CH 14 PSYC

CHAPTER 14

Attention and Higher Cognition

Neil V. Watson

Simon Fraser University

S. Marc Breedlove

Michigan State University

Attention to Details

Some heart conditions can launch blood clots that block

the arteries supplying the brain, causing several strokes

within a short span of time. What made Parminder’s

case so exceptional was that in the span of a couple of

months she had two strokes that were mirror images of

each other—they damaged identical regions of the left

and right parietal lobes.

Parminder’s unlikely brain lesions produced equally

unlikely symptoms. A few weeks after her second

stroke, Parminder had regained many of her intellectual

powers—she could converse normally and remember

things. Her visual fields were apparently normal too, but

her visual perception was anything but normal.

Parminder had lost the ability to perceive more than one

thing at a time. For example, she could see her

husband’s face just fine, but she couldn’t judge whether

he had glasses on or not. It turned out that she could

see the glasses or she could see the face, but she

couldn’t perceive them both at the same time. When

shown a drawing of several overlapping items, she

could perceive and name only one at a time.

Furthermore, she couldn’t understand where the objects

she saw were located. It was as if Parminder was lost in

space, able to pay attention to only one object or detail

at a time, apparently alone in a world of its own. What

could explain Parminder’s symptoms?

What is attention? William James, the great American psychologist,

wrote in 1890:

Everyone knows what attention is. It is the taking possession by the mind, in clear

and vivid form, of one out of what seem several simultaneously possible objects or

trains of thought. Focalization, concentration, of consciousness are of its essence. It

implies withdrawal from some things in order to deal effectively with others, and is a

condition which has a real opposite in the confused, dazed, scatterbrained state.

Clearly, James understood that attention can be effortful, improves

perception, and acts as a filter. This continual shifting of our focus

from one interesting stimulus to the next lies at the heart of our

innermost conscious experiences, our awareness of the world around

us, and our place in it. So, we open this chapter by exploring the

behavioral and neural dimensions of attention before turning to the

more general question of our conscious experience of the world.

14.1 Attention Focuses Cognitive

Processing on Specific Targets or

Information

The Road Ahead

The first part of this chapter concerns the consequences of

attention processes: the ways attention filters the world

and affects our processing of information. At the

conclusion of this section, you should be able to:

14.1.1 Provide a general definition of attention, and

distinguish between overt and covert forms of

attention, with examples.

14.1.2 Describe the limitations of our attention,

situations in which our attention may be

overextended, and the behavioral

manifestations of our limited attention.

14.1.3 Speculate about the ways in which evolution

may have shaped attention.

14.1.4 Distinguish between voluntary and reflexive

attention, and describe general experimental

designs for studying each.

14.1.5 Describe the use of focused attention to search

the world for particular objects (using either a

feature search or a conjunction search), and

discuss the significance of the “binding

problem.”

Despite taking delight in pretending otherwise, the average 5-yearold knows exactly what it means when an exasperated parent shouts,

“Pay attention!” We all share an intuitive understanding of the term

attention, but it is tricky to formally define. In general, attention

(or selective attention) is the process by which we select or focus on

one or more specific stimuli—either external phenomena or internal

thoughts—for enhanced processing and analysis. It is the selective

quality of attention that distinguishes it from the related concept of

vigilance, the global level of alertness of the individual. Most of the

time we direct our eyes and our attention to the same target, a

process known as overt attention. For example, as you read this

sentence, it is both the center of your visual gaze and (we hope) the

main item that your brain has selected for attention. But if we choose

to, we can also shift the focus of our visual attention covertly,

keeping our eyes fixed on one location while “secretly” scrutinizing

something in peripheral vision (Helmholtz, 1962; original work

published in 1894). Remember that teacher who, even when looking

out the window, somehow knew instantly when someone checked

their phone? That’s an example of what is known as covert

attention (FIGURE 14.1).

FIGU R E 1 4 . 1 Covert Attention View larger image

Selective attention isn’t restricted to visual stimuli. Imagine yourself

chatting with an old friend at a noisy party. Despite the background

noise, you would probably find it relatively easy to focus on what

your friend was saying, even if speaking quietly, because paying close

attention to a friend enhances your processing of their speech and

helps filter out distracters. This phenomenon—the ability to “tune in”

to one voice and “tune out” everything else—is known as the

cocktail party effect, and it nicely illustrates how attention acts to

focus cognitive processing resources on a particular target. If your

attention drifts to a different stimulus—for example, if you start

eavesdropping on a more interesting conversation nearby—it

becomes almost impossible to simultaneously follow what your

friend is saying. (The term cocktail party effect is also sometimes

used in the special case where a highly salient word, such as one’s

own name, captures attention in a noisy environment.)

There are limits on attention

The powers of attention that help you to easily chat with a friend in a

noisy room normally rely on cues in several different sensory

modalities, such as where their speech sounds are coming from, the

movements of their lips while speaking, and their unique tone of

voice. But what if we restrict our attention to just one type of

stimulus?

In shadowing experiments, participants must focus their attention

on just one out of two or more simultaneous streams of stimuli. In a

classic example, Cherry (1953) presented different streams of speech

simultaneously to people’s left and right ears via headphones—the

technique is called dichotic listening—and asked them to focus their

attention on one ear or the other and report what they heard.

Participants were able to accurately report what they heard in the

attended ear, but they could report very little about what was said in

the nonattended ear, aside from simple characteristics, such as the

sex of the speaker. In fact, if a shadowing task is difficult enough,

people may fail to detect even their own names in the unattended ear

most of the time (Röer and Cowan, 2021)!

Similar restrictions of attention can be seen in other sensory

modalities, such as musical notes (Zendel and Alain, 2009) and

visual stimuli. Participants closely attending to one complex visual

event against a background of other moving stimuli—dancers

weaving through a basketball game, for example—may show

inattentional blindness: a surprising failure to perceive

nonattended stimuli. And the unperceived stimuli can be things that

you might think impossible to miss, like a gorilla strolling across the

screen out of the blue (Simons and Chabris, 1999; Simons and

Jensen, 2009). Even highly trained experts can have this problem. In

one study, over 80 percent of radiologists screening CT scans for

lung cancer didn’t notice a seemingly obvious image of a gorilla

inserted into one of the scans (Drew et al., 2013) (you can see an

example on the website). Inattentional blindness even occurs when

the nonattended stimulus could have life-or-death consequences for

the observer; for example, a significant fraction of police officers and

trainees will fail to notice a gun placed in plain view during a

simulated traffic stop (Simons and Schlosser, 2017).

In general, divided-attention tasks—in which a person is asked to

process two or more simultaneous stimuli—confirm that attention is

a limited resource and that it’s very difficult to effectively attend to

more than one thing at a time, even if we feel like we are

multitasking just fine (Bonnel and Prinzmetal, 1998; Konishi et al.,

2020). So, our limited selective attention generally acts like an

attentional spotlight (see Figure 14.1), shifting around the

environment, highlighting stimuli for enhanced processing. It’s an

adaptation that we share with many other species because, like us,

they are confronted with the problem of extracting important signals

from a noisy background (Bee and Micheyl, 2008). Birds and bats,

for example, must isolate critical vocalizations from a cacophony of

calls and other noises in the environment—their version of the

cocktail party problem (Lewicki et al., 2014). Having a single

attentional spotlight helps us focus cognitive resources and

behavioral responses toward the most important things in the

environment at any given moment (the smell of smoke, the voice of a

potential mate, a glimpse of a big spotted cat), while ignoring

extraneous information.

