1984) originally assumed that successful multitasking depends on the compatibility of input systems, representational format, and output systems. From this account, one would have to argue that (auditory–verbal–vocal) phone conversation may be performed concurrently with little or no costs to a (visual–spatial–manual) task as continuous mental arithmetic. Given the apparent three-dimensional compatibility of this dual-task combination, phone conversation and continuous mental arithmetic should be performed together with no interference, which was obviously not the case in our study (cf. Strayer and Drews, 2007).

The basic tenet of a spare–utilized capacity threading (monitoring–focus) model is that there is an intermittent exchange between capacity for task operations and for monitoring. Crucial is the notion of intermittency, as task processing is interrupted during monitoring, which means that as individuals engage in active task operations, spare capacity is conveyed to utilized capacity. Thus, a temporary increase in task focus would yield a corresponding (temporary) decrease in monitoring. A spare–utilized capacity threading model is not only consistent but even relies on the notion of a processing bottleneck, as it assumes that individuals can (effectively) engage in only one of the two, task processing or monitoring. In this way, it is mutually exclusive with the notion of (temporarily–punctual) sharing of capacity. This means that the relation of utilized versus spare capacity is constantly fluctuating across subsequent trials as this relation is continually evaluated and re-adjusted, which means that capacity for active task operations varies across trials. Remind that several empirical findings of Strayer et al. (2003) support this position, suggesting that even when talking drivers direct their gaze at objects in the environment, they often fail to see them. To put it more precisely, there is an increased probability for drivers currently engaged in active conversation to commit attention failure (in a temporarily punctual fashion) to recognize objects in the environment.

A theoretical alternative to the prevalent multiple-resource framework in applied-multitasking research (Wickens, 1980, 1984), therefore, is that conversation-related interference stems from an intermittent postponement imposed by a discrete-processing bottleneck such that attending to the phone conversation temporarily blocks information processing in the primary task, because the bottleneck forces serial processing between talking and performing continuous arithmetic. Such

a view of trial-by-trial intermittent resource allocation offers a completely natural way to explain variability that is usually observed in RT experiments. In (low-error domain) RT tasks, these trial-by-trial fluctuations in the rate of utilized capacity (task focus) are reflected in the right tail (skewness) of the intraindividual RT distribution. The results of the present study are completely in line with such a perspective: As visually displayed in **Figures 2**, **3**, individuals were partially capable to retain a high level of primary-task performance during talking (as compared to the standard condition), which is particularly true for automatic (vs. controlled) processing, but are partially prone to commit a failure to engage in processing the primary

task. That is, they are not very different at the shortest percentiles of the CDF while the difference increases substantially toward the longest percentiles, and this effect differentially depends on the demand imposed by the primary task. According to Strayer and Drews (2007), conversation is special in that thought packages cannot be broken into arbitrary units but instead is composed of turns that engage the central-processing bottleneck.

#### Final Conclusion

A final word ought be devoted to the fundamental question of how an applied multitasking study should be conducted in order to satisfy the requirements of ensuring experimental

control (i.e., internal validity), on the one hand, and to provide representativeness of the created micro case (i.e., external validity), on the other hand. Although the problem has been recognized by theoreticians of outstanding reputation (e.g., Pashler, 1998, pp. 5–31; Sanders, 1998, pp. 452–506; Salvucci and Taatgen, 2011, pp. 237–253), no definite solution has been offered probably because the problem is unsolvable as it is a problem of perspective. An essential characteristic of applied research relates to the complexity of the real-life situation and the variety of possible influences and effect mechanisms. The dilemma is that isolating the separate influence of the independent variables increases internal validity but decreases representativeness. For example, Drews et al. (2008) criticized the frequently observed practice of reducing complexity to increase experimental control to study conversation-related interference, arguing that many tasks employed to simulate conversation in studies on cell phone use on driving suffer from serious ecological-validity concerns. For example, several studies used "verbal tasks" as representative for conversation, administering participants to decide between words and non-words, or to perform verbal-reasoning tasks as secondary-task assumed to interfering with primary-task performance. In any case, artificial tasks fail to mimic the features of real conversation.

We used the method of story-based natural conversation, using a scripted interview guideline, to simulate the selfregulated dyadic-activity characteristics of naturalistic everyday small talk conversation among students. The interview was semi-structured but contained optional elements to ensure the conversation to flow appropriately. We intended to establish a talking-load condition with balanced conversation's proportion, being aware that varying the proportion between listening and speaking might be a potential source of interference measurable in continuous dual-task situations. For example, McCarley et al. (2004) found impairments in the ability of participants to detect changes in real-world traffic scenes when they were conversing on a handsfree device, however, no such performance decrements were observed when participants listened to prerecorded conversations from other participants. These findings are important since they demonstrate that listening to verbal material is by itself not sufficient to produce the dual-task interference associated with using a cell phone while driving. In any case, a more in-depth analysis of the particular components of real-life conversation is vital for the complete understanding of conversation-related interference in future studies. For the time being, we conclude that phonerelated interference effects on cognition does not arise from a constant slow down but from an occasional break down of mental efficiency during continuous mental arithmetic performance.

The key contribution of our study embraces two aspects, knowledge related to the particular component processes affected by conversation in continuous dual-task situations, methodology of design and experimental set-up (Steinborn et al., 2017), and advanced measurement technology (Steinborn et al., 2016b). First, our results provide knowledge to the community since we determined component processes related to automatic and controlled information processing as they were affected by concurrent phone conversation. Second, we provide a methodical advancement to study conversationrelated interference on primary-task performance within the framework of mental chronometry. In the focus of our research project stands the goal of measuring the effects of phone talking on automatic and controlled information processing with high precision, by analyzing the entire RT distribution instead of only analyzing RT means. The main conclusion our study provides is that interference by phone conversation is not due to a constant slowdown but rather due to an occasional breakdown of continuous information processing, which differentially affects automatic and controlled components of information processing. In effect, we argue that phone conversation makes individuals vulnerable to attention failure (being greater for controlled vs. automatic components), and in this way, hampers stability of information-processing throughput (Humphreys and Revelle, 1984; Steinborn et al., 2016b).

# ETHICS STATEMENT

Written informed consent was obtained from the participants regarding their agreement with their participation in this research. Our study was in accordance with the ethical standards of the institutional and national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

#### AUTHOR CONTRIBUTIONS

Ideas and theorizing: MS and LH. Experiment and data analysis: MS. Interpreting results: MS and LH. Writing of the manuscript: MS.

# FUNDING

The present research was funded by the German Research Foundation (DFG) and the University of Würzburg in the funding programme Open Access Publishing.

#### ACKNOWLEDGMENTS

We would like to thank the following student research assistants for help with data collection at our lab: Wiebke Herter, Jonas Ebert, Janine Rathmann, Leila Hetzke, Lena Schuster, and Serena Grätz.

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**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2017 Steinborn and Huestegge. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# High-Frequency Binaural Beats Increase Cognitive Flexibility: Evidence from Dual-Task Crosstalk

Bernhard Hommel<sup>1</sup> , Roberta Sellaro<sup>1</sup> , Rico Fischer<sup>2</sup> , Saskia Borg<sup>1</sup> and Lorenza S. Colzato<sup>1</sup> \*

1 Institute for Psychological Research, Leiden Institute for Brain and Cognition, Leiden University, Leiden, Netherlands, <sup>2</sup> Department of Psychology, University of Greifswald, Greifswald, Germany

Increasing evidence suggests that cognitive-control processes can be configured to optimize either persistence of information processing (by amplifying competition between decision-making alternatives and top-down biasing of this competition) or flexibility (by dampening competition and biasing). We investigated whether highfrequency binaural beats, an auditory illusion suspected to act as a cognitive enhancer, have an impact on cognitive-control configuration. We hypothesized that binaural beats in the gamma range bias the cognitive-control style toward flexibility, which in turn should increase the crosstalk between tasks in a dual-task paradigm. We replicated earlier findings that the reaction time in the first-performed task is sensitive to the compatibility between the responses in the first and the second task—an indication of crosstalk. As predicted, exposing participants to binaural beats in the gamma range increased this effect as compared to a control condition in which participants were exposed to a continuous tone of 340 Hz. These findings provide converging evidence that the cognitive-control style can be systematically biased by inducing particular internal states; that high-frequency binaural beats bias the control style toward more flexibility; and that different styles are implemented by changing the strength of local competition and top-down bias.

Keywords: PRP, Dual-task, Binaural beats, gamma

# INTRODUCTION

The concept of cognitive control refers to processes that are not directly involved in processing and selecting stimulus events or actions but that rather orchestrate the processes responsible for these basic functions. Control processes are commonly characterized in terms of their capacity limitations but there is increasing evidence that they can also vary in style. Both functional (Goschke, 2003; Dreisbach and Goschke, 2004) and neural (Cools, 2008, 2012; Cools and D'Esposito, 2011) considerations suggest that cognitive-control states can vary both intra- and inter-individually to the degree that they either focus the available processing capacity on one single event or task or distribute capacity more widely across various processes or tasks. Following these leads, Hommel (2015) has suggested that the style of control varies between persistence and flexibility: while the former implies highly focused, exclusive processing, the latter implies a broad distribution of resources and rather integrative processing. As the current settings on the persistence-flexibility dimension affect both the basic cognitive operations and the operation

#### Edited by:

Tilo Strobach, MSH Medical School Hamburg – University of Applied Science and Medical University, Germany

#### Reviewed by:

Antonino Vallesi, University of Padua, Italy Leila Chaieb, University of Bonn, Germany

> \*Correspondence: Lorenza S. Colzato<EMAIL_ADDRESS>
#### Specialty section:

This article was submitted to Cognition, a section of the journal Frontiers in Psychology

Received: 14 June 2016 Accepted: 12 August 2016 Published: 24 August 2016

#### Citation:

Hommel B, Sellaro R, Fischer R, Borg S and Colzato LS (2016) High-Frequency Binaural Beats Increase Cognitive Flexibility: Evidence from Dual-Task Crosstalk. Front. Psychol. 7:1287. doi: 10.3389/fpsyg.2016.01287

characteristics of the superordinate cognitive-control processes, Hommel (2015) refers to the process of adjusting and changing the settings as "metacontrol" and to the resulting settings or states as "metacontrol states."

