Session 8: Neuroimaging of the default mode

Fox et al. (2005)* described a pair of anticorrelated networks of brain structures in connection with attention-demanding cognitive tasks: a task-positive network that increases activity during attention to the task and a task-negative network including the default network that decreases activity during attention to the task. The other articles for this session attempt to establish what the default network does when it becomes active. Mason et al. (2007)** found a correlation between increased activity in the default network and both more practiced tasks that allow for more mind wandering and individuals’ self-reported tendency to mind wander. Andrews-Hanna et al. (2010)*** used fMRI imaging during tasks requiring different levels of visual attention and self-reports of mind wandering on the task to determine whether default network activity is connected with spontaneous (particularly internal) cognition (i.e., mind wandering) or a broadening of external attention. They concluded that default network activity is associated with spontaneous cognition, not broadening of external attention (Andrews-Hanna et al., 2010). Stawarczyk et al. (2011)**** categorized thoughts during a task by both task-relatedness and stimulus-dependency to identify whether and how default network activity is associated with task-unrelated and stimulus-independent thoughts. They found evidence for default network involvement in both categories of thought, and they found that the activation associated with task-unrelated and stimulus-independent thoughts is additive, such that mind-wandering (task-unrelated and stimulus-independent thought) is associated with greater activity in some areas of the default network than either task-unrelated but stimulus-dependent (i.e., outside distractions) or stimulus-independent but task-related (i.e., evaluating task difficulty or performance) thoughts are (Stawarczyk et al., 2011).

The explanations for default network activity provided in Mason et al. (2007), Andrews-Hanna et al. (2010), and Stawarczyk et al. (2011) build on Fox et al.’s (2005) findings and are consistent with them: Fox et al. (2005) described what the default network does during attention to an external task (i.e., it deactivates). The other studies described what the default network does when attention is not focused on performing the external task.

Andrews-Hanna et al. (2010) and Stawarczyk et al. (2011) gave what initially seem like contradictory explanations for default network activity: Andrews-Hanna et al. (2010) suggested that default network activity is not associated with external focus characteristics, while Stawarczyk et al. (2011) linked some default network activity to attention to external task-unrelated stimuli. But Andrews-Hanna et al. (2010) specifically observed that their study was limited to external stimuli within the same visual display and that default network activity could actually be associated with broadening of attention to a much larger scale, such as visual attention to other parts of the room or attention in some other modality such as sound or smell. And Stawarczyk et al. (2011) did not have their participants identify the specific external stimuli they were distracted by during task-unrelated stimulus-dependent thoughts. So the two sets of findings could be consistent in that both internal and stimulus-dependent thoughts can increase default network activity, provided that the stimulus triggering the stimulus-dependent thoughts comes from a focus broader than the screen the primary task is on or the headphones or speakers some task-related auditory stimulus is coming from.

Because the Stawarczyk et al. (2011) study monitored participants’ thoughts during the fMRI scans—and particularly because it used thought probes during the tasks/scans—it seems to provide a particularly valid correlation between actual thought content and default network activity. Andrews-Hanna et al. (2010) did not use real-time thought profiles, but they did create conditions that specifically differed in the scope of external attention required, so their results are convincing in the context of whether default network activity tends to be associated more with a broad external focus or mind-wandering. And they also had participants provide post-task self reports of their thoughts during the task (Andrews-Hanna et al., 2010), which might not result in the most accurate information but does at least provide some information specific to the participants and tasks represented in each brain scan.

By contrast, Mason et al.’s (2007) evidence for the connection between default network activity and mind-wandering seems awfully indirect: they did associate increased default network activity with practiced tasks, which are associated with more mind wandering, and also with their participants’ self-reported general mind wandering tendencies, but they did not actually monitor the participants’ thoughts while they were performing the tasks in the scanner either through thought probes during the tasks or through post-task self reports. This seems like a major weakness for correlating brain activity to thought content.

