Attention and task difficulty when is performance facilitation

Of note, subjects with higher EC scored much better on all but one of the cognitive tests Digit Symbol, which did not reach significance. TABLE 2. Demographic and neuropsychological characteristics of each Executive Capacity EC group. As illustrated in Table 3 , accuracy was lower and RTs were longer under high than low WM load, indicating that the high load task was more difficult to perform. Figure 2A presents the grand average waveforms in response to target stimuli under low and high load conditions at specified ROIs, and Figure 2B illustrates the surface potential maps of the posterior SW and P3b.

Event-related potential ERP data in response to target visual stimuli under the low and high load conditions. A Illustrates the grand average waveforms arrows point to the P3b component and posterior SW ; and B shows the surface potential maps for the P3b component and the posterior SW.

The load effect was present because the P3b amplitude was smaller under the high than low load condition, as illustrated in Figure 3A. Bar graphs illustrating the mean amplitudes of the ERP components under the low and high load conditions. A Shows mean amplitude of P3b component and posterior SW in response to target visual stimuli; and B illustrates mean amplitude of the N1 component in response to standard auditory stimuli.

Error bars indicate standard error of the mean. Based on temporal course and spatial distribution, one temporospatial PCA factor TF2SF1 was identified as reflecting the P3b, peaking at electrode site A4 near Pz at ms, accounting for Similarly, based on its temporal course and spatial distribution, one temporospatial PCA factor TF3SF1 was identified as reflecting the posterior SW, peaking at electrode site C2 near Cz at ms, accounting for 8.

Scalp topographies and waveforms of PCA factors under the low and high load conditions. TF, Temporal Factor. SF, Spatial Factor. For the P3b component, the factor score amplitude was larger under the low load than the high load, whereas for the posterior SW component, the factor score was larger under high than low load.

In summary, the main results of the PCA were consistent with those found of the average waveforms. However, in contrast to the average waveform analysis, there was no trend toward an interaction between load and EC for the PCA factor reflecting the posterior SW. Figure 5A presents the grand average waveforms in response to auditory stimuli under low and high load conditions at specified ROIs, and Figure 5B illustrates the surface potential maps of the posterior N1.

Event-related potential data in response to standard auditory stimuli under the low and high load conditions. A Illustrates the grand average waveform arrow points to the N1 component ; and B shows the surface potential maps for the N1 component. It peaked at electrode site FCz at ms and accounted for 5.

The focus was on load-related differences in the variables. Also of note, there was no correlation between performance on the visual task i. A potential explanation for the load-related decrease in the N1 amplitude is that it is a direct reflection of the impact of the load-related increase in the posterior SW, which overlaps temporally with the subsequent presentation of auditory stimuli see Figure 1.

Within this framework, the two components would be the result of the same effect, time-locked to different experimental stimuli. For this account to be plausible, there must be not only a temporal, but also a spatial overlap between these two components. Of note, there is no spatial intersection between the electrode cluster used to measure the posterior SW and the cluster used to measure the N1 component.

However, this does not preclude the possibility that the electrophysiologic activity of the posterior SW extends to sites more anterior than those included in the measurement of the posterior SW. To address this issue, SW activity in response to visual target stimuli was re-measured at the Fz electrode cluster used to measure N1 amplitude to determine if there was an effect of load. Nor was there an inverse correlation between the amplitude of the SW measured at the Fz cluster and the N1 measured at the same location.

As discussed in the Introduction, one hypothesis for the decline in the N1 amplitude under the high load condition is that it reflects a passive process i.

If this were the case, one would expect to also find an inverse correlation between the amplitude of the N1 to auditory distracters and the amplitude of the posterior SW to visual targets when analyzed for each load separately, which, as noted, was not found.

Taken together, these results suggest that the load-related decrease in N1 amplitude may reflect a more active, top—down process, rather than a passive process. The present study employed a cross-modal paradigm to investigate the impact of WM load on distracter processing, using ERPs as critical dependent variables. To address a potential source of inconsistent findings in the LT literature, the study aimed to control for the confounding effects of task difficulty and EC across subjects.

Well-normed, standardized neuropsychological tests rather than experimental tasks were used to define EC, which may allow for greater generalization of results.

The WM load manipulation of the primary visual task was effective: relative to the low load condition, the high load condition was associated with lower accuracy and longer RTs. The success of the titration process in controlling the level of task difficulty across subjects was demonstrated by finding that under high WM load, behavioral performance on the primary task was not modulated by EC group.