By acting as a filter, attention narrows our focus and directs our

cognitive resources toward only the most important stimuli around

us, thereby protecting the brain from being overwhelmed by the

world. But the details of this attentional bottleneck have been

elusive. Initial research gave evidence of an early-selection model of

attention, in which unattended information is filtered out right away,

at the level of the initial sensory input, as in the shadowing

experiments we just described (Broadbent, 1958). But others noted

that important but unattended stimuli (such as your name) may

undergo substantial unconscious processing, right up to the level of

semantic meaning and awareness, before suddenly capturing

attention (Röer and Cowan, 2021), thus illustrating a late-selection

model of attention. Many contemporary models of attention now

combine both early- and late-selection mechanisms, and debate

continues over their relative importance.

Gorillas in the Midst Who could miss the gorilla in the video from which this still is taken?

Most people do, if they are concentrating on some other task, such as counting the number

of times people in white shirts touch a ball that is being passed around. View larger

image

If we view attention as a limited resource, then we only have enough

of it to do one complex task at a time, or a few very simple ones. This

finding implies that attention is continually rebalanced between

early and late selection, depending on perceptual load: the

processing demands imposed by the task at hand. When we focus on

a very complex stimulus, the load on our perceptual processing

resources is so great that there is nothing left over, and extra stimuli

are excluded right from the outset: an early-selection process (S.

Murphy et al., 2017).

Attention is deployed in several

different ways

We’ve seen that through willpower we can direct our attention to

specific stimuli without moving our eyes or otherwise reorienting.

Early experiments on this phenomenon employed sustainedattention tasks, like the one depicted in Figure 14.1, where a single

stimulus location must be held in the attentional spotlight for an

extended period. Although these tasks are useful for studying basic

phenomena, other important questions about attention require

another approach. For example, how do we shift our attention

around? How does attention enhance the processing of stimuli, and

which brain regions are involved? To answer these questions,

researchers devised clever tasks that employ stimulus cuing to

control attention, which revealed two general categories of attention,

as we’ll discuss next.

The kind of attention that we have been discussing thus far is what

researchers call voluntary attention (or endogenous attention).

As the name implies, voluntary shifts of attention come from within;

they are the conscious, top-down directing of our attention toward

specific aspects of the environment, according to our interests and

goals. FIGURE 14.2 features the symbolic cuing task (or spatial

cuing task), developed by Michael Posner (2016) and used

extensively to study voluntary attention. Studies using cuing tasks

have confirmed that consciously directing your attention to the

correct location or stimulus improves processing speed and accuracy.

Conversely, directing your attention to an incorrect location or

stimulus impairs processing efficiency.

FIGU R E 1 4 . 2 Measuring the Effects of Voluntary Shifts of Attention View

larger image

How much does it help to shift your attention to a location before a

stimulus occurs there? Posner’s symbolic cuing task allows us to

quantify how voluntary attention benefits processing. In a symbolic

cuing task, participants stare at a point in the center of a computer

screen and must press a key as soon as a specific target (the

stimulus) appears on the screen; this technique thus measures

reaction time. The stimulus is preceded by a cue that briefly

flashes on the screen, hinting where the stimulus will appear. Most of

the time, as in FIGURE 14.2A, the participant is provided with a

valid cue; for example, a rightward arrow flashes on the screen

moments before the stimulus appears on the right side of the screen.

In a few trials, like the one in FIGURE 14.2B, the arrow points the

wrong way and thus provides an invalid cue. And in “neutral” control

trials (FIGURE 14.2C), the cue doesn’t provide any hint at all. Both

the cue and the stimulus are on the screen so briefly that participants

don’t have time to shift their gaze (and in any case, the researchers

monitor their eyes to ensure they stare at the fixation point).

Averaged over many trials, the reaction-time data (FIGURE 14.2D)

clearly show that people swiftly learn to use cues to predict stimulus

location, shifting their attention without shifting their gaze, in

anticipation of the appearance of the target stimulus. Compared with

neutral trials, processing is significantly faster for validly cued trials,

and participants pay a price for misdirecting their attention on those

few trials in which the cue is invalid, misdirecting attention to the

wrong side of the display. Many variants of the symbolic cuing

paradigm have been developed—varying the timing of the stimuli,

altering their complexity, requiring a choice between different

responses—all of which can affect reaction time, which we discuss

next.

Some types of stimuli just grab our

attention

There is a second way in which we pay attention to the world,

involving more than just consciously steering our attentional

spotlight around. Flashes, bangs, sudden movements—any striking

or important change—can instantly snatch our attention away from

whatever we’re doing, unless we are very focused. Drop your glass in

a restaurant, and every conversation stops, every head in the place

swivels, seeking the source of the sound (you, embarrassingly). This

sort of involuntary reorientation toward a sudden or important event

is an example of reflexive attention (or exogenous attention). It is

considered to be a bottom-up process, because attention is being

seized by sensory inputs from lower levels of the nervous system,

rather than being directed by voluntary, conscious top-down

processes of the forebrain.

RESEARCHERS AT WORK

Reaction Times Reflect Brain Processing, from Input to

Output

Reaction-time measures are a mainstay of cognitive

neuroscience research. In tests of simple reaction time,

participants make a single response—for example, pressing a

button—in response to an experimental stimulus (the

appearance of a target, the solution to a problem, a tone, or

whatever the experiment is testing). In tests of choice reaction

time, the situation is slightly more complicated: a person is

presented with alternatives and has to choose among them

(e.g., correct versus incorrect, same versus different) by

pressing one of two or more buttons.

Reaction times in an uncomplicated choice reaction-time

test, in which the participant indicates whether two stimuli are

the same or different, average about 300–350 milliseconds

(ms). The delay between stimulus and response varies

depending on the amount of neural processing required

between input and output. The neural systems involved in this

sort of task, and the timing of events in the response circuit, are

illustrated in FIGURE 14.3. Brain activity proceeds from the

primary visual cortex (V1) through a ventral visual object

identification pathway (see Chapter 7) to prefrontal cortex, and

then through premotor and primary motor cortex, down to the

spinal motor neurons and out to the finger muscles. In the

sequence shown in the figure—proceeding from the

presentation of visual stimuli to a discrimination response—

notice that it takes about 110 ms for the sensory system to

recognize the stimulus (somewhere in the inferior temporal

lobe), about 35 ms more for that information to reach the

prefrontal cortex, and then about 30 ms more to determine

which button to push. After that, it takes another 75 ms or so for

the movement to be executed (i.e., 75 ms of time elapses

between the moment the signal from the prefrontal cortex

arrives in premotor cortex and the moment the finger pushes

the button). It is fascinating to think that something like this

sequence of neural events happens over and over in more-

complicated behaviors, such as recognizing a long-lost friend or

composing an opera.