* Fox, M. D., Snyder, A. Z., Vincent, J.L., Corbetta, M., Van Essen, D. C., & Raichle, M. E. (2005). The human brain is intrinsically organized into dynamic, anticorrelated functional networks. PNAS, 102, 9673-9678.

** Mason, M. F., Norton, M. I., Van Horn, J. D., Wegner, D. M., Grafton, S. T., & Macrae, C. N. (2007). Wandering minds: the default network and stimulus-independent thought. Science, 315, 393-395.

*** Andrews-Hanna, J. R., Reidler, J. S., Huang, C., & Buckner, R. L. (2010). Evidence for the default network's role in spontaneous cognition. Journal of Neurophysiology, 104, 322-335.

**** Stawarczyk, D., Majerus, S., Maquet, P., & D'Argembeau, A. (2011). Neural correlates of ongoing conscious experience: Both task-unrelatedness and stimulus-independence are related to default network activity. PLoS ONE, 6(2): e16997.

Session 7: Mindfulness and meditation-based attentional training

A week ago, Elizabeth and I attended a mediation class organized by the Art of Living Foundation and I Meditate NY. The class included roughly three sections: first, introductions of the instructor and students, during which we shared our reasons for being there and what we hoped to accomplish; second, learning a breathing technique; and third, a 20-minute guided meditation.

The instructor emphasized the importance of breathing as both a focus point to stay in the present and as a relaxation or energizing technique because different breathing patterns are associated with different emotional and arousal states. We practiced what the instructor called “ocean breaths,” which supposedly mimic sleep. This portion of the class did not seem related to training attention, though the instructor did say it was to put us in a proper mindset for the actual meditation.

Then, in the meditation itself, we focused in sequence on various aspects of the present: our surroundings, particularly the ambient sounds; different parts of our bodies; our thoughts; and our feelings. The instructor specifically told us to focus but not judge and to accept and become at one with the experiences we focused on.

Based on the descriptions of meditation techniques in Lutz et al. (2008)*, our meditation fits into the category of focused attention meditation, which focuses and sustains attention on an object and trains monitoring of attention and distractions and re-engaging attention onto the intended object and away from distractions. But the elements of our meditation in which we monitored our thoughts and emotions resembled open monitoring meditation, in which one monitors one's experience without focusing on any explicit object (Lutz et al., 2008).

Regardless of the category of meditation we did, we did seem to practice certain aspects of mindfulness that are associated with meditation more broadly, specifically observing, acting with awareness (via the explicit instructions to focus on particular aspects of our experience), and non-judgment and also possibly emotional non-reactivity (Baer et al.,2006**). Something unexpected in terms of non-reactivity happened when I focused on the ambient sounds during our meditation: I have always found the traffic (especially the emergency vehicles) noises of New York City jarring and distracting, but by focusing on those sounds and letting go of judgment, I felt the discords resolve into an almost soothing background noise. So anecdotally, at least, I could believe that meditation in the form that we learned could improve attention, if nothing else than because it can help one let go of the distracting quality of one's surroundings.

But if our meditation fits into the focused attention attention category, there is also empirical evidence that it might be a way to train attention, albeit indirectly. MacLean et al. (2010)*** found evidence that focused attention meditation in relatively experienced meditators improves perceptual sensitivity and thus reduces vigilance decline on sustained attention tasks by making the tasks less cognitively demanding. Similarly, Jensen et al. (2011)**** suggested that mindfulness-based stress reduction (MBSR), which is based on meditation and attempts to train present focus and non-judgment, can reduce vigilance decrements, improve perceptual thresholds (consistent with MacLean et al., 2010), and improve working memory capacity in relatively inexperienced meditators. While neither of these studies found direct effects of meditation on sustained attention capacity, these indirect improvements are a way in which focused attention meditation can improve performance on tasks requiring sustained attention.

Thus, even though the empirical evidence does not show direct effects of meditation on the mechanisms of sustained attention, the MacLean et al. (2010) and Jensen et al.(2011) studies and my own experience as a novice in a meditation class suggest that meditation could be beneficial to sustained attention even if it does not train attention directly.