There are increasing attempts to identify means to bias metacontrol states in systematic ways, which is of both theoretical and practical relevance. It is of theoretical relevance because the characterization of effective means to bias the metacontrol state or control style provides constrains for understanding its underlying functional and neural mechanisms. And it is of practical relevance because effective means point to interesting methods for individually tailored cognitive enhancement, which for instance might seek to support individuals to implement particularly adaptive states according to their needs. The present study assessed an enhancement technique that has been frequently claimed to target cognitive control functions: binaural beats—the subjective experience of a beating tone with a frequency that corresponds to the frequency difference between two binaurally presented tones (Oster, 1973). Originally, binaural beats of low frequency have been argued to induce mental relaxation while high frequencies were assumed to induce alertness and attentional concentration (Vernon, 2009; Turow and Lane, 2011). This would suggest that high-frequency beats bias cognitive control toward persistence and focus, but recent findings suggest the exact opposite.

In a recent study, we presented participants with highfrequency binaural beats (gamma range), low-frequency binaural beats (alpha range), or a continuous tone of 340 Hz (Reedijk et al., 2015) before they performed an attentional blink task (Raymond et al., 1992). In this task, participants are presented with two visual targets in a rapid stream of stimuli, which commonly leads to the observation that they often miss the second target if it is presented briefly after the first. The impact of the low-frequency beats on the attentional blink did not differ from the control condition, while the high-frequency beats reduced the attentional blink significantly in individuals with low striatal dopamine. The presence of the attentional blink has been attributed to over-control (Olivers and Nieuwenhuis, 2006) i.e., a too strong focus on the first target, which leaves too few resources for the second. This suggests that high-frequency beats lead to a broader distribution, rather than to a stronger focus, of available resources—to more cognitive flexibility that is. This interpretation would fit the observation that binaural beats in the gamma range can improve performance in a divergent thinking task, but not in a convergent thinking task (Reedijk et al., 2013), as divergent thinking should benefit more from broadly distributed resources than convergent thinking.

The present study sought for converging evidence for the idea that binaural beats in the gamma range might bias cognitive control toward flexibility. In previous studies, control biases toward persistence or flexibility have been assessed by means of crosstalk between different event representations or across multiple tasks (e.g., Dreisbach and Goschke, 2004). Of particular relevance for our present study, Fischer and Hommel (2012) have tested participants in a dual-task paradigm after having primed them with a convergent-thinking task or a divergentthinking task. The dual-task paradigm was chosen to produce the well-established psychological refractory period (PRP) effect (see Pashler, 1998, for an overview): the observation that a response (R2) to a stimulus (S2) is slower the sooner this stimulus appears after the presentation of another stimulus (S1) signaling another response (R1). In other words, the reaction time for the second of two responses (RT2) increases as the interval between S1 and S2 (the stimulus onset asynchrony or SOA) decreases. The idea was that a convergent or divergent priming task would bias the control style toward persistence versus flexibility, respectively. The dependent measure of interest was the degree of crosstalk from the second on the first task. As previously demonstrated, RT1 (the reaction time in the first-performed task) is sensitive to the compatibility between the response in the first task (R1) and the response in the second (R2: Hommel, 1998; Logan and Schulkind, 2000); for instance, the time it takes to press the left of two keys in the first task (R1) is faster if the second task also requires a left keypress (R2). This demonstrates that R2 is activated before R1 selection is completed, which makes the response-compatibility effect (RCE) an indicator of the degree of distributed, parallel processing (Logan and Gordon, 2001; Lien and Proctor, 2002).

As one would expect from this reasoning, Fischer and Hommel (2012) found a smaller RCE if participants were primed with a convergent-thinking rather than a divergent-thinking task. If we assume that engaging in divergent thinking leads to a more broadly distributed allocation of processing resources, and that this bias toward more flexibility was sufficiently inert to affect performance in the overlapping dual task, we can conclude that the size of the RCE reflects the relative bias toward persistence and flexibility. If our hypothesis that high-frequency binaural beats bias the cognitive control style toward flexibility is correct, presenting participants with high-frequency beats should thus increase their RCE in a dual task that manipulates R1-R2 compatibility. We tested this prediction by adopting a task comparable to that used by Fischer and Hommel (2012) and having participants perform it after presenting them with either high-frequency binaural beats (the gamma group) or with a continuous tone of 340 Hz (the control group). Given that binaural beats may impact mood (Chaieb et al., 2015), heart rate, and human blood pressure (Carter, 2008), we also assessed participants' subjective affective states, heart rate, and blood pressure before and after the dual-task performance.

# MATERIALS AND METHODS

# Participants

Forty students (32 female, eight male; aged 18–27 years old) from Leiden University took part in exchange for course credit or pay. All had normal or corrected-to-normal sight and hearing. Participants were selected individually using the Mini International Neuropsychiatric Interview (M.I.N.I.; Sheehan et al., 1998), a well-established brief diagnostic tool in clinical, drug and stress research that screens for several psychiatric disorders and drug use (Sheehan et al., 1998; Colzato and Hommel, 2008; Colzato et al., 2008). A group of randomly selected 20 participants (15 female, five male) was exposed to gamma-frequency (40 Hz) binaural beats and the other 20 (17 female, three male) were assigned to a control condition, in which they were exposed to a constant tone of 340 Hz.

#### Ethical Statement

fpsyg-07-01287 August 22, 2016 Time: 11:40 # 3

Written informed consent was obtained from all subjects; the protocol and the remuneration arrangements of 5 euro were approved by the local ethical committee (Leiden University, Institute for Psychological Research).

# Dual-Task Paradigm

Like Fischer and Hommel (2012), we adopted the dual-task paradigm from Fischer et al. (2007; see also Logan and Schulkind, 2000), in which both tasks required participants to categorize the stimuli as being smaller vs. larger than 5. To avoid identical stimuli in both tasks (e.g., perceptual match) and to maintain the numerical distance to 5, the digits 3 and 7 and digits 2, 4, 6, and 8, presented in white on black background, served as stimuli for Task 1 (S1) and Task 2 (S2), respectively (Fischer et al., 2007). The same categorization of S1 and S2 (i.e., either both smaller or both larger than 5) implied a match between R1 and R2 categories, that is, response-category compatibility or response compatibility for short. Accordingly, opposite categorizations (i.e., S1 smaller and S2 larger than 5 or vice versa) implied response incompatibility, so that performance differences between response-compatible and response-incompatible trials reflect the RCE.

Participants were to press one of two keys in each task: the "," and "." key of the QWERTZ keyboard to S1, by using their right index and middle finger, and the "Y" and the "X" key to S2, by using their left middle and index finger. The stimulus-response mappings were counterbalanced across participants. Each trial began with a 500-ms fixation display, next to which S1 appeared above the screen center. Following an SOA of 40, 130, 300, or 900 ms, S2 appeared below screen center for 1000 ms. Both stimuli were replaced by a 2-s blank screen, followed by the 300 ms feedback "correct" or, in case of an incorrect response in either task, a missing response, or incorrect response order, the feedback "error." Participants were asked not to group responses and to respond as fast and as accurately as possible, first to S1 and only second to S2 (Task 1 priority). Participants performed 64 practice trials, followed by three experimental blocks of 64 trials each.

# Procedure

Participants were tested individually. Upon arrival, they were asked to rate their mood on a 9 × 9 Pleasure × Arousal grid (Russell et al., 1989), with values ranging from –4 to 4. The resulting score thus indicated the location of the participant's affective state within a two-dimensional space defined by hedonic tone and activation. Subsequently, participants listened to gamma-frequency (40 Hz) binaural beats or a constant tone of 340 Hz (control condition), all embedded in white noise to enhance clarity of the beats (Oster, 1973), for 3 min before and during the dual-task paradigm (training and experimental blocks). Binaural beats were presented through inear headphones (Etymotic Research ER-4B microPro), which provide 35 dB noise attenuation. The binaural beats were based on a 340 Hz carrier tone, which was used as the constant tone in the control condition. After the dual-task paradigm, participants rated their mood for the second time. After these measurements the experimental session ended and participants were paid, debriefed, and dismissed.

# Statistical Analyses

In view of the relatively small number of trials in each design cell we did not trim the data but analyzed median rather than mean RTs to reduce the impact of outliers. To assess whether binaural beats modulate dual-task performance, median RTs data for T1 and T2 were submitted to two separate repeated-measures analyses of variance (ANOVAs) with SOA (40 vs. 130 vs. 300 vs. 900) and Response Compatibility (R1-R2 compatible vs. R1-R2 incompatible) as within-participants factors and group (control vs. gamma) as between-participants factor.

Incorrect T1 (M = 1.4%, SEM = 0.2) and T2 responses (M = 4.2%, SEM = 0.6) were excluded and the analyses were restricted to trials in which both responses were correct. Mood (pleasure and arousal scores), heart rate (HR), systolic blood pressure (SBP) and dystolic blood pressure (DBP) were analyzed separately by means of repeated-measures ANOVAs. Effect of time (first vs. second measurement) served as within-subjects factor and group (gamma vs. control) as between-subject factor. A significance level of p < 0.05 was adopted for all statistical tests. In case of significant interaction, post hoc analyses were conducted using Tukey HSD test.

# RESULTS

# Participants

No significant group differences were observed in terms of age (M = 19.8, SEM = 0.6 and M = 20.25, SEM = 0.7 for the control and gamma group, respectively), t(38) < 1, p = 0.64, or gender distribution (F/M = 17/3 and 15/5 for the control and gamma group, respectively), χ2 < 1, p = 0.43.

# RT1

The main effect of SOA was significant, F(3,114) = 10.524, p < 0.001, η 2 <sup>p</sup> = 0.22, indicating faster RTs with increasing SOA. Post hoc analyses showed that RTs were significantly slower at SOA-40 and SOA-130 than at SOA-300 and SOA-900 (p<sup>s</sup> ≤ 0.007). No significant differences were observed between SOA-40 and SOA-130 (p = 0.999), or between SOA-300 and SOA-900 (p = 0.62).

