Research Briefing: organising the contents of working memory

Figure 1. Nicholas Myers

Research Briefing, by Nicholas Myers

Everyone has been in this situation: you are stuck in an endless meeting, and a colleague drones on about a topic of marginal relevance. You begin to zone out and focus on the art hanging in your boss’s office, when suddenly you hear your name mentioned. On high alert, you suddenly shift back to the meeting and scramble to retrieve your colleague’s last sentences. Miraculously, you are able to retrieve a few key words – they must have entered your memory a moment ago, but would have been quickly forgotten if hearing your name had not cued them as potentially vital bits of information.

This phenomenon, while elusive in everyday situations, has been studied experimentally for a number of years now: cues indicating the relevance of a particular item in working memory have a striking benefit to our ability to recall it, even if the cue is presented after the item has already entered memory. See our previous Research Briefing on how retrospective cueing can restore information to the focus of attention in working memory.

In a new article, published in the Journal of Cognitive Neuroscience, we describe a recent experiment that set out to add to our expanding knowledge of how the brain orchestrates these retrospective shifts of attention. We were particularly interested in the potential role of neural synchronization of 10 Hz (or alpha-band) oscillations, because they are important in similar prospective shifts of attention.

Figure 2. Experimental Task Design. [from Myers et al, 2014]

We wanted to examine the similarity of alpha-band responses (and other neural signatures of the engagement of attention) both to retrospective and prospective attention shifts. We needed to come up with a new task that allowed for this comparison. On each trial in our task, experiment volunteers first memorized two visual stimuli. Two seconds later, a second set of two stimuli appeared, so that a total of four stimuli was kept in mind. After a further delay, participants recalled one of the four items.  

In between the presentation of the first and the second set of two items, we sometimes presented a cue: this cue indicated which of the four items would likely be tested at the end of the trial. Crucially, this cue could have either a prospective or a retrospective function, depending on whether it pointed to location where an item had already been presented (a retrospective cue, or retrocue), or to a location where a stimulus was yet to appear (a prospective cue, or precue). This allowed us to examine neural responses to attention-guiding cues that were identical with respect to everything but their forwards- or backwards-looking nature. See Figure 2 for a task schematic.

Figure 3. Results: retro-cueing and pre-cueing
trigger different attention-related ERPs.
[from Myers et al, 2014]

We found marked differences in event-related potential (ERP) profiles between the precue and retrocue conditions. We found evidence that precues primarily generate an anticipatory shift of attention toward the location of an upcoming item: potentials just before the expected appearance of the second set of stimuli reflected the location where volunteers were attending. These included the so-called early directing attention negativity (or 'EDAN') and the late directing attention-related positivity (or 'LDAP'; see Figure 3, middle panel; and see here for a review of attention-related ERPs). Retrocues elicited a different pattern of ERPs that was compatible with an early selection mechanism, but not with stimulus anticipation (i.e., no LDAP, see Figure 3, upper panel). The latter seems plausible, since the cued information was already in memory, and upcoming stimuli were therefore not deserving of attention. In contrast to the distinct ERP patterns, alpha band (8-14 Hz) lateralization was indistinguishable between cue types (reflecting, in both conditions, the location of the cued item; see Figure 4).

Figure 4. Results: retro-cueing and pre-cueing trigger similar patters
of de-synchronisation in low frequency activity (alpha band at ~10Hz).
[from Myers et al, 2014]

What did we learn from this study? Taken together with the ERP results, it seems that alpha-band lateralization can have two distinct roles: after a precue it likely enables anticipatory attention. After a retrocue, however, the alpha-band response may reflect the controlled retrieval of a recently memorized piece of information that has turned out to be more useful than expected, without influencing the brain’s response to upcoming stimuli.

It seems that our senses are capable of storing a limited amount of information on the off chance that it may suddenly become relevant. When this turns out to be the case, top-down control allows us to pick out the relevant information from among all the items quietly rumbling around in sensory brain regions.

Many interesting questions remain that we were not able to address in this study. For example, how do cortical areas responsible for top-down control activate in response to a retrocue, and how do they shuttle cued information into a state that can guide behaviour? 



Key Reference: 

Myers, Walther, Wallis, Stokes & Nobre (2014) Temporal Dynamics of Attention during Encoding versus Maintenance of Working Memory: Complementary Views from Event-related Potentials and Alpha-band Oscillations. Journal of Cognitive Neuroscience (Open Access)

In the News: Death wave

Near-death Experience
(Wiki Commons)

Can neuroscience shed light on one of life's biggest mysteries - death? In a paper just published in PNAS, researchers describe a surge of brain activity just moments before death. This raises the fascinating possibility that they have identified the neural basis for near death experiences.