*Lutz, A., Slagter, H. A., Dunne, J. D., & Davidson, R. J. (2008). Attention regulation and monitoring in meditation. Trends in Cognitive Sciences, 12, 163-169.

 **Baer, R. A., Smith, G. T., Hopkins, J., Krietemeyer, J., & Toney, L. (2006). Using self-report assessment methods to explore facets of mindfulness. Assessment, 13, 27-45.

***MacLean, K. A. et al. (2010). Intensive meditation training improves perceptual discrimination and sustained attention. Psychological Science, 21, 829-839.

****Jensen, C. G., Vangkilder, S., Frokjaer, V., & Hasselbalch, S. G. (2011). Mindfulness training affects attention--Or is it attentional effort? Journal of Experimental Psychology, General, 141, 106-123.

Session 5: Attention across the lifespan

This week's articles address sustained attention and mind wandering over the lifespan. Richards (2008)* discussed the use of heart rate and eye movement measurements to study the development of sustained attention in infants. The eye movement studies showed that infants' general arousal systems develop along with their voluntary control of eye movements, that arousal both increases voluntary eye movement and is associated with voluntary shifts in the type of eye movement appropriate to tracking stimuli moving at different speeds in infants about 20 weeks and older, and that top-down control over eye movements may increase between 4 months and 2 years of age (Richards, 2008). Lin, Hsiao, and Chen (1999)** studied development of sustained attention in school-age children using versions of the Continuous Performance Test (CPT), which requires participants to respond to rare target stimuli interspersed among non-target stimuli presented at a rapid fixed rate. They found that performance on the CPT improves—first rapidly and then more slowly—in children between ages 6 and 15 and particularly between ages 6 and 12, suggesting that the cognitive inhibition aspect of sustained attention develops during that period (Lin et al., 1999). Carriere et al. (2010)*** and Jackson and Balota (2011)**** studied decreases in reported mind wandering and observed errors on sustained attention tasks in older adults. Carriere et al. (2010) found that adults tend to respond more slowly and accurately on the sustained attention to response task (SART) as they age but that measures of actual task disengagement on the SART decreased only in early adulthood (age 20 to 30) and remained stable afterwards, suggesting that sustained attention ability does not change with aging but that older adults tend to use a more cautious response strategy. Jackson and Balota (2011), measuring reaction times and reported mind wandering, found that older adults are less likely to exhibit mind wandering than younger adults but were generally no slower or more accurate and showed just as much reaction time speeding immediately prior to a SART error as younger adults after correcting for general age-related slowing of mental processing. They did find that older adults were slower and more accurate on an “easier” version of the SART, though only the accuracy difference was significant, and they noted that older adults show more slowing after making an error on the SART, indicating that either reestablishing cognitive control after an error becomes more difficult with age or that older adults engage in more self-evaluative (and in this case, task-related) mind wandering (Jackson & Balota, 2011). Thus, taken together, these studies indicate that sustained attention capacity develops through childhood and early adolescence and remains stable with aging but that older adults mind wander off task less (though they might engage in more task-related mind wandering) and may develop different strategies for sustained attention tasks.

Studying differences in sustained attention across the lifespan raises certain experimental challenges. First, infants and very young children cannot be tested using standard measures like the SART. Thus, as Richards (2008) described, measures like heart rate and eye movements are often used to study attention in infants. Richards (2008) mentioned that some of the eye movement studies included children up to 7 years old, providing some basis for comparison with older children and adults who tend to be tested using more behavioral measures. But the techniques used for infants and for older children and adults appear not to overlap much. Similarly, Lin et al.'s (1999) study with school-age children used a different task than Carrier et al.'s (2010) and Jackson and Balota's (2011) studies with adults did. The tasks seem to measure the same sustained attention abilities, but comparing children's and adults' abilities would be more reasonable if they were tested on the same task or if the tasks were explicitly validated against each other.

