Marlene R. Cohen

Attention improves performance primarily by reducing interneuronal correlations

Visual attention can improve behavioral performance by allowing observers to focus on the important information in a complex scene. Attention also typically increases the firing rates of cortical sensory neurons. Rate increases improve the signal-to-noise ratio of individual neurons, and this improvement has been assumed to underlie attention-related improvements in behavior. We recorded dozens of neurons simultaneously in visual area V4 and found that changes in single neurons accounted for only a small fraction of the improvement in the sensitivity of the population. Instead, over 80% of the attentional improvement in the population signal was caused by decreases in the correlations between the trial-to-trial fluctuations in the responses of pairs of neurons. These results suggest that the representation of sensory information in populations of neurons and the way attention affects the sensitivity of the population may only be understood by considering the interactions between neurons.

Stimulus onset quenches neural variability: a widespread cortical phenomenon

Neural responses are typically characterized by computing the mean firing rate, but response variability can exist across trials. Many studies have examined the effect of a stimulus on the mean response, but few have examined the effect on response variability. We measured neural variability in 13 extracellularly recorded datasets and one intracellularly recorded dataset from seven areas spanning the four cortical lobes in monkeys and cats. In every case, stimulus onset caused a decline in neural variability. This occurred even when the stimulus produced little change in mean firing rate. The variability decline was observed in membrane potential recordings, in the spiking of individual neurons and in correlated spiking variability measured with implanted 96-electrode arrays. The variability decline was observed for all stimuli tested, regardless of whether the animal was awake, behaving or anaesthetized. This widespread variability decline suggests a rather general property of cortex, that its state is stabilized by an input.

Measuring and interpreting neuronal correlations

Mounting evidence suggests that understanding how the brain encodes information and performs computations will require studying the correlations between neurons. The recent advent of recording techniques such as multielectrode arrays and two-photon imaging has made it easier to measure correlations, opening the door for detailed exploration of their properties and contributions to cortical processing. However, studies have reported discrepant findings, providing a confusing picture. Here we briefly review these studies and conduct simulations to explore the influence of several experimental and physiological factors on correlation measurements. Differences in response strength, the time window over which spikes are counted, spike sorting conventions and internal states can all markedly affect measured correlations and systematically bias estimates. Given these complicating factors, we offer guidelines for interpreting correlation data and a discussion of how best to evaluate the effect of correlations on cortical processing.

Estimates of the contribution of single neurons to perception depend on timescale and noise correlation.

The sensitivity of a population of neurons, and therefore the amount of sensory information available to an animal, is limited by the sensitivity of single neurons in the population and by noise correlation between neurons. For decades, therefore, neurophysiologists have devised increasingly clever and rigorous ways to measure these critical variables (Parker and Newsome, 1998). Previous studies examining the relationship between the responses of single middle temporal (MT) neurons and direction-discrimination performance uncovered an apparent paradox. Sensitivity measurements from single neurons suggested that small numbers of neurons may account for a monkeys psychophysical performance (Britten et al., 1992), but trial-to-trial variability in activity of single MT neurons are only weakly correlated with the monkeys behavior, suggesting that the monkeys decision must be based on the responses of many neurons (Britten et al., 1996). We suggest that the resolution to this paradox lies (1) in the long stimulus duration used in the original studies, which led to an overestimate of neural sensitivity relative to psychophysical sensitivity, and (2) mistaken assumptions (because no data were available) about the level of noise correlation in MT columns with opposite preferred directions. We therefore made new physiological and psychophysical measurements in a reaction time version of the direction-discrimination task that matches neural measurements to the actual decision time of the animals. These new data, considered together with our recent data on noise correlation in MT (Cohen and Newsome, 2008), provide a substantially improved account of psychometric performance in the direction-discrimination task.

Context-Dependent Changes in Functional Circuitry in Visual Area MT

Animals can flexibly change their behavior in response to a particular sensory stimulus; the mapping between sensory and motor representations in the brain must therefore be flexible as well. Changes in the correlated firing of pairs of neurons may provide a metric of changes in functional circuitry during behavior. We studied dynamic changes in functional circuitry by analyzing the noise correlations of simultaneously recorded MT neurons in two behavioral contexts: one that promotes cooperative interactions between the two neurons and another that promotes competitive interactions. We found that identical visual stimuli give rise to differences in noise correlation in the two contexts, suggesting that MT neurons receive inputs of central origin whose strength changes with the task structure. The data are consistent with a mixed feature-based attentional strategy model in which the animal sometimes alternates attention between opposite directions of motion and sometimes attends to the two directions simultaneously.