First, to put this research into context, death-related brain activity was examined in rats, not humans. For obvious reasons, it is easier to study the death process in animals rather than humans. In this study, nine rats were implanted with electrodes in various brain regions, anaesthetised then 'euthanized' (i.e., killed). The exact moment of death was identified as the last regular heartbeat (clinical death). Electroencephalogram (EEG) was recorded during normal waking phase, anaesthesia and after cardiac arrest (i.e., after death) from right and left frontal (RF/LF), parietal (RP/LP) and occipital (RO/LO) cortex (see Figure below). Data shown in panel A ranges from about 1hr before death to 30mins afterwards. At this coarse scale you can see some patterns in the waking data that generally reflect high frequency brain activity (gamma band, >40Hz). During anaesthesia, activity becomes synchronised at lower frequency bands (especially delta band: 0.1–5 Hz), but everything seems to flatline after cardiac arrest. However, if we now zoom in on the moment just after death (Panels B and C), we can see that the death process actually involves a sequence of structured stages, including a surge of high-frequency brain activity that is normally associated with wakefulness.

Adapted from Fig 1 of Borjogin et al. (2013)

In the figure above, Panel B shows brain activity zoomed in at 30min after death, and Panel C provides an even closer view, with activity from each brain area overlaid in a different colour. The authors distinguish  four distinct cardiac arrest stages (CAS). CAS1 reflects the time between the last regular heartbeat and the loss of oxygenated blood pulse (mean duration ~4 seconds). The next stage, CAS2 (~6 seconds duration) ended with a burst in delta waves (so-called 'delta blip' ~1.7 seconds duration), and CAS3 (~20 seconds duration) continued until there was no more evidence of meaningful brain activity (i.e., CAS4 >30mins duration). These stages reflect an organized series of brain states. First, activity during CAS1 transitions from the anaesthetised state with an increase in high-frequency activity (~130Hz) across all brain areas. Next, activity settles into a period of low-frequency brain waves during CAS2. Perhaps most surprisingly, during CAS3 recordings were dominated by mid-range gamma activity (brain waves ~35-50Hz). In further analyses, they also demonstrate that this post-mortem brain activity is also highly coordinated across brain areas and different frequency bands. These are the hallmarks of high-level cognitive activity. In sum, these data suggests that long after death, the brain enters a brief state of heightened activity that is normally associated with wakeful consciousness.

Heightened awareness just after death  

Adapted from Fig 2 of Borjogin et al. (2013)

The authors even suggest that the level of activity observed during CAS3 may not only resemble the waking state, but might even reflect a heightened state of conscious awareness similar to the “highly lucid and realer-than-real mental experiences reported by near-death survivors”. This is based on the observation that there is more evidence for consciousness-related activity during this final phase of death than during normal wakeful consciousness. This claim, however, depends critically on their quantification of 'consciousness'. To date, there is no simple index of 'consciousness' that can be reliability measured to infer the true state of awareness. And even if we could derive such a consciousness metric in humans (see here), to generalise to animals could only ever be speculative. Indeed, research in animals can only ever hint at human experience, including near-death experiences.

Nevertheless, as the authors note, this research certainly demonstrates that activity in the brain is consistent with active cognitive processing. The results demonstrate that a neural explanation for these experiences is at least plausible. They have identified the right kind of brain activity for a neural explanation of near-death experiences, yet it remains to be verified whether these signatures do actually relate directly to the subjective experience.

Future directions: The obvious next step is to test weather similar patterns of brain activity are observed in humans after clinical death. Next, it will be important to show that such activity is strongly coupled to near-death experience. For example, does the presence or absence of such activity predict whether or not the person would report a near death experience. This second step is obviously fraught with technical and ethical challenges (think: The Flatliners), but would provide good evidence to link the neural phenomena to the phenomenal experience.

Key Reference:

Borjigin, Lee, Liu, Pal, Huff, Klarr, Sloboda, Hernandez, Wang & Mashour (2013) Surge of neurophysiological coherence and connectivity in the dying brain. PNAS

Related references:

Tononi G (2012) Integrated information theory of consciousness: An updated account. Arch Ital Biol 150(2-3):56–90.

Auyong DB, et al. (2010) Processed electroencephalogram during donation after
cardiac death. Anesth Analg 110(5):1428–1432

Related blogs and news articles:

BBC News
Headquarters Hosted by the Guardian
National Geographic
The Independent

Journal Club: Brains Resonating to the Dream Machine

By George Wallis

On: VanRullen and Macdonald (2012). PerceptualEchoes at 10Hz in the Human Brain

One day in 1958 the artist Brion Gysin was sleeping on a bus in the south of France. The bus passed a row of trees, through which the sun was shining. As the flickering light illuminated Gysin, he awoke and with his eyes closed, began to hallucinate, seeing:

“an overwhelming flood of intensely bright patterns in supernatural colours… Was that a vision?”.  

By the turn of the decade Gysin was living with William S Burroughs in the flophouse in Paris that became known as the Beat Hotel. Gysin told Burroughs of his experience, and they decided to build a device to recreate the flickering stimulation. The ‘Dream Machine’ is a cylinder of cardboard, cut at regular intervals with windows, which can be spun on a 78rpm record player, a light bulb inside to throw off a flickering light.   The light flickers around ten times per second (10Hz). Some, like the poet Ginsberg (“it sets up optical fields as religious and mandalic as the hallucinogenic drugs”), claim to have experienced vivid hallucinations when seated eyes closed before a spinning Dream Machine (although, most devotees admitted that the effect was much stronger in combination with psychedelic drugs).