The main effect of Response Compatibility was also significant, F(1,38) = 15.799, p < 0.001, η 2 <sup>p</sup> = 0.29, with participants being faster in categorizing S1 when S1 and S2 belonged to the same response category (M = 566, SEM = 12.7) than when they did not (M = 591, SEM = 17.7) (see **Figure 1** and **Table 1**). This effect was significant for short SOAs only (68 and 43 ms for SOA-40 and SOA-130, respectively, p<sup>s</sup> < 0.001), but not for long SOAs (12 and 0 ms, for SOA-300 and SOA-900, respectively, p<sup>s</sup> ≥ 0.88), as revealed by a significant interaction between SOA and Response Compatibility, F(3,114) = 17.78, p < 0.001, η 2 <sup>p</sup> = 0.32. More importantly,

we observed a significant interaction between group and Response Compatibility, F(1,38) = 4.33, p = 0.04, η 2 <sup>p</sup> = 0.10, showing that the compatibility effect was significantly (and more than three times) larger in the gamma group (38 ms) than in the control group (12 ms). No other significant effects were found, F<sup>s</sup> ≤ 1.79, p<sup>s</sup> ≥ 0.15.

#### RT2

The main effect of SOA was significant, F(3,114) = 450.126, p < 0.001, η 2 <sup>p</sup> = 0.92, reflecting the typical PRP effect with steeply increasing reaction times as SOAs get shorter (Pashler, 1994). Post hoc analyses revealed significant differences between all SOAs, p<sup>s</sup> < 0.001. The main effect of Response Compatibility was significant too, F(1,38) = 90.394, p < 0.001, η 2 <sup>p</sup> = 0.70, indicating faster RTs for compatible trials (M = 563, SEM = 10.9) than for incompatible trials (M = 642, SEM = 15.7). This effect varied as a function of SOA, as indicated by a significant Response Compatibility × SOA interaction, F(3,114) = 50.681, p < 0.001, η 2 <sup>p</sup> = 0.57. Post hoc analyses revealed that the RCE was significant for SOAs 40, 130, and 300 (155, 104, and 45 ms, respectively, p<sup>s</sup> < 0.001), but not for the SOA-900 (11 ms, p = 0.91). There was no other significant effect, F<sup>s</sup> ≤ 1.79, p<sup>s</sup> ≥ 0.36.

#### Physiological and Mood Measurements

ANOVAs showed a main effect of time for pleasure, F(1,38) = 8.792, p = 0.005, η 2 <sup>p</sup> = 0.19, arousal F(1,38) = 11.868, p = 0.001, η 2 <sup>p</sup> = 0.24, and HR, F(1,38) = 9.727, p = 0.003, η 2 <sup>p</sup> = 0.20, but not for SBP and DBP, F<sup>s</sup> < 1, p<sup>s</sup> ≥ 0.43. Pleasure, arousal and HR levels decreased during the experiment [Pleasure: MTime 1 = 1.6 (SEMTime 1 = 0.2) vs. MTime 2 = 1.1 (SEMTime 2 = 0.2); Arousal: 0.6 (0.2) vs. −0.4 (0.3); HR: 86.0 (2.4) vs. 77.5 (2.3)], whereas SBP [124.0 (2.4) vs. 122.5 (2.8)] and DPB [72.4 (1.4) vs. 72.8 (2.0)] did not vary across time. Importantly, neither the group effect nor the interaction was significant, F<sup>s</sup> ≤ 2.6, p<sup>s</sup> ≥ 0.11, suggesting that physiological and mood changes were comparable across groups: Pleasure [Control: 1.4 (0.2) vs. 1.1 (0.3); Gamma: 1.9 (0.2) vs. 1.1 (0.3)], arousal [Control: 0.5 (0.3) vs. −0.7 (0.4); Gamma: 0.7 (0.3) vs. −0.1 (0.4)], HR [Control: 89.3 (3.4) vs. 79.8 (3.3); Gamma: 82.7 (3.4) vs. 75.2 (3.3)], SBP [Control: 120.3 (3.4) vs. 120.3 (3.9); Gamma: 127.8 (3.4) vs. 124.8 (3.9)] and DBP [Control: 71.5 (2.0) vs. 74.8 (2.8); Gamma: 73.2 (2.0) vs. 70.9 (2.8)]. This suggests that we can rule out an account of our results in terms of physiological and/or mood changes.

## DISCUSSION

We tested the possibility that high-frequency binaural beats in the gamma range bias cognitive control toward more flexibility. We hypothesized that this would induce more crosstalk between the two tasks in a dual-task paradigm, resulting in a more pronounced RCE in the first task after being exposed to gamma beats than in a control condition. The findings show the predicted result and there was no indication that mood or other physiological changes were responsible for, or related to this effect (even though we acknowledge that a possible moderation

by mood need not be inconsistent with our prediction, as both cognitive control and mood rely on dopaminergic supply and are thus sensitive to changes therein: e.g., Akbari Chermahini and Hommel, 2012). We thus consider the present findings to support the assumption that gamma beats promote cognitive flexibility. This has both theoretical and practical relevance, as it shows that control states can be affected and be systematically biased by

TABLE 1 | Reaction times (in ms) for Task 1 (RT1) and Task 2 (RT2) as a function of group (control and gamma), response-category compatibility (R1-R2 compatibility), and stimulus onset asynchrony (SOA).

Standard errors of the means are presented in parenthesis.

task-irrelevant stimulation. This seems to suggest that cognitivecontrol states can be triggered exogenously, which challenges the traditional idea that stimulus processing and response selection emerges from the competition between endogenous control operations and exogenous, stimulus-induced tendencies (e.g., Verbruggen et al., 2014). On the positive side, our findings suggest that binaural beats provide the opportunity for cognitive enhancement by providing people with tools to tailor their cognitive-control states to situational demands. In particular, binaural beats seem to provide the opportunity to increase people's cognitive flexibility in a rather automatic fashion, that is, without any particular instruction or task-relevance of the beats. We note that our sample is predominantly female, a common limitation for studies using psychology students as participants. On the one hand, the two experimental groups were matched for gender, so that this general gender imbalance cannot account for our main findings. On the other hand, however, more research will be necessary to see whether these findings generalize to males.

Before speculating on the possible neural mechanisms underlying the impact of binaural beats, we would like to discuss a recent finding that does not seem to fit with our flexibility hypothesis. In particular, Colzato et al. (2015) observed that binaural gamma beats reduced the global-precedence effect in a Navon task (i.e., better performance to the global than to the local features of a visual stimulus) and interpreted this finding in terms of a stronger and/or more efficient focusing of attention on the relevant dimension. One possible implication of this finding could be that binaural gamma beats affect the choice between alternative interpretations of the same stimulus (as in the Navon task) differently than the choice between alternative stimulus events (as in Reedijk et al., 2015), alternative verbal concepts (as in Reedijk et al., 2013), and alternative responses (as in the present task). For instance, focusing visual attention on global features relies on information from different frequency channels than focusing on local features (Hills and Lewis, 2009) and it might be impossible to process both kinds of information at the same time. Another possibility is that a less pronounced globalprecedence effect actually represents a broader distribution of resources rather than more focusing. Global precedence might reflect an unequal distribution of attentional resources to the benefit of global information (Robertson, 1996), a rather strong focus that is, so that a reduction of the precedence effect reflects a more equal distribution. If so, the findings from the Navon task would fit reasonably well with our flexibility hypothesis. In any case, the question whether the flexibility hypothesis also holds for the processing and selective attention to global and local features of visual stimuli requires further study.

More research will also be needed to better understand the neural mechanisms underlying both the perceptual illusion that binaural beats induce and the way they affect cognitive-control states. The impact of auditory stimulation on cognitive control is unlikely to be a result of local cortical priming or interactions but rather seems to point neural communication at a larger scale. Larger-scale neural communication has been argued to rely on brain rhythms (Fries, 2009; Brunet et al., 2014), which might be sensitive to binaural beats of particular frequency bands.  These findings support the idea that the impact of binaural beats on cognitive processes might be mediated by neural phase locking (Karino et al., 2006; see, Chaieb et al., 2015, for a recent review on the effect of binaural beats on cognition and mood), in the sense that the beats induce or entrain a particular neural pattern that promotes or impairs neural communication underlying particular cognitive processes, such as cognitive control. Hence, binaural beats may act as a neural entrainment technique that operates by modulating the brain oscillations that particular cognitive processes require or benefit from, and oscillations in the gamma-frequency band might be particularly relevant for this purpose (Pastor et al., 2002; Schwarz and Taylor, 2005). To test that, future studies may make use of electroor magneto-encephalographic methods, which would permit

assessing the relationship between binaural beats and the auditory entrainment of brain oscillations (e.g., Galambos et al., 1981; Picton et al., 1987) and the role of oscillations in the gamma range for local brain communication (Kopell et al., 2000; Quilichini et al., 2010) more directly. Pharmacological studies would also be useful to test, for instance, the possibility that binaural beats involve norepinephrine/glutamate dynamics and increase phasic norepinephrine to enhance cognitive processing (Mather et al., 2015).

In any case, our findings suggest three main conclusions. First, they provide converging evidence for the idea that the current metacontrol state, which we argue implements a particular degree of persistence versus flexibility of cognitive control, can be systematically biased. This supports the general idea that control processes can vary in style (e.g., Goschke, 2003; Cools and D'Esposito, 2011; Hommel, 2015) and the assumption that inducing particular internal states provides an effective means to promote particular styles (e.g., Dreisbach and Goschke, 2004). Second, our findings provide converging evidence for the idea that binaural beats in the gamma range have an impact on the current metacontrol state. While the functional and neural mechanism underlying this impact is not yet entirely understood, the empirical link between the processing of rather lowlevel auditory stimuli and broadly operating control processes provides rather strong constraints on how this mechanism might work. The question how binaural beats affect brain rhythms related to cognitive control might be key in getting more insight on this issue. Third, together with our previous observations (Reedijk et al., 2013, 2015), the present findings point to some interesting commonalities of, and functional overlap between the selection and consolidation of successive visual stimuli, the sequential search of verbal stimuli in memory, and the separation of sequentially performed tasks. These commonalities seem to support Hommel's (2015) claim that metacontrol states operate on (i) the degree to which alternative representations compete with each other and (ii) the degree to which their mutual competition is top-down biased through the current goal. In particular, a tendency toward persistence would imply

#### REFERENCES

strong competition and top-down bias while a tendency toward flexibility would imply weak competition and top-down bias. If we assume that gamma beats reduce competition and top-down bias, this would explain why processing the second of two targets is less hampered by the first (Reedijk et al., 2015), why searching for multiple words related to the same concept is easier (Reedijk et al., 2013), and why response representations belonging to two different tasks show more crosstalk, as in the present study. Further studies will be necessary to investigate whether and to what degree the biasing of metacontrol states can affect not only the crosstalk between two tasks but also the efficiency to which they can be performed.