FIGU R E 1 4 . 3 A Reaction-Time Circuit in the Brain LGN, lateral geniculate

nucleus; V1, primary visual cortex; V2 and V4, extrastriate visual areas. View

larger image

Researchers study reflexive attention using a different kind of cuing

task, called peripheral spatial cuing. In this task, instead of a

meaningful symbol like an arrow, the cue that is presented is a

simple sensory stimulus, such as a flash of light, occurring in the

location to which attention is to be drawn. Research with this type

of simple cuing confirmed that valid reflexive cues enhance the

processing of subsequent stimuli at the same location, but only when

the target stimulus closely follows the cue. At longer intervals

between the cue and target, starting at about 200 ms, a curious

phenomenon is observed: detection of stimuli at the location where

the valid cue occurred is actually impaired (Satel et al., 2019). It’s as

though attention has moved on from where the cue occurred and is

reluctant to return to that location. This inhibition of return

probably evolved because it prevented reflexive attention from

settling on unimportant stimuli for more than an instant, an effective

strategy in animals foraging for food or scanning the world for

threats.

Normally, reflexive and voluntary attention work together to direct

cognitive activities (FIGURE 14.4), probably relying on somewhat

overlapping neural mechanisms. Anyone who has watched a squirrel

at work has seen that twitchy interplay. When it comes to singlemindedly searching for tasty morsels (an example of voluntary

attention), a squirrel has few rivals. But even slight noises and

movements (cues that reflexively capture attention) cause the

squirrel to stop and scan its surroundings—a sensible precaution if,

like a squirrel, you are yourself a tasty morsel. So, it’s no surprise

that emotional cues—a sudden gasp from a companion, for example

—can likewise reflexively capture attention and augment sensory

processing (Carretié, 2014). And effective cues for reflexive attention

may involve multiple sensory modalities: a sudden sound coming

from a particular location, for example, can improve the visual

processing of a stimulus that appears there (Hillyard et al., 2016;

Störmer, 2019).

FIGU R E 1 4 . 4 Voluntary and Reflexive Attention Are Complementary View

larger image

Attention helps us to search for

specific objects in a cluttered world

Another familiar way that we use attention is in visual search:

systematically scanning the world to locate a specific object among

many—your car in a parking lot, for example, or your friend’s face in

a crowd. If the sought-after item varies in just one key attribute, the

task can be pretty easy—searching for your red car among a bunch of

silver and black ones, for example. In a simple feature search like

this (FIGURE 14.5A), the sought-after item “pops out”

immediately, no matter how many distracters are present (Joseph et

al., 1997). Effortful voluntary attention isn’t needed.

FIGU R E 1 4 . 5 Visual Search View larger image

More commonly, however, we must use a conjunction search—

searching for an item on the basis of a combination of two or more

features, such as size and color (FIGURE 14.5B and C). This can

become very difficult when, for example, you must simultaneously

consider the hair, nose, eyes, and smile of your friend’s face in a

crowd—and the bigger the crowd grows, the harder the task becomes

(unless your friend waves, thereby reflexively grabbing your

attention—phew!).

Experimental results (FIGURE 14.5D) confirm what you probably

already know intuitively: conjunction searches can be relatively slow

and laborious, involving a large cognitive effort. That’s because your

brain has to deal with what is known as the binding problem (A.

M. Treisman, 1996; Hitch et al., 2020), which is this: How do we

know which different stimulus features—colors, shapes, sounds, etc.,

each processed by different regions of the brain—are bound together

in a single object? And if those objects appear only infrequently—say,

weapons in luggage or tumors in X-rays—they may go undetected

with alarmingly high frequency, even by highly trained screeners

(Wolfe et al., 2013). Our natural tendency is to let our attention

wander between the various sources of stimuli in our environment.

Where’s Waldo? Puzzles like the “Where’s Waldo?” series are classic examples of

conjunction searches: you can find Waldo only if you search for the right combination of

striped sweater, hat, glasses, and slightly goofy expression. Imagine how much easier it

would be to find Waldo if everyone else on the beach were wearing green! In that case,

finding Waldo would be a feature search (the only person not wearing green) and he would

“pop out” in the picture … but that wouldn’t be any fun. View larger image

To uncover finer details of attentional mechanisms, neuroscientists

take two complementary perspectives on attention experimental

strategies. First, we can look at consequences of attention in the

brain, asking how neural systems are affected by selective attention

to enhance the processing of stimuli. So, in these studies, the

question is, What are the neural targets of attention? Second, we can

try to uncover the mechanisms of attention, the brain regions that

produce and control attention, shifting it between different stimuli in

different sensory modalities. Here, the question is, What are the

neural sources of attention? In selecting experimental techniques to

address these different objectives, researchers must juggle the need

for good temporal resolution—the ability to track changes in the

brain that occur very quickly—with the need for excellent spatial

resolution, the ability to observe the detailed structure of the brain.

In general, electrophysiological approaches offer the speed (temporal

resolution) necessary to distinguish the consequences of attention

from the mechanisms that direct it, while brain-imaging techniques

like fMRI offer the anatomical detail (spatial resolution) to figure out

where these neural actions are taking place. This speed-versusaccuracy trade-off permeates the research that we discuss in the

following section.

How’s It Going?

1. How do you define attention? Distinguish between overt

and covert attention, giving examples of each. What is

the attentional spotlight?

2. What is inattentional blindness, and under what

circumstances might it occur?

3. How do early-selection effects of attention differ from

late-selection effects? What single aspect of a stimulus

may determine whether early or late selection occurs?

4. Summarize Posner’s symbolic cuing task. What did this

task reveal?

5. Compare and contrast voluntary attention and reflexive

attention, and identify the principal ways in which they

differ. What is inhibition of return, and does it relate to

voluntary attention or to reflexive attention?

6. While conducting a visual search for something, we

sometimes experience “pop-out.” What is it? Is pop-out

more closely associated with feature search or with

conjunction search, and how do those differ?

7. Distinguish between temporal resolution and spatial

resolution as they apply to brain-imaging techniques.

How are they related?

FOOD FOR THOUGHT

Self-help articles and books promise to help us get better at

multitasking. What does the science of attention tell us about

this goal?

14.2 Targets of Attention:

Attention Alters the Functioning of

Many Brain Regions

The Road Ahead

The next section turns to the impact of attention on brain

processes. Once you have finished studying this section,

you should be able to:

14.2.1 Describe how and why scientists use the

electrical activity of the brain to study attention.

14.2.2 Name and describe the main components seen

in event-related potentials (ERPs) as they relate

to auditory versus visual attention, and

particularly compare the auditory N1 effect and

the visual P1 effect.

14.2.3 Describe the electrophysiological phenomena

associated with visual search tasks.

14.2.4 Describe experimental evidence that selective

attention to stimuli enhances neural activity in

the brain regions processing those stimuli.

Recording electrical activity directly from the neurons of people’s

brains would be a way to obtain excellent temporal and excellent

spatial resolution, but of course we can’t just stick recording

electrodes directly into the brains of healthy participants. Instead, we

must find noninvasive ways to assess brain activity.

When many cortical neurons work together on a specific task, their

activity becomes synchronized to some degree. You might think this

would be easy to see in a standard EEG recording (i.e., an

electroencephalogram, where the brain’s electrical activity is

recorded from the scalp, as we described in Chapter 2), but it isn’t.

