The attentional blink and temporal selection

 

While our retinas process the world around us continuously with little to no interruption, certain mental processes are very limited. One way to show this it to present someone with a rapid stream of letters, one at a time in the center of the screen, at a rate of perhaps 10 letters per second. Give them the task of reporting one or more letters, with each target letter to report designated by presenting a circle around it.

When only one letter is designated for report, the task is pretty easy. When two letters are designated for report and they are far apart in time (say, a full second apart), performance is also high. But when the second letter appears within several hundred milliseconds of the first, something strange happens. Often one feels one not did not see the second ring or letter at all. This is the attentional blink (AB).

For the last twenty years, researchers have investigated which aspects of mental processing are impaired for the second, seemingly unseen target. For many years, the leading theory was that the second target is successfully selected by attention but that later stages are fully occupied with the first target, so that the second target decays before being fully processed.

More recent theories suggest that not only is post-selection target processing impaired, but selection itself is also impaired. After selecting the first target, the attempt to select the second target goes awry. 

The idea that selection is disrupted has had legs, empirically and theoretically. Yet we know surprisingly little about the temporal characteristics of selection even when it succeeds. Vul, Nieuwenstein, & Kanwisher (2008) made a start at remedying this by pointing out that temporal selection could be thought of as having two different aspects – latency and precision.

Latency is the delay in time between the appearance of the target and the stimulus that is ultimately sampled from the scene and reported. Precision is the variability of this latency on different occasions. The attentional blink might affect either, both, or neither of these parameters. To understand how it could be neither, consider that the blink might prevent selection independently of whether or not its time-course is affected.

Vul, Nieuwenstein, & Kanwisher (2008) had a go at quantifying latency and precision. Their methodology was flawed (all the details are in our in-press manuscript), but their basic idea was sound. We applied it to six different datasets from five different labs, and found a similar pattern of results in each. I’ll tell you some of the highlights.

The basic experimental set-up is to present a rapid stream of letters or objects with two of the stimuli indicated as targets, e.g. by presenting a circle around them. The task in this case is to report the two stimuli that were circled.

Sometimes participants report both stimuli correctly and sometimes they get one or both wrong, and this varies with the amount of time between the targets. Traditionally, such percent-correct measures are where the data analysis ends. But Vul et al. had the bright idea of scrutinising the nature of participants’ incorrect responses. Some errors appear to be completely random, but frequently a participant will report the stimulus a few items before or after the target. These near-misses are conspicuous on the serial position error histogram, below, in which the response on every trial is coded relative to the target. If a participant reports the item two before the target, we add it to the “-2” bar of the histogram. If the item reported occurred three items after the target, it’s coded as “3”, and so on.

histogramExplain

The first thing that jumps out is that the error distribution is rather symmetrical, meaning that there are approximately as many items reported that occurred before the target as after (in the histogram pictured above, it looks somewhat positively skewed, but that’s largely because the mean falls between position zero and position one, while the histogram bars correspond to whole numbers).

The near-symmetry in time suggests that temporal selection works in a particular way. Before looking at data like these, we assumed that when the circle appeared, it triggered attentional sampling of a stimulus from the scene. If that occurred, however, one would expect a histogram with a substantial positive skew, as variability in time until sampling could widen the distribution only forward in time. Our finding of nearly as many reports from before the target as after hints that rather the cue triggering the commencement of sampling from the stream, the stream is being sampled all along. Which item is ultimately reported may be determined by a subsequent process that binds the ring with one of the persisting representations evoked by the stimuli in the stream.

Which brings me to another remarkable thing: the temporal precision of selection is not affected by the blink. That is, the spread of the histogram, once random guesses were subtracted out, is the same for T1 and T2, for short lags and for long. In unpublished experiments, we found that regardless of stream presentation rate, it is around 80-100 ms (standard deviation of a fitted normal distribution), depending on the participant and the experiment. Looks like the binding process is something that attentional blink theories ought to theorise about.

Unlike the precision of selection, the latency was quite affected by the blink. At the intervals between the first and second target that yield a blink, selection was quite delayed. That is, the histogram was centered on 1-2 items after the target. Except for very short intervals, where it seemed only a single histogram was present, indicating that people manage to sample both targets from a single attentional episode (the explanation we favor for “lag-1 sparing”). This supports those theories that include a disruption to selection, although none have yet addressed temporal precision.

While the sheer number of studies and findings in the attentional blink literature is quite intimidating, a number of theorists have risen to the challenge, developing computational theories that purport to fit the most important empirical results (e.g. 12, 3). As a result of such explicitness, many theories in this area have strong potential to be disconfirmed. One thing I’d like to understand is whether any can explain the contrast between our attentional blink results and some results we found previously (paper, blogpost).

In that previous work, we found that not just precision but also latency could be robust to the demands of encoding a second target. We presented two streams concurrently and on some trials presented a target in each stream. Both targets appeared at the same time. In those circumstances, participants’ performance decreased substantially in the two-target condition, but precision and latency were both unaffected. That the second target impaired performance by approximately as much as it is during an attentional blink, while the properties of selection were unaffected, seems to be a problem for theories that attribute the blink largely to the disruption of selection.

Patrick Goodbourn deserves most of the credit for this work, plus a badge, or maybe two, for putting everything together as open data and open materials (we actually are getting two badges, thanks to the policy of Psychological Science to reward open practices!).

Goodbourn PT, Martini P, Harris I, Livesey E, Barnett-Cowan M, & Holcombe AO (in press). Reconsidering temporal selection in the attentional blink. Psychological Science (postprint, data).

4 thoughts on “The attentional blink and temporal selection

  1. Excellent work Alex (and Patrick who I hear did the hard parts). It’s interesting that you find such symmetrical response distributions when previous work has found a fairly consistent post-cue shift. For example you can find this in a lot of Juan Botella’s excellent experiments on RSVP in the early 90’s. (e.g. Filtering versus parallel processing in RSVP tasks , Botella & Eriksen 1992). Any opinions about the reason for the difference and do those findings have any implications for your ideas about binding between internal representations rather than sampling?

    • Thanks Brad for pushing me on this, I want to go through all the old papers, but won’t have time to do that until after I return from Norway and Sweden. Botella & Eriksen and an older Broadbent & Broadbent paper talk about those shifts with task, as you mention, but they are what we would call latency shifts, and not very large. The histograms, to the extent that they report them, look pretty symmetrical despite having, as did our data (and the data of the other labs we analysed), nonzero latency. Botella uses the term “symmetrical” to mean what we would call zero latency.

      • I think my own thoughts are getting pushed here actually. I’ve always taken the post-shift and an asymmetrical distribution for granted so these data are really interesting.

    • There’ll definitely be cases where it’s positively skewed, and some of the circumstances should be very illuminating. We’ve only found positive skew so far, I think, when we use an exogenous cue or otherwise unsalient cue. And that recalls your suggestion that salience might help explain the different findings of Bay & Wyble vs. Goodbourn & Holcombe, although if anything I’d think that reduced salience would worsen two-target performance, not allow you to do both targets equally well.. Something interesting is going on.

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out / Change )

Twitter picture

You are commenting using your Twitter account. Log Out / Change )

Facebook photo

You are commenting using your Facebook account. Log Out / Change )

Google+ photo

You are commenting using your Google+ account. Log Out / Change )

Connecting to %s