Second, the Lin et al. (1999), Carrier et al. (2010), and Jackson and Balota (2011) studies were all cross-sectional, leaving the possibilities of demographic differences and generation effects among the age groups. These potential non-age differences are probably relatively small with school-age children within a relatively narrow age range recruited from the same schools as in the Lin et al. (1999) study. And Jackson and Balota (2011) explicitly examined differences in education, vocabulary, and personality to separate the effects of those variables from age effects in their study. But considering the large age ranges in the Carrier et al. (2010) and Jackson and Balota (2011) studies, the participants in different age groups might be non-equivalent in ways that the studies did not control for because of such factors as generational experience with computers and other technology and qualitative differences in education over time. Indeed, one reason why older adults tend to find the SART more engaging and more challenging than younger adults do (cf. Jackson & Balota, 2011) might be because the older adults in the participant pool were not exposed to computers early in life the way the younger adults were. Two ways to overcome potential generation effects might be to conduct a longitudinal study on the same participants as they age or to repeat the studies later as the populations from which the older participants are drawn begin to resemble the populations from which the younger participants are currently drawn and compare the results to those in the current studies. But either of those types of studies would not produce results for years into the future.

* Richards, J. E. (2008). Attention in young infants: A developmental psychophysiological perspective. Handbook of Developmental Cognitive Neuroscience. C.A. Nelson & M. Luciana. Cambridge, MA, MIT Press.

** Lin, C. C., Hsiao, C. K., & Chen, W. J. (1999). Development of sustained attention assessed using the Continuous Performance Test among children 6-15 years of age. Journal of Abnormal Child Psychology, 27, 403-412.

*** Carriere, J. S., Cheyne, J. A., Solman, G. J., & Smilek, D. (2010). Age trends for failures of sustained attention. Psychology and Aging, 25, 569-574.

**** Jackson, J. D. & Balota, D. A. (2011). Mind-wandering in younger and older adults: Converging evidence from the sustained attention to response task and reading for comprehension. Psychology and Aging, 27, 106-119.

Session 4: Consequences & functionality of mind wandering

Today's articles consider positive and negative consequences of mind wandering. Killingsworth and Gilbert(2010)* found that mind wandering is associated with unhappiness: specifically, people tend to be no happier when mind wandering to pleasant topics than they are when they are on task, and they tend to be unhappier when mind wandering to negative or neutral topics. McVayand Kane (2012)** described the tendency to mind wander as a major mediating factor for why lower working memory capacity (WMC) predicts poorer reading comprehension. Delaney et al. (2010)*** described how mind wandering, particularly to topics that are distant in time or space from the current environment, can lead to forgetting of information learned immediately before the mind wandering episode. By contrast, Baird, Smallwood, and Schooler (2011)**** suggested that mind wandering can be a form of problem solving related to an individual's general goals for the future. These consequences do not seem mutually exclusive, with the possible exception of McVay and Kane's (2012) findings and Baird et al.'s (2011) findings related to working memory, and they suggest other potential positive consequences of mind wandering.

First, Baird et al.'s (2011) positive consequence of mind wandering is compatible with Killingsworth and Gilbert's (2010) and Delaney et al.'s (2010) negative consequences. Baird et al. (2011) proposed that mind wandering varies in temporal focus (i.e., past, present, or future) and on certain cognitive dimensions including whether the thoughts are self-related or goal-directed. They found that self-related and goal-directed thoughts tend to be associated with future-focused mind wandering (Baird et al., 2011). None of these categories of thought are incompatible with unhappiness or forgetting of recently learned present-focused information. If someone is thinking about a potential future problem, he or she could very well be unhappier than someone focusing on a more mundane present task. And thinking about a future problem can be expected to shift someone's mental context away from a current task, as Delaney et al. (2010) described, and the farther in the future or the more dissimilar from the current task the future problem is, the greater the shift would be, resulting in more forgetting of information learned just before the mind wandering began. Thus, Delaney et al.'s (2010) findings could be paired with Baird et al.'s (2011) findings to predict that someone mind wandering to future topics is likely to be thinking about a goal or problem unrelated to the current task and is also likely to forget information related to the current task that was learned just before the mind wandering episode.