Because of variation in the firing of the neurons, not to mention

regional differences in the timing of brain activity, a real-time EEG

recorded during an attention task looks surprisingly random. So

instead, researchers record participants doing a task (FIGURE

14.6A) over and over again, and they average all the EEGs recorded

during those repeated trials (FIGURE 14.6B). Over enough trials,

the random variation averages out, and what’s left is the overall

electrical activity specifically associated with task performance

(FIGURE 14.6C). This averaged activity, called the event-related

potential (ERP) (Helfrich and Knight, 2019), tracks regional

changes in brain activity much faster than brain-imaging techniques

like fMRI do. For this reason, ERP has become the favorite tool of

neuroscientists studying moment-to-moment consequences of

attention in the brain.

FIGU R E 1 4 . 6 Event-Related Potentials View larger image

Distinctive patterns of brain electrical

activity mark shifts of attention

Consciously directing your attention to a particular auditory stimulus

—for example, shadowing one ear, as we described earlier—has a

predictable effect on the ERP. Between about 100 and 150 ms after

the onset of a sound stimulus, two large waves are seen in the ERP

from the auditory cortex: an initial positive-going wave called P1,

immediately followed by a larger negative-going wave called N1 (see

Figure 14.6C). The N1 wave reflects an important aspect of auditory

attention: it is much larger following a stimulus that is being

attended to than it is for the very same stimulus presented at the

same ear but not attended to (Hillyard et al., 1973). Because the only

thing that changes between conditions is the participants’ attention

to the stimuli, this auditory N1 effect must be a result of selective

attention somehow acting on neural mechanisms to enhance

processing of that particular sound. Auditory attention may also

affect much later ERP components, such as the wave called P3 (or

auditory P300) (see Figure 14.6C). Changes in late-occurring

components like P3 are tricky to interpret, because they can be

associated with multiple different cognitive operations, ranging from

memory access to reactions to unexpected events (Wessel and Aron,

2017). Nevertheless, some researchers believe that P3 is especially

sensitive to higher-order processing of the stimulus—qualities like

the underlying meaning of the stimulus, unexpected language,

identity of the speaker, and other cognitive processes—in which case

the P3 effect provides an example of a late-selection effect of

attention. Researchers are debating whether P3 therefore is

(Mashour et al., 2020) or is not (Pitts et al., 2014) an

electrophysiological marker of consciousness.

What about effects of attention on ERPs from visual stimuli?

Because the neural systems involved in visual perception are

different from those involved in audition, voluntary visual attention

causes its own distinctive changes in the ERP. We can study these

visual effects by collecting ERP data over occipital cortex—the

primary visual area of the brain—while a participant performs a

symbolic cuing task, as depicted in FIGURE 14.7. On valid trials

(remember, this is when the target appears where expected, in the

location indicated by the cue, as in FIGURE 14.7A), electrodes over

occipital cortex show a substantial enhancement of the ERP

component P1, the positive wave that occurs about 70–100 ms after

stimulus onset, often carrying over into an enhancement of the N1

component immediately afterward (FIGURE 14.7C). A similar

effect on P1 is evident when attention is instead oriented reflexively

to a flash or sound (McDonald et al., 2005), but only when the

interval between the cue and the appearance of the target is brief. At

longer intervals, the P1 effect may actually be reduced, as an

electrophysiological manifestation of the inhibition of return (Tang

et al., 2021; but see also Satel et al., 2019). And for invalid trials

(FIGURE 14.7B), where attention is being directed elsewhere, the

visual P1 effect isn’t evident at all, even though the visual stimulus

is identical and in the same location as in the validly cued trials.

Interestingly, the P1 effect is evident only in visual tasks involving

manipulations of spatial attention (where is the target?)—not other

features, like color, orientation, or more complex properties that

would be characteristic of late-selection tasks.

FIGU R E 1 4 . 7 ERP Changes in Voluntary Visual Attention View larger image

What happens to ERPs during visual search tasks, where we are

directing attention so as to find a particular target in an array and

ignore distracters? Under these conditions, a subcomponent of N2

(see Figure 14.6), called N2pc, is triggered at occipitotemporal sites

contralateral to the visual target (Luck and Hillyard, 1994; Hickey et

al., 2009).

The neural mechanisms of visual attention may be quite plastic. For

example, extensive experience with action video games, which

heavily rely on visual attention, is associated with neural changes (S.

Tanaka et al., 2013; Kowalczyk et al., 2018) and corresponding

enhancements of longer-latency ERP components (Mishra et al.,

2011; Palaus et al., 2017). Possible trade-offs for all this gaming,

however, may include impaired social and emotional function (no,

we’re not kidding: K. Bailey and West, 2013; Yan et al., 2021). And of

course, some people could be drawn to gaming simply because they

are already good at visuospatial processing.

Attention affects the activity of

neurons

PET and fMRI operate too slowly to track the rapid changes in brain

activity that occur in reaction-time tests. Instead, researchers have

used “sustained-attention tasks” to confirm that attention enhances

activity in brain regions that process key aspects of the target

stimulus. In these experiments, participants are asked to pay close

and lasting attention to one particular aspect of a complex stimulus—

just the faces in a complex scene, or changes in the pattern of

selected dots within an array, for example. Concurrent fMRI

generally confirms that attention somehow acts directly on neurons,

boosting the activity of those brain regions that process whichever

stimulus characteristic was targeted. So, in these particular

examples, enhancement is seen in the cortical fusiform face area

during attention to faces (O’Craven et al., 1999), or in the subcortical

superior colliculus and lateral geniculate (important for spatial

processing of visual stimuli) during attention to spatial arrays

(Schneider and Kastner, 2009).

In Chapter 7 we discussed the distinctive receptive fields of visual

neurons and how stimuli falling within these fields can excite or

inhibit the neurons, causing them to produce more or fewer action

potentials. In an important early study, Moran and Desimone (1985)

recorded the activity of individual neurons in visual cortex while

attention was shifted within each cortical cell’s receptive field. Using

a system of rewards, the researchers trained monkeys to covertly

attend to one spatial location or another while recordings were made

from single neurons in visual cortex. A display was presented that

included the cell’s most preferred stimulus, as well as an ineffective

stimulus (one that, by itself, did not affect the cell’s firing) a short

distance away but still within the cell’s receptive field. As long as

attention was covertly directed at the preferred stimulus, the cell

responded by producing many action potentials (FIGURE 14.8).

But when the monkey’s attention was shifted elsewhere within the

cell’s receptive field, even though the animal’s gaze had not shifted,

that same stimulus provoked far fewer action potentials from the

neuron. Only the shift in attention could account for this sort of

modulation of the cell’s excitability. Subsequent work has confirmed

that attention can also remold the receptive fields of neurons in a

variety of ways (Womelsdorf et al., 2008; Speed et al., 2020).

FIGU R E 1 4 . 8 Effect of Selective Attention on the Activity of Single Visual

Neurons View larger image

How’s It Going?

1. Define EEG and ERP, and explain how ERPs are

measured. Why is the ERP a favored technique in

cognitive neuroscience?

2. Match each of the following ERP phenomena—N1, P1,

P3, N2pc—with one of these terms: pop-out, early

selection, auditory attention, late selection, visual

attention, distractors.

3. Describe an experimental procedure that can

demonstrate the effects of selective attention on the

activity of an individual neuron.