As expected, to achieve this goal, under the more demanding load condition, subjects with higher EC needed to be given a significantly greater number of target stimuli to make their performance comparable to that of subjects with lower EC. A trend was noted in the average waveforms suggesting a greater load-related augmentation of posterior SW activity to visual targets for the high than average EC group. The N1 component served as a marker of early processing of task-irrelevant auditory distracters.

The main findings of the experiment are not consistent with LT. Increasing WM load was linked to a decrease, not an increase, in N1 amplitude in response to task-irrelevant auditory distracters. In line with our findings, other groups have also reported reduced distracter processing under high compared to low WM load in cross-modal paradigms Munka and Berti, ; SanMiguel et al.

Importantly, this pattern of findings was upheld after controlling for the potential influence of differences in subjective task difficulty and EC across participants.

Our results demonstrated that the amplitude of the posterior SW increased under the high load condition, consistent with previous work Ruchkin et al. There is evidence that the posterior SW is an index of sustained attention and effort processes that likely underlie the steadfastness of attention Ruchkin et al.

Correlation analyses indicated that when WM load is augmented, the greater the increase in attention to the primary task as measured by the posterior SW , the greater the reduction of the processing of task-irrelevant auditory standards as measured by the N1. Moreover, as WM load increases, a smaller decline in target accuracy is associated with ERP measures of enhanced attention to the primary task indexed by the posterior SW and reduced processing of irrelevant stimuli indexed by the N1.

One concern is that the load—related increase in posterior SW to visual targets directly influences the measurement of the subsequent N1 to auditory standards, reflecting the same effect that is time-locked to different experimental stimuli. This account could explain why the load—related differences in the average waveforms to auditory standards appear to begin prior to the N1 itself. There are several reasons why we believe this explanation to be unlikely.

First, the interstimulus interval ISI between visual and auditory events was jittered between and ms, reducing the likelihood that electrophysiologic responses to auditory events were time-locked to visual events Luck, Second, there was limited overlap in the spatial distribution of the posterior SW to visual targets and N1 to auditory standards, which would be necessary to directly link the measurement of one component to that of the other.

Also, when the SW was re-measured at the Fz cluster where the N1 was measured no load effect was observed.

Moreover, if the posterior SW and N1 reflected the same experimental effect, one would anticipate a close correlation between the two components within each load, but none was found. Finally, the pattern of results was confirmed using PCA, a method that can parse variance due to the N1 factor from that reflecting electrophysiologic activity that precedes or follows it Dien, To further address this issue, future studies should include trials with longer ISIs to eliminate the possibility of temporal overlap between electrophysiologic responses to visual and auditory stimuli.

The dissociation found between the pattern of response for the posterior SW and the P3b component was predicted. In contrast to the SW, the amplitude of the P3b diminished under the high load condition, which is consistent with many other reports Mecklinger et al.

Another potential contribution to load-related reduction of the size of P3b might be increased trial-to-trial variability in the peak latency of the P3b due to greater task difficulty. We tried to mitigate this potential confounder by measuring mean, and not peak, amplitude for the P3b, as previously recommended Luck, There are limitations to the current study, which focused on controlling for task difficulty and EC.

It did not manipulate variables that have been shown to impact distracter processing using within-modality paradigms, such as which component of WM load is varied e. Although our sample size was at least as large as many in the literature, it was nonetheless relatively modest and thus the findings need to be replicated.

The inability to find a main effect of EC group or an interaction between EC group and load for our experimental variables could be the result of insufficient power. It is worth noting that almost none of the p -values from these analyses even approached a trend. Additional work is needed to determine whether a similar pattern of response would be observed for more salient events.

It would also be informative for future studies to include more challenging levels of WM load. We strongly suspect that the relationship between task difficulty and protection from distraction may follow an inverted U—shaped curve. If demands are sufficiently augmented, capacity—limited control resources would eventually be depleted, leading to an increase in the processing of task—irrelevant distracters.

If so, it would require much more demanding conditions for high EC adults to exhaust resources that mediate top—down control, resulting in an increase in distractibility. Finally, although we suspect that the correlation between the load-related increase in posterior SW to visual targets and the load-related decrease in N1 to auditory distracters is the result of active, top—down process and not a passive one i.

In summary, an increase in WM load was associated with electrophysiological evidence of an increased steadfastness of attention on the primary visual task and a reduction in the early processing of auditory distracters. These findings occurred within the context of controlling for the confounding effects of task difficulty and WM capacity across subjects, an approach that future studies should take into consideration.

The results of this study present a challenge to LT, which may need to be modified. KD and PH designed the experiment. ET analyzed the data and prepared the figures. SS drafted the manuscript. 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. The authors would like to thank Brittany Alperin for early work on the project, and Sarah Fackler for her excellent administrative assistance.