But Baird et al.'s (2011) findings regarding working memory capacity (WMC) appear to conflict with McVay and Kane's (2012) findings. McVay and Kane (2012) found a negative correlation between WMC and reading comprehension that was partially mediated by general mind wandering tendency (as measured on other, non-reading comprehension tasks). In other words, they found that low WMC predicts more mind wandering and poorer reading comprehension and that the tendency to mind wander, which decreases with higher WMC, explains a significant portion of WMC's predictive value for reading comprehension (McVay & Kane, 2012). By contrast, Baird et al. (2011) found no significant correlation between WMC and mind wandering in general but found a positive correlation between WMC and prospective mind wandering, which means that people with higher WMC actually tend to mind wander more, not less, about the future, than people with lower WMC do. Thus, based on Baird et al.'s (2011) findings, one would not predict that the relationship between WMC and reading comprehension could be mediated by mind wandering tendency as McVay and Kane (2012) found because the high-WMC people who mind wander more about future events should have better reading comprehension based on their WMC but poorer reading comprehension based on mind wandering tendency. Perhaps there are differences in individuals' tendencies to mind wander about different topics, and those topic-based differences are important to predicting the positive and negative consequences of mind wandering.

Finally, Delaney et al.'s (2010) and Baird et al.'s (2011) findings suggest other potentially positive functions of mind wandering. Baird et al. (2011) described mind wandering as solving future problems, but people could also have ongoing problems and goals associated with a past event, such as accepting a rejection or the loss of a loved one. If mind wandering can be a mechanism to resolve future problems, could it not also be a mechanism to resolve past and present conflicts? Also, Delaney et al. (2010) asserted that the context shift accompanying daydreaming causes forgetting of just-learned material by reducing the effectiveness of retrieval cues for that information. Thus, returning to the earlier mental context should reduce this form of forgetting (Delaney et al., 2010). But the contextual retrieval cues process brings to mind the study technique of associating information with particular locations during studying and then recalling those locations as a retrieval cue during a test. If so, is it possible that mind wandering could serve as a retrieval cue for information related to some personal goal?

* Killingsworth, M. A. & Gilbert, D. T. (2010). A wandering mind is an unhappy mind. Science, 330, 932.

** McVay, J. C. & Kane, M. J. (2012). Why does working memory capacity predict variation in reading comprehension? On the influence of mind wandering and executive attention. Journal of Experimental Psychology: General, 141, 302-320.

*** Delaney, P. F., Sahakyan, L., Kelley, C. M., & Zimmerman, C. A. (2010). Remembering to forget: the amnesiac effect of daydreaming. Psychological Science, 21, 1036-1042.

**** Baird, B., Smallwood, J., & Schooler, J. W. (2011). Back to the future: Autobiographical planning and the functionality of mind-wandering. Consciousness and Cognition, 20, 1604-1611.

Session 3: Mind Wandering & Meta-attention (Theories of mind-wandering)

Smallwood and Schooler (2006)* and McVay and Kane (2010)** proposed two competing theories to explain mind wandering: an “executive function” theory and an “executive failure” theory. The “executive function” theory posits that mind wandering requires executive control and uses the same cognitive resources needed to complete a conscious task (Smallwood & Schooler, 2006). Thus, mind wandering should involve the same brain regions associated with executive control and should increase with ease and practice of the task the person should be focusing on because both ease and practice free up cognitive resources for mind wandering (Smallwood & Schooler, 2006; Schooler et al., 2011***). By contrast, the executive failure theory explains mind wandering as a failure of executive control over off-task thoughts that are generated automatically (McVay & Kane, 2010). Under this theory, mind wandering should involve different brain regions than executive control, particularly the default network that is responsible for self-projection even in the absence of external cues, and mind wandering should increase with sleepiness, inebriation, lower working memory capacity, and disorders like ADHD that involve executive function deficits (McVay & Kane, 2010). In general, the executive failure theory seems to better explain the available evidence on individual differences and compromised states, but it does not satisfactorily explain one set of neuroimaging data that appears to support the executive function theory.