Gysin and Burroughs had rediscovered a phenomenon that had been known to scientists for some time. The great neurophysiologist Purkinje documented the hallucinatory effect of flickering light by waving an open-fingered hand in front of a gaslight. Another neuro-luminary, Hermann von Helmholtz, investigated the same phenomenon in Physiological Optics, calling the resulting hallucinations ‘shadow patterns’. In the 1930s Adrian and Matthews, investigating the rhythmic EEG signal recently discovered by Hans Berger, shone a car headlamp through a bicycle wheel and found that they could ‘entrain’ the EEG recording of their subject to the stimulation, in ‘a coordinated beat’. And from there investigation of the magical 10Hz flicker continued, on and off, until the present day (for a very readable review, see the paper by ter Meulen, Tavy and Jacobs referenced at the bottom of this post – from which the above quotations from Gysin and Ginsberg are taken; see also a relate post by Mind Hacks).

This week’s journal club paper is not about flicker-induced hallucinations. However, it does use EEG to address the related idea that there is something rather special to the visual system about the 10Hz rhythm. The paper, by Rufin VanRullen and James Macdonald, and published this month in Current Biology, used a very particular type of flickering stimulation to probe the ‘impulse response’ of the brain. They found – perhaps to their surprise – that the brain seems to ‘echo back’ their stimulation at about 10 echoes per second.

Macdonald and VanRullen’s participants were ‘plugged in’ during the experiment – electroencephalography (EEG) was used to measure the tiny, constantly changing voltages on their scalps that reflect the workings of the millions of neurons in the brain beneath. The stimulus sequence presented (with appropriate controls to ensure the participants paid attention) was a flickering patch on a screen. The flicker was of a very particular kind. It was a flat spectrum sequence, a type of signal used by engineers to probe the ‘impulse response’ of a system. The impulse response is the response of a system to a very short, sharp stimulation. Imagine clicking your fingers in an empty cathedral – that short, sharp click is transformed into a long, echoing sound that slowly dies away. This is the impulse response of the cathedral: VanRullen and MacDonald were trying to measure the impulse response of the brain’s visual system. Because of its property of very low autocorrelation (the value of the signal at one point in time says nothing about what the value of the signal will be at any other time), the kind of signal the authors flashed at their participants can be used to mathematically extract the impulse response of a system (for more details, see the paper by Lalor at al., referenced at the bottom of this post).

To extract the impulse response, you do a ‘cross-correlation’ of the input signal (the flickering patch on the screen) with the output of the system – which, in this case, was the EEG signal from over the visual cortex of the participants (the occipital lobe). Cross-correlation involves lining up the input signal with the output at many different points in time and seeing how similar the signals are. So, you start with the input lined up exactly with the output, and ask how similar the input and output signals look. Then you move the input signal so it’s lined up with the output signal 1ms later – how similar now? And so on… all the way up to around 1s ‘misalignment’, in this paper.  Here, for two example subjects (S1 and S2), is the result:

The grey curves are the cross-correlation functions, stretched out over time. Up until about 0.2 seconds you see the classic ‘visual evoked potential’ response, but after that time a striking 10Hz ‘echo’ emerges. The authors perform various controls, to show, for example, that these ‘echoes’ are not induced only by the brightest or darkest values in their stimulus sequence. They argue that because of the special nature of the stimuli they used, this effect must represent the brain actually ‘echoing back’ the input signal at a later time. In their discussion, they propose that this could be a mechanism for remembering stimuli over short periods of time: replaying them 10 times per second.

This is a bold hypothesis. Are these 10Hz reverberations really ‘echoes’ of the visual input, used for visual short term memory? We weren’t sure. We already know that the EEG resonates by far the most easily to flickering stimuli at 10Hz (see the paper by Hermann, referenced below), so despite the sophisticated stimulus used here, it is easy to suspect that the result of this experiment depends more on this ‘ringing’ quality of the EEG than on mnemonic echoes of stimuli themselves. We felt that in order to really nail this question you would need to show, for example, that our sensitivity to specific stimuli we have just been shown changes with a 10Hz rhythm in the seconds after we encounter it. However, this is the sort of thing that could be achieved with behavioural experiments.

Perhaps a new theory of short term memories will emerge.  

In the meantime, why not build yourself a dream machine and see if you can have your own visionary insights with the help of some 10Hz flickering light?  You’ll need the diagram below (blow up; cut out; fold into a cylinder), an old 78rpm record player, and a light-bulb.

References:

Current Biology, 2012: Perceptual Echoes at 10Hz in the Human Brain, Rufin VanRullen and James S.P. Macdonald

European Neurology, 2009: FromStroboscope to Dream Machine: A History of Flicker-Induced Hallucination,  B.C. ter Meulen, D. Tavy and B.C. Jacobs

NeuroImage, 2006: The VESPA: a method for the rapid estimation of a visual evoked potential.  Edmund C. Lalor, Barak A. Pearlmutter, Richard B. Reilly, Gary McDarby and John J. Foxe

Experimental Brain Research, 2001: Human EEG responses to 1-100 Hzflicker: resonance phenomena in visual cortex and their potential correlationto cognitive phenomena.