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Pasternak, T. Working memory in primate sensory systems. Porto, F. To support this cover story, a black-and-white drawing of a hand holding a reversed Ketchup bottle was presented on-screen throughout the task and a black-and-white drawing of a hand holding a bottle ejecting Ketchup replaced the default image at the end of successful trials.

The task consisted of 60 trials each one starting with a four-second countdown followed by a two-second measurement period and a three-second feedback period.

If the peak maximum exerted force was equal to or greater than the standard, the trial counted as success and the positive feedback drawing was displayed during the feedback period. The force exerted in this isometric hand grip task is proportional to the amount of energy adenosine triphosphate invested for task success and provides thus a valid measure to test effort-related predictions in instrumental tasks [ 37 ]. To create a task version with unclear, unpredictable difficulty Study 1 and Study 2 , force standards were varied from trial to trial.

Thirty different force standards were presented twice in random order to create the 60 task trials. To create the task version with clear difficulty Study 2 , the same easy force standard of 60 N corresponding to the strength of a weak, low-effort handshake [ 39 , 40 ] was used in all trials and for all participants. To increase the likelihood that the hand grip task would activate the explicit achievement motive, we provided participants with two sources of feedback about their performance during the task.

First, exerted force was continuously displayed during the measurement period. Second, during a three-second feedback period following the measurement, the peak force that participants had exerted in the preceding measurement period was displayed together with the required force standard.

Statements from the three scales were presented alternating. After having provided informed consent, participants were seated at a table in front of a computer screen and introduced to the hand dynamometer that was mounted at the table in a vertical position. They learned that the device measured grip force exerted on it and were then allowed to freely explore its functionality during the next 30 seconds.

During this time, the force that participants were exerting on the dynamometer was displayed on the screen, and the experimenter encouraged participants to exert varying levels of force to see how the device responded. After this familiarization period, participants performed three maximum force trials see the maximum force trails section for details before the experimenter gave them a sheet with instructions for the main hand grip task and left the room.

For the duration of the hand grip task, participants remained alone in the room. The instruction sheet informed participants that they would be performing 60 trials of a hand grip task.

They were asked to imagine that the dynamometer represented a clogged Ketchup bottle that they could free by exerting a force equal to or higher than a force standard set by the computer. All participants received the general information about the hand grip task procedure presented in the hand grip task section but did not receive any information about the specific force standards prior to or during the measurement period. Participants in the unclear task difficulty conditions Study 1 and Study 2 learned that the force standards would vary randomly from one trial to the next one, whereas participants in the clear task difficulty condition Study 2 were informed that the force standard would be the same in every trial.

After having performed the 60 hand grip trials, participants performed again three maximum force trials in the presence of the experimenter. Finally, all participants were carefully debriefed and given an Amazon voucher worth GBP 5 for their participation.

Given that the dynamometer sampled exerted force at 10 Hz during the two-second measurement periods, 20 force values were available for each trial. The highest value of the 20 force values of a trial was used as trial peak force value and a force-time-integral FTI was computed by adding up all 20 force values [ 42 ]. FTIs thus indicated the total amount of effort exerted during the two-second measurement periods. However, peak force constituted our primary measure given that it was instrumental for success exerted peak force was compared with the force standard to determine success in a trial.

In Study 1, this was based on all six maximum force trials but in Study 2, we only used the three post-task maximum force trials to determine maximum force scores given that a programming error prevented our software from saving exerted force during the pre-task maximum force trials in the clear-difficulty condition. For each participant, we computed a task peak force score as arithmetic average of all 60 peak force values.

In both studies, we tested our predictions about the impact of sanAch on exerted force under conditions of unclear task difficulty by calculating Pearson correlation coefficients between peak force scores and sanAch. In Study 2, this analysis only considered participants in the unclear-task difficulty condition. All analyses were conducted using R [ 46 ] and the psych [ 47 ], cocor [ 48 ], and BICpack [ 49 ] packages. P -value based tests that addressed the following a-priori directional hypotheses were conducted one-tailed: 1 the postulated positive relationship between sanAch strength and exerted force under conditions of unclear task difficulty, 2 the prediction that sanAch would be more positively associated with exerted force than sanAff or sanPow under conditions of unclear task difficulty, and 3 the hypothesis that the sanAch-exerted-force relationship would be more positive under conditions of unclear task difficulty than under conditions of fixed task difficulty.

All other p -value based tests were conducted two-tailed. The strength of evidence provided by the observed Bayes Factors was interpreted according to Andraszewicz et al. Means and standard deviations of force values and motive scores as well as the correlation coefficients between these variables can be found in Table 1.