Both theories provide explanations for why mind wandering increases when people are doing easier tasks or tasks they have practiced extensively. The executive function theory is possibly more elegant here: it predicts that tasks requiring fewer executive resources, especially working memory resources, free up more resources for mind wandering (Smallwood & Schooler, 2006). Thus, easier tasks and well-practiced tasks that a person does relatively automatically should be associated with more mind wandering. But the executive failure theory can also explain this phenomenon. McVay and Kane (2010) explained harder tasks as requiring a more concrete level of construal to complete and easier or more practiced tasks as needing only a more abstract level of construal. More concrete levels of construal require more executive control processes to match to the current situation, and the increased executive control resources also tend to block off-task thoughts (McVay & Kane, 2010).

Similarly, both theories can explain findings that mind wandering interferes with performance on the task at hand. Smallwood and Schooler (2006) explain the interference as a diversion of executive resources away from the task. McVay and Kane (2010) do not explain the interference directly, but it seems plausible that distraction, even if the distracting thoughts did not use the same cognitive resources as the task, would degrade performance.

But the executive failure theory better accounts for individual differences in mind wandering tendencies than does the executive function theory. As McVay and Kane (2010) pointed out, if mind wandering uses executive resources then people with fewer resources to spare—those with low working memory capacity or disorders like ADHD or merely people who are tired or drunk—should mind wander less than those with more resources to spare. Instead, these individuals have been shown to mind wander more (McVay & Kane, 2010; Schooler et al., 2011). Schooler et al. (2011) discussed drinking and nicotine craving as increasing mind wandering while decreasing meta-awareness of mind wandering, but they did not seem to reconcile the increase in mind wandering with the executive function theory.

However, one neuroimaging study tends to support the executive function theory of mind wandering and seems inadequately explained by the executive failure theory. Mind wandering episodes have been found to be associated with activity in regions of the brain (certain areas of the prefrontal cortex, PFC, and anterior cingulate cortex, ACC) that are generally associated with executive control (McVay & Kane, 2010; Schooler et al., 2011). Mind wandering without meta-awareness (i.e., when the person is unaware that he or she is mind wandering) also is associated with stronger activity in those regions than mind wandering with meta-awareness is (McVay & Kane, 2010). McVay and Kane (2010) suggested that this activation reflects the effort of refocusing on the task at hand and that conscious awareness of mind wandering is not necessary for the refocusing to happen. But that seems to suggest a time lag between the onset of mind wandering and PFC and ACC activation, and McVay and Kane (2010) did not suggest that any such time lag occurred. Their suggestion that refocusing may require more control activity during mind wandering without meta-awareness than during mind wandering with meta-awareness (McVay & Kane, 2010) also begs the question of why that would be the case. Thus, without temporal data or a theoretical reason for why refocusing attention requires more control resources in mind wandering without meta-awareness, activation of the PFC and ACC during mind wandering seems to support the executive function theory and contradict the executive failure theory of mind wandering.

As a side note, Schooler et al. (2011) suggested that reductions in neural responses to task stimuli during mind wandering indicate that “reduction in task focus due to mind wandering arises from the internal focus necessary to maintain an internal train of thought, rather than a process of distraction” (p. 320). To a novice, this is not apparent at all—why could a process of distraction not produce the same response reductions?

* Smallwood, J. & Schooler, J. W. (2006). The restless mind. Psychological Bulletin, 132, 946-958.

** McVay, J. C. & Kane, M. J. (2010). Does mind wandering reflect executive function or executive failure? Comment on Smallwood and Schooler (2006) and Watkins (2008). Psychological Bulletin, 136, 188-197.