In the present study, we found crosstalk effects but no impact of binaural beats on the SOA effect on R2, which is considered to diagnose the bottleneck underlying multitasking. On the one hand, this dissociation between crosstalk and multitasking effects might be taken to challenge the claim that multitasking costs reflect inter-task crosstalk (Navon and Miller, 1987). On the other hand, however, it is still possible that the bottleneck underlying multitasking costs is functional, rather than structural, in nature and that the respective serial processing style is chosen to minimize crosstalk (Miller et al., 2009). In fact, it is possible that increasing crosstalk provides even stronger motivation to serialize as many (other) processes as possible, even though the size of our crosstalk effect might have been too small to make that visible in the SOA effect. To investigate these possibilities more systematically, it would seem to make sense to choose more powerful manipulations to target metacontrol states than those provided by binaural beats, but we leave that to future studies.

# AUTHOR CONTRIBUTIONS

Authors LC and BH designed the study and wrote the protocol. Authors RS, RF, and SB managed the literature searches and analyses. Author SB collected the data. Authors LC, BH, and RS wrote the first draft of the manuscript. All authors contributed to and have approved the final manuscript.

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2016 Hommel, Sellaro, Fischer, Borg and Colzato. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Transferability of Dual-Task Coordination Skills after Practice with Changing Component Tasks

Torsten Schubert<sup>1</sup> \*, Roman Liepelt<sup>2</sup> , Sebastian Kübler<sup>1</sup> and Tilo Strobach<sup>3</sup> \*

<sup>1</sup> Department of Psychology, Martin-Luther University Halle-Wittenberg, Halle, Germany, <sup>2</sup> Institute of Psychology, German Sport University Cologne, Cologne, Germany, <sup>3</sup> Department of Psychology, Medical School Hamburg, Hamburg, Germany

Recent research has demonstrated that dual-task performance with two simultaneously

presented tasks can be substantially improved as a result of practice. Among other mechanisms, theories of dual-task practice-relate this improvement to the acquisition of task coordination skills. These skills are assumed (1) to result from dual-task practice, but not from single-task practice, and (2) to be independent from the specific stimulus and response mappings during the practice situation and, therefore, transferable to new dual task situations. The present study is the first that provides an elaborated test of these assumptions in a context with well-controllable practice and transfer situations. To this end, we compared the effects of dual-task and single-task practice with a visual and an auditory sensory-motor component task on the dual-task performance in a subsequent transfer session. Importantly, stimulus and stimulus-response mapping conditions in the two component tasks changed repeatedly during practice sessions, which prevents that automatized stimulus-response associations may be transferred from practice to transfer. Dual-task performance was found to be improved after practice with the dual tasks in contrast to the single-task practice. These findings are consistent with the assumption that coordination skills had been acquired, which can be transferred to other dual-task situations independently on the specific stimulus and response mapping conditions of the practiced component tasks.

Keywords: dual tasks, practice, executive functions, task coordination skills, transfer

# INTRODUCTION

Performing two component tasks simultaneously at the same time (i.e., dual tasks) can be extremely difficult but this difficulty is often reduced after practice. For example, during the first lessons of driving school students find it challenging to coordinate the large number of different activities and components of car driving (e.g., changing gear, lane change, navigation, etc.). At the end of the lessons students are, however, able to coordinate these activities enabling them to drive safely in road traffic. In other words, an improved coordination of multiple task requirements may result from ongoing practice and may lead to improved performance in dual-task-like situations of car driving at the end of practice. However, while this example illustrates a plausible every day situation of practice-related improvement in dual-task coordination, findings in the literature are not conclusive concerning empirical evidence for the acquisition of task coordination skills to explain dual-task improvement.

#### Edited by:

Motonori Yamaguchi, Edge Hill University, United Kingdom

#### Reviewed by:

Miriam Gade, Catholic University of Eichstätt-Ingolstadt, Germany Kim-Phuong L. Vu, California State University, Long Beach, United States Cristina Iani, University of Modena and Reggio Emilia, Italy

\*Correspondence:

Torsten Schubert<EMAIL_ADDRESS>Tilo Strobach<EMAIL_ADDRESS>
#### Specialty section:

This article was submitted to Cognition, a section of the journal Frontiers in Psychology

Received: 23 December 2016 Accepted: 23 May 2017 Published: 13 June 2017

#### Citation:

Schubert T, Liepelt R, Kübler S and Strobach T (2017) Transferability of Dual-Task Coordination Skills after Practice with Changing Component Tasks. Front. Psychol. 8:956. doi: 10.3389/fpsyg.2017.00956

The present study tackles this issue, assessing whether task coordination skills are acquired with dual-task practice and whether this represents a mechanism underlying improved dualtask performance, which may explain practice-related reduction of dual-task costs. The improvement of task coordination skills is typically associated with an optimized control of two simultaneous task processing streams in dual-task situations (Hirst et al., 1980). From a learning perspective, it is important to know whether there is evidence for such skill acquisition (Anderson, 1988; Taatgen, 2013) in the context of complex situations (i.e., situations with two simultaneous tasks). Such evidence would be important for learning accounts assuming that task coordination skills can be acquired during dual-task training and that these skills are assumed to affect the practicerelated improvement of dual-task performance in addition to task automatization (Schneider and Shiffrin, 1977; Logan, 1988) or learning of the component tasks during dual-task practice (Ahissar et al., 2001; Ruthruff et al., 2006; Maquestiaux et al., 2008; Strobach et al., 2013; Strobach and Schubert, 2017).

# Prior Tests of Task Coordination Skills

According to theoretical considerations, task coordination skills are acquired and improved under dual-task practice conditions, but not when practicing each task in isolation under singletask practice conditions (Hirst et al., 1980; Kramer et al., 1995; Strobach et al., 2014). That is, these skills evolve from practicing two tasks simultaneously, rather than being attributable to learning the component tasks (Damos and Wickens, 1980; Oberauer and Kliegl, 2004; Silsupadol et al., 2009). Furthermore, once acquired, improved task coordination skills should at least be partially independent of the specific properties of the component tasks presented during dual-task practice. Consequently, these skills should be (at least to some extent) transferable across different dual-task situations (Kramer et al., 1995; Bherer et al., 2006; Liepelt et al., 2011).

Several studies have provided preliminary evidence, which supports the assumption that under certain conditions task coordination skills can indeed be acquired during long lasting training. As an example, studies testing the consequences of experience with dual tasks in comparison with the consequences of single task training were conducted in persons having extensive experience in playing a particular type of video games, known as action video games and its common subgenre ego-shooters. These games typically require the fast performance of several actions such as follow and maintain aims of the game, fight enemies, locate supplies, etc. The actions are performed under extreme temporal processing demands either at the same time or within close temporal proximity during the games. In contrast to persons without game experience, action video gamers show an optimized ability to perform and coordinate two simultaneous tasks in a dual-task paradigm of the Psychological Refractory Period (PRP) type (Pashler, 1994; Schubert, 1999) including well-controllable sensorimotor tasks (e.g., Strobach et al., 2012c; Chiappe et al., 2013).

Further evidence suggesting that task coordination skill may differ between persons with different degree of multitasking experience, stems from studies comparing the dual-task performance of highly skilled simultaneous interpreters and control participants. Simultaneous interpreting is an activity that is highly complex and requires the performance of multiple simultaneous tasks. Among others, these tasks include the analysis and understanding of the discourse in a first language, reformulating linguistic material, language production in a second language, and storing of intermediate processing steps. In two studies, we explored whether persons with experience in simultaneous interpreting possess superior skills in coordination of multiple tasks and whether they are able to transfer these skills to PRP dual tasks (Strobach et al., 2015a; Becker et al., 2016). In fact, we found faster dual-task reaction times (RTs) in persons with experience in simultaneous interpretation in contrast to control participants without such experience. Thus, action video gamers and simultaneous interpreters seem to possess superior skills to coordinate multiple tasks in lab-based dual-task situations. However, both cases, action video gaming as well as simultaneous interpreting, are no well-controllable practice situations and, therefore, do not allow for a systematic analysis of the specific underlying mechanisms and practice components as well as of the amount of practice enabling the acquisition of task coordination skills. For instance, in the context of video games, the number and frequency of situations with the presentation of simultaneous tasks is uncontrolled, so are the types of tasks combined. Further, it might be the sheer complexity of the situations (i.e., the combination of multiple tasks in action video games or during simultaneous interpreting without temporal overlap of these tasks), but not the experience of simultaneous tasks per se, that had led to the acquisition of task coordination skills (Schneider et al., 2002).

Recently, several studies proposed a well-controllable situation of dual-task practice, which is promising to investigate the characteristics of task coordination skill acquisition (Liepelt et al., 2011; Strobach et al., 2012d). In that situation, two groups of participants experience different types of practice with two sensorimotor component tasks, a visual-manual (i.e., the visual task) and an auditory-verbal tasks (i.e., the auditory task). In the visual task, there were spatially compatible mappings of circle positions on the screen to manual finger responses. In the auditory task, different tone pitches (low, medium, high) had a compatible mapping on number words ("ONE," "TWO," "THREE"). While hybrid practice included single-task and dual-task trials (see also Schumacher et al., 2001; Strobach et al., 2015b), single-task practice included single-task trials alone. After extended practice the authors compared the performance of the two different practice groups, i.e., the single-task and the hybrid practice groups, in a dual-task transfer situation in which the auditory and the visual task were processed simultaneously. In fact, after hybrid practice dual-task performance was better than after single-task practice.