Fig 2 illustrates this relationship and Fig 3 presents the evolution of exerted force across the 2-second measurement period as a function of sanAch. The dashed line shows the best fit regression line. The graph is based on predicted values generated from regression equations for individuals one SD above and below the mean explicit achievement motive sanAch strength. The x-axis refers to the 20 force samples collected during each task trial. Table 2 displays means, standard deviations, and bivariate correlations of force and motive scores.

Fig 4 illustrates the relationship between sanAch and relative peak force in the two difficulty conditions and Fig 5 shows the evolution of exerted force across the 2-second measurement period as a function of sanAch and difficulty condition. Dashed lines are best fit regression lines. The graphs are based on predicted values generated from regression equations for individuals one SD above and below the mean explicit achievement motive sanAch strength.

In both studies, we found the predicted positive relationship between the strength of the explicit achievement motive and effort under conditions of unclear, unpredictable task difficulty: the higher the strength of the achievement motive, the higher the force that participants exerted.

Study 2 replicated the positive relationship found in Study 1 and revealed that this relationship was specific to tasks with unclear difficulty. If the difficulty of the task was clear, the strength of the explicit achievement motive did not predict exerted force.

Replicating preceding work [ 54 — 56 ], we also observed that the explicit achievement motive was more predictive of effort investment than the explicit power or affiliation motives. As expected, the employed hand grip task constituted an achievement-related context that activated the explicit achievement motive more than the power or affiliation motives.

Our results may seem obvious from a motivational intensity theory perspective but it should be noted that the achievement motive literature has not yet fully adopted the idea that aroused motives do not always lead to increased effort, persistence, or performance.

A large part of the motive research is still based on the assumption that motives exert a direct impact on behavior once that they have been activated by an opportunity for motive satisfaction [ 9 , 57 , 58 ]. There are a few exceptions that provided more sophisticated models on how motives interact with situational characteristics to influence different aspects of behavior [ 25 — 27 , 59 ] but many publications have an explanatory gap between the aroused motive and the behavioral consequences.

For instance, it often remains unclear how a high motive strength should lead to increased performance. Assuming that high motive strength leads to increased effort and persistence, which in turn increases performance, does not fully close the explanatory gap given that effort and persistence are only two of several determinants of performance [ 60 , 61 ].

Even if our work does not address performance effects in the terms of success and failure, it challenges the idea that motive activation is sufficient for motive effects on behavior. It clearly demonstrates that an activated explicit achievement motive only has a direct impact on the effort invested in instrumental behavior if the difficulty of the behavior is unclear or unpredictable. If the difficulty of the behavior is well-known and fixed to a certain difficulty standard i.

It might seem surprising that we predicted and found effects of the explicit achievement motive on effort given that the energetic and performance-related aspects of behavior have often been associated with the implicit motive, not the explicit motive [ 10 , 26 , 62 ]. However, a closer examination of the behavior that we assessed—exerting force in the Ketchup hand grip task—reveals that it has many features that have been attributed to behavior that should be influenced by the explicit achievement motive [ 25 , 27 ].

For instance, exerting force in the hand grip task constitutes certainly not a spontaneous behavior but a deliberate decision to exert force. Moreover, it is more respondent behavior than operant behavior given that there were specific stimuli eliciting the response for instance, the instruction to squeeze the dynamometer to free the Ketchup bottle.

Exerted force also represents a declarative measure of motivation given that exerting force in the hand grip task is a voluntary, consciously controlled process. In sum, even if exerted force might look at first sight to be a behavioral response for which implicit motive strength should be predictive, a closer look reveals that exerting force in the Ketchup hand grip tasks constitutes the type of deliberate, controlled, and respondent behavior for which explicit motive strength should be relevant.

Our work differs from a large part of the preceding work on motivational intensity theory [ 14 ] regarding the employed effort measure.

Instead of using sympathetic-driven cardiovascular measures to test our effort-related predictions, we assessed the force exerted in an isometric hand grip task. Using exerted force in the presented work had a number of advantages. First, a lot of the current work on the achievement motive examines its impact on physical, mainly sports-related tasks [ 9 , 33 , 34 ]. Assessing physical effort in a motor task is obviously more relevant to this work than cardiovascular responses associated with mental effort.

Second, it only requires a couple of seconds to measure the force response in one trial. Consequently, many trials can be presented in a task to collect a large number of effort measurements. Given that each individual measurement is affected by random noise resulting from factors not related to the main manipulations e. Given the close connection between exerted force in isometric tasks and the amount of consumed adenosine triphosphate—the primary energy for most energy requiring actions of the human body—exerted force enables specific tests of the determinants of the amount of energetic resources that are invested in instrumental tasks [ 37 ].