FOOD FOR THOUGHT

How might psychotherapists exploit the ability of attention to

alter the activity of neurons?

14.3 Sources of Attention: A

Network of Brain Sites Creates

and Directs Attention

The Road Ahead

In the section that follows, we turn our attention to the

anatomy of attention: the network of cortical and

subcortical sites that govern voluntary and reflexive

attention. After studying this material, you should be able

to:

14.3.1 Discuss the functions of the principal

subcortical sites—the superior colliculus and

the pulvinar nucleus—that are associated with

shifts of visual attention.

14.3.2 Summarize the dorsal frontoparietal network

believed to govern voluntary attention,

illustrating this action with examples of

research.

14.3.3 Summarize the right temporoparietal network

associated with reflexive shifts of attention, and

again provide relevant research examples.

14.4.4 Describe some of the most striking forms of

attentional disorders and some medical

approaches to treat them.

Whether attention comes reflexively, from the bottom up, or is

controlled voluntarily, from the top down, it strongly affects neural

processing in the brain, thereby augmenting electrophysiological

activity. That doesn’t mean that the sources of the different forms of

attention are identical, however, or even that they are similar—just

that their consequences are somewhat comparable. So let’s turn to

some of the details of the brain mechanisms that are the source of

attention.

Two subcortical systems guide shifts

of attention

Subcortical structures can be difficult to study because, deep in the

center of the brain and skull, their activity cannot be measured with

EEG/ERP and other noninvasive techniques. Our knowledge of their

roles in attention thus comes mostly from work with animals.

Single-cell recordings from individual neurons have implicated the

superior colliculus, a midbrain structure (FIGURE 14.9), in

controlling the movement of the eyes toward objects of attention,

especially in overt forms of attention (Wurtz et al., 1982; Zhaoping,

2016). When the same eye movements are made but attention is

directed elsewhere, increased firing of the superior colliculus

neurons does not occur. And in people with lesions in just one of the

two superior colliculi, inhibition of return was reduced for visual

stimuli on the affected side (Sapir et al., 1999). So it seems that the

superior colliculus helps direct our gaze to attended objects, and it

ensures that we don’t return to them too soon after our gaze has

moved on. The superior colliculus may also help direct the covert

attentional spotlight: for example, monkeys in which the superior

colliculus has been temporarily inactivated lose the ability to use

selective attention cues (arrows, flashes, etc.) until the inactivation

ends (Krauzlis et al., 2013).

FIGU R E 1 4 . 9 Subcortical Sites Implicated in Visual Attention View larger

image

The pulvinar nucleus, or just pulvinar, making up the posterior

quarter of the human thalamus (see Figure 14.9), is heavily involved

in visual processing, with widespread interconnections between

lower visual pathways, the superior colliculus, and many cortical

areas. The pulvinar nucleus is important for the orienting and

shifting of attention. Monkeys whose pulvinar nuclei are inactivated

with drugs, and humans with strokes affecting the pulvinar nuclei,

may have great difficulty orienting covert attention toward visual

targets (D. L. Robinson and Petersen, 1992; Kraft et al., 2015). The

pulvinar nucleus is also needed to filter out and ignore distracting

stimuli while we’re engaged in covert attention tasks, and in general

it coordinates activity in larger-scale cortical networks according to

attentional demands (Saalmann et al., 2012; Green et al., 2017). In

humans, attention tasks with larger numbers of distracters induce

greater activation of the pulvinar nucleus (M. S. Buchsbaum et al.,

2006), confirming the importance of this nucleus for attention to key

stimuli.

Several cortical areas are crucial for

generating and directing attention

The extensive connections between subcortical mechanisms of

attention and the parietal lobes, along with observations from

clinical cases that we will discuss shortly, point to a special role of the

parietal lobes for attention control. Research indicates that two

integrated networks—dorsal frontoparietal and right

temporoparietal—work together to continually select and shift

between objects of interest, in coordination with subcortical

mechanisms of attention.

A dorsal frontoparietal network for voluntary

(“top-down”) control of attention

In monkeys, recordings from single cells show that a region called

the lateral intraparietal area, or just LIP, is crucial for voluntary

attention. LIP neurons increase their firing rate when attention—not

gaze—is directed to particular locations, and it doesn’t matter

whether the voluntary attention is being directed toward visual or

auditory targets (Shomstein and Gottlieb, 2016). So it’s the top-down

steering of the attentional spotlight that is important to LIP neurons,

not the sensory characteristics of the stimuli.

The human equivalent of this system is a region around the

intraparietal sulcus (IPS) (FIGURE 14.10) that behaves much

like the monkey LIP. For example, on tasks designed so that covert

attention can be sustained long enough to make fMRI images, IPS

activity is enhanced while participants are actively steering their

attention (Corbetta and Shulman, 1998; Hutchinson, 2019). And

when researchers used TMS (transcranial magnetic stimulation; see

Chapter 2) to temporarily inhibit the functioning of the IPS,

participants found it difficult to voluntarily shift their attention

between targets (Koch et al., 2005).

FIGU R E 1 4 . 1 0 Cortical Regions Implicated in the Top-Level Control of Attention

View larger image

People with damage to a frontal lobe region called the frontal eye

field (FEF) (see Figure 14.10) struggle to prevent their gaze from

being drawn away toward peripheral distracters while they’re

performing a voluntary attention task. Neurons of the FEF appear to

be crucial for ensuring that our gaze is directed among stimuli

according to cognitive goals rather than eye-catching characteristics

of the stimuli. In effect, the FEF ensures that cognitively controlled

top-down attention gets priority. It’s no surprise, then, that the FEF

is closely connected to the superior colliculus, which, as we discussed

earlier, is important for planned eye movements.

Functional brain imaging can be used to identify changes in neural

activity during top-down attentional processing tasks (e.g.,

Hopfinger et al., 2010). FIGURE 14.11 shows patterns of activation

while voluntary attention is shifting in response to a symbolic cue.

Enhanced activity is evident in the vicinity of the frontal eye fields

(dorsolateral frontal cortex) and, simultaneously, in the IPS.

Electrophysiological studies of this network indicate that the

attentional control–related activity is first seen in the frontal and

parietal components, followed by anticipatory activation of visual

cortex (if the expected stimulus is visual) or auditory cortex (if the

stimulus is a sound) (McDonald and Green, 2008; Green et al.,

2011). Taken together, these studies support the view that a dorsal

frontoparietal network provides top-down (voluntary) control of

attention.

FIGU R E 1 4 . 11 The Frontoparietal Attention Network View larger image

A right temporoparietal network for reflexive

(“bottom-up”) shifts of attention

A second attention system, located at the border of the temporal and

parietal lobes of the right hemisphere—and named, a little

unimaginatively, the temporoparietal junction (TPJ) (see

Figure 14.10)—is involved in reflexive steering of attention toward

novel or unexpected stimuli (flashes, color changes, and so on).