Importantly, the studies could demonstrate an improvement after hybrid practice, which was exclusively realized by reduced dual-task RTs in the auditory task while there was no evidence for a hybrid practice advantage in the visual task. This is important because the auditory task was processed more slowly as compared to the visual task (see also Schumacher et al., 2001; Tombu and Jolicoeur, 2004; Hartley et al., 2011; Strobach et al., 2012a,b).

The authors proposed a model, which is illustrated in **Figures 1A,B**, to explain the observation of the hybrid-practice advantage in the longer auditory task but not the shorter visual task. As can be seen in the related figures, the model is based on the well-known assumption that sensorimotor tasks can be separated into an initial perception stage, a central response-selection stage and a final motor stage. According to the prominent central bottleneck model, the perception and motor stages in two tasks run in parallel while the central response-selection stages of such tasks are capacity-limited and represent bottleneck processes. This capacity limitation requires the serial processing of these stages and their coordination. **Figure 1A** illustrates the assumption that dual-task processing can be considered as the sequence of a capacity limitation in the faster visual task (e.g., at a central response-selection stage) followed by a switching operation between the responseselection stages, and the capacity limitation in the slower auditory task (Lien et al., 2003; Band and van Nes, 2006; Schubert, 2008). The switching operation is theorized as activating and/or instantiating the rules that map the stimuli of the longer task onto responses (Maquestiaux et al., 2004). It may be that these rules must be moved back into working memory or that the rules remaining in working memory throughout the task must be reestablished during ongoing processing of task 1 and task 2. After hybrid practice (**Figure 1A**) in contrast to single-task practice (**Figure 1B**), activation/instantiation processes are highly efficient due to task coordination skills, leading to a more efficient and therefore faster switching operation; in other words, participants have learned to load task information faster or more efficiently into the working memory as a result of hybrid practice. Therefore, improved dual-task performance after hybrid practice may occur in the longer auditory task, because the shortened switching operation is located between the response-selection stages in the faster visual task and the slower auditory task (Strobach et al., 2014, for a more detailed discussion).

Essentially, the conclusion of a shortened switching operation as an explanation for the findings of Liepelt et al. (2011) and Strobach et al. (2012d) can be distinguished from other explanations focusing on task automatization and/or stage shortening within the component tasks (Ruthruff et al., 2006; Strobach et al., 2013). This is so because, participants of the hybrid and single-task practice groups received the identical number of stimulus contacts and, thus, of experience with each component task during practice. In addition, the performance in the single visual task and single auditory task was similar across groups after single and hybrid practice. This makes it implausible

from a methodological perspective as well as from a results perspective to assume that differences in single-task performance between the training groups can explain the findings of Liepelt et al. (2011) and Strobach et al. (2012d).

Nevertheless, several methodological aspects of the findings of Liepelt et al. (2011) and of Strobach et al. (2012d) require further elucidation of the assumption that hybrid practice with the current type of component tasks can lead to the acquisition of transferable task coordination skills. This is so because in these studies the advantage after hybrid practice in the auditory task was evident when the authors analyzed the dualtask performance in a specific transfer situation, which was presented to the participants after the final training session. More specifically, in the studies of Liepelt et al. (2011) and Strobach et al. (2012d) the dual-task situation in the transfer session was either completely identical to the practiced situation, or it consisted of at least one unchanged but practiced component task while the other component task had changed from practice to transfer; note that in different experiments either the visual or the auditory task remained unchanged from practice to transfer.

The authors interpreted these findings (i.e., transfer in conditions with one task changed while the other task remained unchanged) as evidence for the assumption that task coordination skills are not tied to specific characteristics of the practiced component tasks (Liepelt et al., 2011; Strobach et al., 2012d). While this assumption was based on the fact that transfer was found either in a situation in which the auditory component task had changed and in a different situation in which the visual task had changed from training to transfer, one cannot completely be sure that the observed findings indeed showed the existence of task coordination skills that are unspecific to the two component tasks. Thus, it might be the case that the acquired task coordination skills are tied to either of the two component tasks in a dual-task situation and only one constant task after practice might be sufficient for a successful application of acquired coordination skills during transfer. Note that a situation with at least one unchanged task remaining constant between training and transfer might allow for precisely predicting the time durations when the processes of a component task need to be started in a dual-task situation and/or are expected to be finished; this might produce a benefit for the transfer of coordination skills after practice. We know from investigations on time duration production skills, that transfer from practiced to un-practiced time durations is impaired when a secondary task presented during practice was removed during the transfer test (Healy et al., 2005).

In sum, prior studies lack convincing empirical evidence for the existence of task coordination skills, which can be transferred in task contexts with two changing component tasks during training of well-controllable task and training situations. The aim of the present study is to fill this gap in the dual-task practice literature.

Findings of earlier studies are tempting to assume that an increase in the variability of the practiced learning examples in alternative practice situations may enforce skill transferability after practice (Schmidt and Bjork, 1992; but see Logan, 1990). For instance, the transferability of duration production skills to un-practiced durations were increased after variable practice with mixed durations in contrast to blocked sequences of trials all of the same duration in the study of Schneider et al. (1995). Therefore, in the current study, the specific stimulus and stimulus-response rules of both, i.e., of the visual and of the auditory tasks (Liepelt et al., 2011) were changed between every second practice sessions to increase the variability of the learning examples during practice; this should enforce the need to train general task coordination skills but not specific task automatization.

One group of participants trained the two tasks in 15 hybrid dual-task training sessions and these participants were tested in a final transfer Session 16 at the end of practice. A further group of single-task learners trained the two tasks in single task regimen and were also tested for dual-task performance in this transfer session; these participants also experienced the two session-wise changes of the stimuli and the stimulus-response rules of the component tasks, i.e., the specific character of the visual and auditory tasks was changed between practice sessions equivalently to the changes in the hybrid group. Importantly, the single-task group, had experience only with single-task trials during the 15 practice sessions before the final transfer Session 16. Based on the assumption that an increase in variability increases the chances for transfer of task coordination skills, we predict improved dual-task performance in transfer Session 16 after variable hybrid practice than after single-task practice.

# MATERIALS AND METHODS

# Participants

Participants were randomly assigned to one of the two experimental groups: the hybrid group and the single-task group. In line with previous dual-task learning studies (Liepelt et al., 2011; Strobach et al., 2012d), we included eight participants (six female) with a mean age of M = 24.5 years (SD = 3.5 years) and an age range from 19 to 29 years in the hybrid group. Eight participants (five females) were included in the single-task group with a mean age of M = 23.8 years (SD = 3.2 years, age range from 19 to 28 years). According to the experience from earlier training studies (Liepelt et al., 2011; Strobach et al., 2012d), the administration of frequently changing stimulusresponse rules should require a large number of training sessions in order to get reliable training-related improvements in task performance. A group size of eight participants in each group allows bringing together the requirements for an increased number of training sessions with the requirements of feasible experimental economics (Schubert and Strobach, 2012). Furthermore, an additional analysis with G∗Power (Faul et al., 2007) using values of previous training studies (Liepelt et al., 2011; Strobach et al., 2012d) has shown that a group size of eight participants per group will provide sufficient power (>0.9) with an alpha set at 0.05. The two groups performed altogether 16 sessions, which represents a volume of 256 h of experimentation. All participants of these groups were included into the final data set. Participants were contacted through flyers and electronic mails. All participants had normal or corrected to normal vision

and were not informed of the purpose of the experiment. They were paid for participation at a rate of 8€ per session plus performance-based bonuses.

# Apparatus

Visual stimuli were presented on a 17-inch color monitor and auditory stimuli were presented via headphones, which were connected to a Pentium I IBM-compatible PC. The RT for manual responses was recorded with a button box and the RT of verbal responses was recorded via a voice key connected to the experimental computer. The experimenter typed the actual response on a computer keyboard so that accuracy could be assessed in the analysis. The experiment was controlled by the software package ERTS (Experimental Runtime System; Beringer, 2000).

## Stimuli and Component Tasks

During Sessions 1–16, participants conducted different versions of visual and auditory sensorimotor tasks. All tasks and versions were three-choice tasks and included mappings between 3 stimuli and 3 responses. In the visual task versions, all visual stimuli were white and participants responded with their index, middle, and ring finger of their right hand in accordance to the following lists of stimuli as illustrated in **Figure 2**: circles appearing at the left, central, or right screen position (Sessions 1, 2, 8, and 16), squares appearing at the left, right, or central positions (Sessions 3 and 4), a circle, square, and triangle appearing at the central position (Sessions 5 and 6), a line pattern, semicircle, and cross appearing at the central position (Sessions 7 and 15), triangles of large, medium, and small size appearing at the central position (Sessions 9 and 10), a right-, left-, or top-oriented opening in a square appearing at the central position (Sessions 11 and 12), and a diamond appearing at vertical top, central, and bottom positions (Sessions 13 and 14). In the auditory task versions, participants responded with the verbal number words "ONE," "TWO," and "THREE" in accordance to the following stimuli (**Figure 2**): a low, middle, and high sine-wave tone (Sessions 1, 2, 8, 9, 10, and 16), a low high-pitched, middle high-pitched, and high high-pitched sine-wave tone (Sessions 3 and 4), a whistling sound, middle sine-wave tone, and buzzer tone (Sessions 5 and 6), a "wup"-like sound, signal sound, and clicking noise (Sessions 7 and 15), high white noise, middle sine-wave tone, and low drum sound (Sessions 11 and 12), white noise, middle "wup" like sound, and high "djing"-like sound (Sessions 13 and 14). We selected these sets of stimulus and response mappings across the visual and auditory tasks and the experimental sessions, because these mappings significantly differ from each other and thus increased variability on the one hand (e.g., stimulus-response mapping rules were compatible, incompatible, and arbitrary). On the other hand, we assumed that participants were able to perform these mappings after only a short introduction.