Independent of the differences with the cardiovascular measures employed in most of the research on motivational intensity theory, our findings fit well with preceding work on the theory. Like other researchers who found that variables related to success importance exert a direct impact on effort under conditions of unclear and unfixed task difficulty but not under conditions of fixed task difficulty [ 14 , 63 , 64 ], we observed that the impact of the explicit achievement motive was moderated by clarity of task difficulty.

A critical reader might wonder about the implications of the confound between clarity of difficulty and task difficulty in Study 2. This confound does, however, not challenge our interpretation in terms of clarity of difficulty given that an interpretation in terms of fixed and clear task difficulty fails to explain the observed relationship between achievement motive strength and exerted force.

If participants had perceived the unclear difficulty task as one with a clear and high difficulty, their exerted force should not have increased in proportion to their achievement motive strength. According to motivational intensity theory, task difficulty directly determines effort if participants have a clear idea about task difficulty.

Achievement motive strength, as a variable that influences success importance, should not have a direct impact. Correspondingly, exerted force should either have been high for all participants independent of their achievement motive strength or shown a twofold pattern with some participants exerting a high force and others disengaging.

Fig 4 clearly shows that this was not the case. The only theory-based interpretation that can explain the finding that achievement motive strength was associated with exerted force in one condition but not in the other is thus one in terms of clarity of difficulty. The quasi-experimental design of our studies obviously limits the conclusions that can be drawn from the presented data. Our findings cannot provide strong evidence for the causal link between the explicit achievement motive and effort suggested by our theoretical analysis.

It is possible that the achievement motive was not influencing effort investment but that the effort that participants had invested in the task determined the achievement motive strength that they reported.

It is also not excluded that the observed positive relationship between achievement motive strength and effort was due to a third variable that affected both the explicit achievement motive and exerted force in the same manner. One way to improve the design and provide more direct evidence for the causal effect of the achievement motive on effort might be to manipulate the situational characteristics so that the achievement motive is either activated or not.

However, it is important to note that the impact of clarity of difficulty that we observed in Study 2 is not vulnerable to the quasi-experimental design critique. The observed relationships between the achievement motive and effort may not provide strong evidence for the causal effect of the achievement motive on effort but the manipulation of clarity of difficulty demonstrates that variations in the clarity of task difficulty cause changes in the relationship between achievement motive strength and effort.

In this article, we presented a theoretical analysis of achievement motive effects on effort investment in instrumental tasks and provided empirical evidence for some of the predictions of this analysis. We demonstrated that the impact of the explicit achievement motive on effort depends on the clarity of task difficulty. If the difficulty of a task was known and fixed, achievement motive strength did not predict effort.

However, if the difficulty standard was unknown and unpredictable to participants, achievement motive strength predicted effort. Task characteristics—in particular, the predictability of task difficulty—are thus important factors to consider when using achievement motive strength to predict engagement in various achievement-related tasks. We are grateful to Johanna Berit Slid and Lia Harrison for their help with collecting the presented data.

Browse Subject Areas? Click through the PLOS taxonomy to find articles in your field. Abstract Stable personality dispositions, like motives, are often assumed to exert a direct, stable impact on behavior. Introduction One of the main reasons for the interest in personality traits is the promise that they have stable links with behavior and consequently allow the prediction of behavior [ 1 , 2 ].

Download: PPT. Fig 1. Theoretical predictions for the impact of the explicit achievement motive on effort. Tasks and questionnaires Maximum force trials. Hand grip task. Procedure After having provided informed consent, participants were seated at a table in front of a computer screen and introduced to the hand dynamometer that was mounted at the table in a vertical position.

Results Study 1 Maximum force trials. Relationship between exerted force and motive scores. Fig 2. Relationship between explicit achievement motive strength and relative exerted force in Study 1. Fig 3. Evolution of relative exerted force during a trial as a function of explicit achievement motive strength in Study 1. Table 1. Descriptive statistics and Pearson correlation coefficients for force values and motive scores in Study 1. Study 2 Maximum force trials.

Fig 4. Relationship between explicit achievement motive strength and relative exerted force in Study 2. Fig 5. Evolution of relative exerted force during a trial as a function of explicit achievement motive strength in Study 2.

Table 2. Descriptive statistics and Pearson correlation coefficients for force and motive scores in Study 2. Discussion In both studies, we found the predicted positive relationship between the strength of the explicit achievement motive and effort under conditions of unclear, unpredictable task difficulty: the higher the strength of the achievement motive, the higher the force that participants exerted. Acknowledgments We are grateful to Johanna Berit Slid and Lia Harrison for their help with collecting the presented data.

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