Neuroimaging studies (FIGURE 14.12) confirm that if a relevant

stimulus suddenly appears in an unexpected location, there’s a spike

in activity of the TPJ of the right hemisphere, regardless of whether

the stimulus itself occurs in the left or right side of the world

(Igelström and Graziano, 2017; Krall et al., 2015). Interestingly, the

TPJ system receives direct input from the visual cortex, presumably

providing direct access for information about visual stimuli. The TPJ

also has strong connections with the ventral frontal cortex, a region

that is involved in working memory (see Chapter 13). Because

working memory tracks sensory inputs over short time frames, this

system may specialize in analyzing novelty by comparing present

stimuli with those of the recent past. Overall, the ventral TPJ system

seems to act as an alerting signal, or “circuit breaker,” overriding our

current attentional priority if something new and unexpected

happens.

FIGU R E 1 4 . 1 2 The Right Temporoparietal System for Reflexive Attention View

larger image

Ultimately, the dorsal and ventral attention-control networks need to

interact extensively and function as a single interactive system.

Researchers think that the more dorsal stream of processing is

responsible for voluntary attention, enhancing neural processing of

stimuli and interacting with the pulvinar nucleus and superior

colliculus to steer the attentional spotlight around. At the same time,

the right-sided temporoparietal system scans the environment for

novel salient stimuli (which then draw reflexive attention), rapidly

reassigning attention as interesting stimuli pop up. This basic model

seems to apply across sensory modalities, including both visual and

auditory stimuli (Brunetti et al., 2008; Walther et al., 2010).

Brain disorders can cause specific

impairments of attention

One way to learn about attention systems in the brain is to carefully

analyze the behavioral consequences of damage to specific regions of

the brain. Research on people with attentional disorders shows that

damage of cortical or subcortical attention mechanisms can

dramatically alter our ability to understand and interact with the

environment.

Right-hemisphere lesions

We’ve discussed evidence that the right hemisphere normally plays a

special role in attention (see Figure 14.12). Unfortunately, it is not

uncommon for people to suffer strokes or other types of brain

damage to this part of the brain. The result—hemispatial neglect

—is an extraordinary attention syndrome in which the person tends

to completely disregard the left side of the world. People and objects

to the left of the person’s midline may be completely ignored, as if

unseen, even though the person’s vision is otherwise normal.

Someone with neglect may fail to dress the left side of their body, will

not notice visitors if they approach from the left, and may fail to eat

the food on the left side of their dinner plate. If touched lightly on

both hands at the same moment, the person may notice only the

right-hand touch—a symptom called simultaneous extinction

(despite the name, this is unrelated to extinction in classical

conditioning that you may have learned about previously). People

with this problem may even deny ownership of their left arm or leg

—“My sister must’ve left that arm in my bed; wasn’t that an awful

thing to do?!”—despite normal sensory function and otherwise intact

intellectual capabilities.

It is as if the normally balanced competition for attention between

the two sides has become skewed and now the input from the right

side of the world overrules or extinguishes the input from the left.

Lesions in people with hemispatial neglect (FIGURE 14.13A) neatly

overlap the frontoparietal attention network that we discussed

earlier (shown again in FIGURE 14.13B). This overlap suggests

that hemispatial neglect is a disorder of attention itself, and not a

problem with processing spatial relationships, as was once thought

(Mesulam, 2000; Bartolomeo, 2021). With time, hemispatial neglect

can significantly improve (although simultaneous extinction often

persists), and targeted therapies may help. For example, researchers

are experimenting with the use of special prism glasses to shift vision

to the right during intense physical therapy, in order to recalibrate

the visual attention system (Barrett et al., 2012; O’Shea et al., 2017).

FIGU R E 1 4 . 1 3 Brain Damage in Hemispatial Neglect View larger image

Diagnostic Test for Hemispatial Neglect When asked to duplicate drawings of common

symmetrical objects, people with hemispatial neglect ignore the left side of the model that

they’re copying. View larger image

Bilateral lesions

Parminder, whom we met at the beginning of the chapter, had

bilateral lesions of the parietal lobe regions that are implicated in the

attention network. Although it is rare, bilateral parietal damage can

result in a dramatic disorder called Bálint’s syndrome, made up of

three principal symptoms. First, people with Bálint’s syndrome have

great difficulty steering their visual gaze appropriately (a symptom

called oculomotor apraxia). Second, they are unable to accurately

reach for objects using visual guidance (optic ataxia). And third—the

most striking symptom—people with Bálint’s syndrome show a

profound restriction of attention, to the point that only one object or

feature can be consciously observed at any moment. This problem,

called simultagnosia, can be likened to an extreme narrowing of

the attentional spotlight, to the point that it can’t encompass more

than one object at a time. Hold up a comb or a pencil, and Parminder

has no trouble identifying the object. But hold up both the comb and

the pencil, and she can identify only one or the other. It’s as though

she is simply unable to consciously experience more than one visual

object at a time, despite having little or no loss of vision. Bálint’s

syndrome thus illustrates the coordination of attention and

awareness with mechanisms that orient us within our environment.

SIGNS & SYMPTOMS

Difficulty with Sustained Attention Can Sometimes Be

Relieved with Stimulants

At least 5 percent of all children are diagnosed with attention

deficit hyperactivity disorder (ADHD), characterized as

difficulty directing sustained attention to a task or activity, along

with a higher degree of impulsivity than in other children of the

same age. About three-fourths of those diagnosed are male.

Estimating the prevalence of ADHD (FIGURE 14.14) is

complicated and very controversial; for example, there is

significant variation in ADHD diagnosis and medication between

different (sometimes neighboring) states within the USA, raising

questions about the reliability of current diagnostic practices

(Fulton et al., 2009). Nevertheless, researchers have identified

several neurological changes associated with this disorder.

Affected children tend to have slightly reduced overall brain

volumes (about 3–4 percent smaller than in unaffected

children), with reductions especially evident in the cerebellum

and the frontal lobes, and effective treatments tend to enhance

frontal activity (Arnsten, 2006; Spencer et al., 2015). As we

discuss elsewhere in the chapter, frontal lobe function is

important for myriad complex cognitive processes, including the

inhibition of impulsive behavior, as we will discuss below. (But

remember, correlational studies like these say nothing about

causation; we don’t know whether the brain differences cause,

or are caused by, the behavior.)

FIGU R E 1 4 . 1 4 Prevalence of ADHD in the United States View larger

image

In addition to structural changes, ADHD has been associated

with abnormalities in connectivity between brain regions, such

as within the default mode network, a neural system implicated

in conscious reflection that we will discuss shortly (Cao et al.,

2014). In fact, individual differences in the ability to sustain

attention can be predicted with high accuracy from the strength

of sets of brain connections (Rosenberg et al., 2016, 2017),

even in the resting state, when the individual is not working on

any particular task. Children with ADHD may have abnormal

activity levels in some specific brain systems, such as the

system that signals the rewarding aspects of activities

(Furukawa et al., 2014). Based on a model of ADHD that

implicates impairments in dopamine and norepinephrine

neurotransmission, some researchers advocate treating these

children with stimulant drugs like methylphenidate (Ritalin),

which inhibits the synaptic reuptake of dopamine and

norepinephrine, or with selective norepinephrine reuptake

inhibitors like atomoxetine (Strattera) (Schwartz and Correll,

2014). Stimulant treatment often improves the focus and

performance of children with ADHD within traditional school

settings, but this treatment remains controversial because of the

significant risk of side effects. Furthermore, stimulants improve

focus in everybody, not just people with ADHD, which raises

doubts about the orthodox view that impaired neurotransmission

is the sole cause of ADHD (del Campo et al., 2013). An

emerging alternative view is that what is diagnosed as ADHD

may simply be an extreme on a continuum of normal behavior.