In visual single-task trials, three white dashes served as placeholders for the possible positions of the visual stimuli. They appeared as a warning signal 500 ms before the visual stimulus was presented. The stimulus remained visible until the participant responded or a 2,000 ms response interval had expired. After correct responses, RTs were presented for 1,500 ms on the screen. Following incorrect responses, the word "ERROR" (German: "FEHLER") appeared. A blank interval of 700 ms preceded the beginning of the next trial. An auditory single-task trial started with the presentation of three dashes on the computer screen. After an interval of 500 ms, the tones were presented. The trial was completed when the participant responded verbally or a 2,000 ms response interval had expired. To acquire an accurate measurement of verbal responses, the experimenter typed the actual response on a computer keyboard so that accuracy could be assessed in the analysis. After verbal responses, RTs were presented for 1,500 ms on the screen. Following omitted verbal responses, the word "ERROR" (German: "FEHLER") appeared. A blank interval of 700 ms preceded the beginning of the next trial. Dual-task trials included the visual and the auditory task. These trials were identical to single-task trials with the exception that a visual and an auditory stimulus were presented simultaneously (SOA = 0 ms). As in previous studies on a similar dual-task procedure, participants were not told to respond in any particular order and they should give equal priority to the two tasks. Instructions were designed to encourage participants to perform the tasks as quickly and accurately as possible in all trials and blocks.

# Design and Procedure Hybrid Group

This group performed hybrid practice in Sessions 1–16. Each session lasted <60 min and these sessions were conducted on consecutive days (except weekends). During hybrid practice, there were single-task trials and dual-task trials. Single tasks of the visual or the auditory task were included into single-task blocks of 45 trials. In contrast, 18 dual-task trials were included into mixed blocks combined with 30 mixed single-task trials, 15 of the visual task and 15 of the auditory task. These mixed single-task trials helped to ensure that participants were equally prepared for both tasks in mixed blocks; alternatively, they could prepare for only one task that is executed first in dual-task trials. Participants were instructed to respond to both stimuli as quickly and accurately as possible during all blocks. Response order was free.

In Session 1, participants of the hybrid group performed six visual and six auditory single-task blocks that were presented in alternating order (**Table 1**). Half of the participants started with a visual single-task block and the other half with an auditory singletask block. Session 2 included six single-task blocks (three visual and three auditory task blocks) and eight mixed blocks. After two initial single-task blocks (one visual and one auditory single-task block), sequences of two mixed blocks and one single-task block followed; the type of single-task blocks was alternated. The order of blocks (first visual or auditory task block) was counterbalanced across participants. The design in Sessions 3–16 was identical to that in Session 2 but these sessions included two additional mixed blocks at the end.

#### Single-Task Group

The experimental procedure in the single-task group was similar to the hybrid group with the exception that this group of participants performed single tasks (almost) exclusively in Sessions 1–15 (**Table 1**). To keep the number of stimulus contacts

TABLE 1 | Illustration of the training regime across 16 sessions in the hybrid group/single-task group.

s-short and s-long indicate short single-task blocks (45 trials) and long single-task blocks (66 trials), respectively, of either the visual or the auditory task. mix illustrates mixed blocks (including 15 visual single-task trials, 15 auditory single-task trials, and 18 dual-task trials).

between dual-task conditions (in the hybrid group) and singletask conditions constant, one dual-task trial was replaced by one single-task trial of each task. Consequently, we had singletask blocks with 45 trials (short blocks) or 66 trials (long blocks). Session 1 was identical to the hybrid group. Session 2 included 12 single-task blocks (six visual and six auditory task blocks). Importantly, this session also included two mixed blocks. These mixed blocks were included to analyze initial dual-task performance in the single-task practice group before practice and to match this performance between practice groups. In Session 2, these two initial mixed blocks were introduced after two short single-task blocks. Then, sequences of one short and two long single-task blocks followed. In Sessions 3–15, we presented 16 single-task blocks (eight visual and eight auditory task blocks). After two initial short single-task blocks, sequences of two long single-task blocks and one short single-task block followed. In Sessions 2–15, blocks with the visual and auditory task were alternated and the first type of block (either visual or auditory task) was counterbalanced between subjects. The following Session 16 was identical to this session in the hybrid group.

# RESULTS

## Statistical Analyses

We excluded all trials in which responses were omitted or incorrect (7.0%) prior to statistical RT analyses. The alpha level for significant effects and interaction was set to p = 0.05. Effects sizes were illustrated with partial η 2 for significant main effects and interactions.

To obtain a strong and reliable parameter for dual-task performance, we assessed dual-task performance in dualtask trials and single tasks of single-task blocks: dualtask costs = Performancedual-task trials – Performancesingle-task trials of single-task blocks (Tombu and Jolicoeur, 2004; Strobach et al., 2015c). This parameter of dual-task costs is particularly essential when investigating task coordination skills (Liepelt et al., 2011; Strobach et al., 2012d). It combines trials that are by definition not related to each other (i.e., pure single-task trials and dualtask trials) and is therefore most informative to investigate task coordination skills. We thus excluded mixed single-task trials from the test on skill acquisition because processing associated with this type of trials is less specified. That is, participants will also be partially prepared for a task type that did not occur and this omission of an expected stimulus and task may have thrown off or surprised subjects (Tombu and Jolicoeur, 2004).

We focused on Sessions 2–15 to analyze hybrid practice performance; note that there were no dual-task trials included in Session 1. And we focused on Sessions 1–15 when comparing the single-task performance during hybrid and single-task practice. When testing the acquisition and transfer of task coordination skills, we analyzed single-task and dual-task performance during pre-test and post-test. For the pre-test, we analyzed the dual-task performance by comparing the data in the first two single-task blocks with that of the dual-task trials in the two following mixed blocks in Session 2 in both, the hybrid and the singletask group; note that also the single-task group performed two mixed blocks after two single-task blocks in the beginning of this session. The data of Session 16 (in which both the single-task

and hybrid groups performed single and dual tasks) served as the post-test measure for the performance at the end of practice.

# Hybrid and Single-Task Practice Performance

The RT and error data of the practice sessions are illustrated in **Figure 3** and **Table 2**, respectively. To analyze dual-task performance in the hybrid group across practice, we included the within-subject factors Session (Sessions 2–15) and Trial type (dual tasks vs. single tasks) in mixed measures ANOVAs. The single-task and dual-task RTs varied as an effect of changes in the stimulus-response mapping characteristics and hybrid practice in the auditory task, as indicated by main effects of Session F(13,91) = 28.530, p < 0.001, η 2 <sup>p</sup> = 0.80, and Trial type, F(1,7) = 62.459, p < 0.001, η 2 <sup>p</sup> = 0.90, as well as the interaction, F(13,91) = 16.801, p < 0.001, η 2 <sup>p</sup> = 0.71 (**Figure 3A**). The visual-task RTs showed a similar pattern with main effects of Session, F(13,91) = 31.710, p < 0.001, η 2 <sup>p</sup> = 0.82, and Trial type, F(1,7) = 55.972, p < 0.001, η 2 <sup>p</sup> = 0.89, as well as the interaction of Session and Trial type, F(13,91) = 5.028, p < 0.001, η 2 <sup>p</sup> = 0.42 (**Figure 3B**). Similar to the auditory-task RTs, this task's error rates also varied with hybrid practice and changes in the stimulusresponse mapping characteristics, as indicated by main effects of Session, F(13,91) = 5.502, p < 0.001, η 2 <sup>p</sup> = 0.44, and Trial type, F(1,7) = 97.811, p < 0.001, η 2 <sup>p</sup> = 0.93, as well as their interaction, F(13,91) = 6.823, p < 0.001, η 2 <sup>p</sup> = 0.49. The visualtask error rates showed a similar pattern with main effects of Session, F(13,91) = 5.256, p < 0.001, η 2 <sup>p</sup> = 0.43, and Trial type, F(1,7) = 11.767, p < 0.001, η 2 <sup>p</sup> = 0.63, as well as the interaction, F(13,91) = 4.873, p < 0.001, η 2 <sup>p</sup> = 0.41.

To demonstrate similar levels of component-task processing skills during practice and transfer, we analyzed the singletask trials of single-task blocks in mixed measures ANOVA including the within-subjects factor Session (Sessions 1–15) and the between-subjects factor Group (hybrid group vs. single-task group); note that potential effects and interactions with Group were mainly relevant in these analyses. The auditory singletask RTs (**Figure 3A**) showed no main effect or interaction with Group, Fs < 0.913, ps > 0.545; Session demonstrated variable RTs across practice and changing task characteristics, F(1,14) = 66.456, p < 0.001, η 2 <sup>p</sup> = 0.83. The error data in this task showed no main effect of and interaction with Group, Fs < 0.910, ps > 0.549; Session was significant, F(1,14) = 9.381, p < 0.001, η 2 <sup>p</sup> = 0.40 (**Table 2**). The visual single-task RTs (**Figure 3B**) produced no main effect of and interaction with Group, Fs < 0.910, ps > 0.549; Session demonstrated variable RTs across practice and changing task characteristics, F(1,14) = 69.854, p < 0.001, η 2 <sup>p</sup> = 0.83. The main effect of and interaction with Group were also not evident in the error analysis of the visual task, Fs < 1.219, ps > 0.29; Session was significant, F(1,14) = 8.976, p < 0.001, η 2 <sup>p</sup> = 0.38 (**Table 2**). Thus, component-task processing skills did not statistically differ between both groups across practice.

#### Transfer Test on Task Coordination Skills

In this section, we compare the dual-task costs at the beginning of practice (i.e., pre-test: first two single-task blocks and dualtask trials of the first two mixed blocks in Session 2) and at the end of practice (i.e., post-test: single-task blocks and dualtask trials of the mixed blocks in Session 16) in the hybrid and the single-task group. Reduced dual-task costs and improved dual-task performance in the hybrid group, compared to the single-task group, during post-test would indicate the acquisition and transfer of improved task coordination skills if controlled for possible performance differences in the pre-test. In particular, as illustrated in **Figure 1**, the improved dual-task performance in the hybrid group is expected in the auditory task, because the

TABLE 2 | Error rates (in percent) across the auditory and visual tasks, the single-task, mixed single-task, and dual-task conditions, from Session 1 to Session 16 in the hybrid and single-task

 groups.