*** Schooler, J. W., Smallwood, J., Christoff, K., Handy, T. C., Reichle, E. D., & Sayette, M. A. (2011). Meta-awareness, perceptual decoupling and the wandering mind. Trends in Cognitive Sciences, 15, 319-326.

Session 2: Vigilance & Sustained Attention (Parasuraman & Davies Taxonomy)

Parasuraman and Davies' (1977) taxonomy of vigilance attempted to explain performance degradation on a variety of vigilance tasks in a concept-based rather than task-specific manner. Based on a resource theory of vigilance, which describes vigilance tasks as depleting a limited source (or multiple limited sources) of cognitive resources, the Parasuraman and Davies taxonomy predicts that performance degradation on vigilance tasks will depend on four characteristics of the tasks: task type, event rate (frequency of both relevant and non-relevant stimuli), sensory modality (auditory or visual) engaged by the task, and source complexity (i.e., number of sources and uncertainty of locations the observer must monitor) (Warm & Dember, 1998). The Parasuraman and Davies taxonomy does not capture the variety and complexity of many real-world vigilance tasks (see Donald, 2008), but it does fit well with research indicating that vigilance is an effortful process (see Warm, Parasuraman, & Matthews, 2008) and with the extent to which individual differences in personality, intelligence, and other traits have been able and unable to predict performance on vigilance tasks (see Finomore, Matthews, Shaw, & Warm, 2009).

The task type element of Parasuraman and Davies' taxonomy makes sense if vigilance is an effortful process. The taxonomy classifies tasks as either simultaneous—the observer compares two stimuli to each other to make a judgment—or successive—the observer compares a stimulus to a standard in the observer's working memory to make a judgment (Warm & Dember, 1998). Thus, a successive task, because it includes a working memory component, requires more mental resources and depletes those resources faster, resulting in a greater vigilance decrement than would a simultaneous task (Warm & Dember, 1998; Warm, Parasuraman, & Matthews, 2008). In light of neuroimaging studies showing blood flow in the brain consistent with the idea that vigilance tasks require active processing (Warm, Parasuraman, & Matthews, 2008), it makes sense that vigilance tasks requiring more mental resources would also deplete those resources more quickly and lead to greater performance degradation. Thus, classifying vigilance tasks on whether they require general attentional resources or those resources plus working memory seems intuitive.

The distinction between simultaneous and successive tasks in terms of working memory is also consistent with the finding that working memory capacity better predicts performance on successive vigilance tasks than on simultaneous vigilance tasks (Finomore, Matthews, Shaw, & Warm, 2009). If successive tasks engage working memory but simultaneous tasks do not, then better working memory should be associated with better successive task performance but not necessarily with better simultaneous task performance.

Additionally, if vigilance tasks differ on a number of dimensions, it makes sense that individual differences in personality; intelligence; and tendencies toward sleepiness or boredom, cognitive failures, stress, and particular coping mechanisms would be poor predictors of performance on vigilance tasks as whole (Finomore, Matthews, Shaw, & Warm, 2009). Maybe some of these individual differences would predict performance differences in different categories of vigilance tasks but would not have the same effect across task categories.

One major weakness of the Parasuraman and Davies taxonomy is that it appears to translate poorly to real-world vigilance tasks like CCTV monitoring and air traffic control (Donald, 2008). Donald (2008) particularly criticized the source complexity category because it originally included only the number of sources to be monitored rather than the full variation in types of data and displays and the extent of integration needed to determine whether a particular stimulus is important. This criticism seems apt when one considers the task Warm and Dember (1998) discussed as testing source complexity: observers monitored “oil pressure,” “temperature,” “flanking aircraft distance” and “fuel level” on four simplified displays, each of which required the observer to judge length, height, or distance of bars or dots along a single dimension. Even this experiment produced unexpected results, i.e., that observers showed greater performance degradation on the simultaneous task than on the successive task. This raises the question of whether the successive/simultaneous task studies generalize to vigilance tasks in real life that are even more complex on more dimensions.