Allowing kids diagnosed with ADHD to fidget and engage in

more intense physical activity effectively reduces their

symptoms and improves task performance (Hartanto et al.,

2016; Den Heijer et al., 2017).

How’s It Going?

1. Identify two subcortical structures that are implicated in

the control of attention. What functions do they perform?

2. What is the general name for the cortical system

responsible for conscious shifts of attention? What are

its components, and what happens when those

components are damaged?

3. Name the cortical system implicated in reflexive shifts of

attention. Which specific regions are part of this system,

and what happens if they are damaged?

FOOD FOR THOUGHT

Evidence suggests that the right TPJ provides a circuit breaker

to force attention to shift from a current target; in a modern

setting, how might that function be a help or a hindrance?

14.4 Consciousness, Thought,

and Decision-making Are

Mysterious Products of the Brain

The Road Ahead

The final part of the chapter looks at the most enigmatic,

top-level product of the brain—consciousness—and its

relationships with attention, reflection, and the executive

processes that direct thoughts and feelings. After reading

this section, you should be able to:

14.4.1 Provide a reasonable definition of

consciousness, and name the neural networks

and structures that, when activated, may play a

special role in coordinating conscious states.

14.4.2 Discuss the relationship between

consciousness as experienced by healthy

people and the diminished levels of

consciousness experienced by people in comas

and minimally conscious states.

14.4.3 Discuss the impediments to the scientific study

of consciousness, distinguishing between the

“easy” and “hard” problems of consciousness,

and how free will relates to the study of

consciousness.

14.4.4 Provide an overview of the organization and

function of the frontal lobes, and especially

prefrontal cortex, as they relate to high-level

cognition and executive functions.

There can be no denying the close relationship between attention

and consciousness; indeed, attention is the foundation on which

consciousness is built. Whenever we are conscious, we’re attending

to something, be it internal or external. William James (1890) tried

to capture the relationship between attention and consciousness

when he wrote, “My experience is what I agree to attend to. Only

those items which I notice shape my mind—without selective

interest, experience is an utter chaos.” We all experience

consciousness, so we know what it is, but that experience is so

personal, so subjective, that it’s difficult to come up with an objective

definition. Perhaps a reasonable attempt is to say that

consciousness is the state of being aware that we are conscious

and that we can perceive what’s going on in our minds and all

around us. However, “what’s going on in our minds and all around

us” covers an awful lot of ground. It includes our perception of time

passing, our sense of being aware, our recollection of events that

happened in the past, and our imaginings about what might happen

in the future. Add to that our belief that we employ free will to direct

our attention and make decisions, and we have a concept of immense

scope.

Which brain regions are active when

we are conscious?

Despite definitional complexities, consciousness is an active area of

neuroscience research. So far, there are numerous competing

theoretical models of consciousness, and a growing body of

neuroscientific data. One approach is to look for patterns of

synchronized activity in neural networks as people engage in

conscious, inwardly focused thought. Using fMRI, researchers have

identified a large circuit of brain regions—collectively called the

default mode network, consisting of parts of the frontal,

temporal, and parietal lobes—that seems to be selectively activated

when we are at our most introspective and reflective, and relatively

deactivated during behavior directed toward external goals (Raichle,

2015). In some ways, you could think of it as a daydream network, or

perhaps as a “making-sense” network that we use for reflecting on

daily events and integrating them with our personal memories and

knowledge of the world (Yeshurun et al., 2021). Researchers think

that dysfunction within the default mode network contributes to the

symptoms of various cognitive problems, such as ADHD, autism

spectrum disorder, schizophrenia, and dementia (Whitfield-Gabrieli

and Ford, 2012; Sato et al., 2015). Monkeys and lab rats have circuits

that resemble the human default mode network on structural and

functional grounds, raising the possibility that some nonhuman

species may likewise engage in self-reflection or other introspective

mental activity (Mantini et al., 2011; Sierakowiak et al., 2015). Some

of the basic elements of human consciousness that researchers agree

on are identified in TABLE 14.1, which also lists other species that

may have comparable capacities and experiences.

TA B LE 1 4 . 1 Elements of Consciousness in Humans and Other Animals

Element Definition Other species

Theory of mind Insight into the mental lives of

others; understanding that other

individuals act on their own unique

beliefs, knowledge, and desires

Only chimpanzees, so far

Mirror

recognition

Ability to recognize the self as

depicted in a mirror

All great apes; dolphins;

magpies; some elephants

Imitation Ability to copy the actions of others;

thought to be a stepping-stone to

awareness and empathy

Many species, including

cephalopods like the octopus

Empathy and

emotion

Possession of complex emotions

and the ability to imagine the

feelings of other individuals

Most mammals, ranging from

primates and dolphins, to

hippos and rodents; most

vertebrates able to experience

pleasure (and other basic

emotions)

Tool use Ability to employ found objects to

achieve intermediate and/or

ultimate goals

Chimps and other primates;

other mammals such as

elephants, otters, and

dolphins; birds such as crows

and gulls

Element Definition Other species

Language Use of a system of arbitrary

symbols, with specific meanings

and strict grammar, to convey

concrete or abstract information to

any other individual that has

learned the same language

Generally considered to be an

exclusively human ability,

with controversy over the

extent to which the great apes

can acquire language skills

Metacognition “Thinking about thinking”: the

ability to consider the contents of

one’s own thoughts and cognitions

Nonhuman primates; dolphins

A practical alternative approach has been to study consciousness by

focusing on people who lack it—people in comas or other states of

reduced consciousness. Maps of brain activity in such people—or

more precisely, maps of deactivated areas (Tsuchiya and Adolphs,

2007; Lemaire et al., 2021)—suggest that consciousness depends on

a specific frontoparietal network (FIGURE 14.15) that includes

much of the cortical attention network we’ve been discussing, along

with regions of medial frontal and cingulate cortices. Some

researchers have proposed that the claustrum (FIGURE 14.16)—a

slender sheet of neurons buried within the white matter of the

forebrain lateral to the basal ganglia—may play a role in the

experience of being conscious (Crick and Koch, 2005; Smith et al.,

2020), by virtue of its remarkable reciprocal connections with

virtually every area of cortex, suggestive of a network hub. A sudden

change in consciousness has been reported after electrical

stimulation of the claustrum in some studies (Koubeissi et al., 2014;

Quraishi et al., 2017) but not all (Bickel and Parvizi, 2019), and it

remains to be determined exactly what role the claustrum plays in

coordinating the various cognitive elements of the networks

underlying consciousness (Atilgan et al., 2022). Some consciousness

researchers argue that further searching for specific neural correlates

of consciousness is unlikely to solve the problem anyway, because

the enormous variability of conscious experiences means that large

and shifting collections of brain regions—sometimes almost the

entire brain—will participate (Seth, 2021).

FIGU R E 1 4 . 1 5 The Unconscious Brain View larger image

FIGU R E 1 4 . 1 6 Consciousness Controller? View larger image

So is clinical unconsciousness really the inverse of consciousness?