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anticipated speed-up switching operation is located between the central response-selection stages of the shorter visual and the longer auditory task. Thus, dual-task costs should be reduced at post-test after hybrid practice primarily in the auditory task and less so in the visual task. To test these assumptions, we performed mixed measures ANOVAs on the RT and error data with the within-subject factors Testphase (pre-test vs. post-test), Trialtype (single-task trials vs. dual-task trials), Task (auditory, visual task) and the between-subject factor Group (hybrid group vs. single-task group). This ANOVA revealed a significant fourway interaction, F(1,14) = 8.528, p = 0.01, η 2 <sup>p</sup> = 0.38, for the RT data, suggesting changes in dual-task costs that differed between the auditory and the visual task. Accordingly, we conducted subsequent ANOVAs with the factors Testphase, Trialtype, and Group separately for the auditory and the visual task to assess whether the different types of practice led to changes in dual-task costs in these different tasks.

The RT results of the auditory task point to the acquisition and transfer of improved task coordination skills after hybrid practice. In fact, we found a three-way interaction between Testphase, Trialtype, and Group, F(1,14) = 12.671, p < 0.01, η 2 <sup>p</sup> = 0.48. As illustrated in **Figure 4A**, at post-test, dualtask costs were significantly reduced after hybrid practice (M = 40 ms) in contrast to single-task practice (M = 110 ms), t(14) < 2.135, p < 0.05. At pre-test, the difference between dual-task cost between the hybrid group and single-task group was not significant, t(14) = 1.305, p = 0.21. Thus, improved dual-task performance in the hybrid group at post-test cannot be explained by improved initial dual-task performance levels in this group relative to the single-task group. Furthermore, the improvement in dual-task performance is dual-task-specific, since it cannot be explained with differences in single-task RTs between groups, t(14) = 0.714, p = 49. The remaining effects and interactions in this RT analysis were as follows: Testphase, F(1,14) = 257.679, p < 0.001, η 2 <sup>p</sup> = 0.95, Trialtype, F(1,14) = 150.129, p < 0.001, η 2 <sup>p</sup> = 0.92, Group, F(1,14) = 0.046, p = 0.83, Testphase × Trialtype, F(1,14) = 183.536, p < 0.001, η 2 <sup>p</sup> = 0.93, Testphase × Group, F(1,14) = 1.123, p = 0.31, Trialtype × Group, F(1,14) = 0.057, p = 0.82. The error analysis of the auditory task showed no three-way interaction of Testphase, Trialtype, and Group, F(1,14) = 0.518, p = 0.49. The remaining effects and interactions in this analysis were as follows: Testphase, F(1,14) = 1.070, p = 0.32, Trialtype, F(1,14) = 51.683, p < 0.001, η 2 <sup>p</sup> = 0.79, Group, F(1,14) = 1.104, p = 0.31, Testphase × Trialtype, F(1,14) = 3.646, p = 0.08, Testphase × Group, F(1,14) = 0.014, p = 0.91, Trialtype × Group, F(1,14) = 2.649, p = 0.13 (**Table 2**).

In order to test whether, the advanced dual-task performance (i.e., decreased dual-task costs) in the auditory task after hybrid practice compared to single-task practice is based on only a few participants with mean values strongly deviating from those of the rest, we conducted a non-parametric test on the dual-task RT costs in the auditory task. This test includes the rank of each participant according to its dual-task costs in the auditory task and it ignores the absolute dual-task costs. A non-parametric Mann–Whitney U test showed a significant difference between the ranks of the hybrid group (mean rank = 5.88) and the singletask group (mean rank = 11.13), p < 0.05 (lower rank value indicates a lower amount of dual-task costs). This result shows that the present finding of reduced dual-task costs after hybrid practice is not the result of only a few outlier participants.

In the visual task, there was no advantage in the RT data and thus no evidence for the acquisition and transfer of improved task coordination skills after hybrid practice. This conclusion results from the finding of a non-significant three-way interaction of Testphase, Trialtype, and Group, F(1,14) = 0.874, p > 0.37 (**Figure 4B**). The remaining effects and interactions in this RT analysis were as follows: Testphase, F(1,14) = 80.229, p < 0.001, η 2 <sup>p</sup> = 0.85, Trialtype, F(1,14) = 117.221, p < 0.001, η 2 <sup>p</sup> = 0.89, Group, F(1,14) = 0.569, p = 0.46, Testphase × Trialtype, F(1,14) = 49.712, p < 0.001, η 2 <sup>p</sup> = 0.78, Testphase × Group, F(1,14) = 0.909, p = 0.36, Trialtype × Group, F(1,14) = 0.774, p = 0.39. Analogous, the error analysis of the visual task also showed no interaction of Testphase, Trialtype, and Group, F(1,14) = 0.153, p = 0.70. The remaining effects and interactions in this analysis on error rates in the visual task were as follows: Testphase, F(1,14) = 0.050, p = 0.84, Trialtype, F(1,14) = 3.630, p = 0.08, Group, F(1,14) = 0.035, p = 0.85, Testphase × Trialtype, F(1,14) = 7.177, p < 0.05, η 2 <sup>p</sup> = 0.34, Testphase × Group, F(1,14) = 1.610, p = 0.23, Trialtype × Group, F(1,14) = 2.119, p = 0.17 (**Table 2**). In sum, the present data pattern is consistent with the assumption of an acquisition of transferable task coordination skill and the assumption of a speed-up switching operation between tasks after hybrid practice.

# Follow-Up Analyses

fpsyg-08-00956 June 10, 2017 Time: 15:43 # 10

At this point, critics may say that the transferable character of task coordination skills has not yet completely demonstrated. This is because the specific combination of component tasks in Session 16 was previously experienced in Sessions 1, 2, and 8. The dual-task performance advantage in the hybrid group may thus exclusively result from practice in these tree sessions and it might not result from learning processes during the other practice sessions and the related variations of the component tasks. To test this counter argumentation, we conducted a new group of 10 participants with single-task practice of eight sessions only. Importantly, the changes in the characteristics of the stimulusresponse mappings in these eight sessions were identical to the changes in the hybrid group's first eight sessions. In addition, this new single-task group had single-task practice in the first seven sessions (with the exception of a pre-test and its two mixed blocks in the beginning of Session 2) and performed single-task and mixed blocks in the final test Session 8. We compared the dual-task performance of this new single-task practice group with the dual-task performance of the hybrid training group in the 8th session. This comparison showed no main effect of Group and no interaction with Group for the analysis of the auditory-task RTs during pre-test and post-test under singletask and dual-task conditions, both Fs(1,18) < 2.402, ps > 0.14, η 2 p s < 0.15. This finding is important because it shows equal dual-task performance of the hybrid and the new single-task practice group after eight training sessions, which include the sessions with identical stimulus-response characteristics of the component tasks as those in Session 16 of the hybrid group. The fact that we could not find a difference between the new singletask and hybrid group at Session 8, but a significant difference in dual-task performance between the (initial) single-task and the hybrid group at Session 16, suggests the latter dual-task advantage has occurred because of the additional training sessions between Sessions 9 and 16. However, the trained component tasks during these additional training sessions, i.e., Sessions 9–15, did differ from the component tasks in Session 16. Therefore, we can conclude that the observed hybrid-practice advantage after 16 sessions cannot be explained by the repetition of the component task situation in Session 16 with that from the Sessions 1, 2, and 8. Differently to that participants of the hybrid-practice task have acquired skills from the training with task situations that differed to those from the component tasks in Session 16 and the acquired skills have been transferred between task situations. The results showed further that this transfer requires more than eight sessions of practice in the current protocol of hybrid practice.

# DISCUSSION

The present study investigated whether hybrid-practice-related task coordination skills are independent from the specific

characteristics of the practiced component tasks and are thus transferable in a well-controllable practice and transfer context. In particular, transferable skills were shown in the data of the longer auditory task, but not for the data of the shorter visual task of the present task design when both component tasks were changed between practice and transfer. This data is in line with and extents the findings of Liepelt et al. (2011) as well as Strobach et al. (2015b) that provided evidence of skill transfer to dual tasks with only one changed task. These prior findings did not rule out that improved dual-task coordination skills may require constant features between practice and transfer, such as at least one nonchanged component task. The present dual-task transfer test (Session 16) points to a hybrid-practice advantage with changed characteristics in two tasks. Furthermore, our data provide hints for the dose-dependency of transferable task coordination skills, since there is no hybrid-practice advantage after eight sessions when compared with the new single-task group. The hybridpractice advantage in dual tasks emerged only after a doubling of the practice amount. In general, our findings suggest that the automatization

of the combined component tasks (e.g., Ahissar et al., 2001; Ruthruff et al., 2006; Maquestiaux et al., 2008; Strobach et al., 2013; Strobach and Schubert, 2017) is complemented by the acquisition of task coordination skills. Both mechanisms, i.e., task automatization and improvement of task coordination contribute to the practice-related optimization of dual-task processing (Hirst et al., 1980; Kramer et al., 1995). The present data suggest that task automatization has played a rather minor role in the present dual-task context since the component tasks of the post-test (i.e., Session 16) received a small dose of repetitions during prior sessions. Note that the component tasks of the post-test Session 16 were repeated only in Sessions 1, 2, and 8. In all other sessions, we changed the stimuli and stimulus-response mapping rules, which precluded a repeated learning of specific stimulusresponse episodes, which, however, would be needed to enable task automatization (Ruthruff et al., 2006). Moreover, we found large transfer effects in the final Session 16, which was preceded by permanently changing component task situations especially from Session 8 and, partially, also during the Sessions 1–8.

