Stay focussed – muffle that resonant bass!

Alexander Thiele

Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, NE2 4HH, UK

Spotlights

Increased rhythmic brain activity in the high frequencyrange and decreased rhythmic activity in the low fre-quency range is a hallmark of cognitive function andability. A novel study links these findings to the cholin-ergic theory of attention, demonstrating that attention-induced rebalancing of brain rhythms is augmented byacetylcholine.

The strength of oscillatory neuronal activity in specificfrequency bands is linked to cognitive states [1]. Attentionand visual perception are associated with increased oscil-latory activity in the g-frequency range (30–80 Hz) [2] (butsee [3]), and reduced oscillatory activity in low frequencyranges (e.g. 7–14 Hz, a-band activity) [2,3] (but see [4]).The increased synchronous activity in the g-frequencyband may aid information transfer, as synchronous spikesmore efficiently activate distant neuronal populations. Thesynchronous spikes could also exploit windows of excitabil-ity at their targets, which are invariably produced duringoscillatory cycles, thereby creating an effective selectionand filter mechanism [2]. It is thus beneficial if oscillatorypower is altered by cognitive operations, as it aids theprocessing of task-relevant information. Exactly how suchan alteration is implemented is poorly understood, al-though the cholinergic system might be involved.

The cholinergic system mediates state dependent re-balancing of oscillatory power in low and high frequencybands [1]. Activation of cholinergic nuclei in animalsreduces low frequency oscillations, and it can increase highfrequency gamma oscillations. Acetylcholine also enablesattentional modulation of sensory processing in visualcortex [5]. Thus, acetylcholine might aid attention-inducedrebalancing of oscillatory power. Novel data by Bauer et al.[6] support this idea, but the authors report a surprisingtwist to the story. They found that visual attention reducesthe a/b-band activity and increases g-band activity invisual cortex contralateral to the attended location.By augmenting cholinergic drive through systemic admin-istration of an acetylcholine-esterase inhibitor, theyreduced the a/b-band activity even further, whereas theirmanipulation had no influence on attention-inducedg-band activity. Importantly, the strength of acetylcho-line-induced reduction in a/b-band activity correlated withan individual’s performance: the more low frequency oscil-lations were muffled by cholinergic drive, the faster asubject detected relevant stimuli.

At first glance this finding appears incompatible with aproposed role of enhanced g-band activity in attention.However, the story is more complex. Alpha-band oscillatoryactivity in the cortex is not simply related to a state of idling,

Corresponding author: Thiele, A. (alex.thiele@ncl.ac.uk).

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even if idling is associated with strong a-band oscillatoryactivity. The amount of a-band reduction in detection taskscorrelates with performance. Importantly, the expectationof distracters at irrelevant locations increases a-band activ-ity in those areas representing the irrelevant locations, as ifenhanced a-band activity functions as an active filter, notsimply an oscillatory rhythm unfavourable to sensory pro-cessing [7]. Moreover, the attention-induced enhancementof g-band activity, although prominent in many studies, isrestricted to neuronal activity in superficial layers of thevisual cortex [8]. Attention has no effect on g-band power ininfragranular layers in visual cortex, but it reduces a-bandactivity in these layers. How could increased a-band activityfunction as an active filter? It has been proposed that it actsthrough pulsed inhibition, whereby a bout of inhibition isgenerated throughout a specific phase of every oscillatorycycle, which efficiently suppresses sensory processing andhinders perception [7]. Reduction of a-band activity andreduction of associated pulsed inhibition would thereforeimprove sensory processing. How could acetylcholine aid theattention induced reduction of a-band activity shown byBauer et al.? Most oscillatory activity strongly depends onthe action of inhibitory interneurons, which are often moresusceptible to cholinergic influences than pyramidal cellsare [9]. While acetylcholine affects the inhibitory inter-neurons proposed to generate pulsed inhibition, thecholinergic influence is opposite to what one would predict.In vitro studies show that acetylcholine reduces theactivity of fast spiking interneurons (which are assumedto aid g-band oscillations) and often increases the activityof the proposed pulsed inhibitors [10]. Thus, there is todate no simple cellular and molecular framework intowhich to fit the current data. Bauer et al.’s paper never-theless neatly demonstrates that acetylcholine augmentsattention-induced low frequency reduction, and therebybenefits performance. One detail to be determined infuture investigations relates to the role of muscarinicand nicotinic receptors. Cholinergic drugs play an impor-tant role in the treatment of age and disease-relatedcognitive decline. An understanding of the specific recep-tors involved may thus aid the development of bettertherapies. Recent preliminary data point towards a roleof muscarinic receptors [1], but it remains to be seenwhether their role is exclusive.

The study by Bauer et al. adds weight to increasingevidence which argues for a more active role of low fre-quency oscillations in cognition. Muffling that resonantbass allows us to focus, and acetylcholine helps us to doso. Did our parents not always tell us to turn down thevolume when doing our homework? Maybe they were rightafter all, even if they did not know in what sense.

Spotlights Trends in Cognitive Sciences April 2012, Vol. 16, No. 4

References1 Harris, K.D. and Thiele, A. (2011) Cortical state and attention. Nat.