Using Neuronal Populations to Study the Mechanisms Underlying Spatial and Feature Attention

Visual attention affects both perception and neuronal responses. Whether the same neuronal mechanisms mediate spatial attention, which improves perception of attended locations, and nonspatial forms of attention has been a subject of considerable debate. Spatial and feature attention have similar effects on individual neurons. Because visual cortex is retinotopically organized, however, spatial attention can comodulate local neuronal populations, whereas feature attention generally requires more selective modulation. We compared the effects of feature and spatial attention on local and spatially separated populations by recording simultaneously from dozens of neurons in both hemispheres of V4. Feature and spatial attention affect the activity of local populations similarly, modulating both firing rates and correlations between pairs of nearby neurons. However, whereas spatial attention appears to act on local populations, feature attention is coordinated across hemispheres. Our results are consistent with a unified attentional mechanism that can modulate the responses of arbitrary subgroups of neurons.

Decision-Related Activity in Sensory Neurons: Correlations Among Neurons and with Behavior

Neurons in early sensory cortex show weak but systematic correlations with perceptual decisions when trained animals perform at psychophysical threshold. These correlations are observed across repeated presentations of identical stimuli and cannot be explained by variation in external factors. The relationship between the activity of individual sensory neurons and the animals behavioral choice means that even neurons in early sensory cortex carry information about an upcoming decision. This relationship, termed choice probability, may reflect the effect of fluctuations in neuronal firing rate on the animals decision, but it can also reflect modulation of sensory responses by cognitive factors, or network properties such as variability that is shared among populations of neurons. Here, we review recent work clarifying the relationship among fluctuations in the responses of individual neurons, correlated variability, and behavior in a variety of tasks and cortical areas. We also discuss the possibility that choice probability may in part reflect the influence of cognitive factors on sensory neurons and explore the situations in which choice probability can be used to make inferences about the role of particular sensory neurons in the decision-making process.

Attention can either increase or decrease spike count correlations in visual cortex

Visual attention enhances the responses of visual neurons that encode the attended location. Several recent studies showed that attention also decreases correlations between fluctuations in the responses of pairs of neurons (termed spike count correlation or rSC). The previous results are consistent with two hypotheses. Attention–related changes in rate and rSC might be linked (perhaps through a common mechanism), so that attention always decreases rSC. Alternately, attention might either increase or decrease rSC, possibly depending on the role the neurons play in the behavioral task. We recorded simultaneously from dozens of neurons in area V4 while monkeys performed a discrimination task. We found strong evidence in favor of the second hypothesis, showing that attention can flexibly increase or decrease correlations, depending on whether the neurons provide evidence for the same or opposite perceptual decisions. These results place important constraints on models of the neuronal mechanisms underlying cognitive factors.Visual attention enhances the responses of visual neurons that encode the attended location. Several recent studies have shown that attention also decreases correlations between fluctuations in the responses of pairs of neurons (termed spike count correlation or rSC). These results are consistent with two hypotheses. First, attention-related changes in rate and rSC might be linked (perhaps through a common mechanism), with attention always decreasing rSC. Second, attention might either increase or decrease rSC, possibly depending on the role of the neurons in the behavioral task. We recorded simultaneously from dozens of neurons in area V4 while monkeys performed a discrimination task. We found strong evidence in favor of the second hypothesis, showing that attention can flexibly increase or decrease correlations depending on whether the neurons provide evidence for the same or opposite choices. These results place important constraints on models of the neuronal mechanisms underlying cognitive factors.

Attention stabilizes the shared gain of V4 populations

Responses of sensory neurons represent stimulus information, but are also influenced by internal state. For example, when monkeys direct their attention to a visual stimulus, the response gain of specific subsets of neurons in visual cortex changes. Here, we develop a functional model of population activity to investigate the structure of this effect. We fit the model to the spiking activity of bilateral neural populations in area V4, recorded while the animal performed a stimulus discrimination task under spatial attention. The model reveals four separate time-varying shared modulatory signals, the dominant two of which each target task-relevant neurons in one hemisphere. In attention-directed conditions, the associated shared modulatory signal decreases in variance. This finding provides an interpretable and parsimonious explanation for previous observations that attention reduces variability and noise correlations of sensory neurons. Finally, the recovered modulatory signals reflect previous reward, and are predictive of subsequent choice behavior. DOI: http://dx.doi.org/10.7554/eLife.08998.001

When Attention Wanders: How Uncontrolled Fluctuations in Attention Affect Performance

No matter how hard subjects concentrate on a task, their minds wander (Raichle et al., 2001; Buckner et al., 2008; Christoff et al., 2009; Killingsworth and Gilbert, 2010). Internal fluctuations cannot be measured behaviorally or from conventional neurophysiological measures, so their effects on performance have been difficult to study. Previously, we measured fluctuations in visual attention using the responses of populations of simultaneously recorded neurons in macaque visual cortex (Cohen and Maunsell, 2010). Here, we use this ability to investigate how attentional fluctuations affect performance. We found that attentional fluctuations have large and complex effects on performance, the sign of which depends on the difficulty of the perceptual judgment. As expected, attention greatly improves the detection of subtle changes in a stimulus. Surprisingly, we found that attending too strongly to a particular stimulus impairs the ability to notice when that stimulus changes dramatically. Our results suggest that all previously reported measures of behavioral performance should be viewed as amalgamations of different attentional states, whether or not those studies specifically addressed attention.