One alternative explanation might be that hybrid practice serves to integrate two tasks more efficiently, to the point of combining them into one single 'super task' (Hazeltine et al., 2002; Ruthruff et al., 2006). According to this super task explanation, one might assume that two separate responseselections processes were performed at the beginning of practice, one response-selection in each component task, while the extensive hybrid practice might have led to the integration of two response-selection processes into one single selection process of a combined task. The processing of only one selection process, instead of two, would reduce dual-task RTs. In fact, the situation of separate practice of two tasks during singletask practice would have prevented integration of both selection processes and would thus prolong RTs in the dual-task situation. However, the integrated selection of two responses after hybrid practice should require that specific pairs of component tasks should be presented constantly throughout the training and that their specific combination should remain constant even in the

post-test session; otherwise in case of permanently changing component tasks, including stimuli and stimulus-response rules, an integrated response-selection process could not emerge and could not transfer from one session to the next; the latter is prevented if the task rules have changed from session n-1 to n, which was precisely the case in the current hybrid training regimen (Hazeltine et al., 2002; Ruthruff et al., 2006). Because we found transfer of skills between changing task situations as a result of hybrid dual-task training, the observed practice-related improvement of dual-task performance cannot be explained by the assumption that both tasks were integrated into one supertask representation.

But how do task coordination skills acquired by participants improve dual-task performance exactly? As illustrated in **Figures 1A,B**, we assume that the present findings favor a shortened switching operation as a potential realization of improved skills of task coordination (Liepelt et al., 2011; Strobach et al., 2014). A shortened switching operation may be located at the end of the central response-selection stage in the shorter task and before the start of this stage in a longer task (Lien et al., 2003; Band and van Nes, 2006); thus, this shortened operation is particularly suited to explain the exclusive hybrid-practice advantage in the longer (auditory) task and the lacking advantage in the shorter (visual) task. Such a location of the switching operation would be in accordance with the assumption that training may lead to an optimized bottleneck processing being it a structural or strategic in nature (Pashler, 1994; Meyer and Kieras, 1997). A shortened switching operation may relate to a more efficient release (for example, by inhibition) of task information from the shorter task (that turns to an irrelevant task after the switch in a current trial) as well as the activation and instantiation of the response mapping rules of the longer task (De Jong, 1995). Due to its particular locus at the end of central processing in task 1, the shortening of a switching operation after hybrid practice would influence dual-task RTs in the longer auditory task, whereas there should be no (or only a minimal) effect on the shorter visual task of the present dual-task situation. These assumptions may explain the observed processing advantage in the current dual-task situation after hybrid practice.

Additionally, we assume that the proposed mechanism is generalizable in the following way: the shortening of a switching operation after hybrid practice would also influence dual-task RTs in any longer task (i.e., the task with the second response), while there should be no (or only a minimal) effect on any shorter task (i.e., the task with a first response); this generalization is based on the assumption that the order of motor responses is equivalent to the order of the tasks' response-selection stages (Ruthruff et al.,

#### REFERENCES

2006). In that case, a hybrid practice effect might lead especially to an earlier start of a task 2 response after the switch and the occurrence of this dual-task training effect should be independent on the specific stimulus and response-selection characteristics of task 2 and task 1 as long as the order of a shorter and a longer task is preserved throughout the training (Strobach et al., 2014). While the current experiment provided evidence for this assumption for the combination of a certain order of a shorter visual motor and an auditory verbal task, other studies may test whether other task combinations would allow for the occurrence of a hybrid dualtask practice advantage located at the longer task (or task 2) of a dual-task situation.

In sum, we demonstrated that task coordination skills improving dual-task performance with practice, are (1) acquired in dual-task situations, (2) transferable, and (3) dose-dependent. Future studies may specify this type of skill acquisition (Taatgen, 2013) and locate its impact in the dual-task processing architecture (Meyer and Kieras, 1997; Schubert, 2008; Strobach et al., 2014).

#### ETHICS STATEMENT

Approval by the local ethics committee was obtained before commencement of the study, which was conducted in strict accordance with the local ethics policies of the Humboldt University Berlin, Germany.

#### AUTHOR CONTRIBUTIONS

ToS, RL, SK, and TiS were involved in every step of this research project.

# FUNDING

The present research was supported by research grants of the German Research Foundation (Deutsche Forschungsgemeinschaft, Schu 1397/3-2, Schu 1397/5-2, Schu 1397/7-1; Str 1223/1-1).

# ACKNOWLEDGMENT

We would like to thank Nina Ammelburg, Anne-Marie Horn, Marina Palazova, and Franziska Plessow for their assistance in data collection.

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**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2017 Schubert, Liepelt, Kübler and Strobach. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Walking-Related Dual-Task Interference in Early-to-Middle-Stage Huntington's Disease: An Auditory Event Related Potential Study

Marina de Tommaso<sup>1</sup> \*, Katia Ricci <sup>1</sup> , Anna Montemurno<sup>1</sup> , Eleonora Vecchio<sup>1</sup> and Sara Invitto<sup>2</sup>

<sup>1</sup> Neurophysiopathology of Pain, Basic Medical Science, Neuroscience and Sensory System Department–SMBNOS-Bari Aldo Moro University, Bari, Italy, <sup>2</sup> Department of Biological and Environmental Sciences and Technologies, University of Salento, Lecce, Italy

Objective: To compare interference between walking and a simple P3 auditory odd-ball paradigm in patients with Huntington's disease (HD) and age- and sex-matched controls.

#### Edited by:

Mike Wendt, Medical School Hamburg, Germany

#### Reviewed by:

Marcus Heldmann, University of Lübeck, Germany Stephen B. R. E. Brown, Leiden University, Netherlands

\*Correspondence: Marina de Tommaso<EMAIL_ADDRESS>
Specialty section:

This article was submitted to Cognition, a section of the journal Frontiers in Psychology

Received: 15 December 2016 Accepted: 14 July 2017 Published: 31 July 2017

#### Citation:

de Tommaso M, Ricci K, Montemurno A, Vecchio E and Invitto S (2017) Walking-Related Dual-Task Interference in Early-to-Middle-Stage Huntington's Disease: An Auditory Event Related Potential Study. Front. Psychol. 8:1292. doi: 10.3389/fpsyg.2017.01292 Methods: Twenty-four early-to-middle-stage HD patients and 14 age- and sex-matched healthy volunteers were examined. EEG—EMG recordings were obtained from 21 scalp electrodes and eight bipolar derivations from the legs. Principal component analysis was used to obtain artifact-free recordings. The stimulation paradigm consisted of 50 rare and 150 frequent stimuli and was performed in two conditions: standing and walking along a 10 by 5 m path. P3 wave amplitude and latency and EEG and EMG spectral values were compared by group and experimental condition and correlated with clinical features of HD.

Results: P3 amplitude increased during walking in both HD patients and controls. This effect was inversely correlated with motor impairment in HD patients, who showed a beta-band power increase over the parieto-occipital regions in the walking condition during the P3 task. Walking speed and counting of rare stimuli were not compromised by concurrence of motor and cognitive demands.

Conclusion: Our results showed that walking increased P3 amplitude in an auditory task, in both HD patients and controls. Concurrent cognitive and motor stimulation could be used for rehabilitative purposes as a means of enhancing activation of cortical compensatory reserves, counteracting potential negative interference and promoting the integration of neuronal circuits serving different functions.

Keywords: dual task, acoustic paradigm, P3, walking, Huntington's disease

# INTRODUCTION

Gait disorders are very common among elderly people and people with neurological disorders, causing 17% of all reported falls (Rubenstein, 2006; Axer et al., 2010). The cognitive contribution to gait has been recognized as a cause of postural instability and risk of falling in people showing normal aging and in people with neurodegenerative diseases. This has led to an increase in de Tommaso et al. P3-Walking Dual Task in Huntington's Disease

research into neural activation during walking, using a variety of techniques including near infrared spectroscopy (NIRS), positron emission tomography (PET), and electroencephalography (EEG) (Hamacher et al., 2015). Evaluation of how motor performance is affected by an additional cognitive load has also been used to assess risk of falling in healthy young and old subjects and patients with motor and cognitive impairments (Al-Yahya et al., 2011). The presence of cognitive engagement during gait refers to performance in multiple tasks, with temporal overlap of simultaneous executive functions. It is well-documented that impairment in dual-task (motor-cognitive) performance characterizes neurodegenerative diseases, including Parkinson's disease (PD) (O'Shea et al., 2002), Alzheimer's disease (Camicioli et al., 1997), and multiple sclerosis (Wajda et al., 2013). Although the functions of the musculoskeletal and biomechanical systems during gait are well-known (Kirtley, 2006; Perry and Burnfield, 2010), an understanding of the neuronal processes underlying stable gait is still lacking (Segev-Jacubovski et al., 2011). The dearth of data on cognitive processes during walking is probably due to the difficulty of assessing neuronal functions during the course of the movement. Changes to the spectral components of EEG rhythms in the alpha and beta ranges reflect activation of the motor system during walking (Jasper and Penfield, 1949; Pfurtscheller and Berghold, 1989; Pfurtscheller and Lopes da Silva, 1999; Androulidakis et al., 2007; Zhang et al., 2008; Pogosyan et al., 2009; Joundi et al., 2012; Solis-Escalante et al., 2012; Hamacher et al., 2015), which need to be assessed to understand the interference induced by cognitive engagement. However, EEG signals are susceptible to physiological and non-physiological artifacts, including motion artifacts, that can compromise the decoding of gait and the separation of neural signals related to bipedal locomotion. The EEG activity related to walking may be separated from physiological and non-physiological artifacts, including motion artifacts, using automatic recognition methods (Nathan and Contreras-Vidal, 2016).

In addition, the impact of cognitive interference on gait efficiency can be investigated by recording brain activity related to the cognitive task, as described in previous studies (De Sanctis et al., 2014).

Huntington's Disease (HD) is an autosomal dominant illness characterized by motor and cognitive impairments and psychiatric disturbances. The HD motor impairment is complex and includes akinesia, bradykinesia, and a progressive loss of coordination that affects functional ability (Van Vugt et al., 2004). A general impairment in motor planning cause difficulties of executive functioning, which may better emerge during multitasking experimental paradigms. Moreover, individuals with HD commonly experience falls (Busse et al., 2009), which may partly be the consequence of the inability to perform multiple executing functions, as walking during a contemporary cognitive task.