Rev. Neurosci. 12, 509–5232 Fries, P. et al. (2001) Modulation of oscillatory neuronal synchronization

by selective visual attention. Science 291, 1560–15633 Chalk, M. et al. (2010) Attention reduces stimulus-driven gamma

frequency oscillations and spike field coherence in V1. Neuron 66,114–125

4 Lakatos, P. et al. (2008) Entrainment of neuronal oscillations as amechanism of attentional selection. Science 320, 110–113

5 Herrero, J.L. et al. (2008) Acetylcholine contributes through muscarinicreceptors to attentional modulation in V1. Nature 454, 1110–1114

6 Bauer, M. et al. (2012) Cholinergic enhancement of visual attention andneural oscillations in the human brain. Curr. Biol. 22, 397–402

Corresponding author: Starns, J.J. (jstarns@psych.umass.edu).

7 Mazaheri, A. and Jensen, O. (2010) Rhythmic pulsing: linking ongoingbrain activity with evoked responses. Front. Hum. Neurosci. 4, 177DOI: 10.3389/fnhum.2010.00177

8 Buffalo, E.A. et al. (2011) Laminar differences in gamma and alphacoherence in the ventral stream. Proc. Natl. Acad. Sci. U.S.A. 108,11262–11267

9 Disney, A.A. and Aoki, C. (2008) Muscarinic acetylcholine receptors inmacaque V1 are most frequently expressed by parvalbumin-immunoreactive neurons. J. Comp. Neurol. 507, 1748–1762

10 Xiang, Z. et al. (1998) Cholinergic switching within neocorticalinhibitory networks. Science 281, 985–988

1364-6613/$ – see front matter � 2012 Elsevier Ltd. All rights reserved.

doi:10.1016/j.tics.2012.02.009 Trends in Cognitive Sciences, April 2012, Vol. 16, No. 4

Modeling single versus multiple systems in implicitand explicit memory

Jeffrey J. Starns1, Roger Ratcliff2 and Gail McKoon2

1 Department of Psychology, Tobin Hall, University of Massachusetts Amherst, Amherst, MA 01003, USA2 Department of Psychology, Ohio State University, Columbus, OH 43210, USA

It is currently controversial whether priming on implicittasks and discrimination on explicit recognition tests aresupported by a single memory system or by multiple,independent systems. In a Psychological Review article,Berry and colleagues used mathematical modeling toaddress this question and provide compelling evidenceagainst the independent-systems approach.

Cognitive psychologists expend enormous effort investigat-ing the proposition that different cognitive phenomenareflect the contribution of separate neurophysiologicalsystems. For example, much research rests on the notionthat separate memory systems underlie priming on implic-it tasks and discrimination on explicit recognition tests [1].Behavioral and neurophysiological research methods havedriven impressive progress in this area, but another pow-erful research tool – mathematical modeling – has beensignificantly under-utilized. In a recently published article,Berry, Shanks, Speekenbrink, and Henson [2] took a stepin the direction of a more rigorous modeling approach.Their results demonstrate the promise of formal modelsfor increasing the clarity of predictions, producing moreappropriate interpretations of empirical patterns, andredefining research questions.

Berry et al. [2] focused on a task in which explicit andimplicit memory could be compared within the same test. Inthis task, participants first identify a test item from adegraded stimulus that becomes clearer over time. Someof the test items have been presented in a previous list,and priming is obtained if these items are identified morequickly than non-studied items. Following the identificationof a test item, participants decide whether or not the itemwas previously presented (i.e., whether it is ‘old’ or ‘new’). Inaddition to the standard ‘old’/‘new’ procedure, Berry et al.obtained recognition responses in terms of confidence rat-ings ranging from 1 (certain ‘new’) to 6 (certain ‘old’), as well

as responses in which participants indicated whether theyspecifically recollected studying an item or just found itfamiliar. To accommodate identification priming and accu-racy in recognition, the authors developed signal detectionmodels under a range of assumptions regarding the rela-tionship between priming and conscious recognition.

At one extreme, a single-system model assumes that thesame memory strength value drives priming and recogni-tion memory. This single-system account makes strongand novel predictions. First, recognition and primingshould be linked: even within old or new items, identifica-tion times should be faster for items called ‘old’ than itemscalled ‘new.’ Consequently, there should be less priming forold items that are not recognized than for recognized items.Second, independent variables cannot have opposite influ-ences on recognition and priming, and third, primingshould not be observed in the absence of recognition.

At the other extreme, one version of a multiple-systemmodel assumes that completely independent memory sig-nals drive identification and recognition (we call this theindependent-systems model). Within a stimulus class (‘old’or ‘new’), this model predicts that identification timesshould be equal for items receiving ‘old’ and ‘new’ judg-ments. The independent-systems model is free to separate-ly vary the memory strength contributions to priming andrecognition and therefore, unlike the single-system model,it can produce priming in the absence of recognition dis-crimination and vice versa.

Covering the middle ground between the single-systemand independent-systems models is a model in which thecontributions of memory to recognition and priming overlapsomewhat but not completely (we call this the overlapping-systems model). Specifically, the memory strengths foridentification and recognition are drawn from a bivariateGaussian distribution with parameters for the mean iden-tification strength, the mean recognition strength, and thecorrelation between the two strengths across items.

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