Perhaps it’s not that simple. For one thing, some people in a

persistent vegetative state (a very deep coma) can be instructed to

use two different forms of mental imagery to create distinct “yes” and

“no” patterns of activity on fMRI and then to use this mental activity

to answer questions (FIGURE 14.17) (Monti et al., 2010;

Fernández-Espejo and Owen, 2013). Most people in a vegetative

state don’t respond to questions and outside stimulation, but others

in minimally conscious states may have considerable cognitive

activity and awareness with little or no overt behavior to indicate

that they are aware of their surroundings (Gosseries et al., 2014;

Sinai et al., 2017). So, it’s increasingly clear that we can’t simply view

a coma as an exact inverse of what we experience as consciousness.

In any case, there seems to be more to consciousness than just being

awake, aware, and attending. How can we identify and study the

additional dimensions of consciousness?

FIGU R E 1 4 . 1 7 Communication in “Unconscious” People View larger image

Some aspects of consciousness are

easier to study than others

Most of the activity of the central nervous system is unconscious.

Scientists call unconscious brain functions cognitively

impenetrable: they involve basic neural processing operations that

cannot be experienced through introspection. For example, we see

whole objects and hear whole words and can’t really imagine what

the primitive sensory precursors of those perceptions would feel like.

Sweet food tastes sweet, and we can’t mentally break it down any

further. But those simpler mechanisms, operating below the surface

of awareness, are the foundation that conscious experiences are built

on.

In principle, then, we might someday develop technology that would

let us directly reconstruct people’s conscious experience—read their

minds—by decoding the primitive neural activity and assembling

identifiable patterns from it. This is sometimes called the easy

problem of consciousness: understanding how particular

patterns of neural activity create specific conscious experiences. Of

course, it’s almost a joke to call this problem “easy,” but at least we

can fairly say that, someday, the necessary technology and

knowledge may be available to accomplish the task of eavesdropping

on large networks of neurons, in real time.

Present-day technology offers a glimpse of that possible future. For

example, if participants are repeatedly scanned while viewing several

distinctive scenes, a computer can eventually learn to identify which

of the scenes the participant is viewing on each trial, solely on the

basis of the pattern of brain activation (FIGURE 14.18A). Of

course, this outcome relies on having the participants repeatedly

view the same static images—hardly a normal state of consciousness.

A much more complex problem is to reconstruct conscious

experience from neural activity during a person’s first exposure to a

stimulus. This sort of reconstruction has been accomplished for

visual stimuli of varying complexity, including letters, shapes, and

uncluttered photos, and shows some promise for reconstructing

remembered scenes (FIGURE 14.18B) (Miyawaki et al., 2008;

Shen et al., 2019). We are still a very long way from directly

capturing the rich ongoing stream of conscious experience, but at

least it’s conceivable.

FIGU R E 1 4 . 1 8 Easy and Hard Problems of Consciousness View larger

image

Alas, there is also the hard problem of consciousness, and it

may prove impossible to crack. How can we understand the brain

processes that produce people’s subjective experiences of their

conscious perceptions? To use a simple example, everyone with

normal vision will agree that a ripe tomato is “red.” That’s the label

that children all learn to apply to the particular pattern of

information, entering consciousness from the color-processing areas

of visual cortex, that is provoked by looking at something like a

tomato. But that doesn’t mean that your friend’s internal personal

experience of “red” is the same as yours. These purely subjective

experiences of perceptions are referred to as qualia (singular quale).

Because they are subjective and impossible to communicate to others

—how can your friend know if “redness” feels the same in your mind

as it does in theirs?—qualia may prove impossible to study

(FIGURE 14.18C). At this point, anyway, we are unable to even

conceive of a technology that would make it possible.

Our subjective experience of consciousness is closely tied up with the

notion of free will: the belief that our conscious self is

unconstrained in deciding our actions and decisions and that for any

given moment, given exactly the same circumstances, we could have

chosen to engage in a different behavior. After centuries of

argument, there’s still no agreement on whether we actually have

free will, but most people behave as though there are always options,

and in any event, there must be a neural substrate for the universal

feeling of having free will. When participants intend to act (push a

button, say), there is selective activation of motor areas, parietal

regions including the IPS (which we implicated earlier in top-down

attention), and dorsal prefrontal cortex (Zapparoli et al., 2017; Si et

al., 2021), suggesting that these regions are important for our

feelings of control over our behavior. However, the conscious

experience of intention may come relatively late in the process of

deciding what to do. Classic research (Libet, 1985), using EEG and a

precise timer, found that an EEG component signaling movement

preparation was evident 200 ms before participants consciously

decided to move. Although controversy initially surrounded this

work, later confirmatory research using fMRI (Soon et al., 2008)

(FIGURE 14.19) found, astonishingly, that brain activity associated

with making a decision was evident in fMRI scans as much as 5–10

seconds before participants were consciously aware of making a

choice!

FIGU R E 1 4 . 1 9 Reading the Future View larger image

These results are sometimes interpreted as meaning we can have no

free will, because our brain decides to push a button before our

conscious self has decided. But even if you are not aware that your

brain has decided to push the left button, it was still your brain that

made that decision, not someone else’s brain. Your conscious self

may be late to join the party when making a decision, but that

doesn’t tell us whether your brain was truly free to choose the left

button rather than the right (or to take the blue pill versus the red

pill, Neo [Google it, youngsters]).

In any event, the earliest indications of the decision-making process

are found in prefrontal cortex. Such involvement of prefrontal

systems in most aspects of attention and consciousness, regardless of

sensory modality or emotional tone, suggests that the prefrontal

cortex is the main source of goal-driven behaviors (Badre and Nee,

2018), as we discuss next.

A flexible frontal system plans and

monitors our behavior

How do we translate our inner thoughts into behavior? Careful

analysis of impairments in people with localized brain damage, along

with functional imaging studies in healthy people, shows that a

network of anterior forebrain sites dominated by the frontal lobes—

but including several other cortical and subcortical sites—is crucial

for executive function, the suite of high-level cognitive processes

that control and organize lower-level cognitive functions in line with

our thoughts and feelings (Alvarez and Emory, 2006; Yuan and Raz,

2014). Some scientists liken executive function to a “supervisory

system” that analyzes important stimuli, weighs competing ideas and

hypotheses, and governs the creation of suitable “plans” for future

action by drawing on cognitive processes like working memory,

attention, feedback utilization, and so on. Executive function

involves multiple interrelated processes, especially (1) smooth task

switching between different cognitive operations, (2) continual

updating of the cognitive plan based on new information and the

contents of working memory, and (3) timely inhibition of thoughts

and behaviors that would compromise the plan (A. Diamond, 2013).

Additional high-level functions that researchers attribute to

executive function include behavioral sequencing, prioritizing of

actions, suppression of interfering inputs, and monitoring of ongoing

performance (Friedman and Robbin, 2022; Menon and D’Esposito,

2022). A closely related account proposes that the crucial function of

the frontal network is hierarchical cognitive control: the ability to

direct shorter-term actions while simultaneously keeping longerterm goals in mind (Koechlin et al., 2003; Badre and Nee, 2018).

Accordingly, a person with executive dysfunction due to frontal

lesions who is given a simple set of errands may be unable to

complete them without numerous false starts, backtracking, and

confusion (Shallice and Burgess, 1991; Rabinovici et al., 2015).

Several of the most widely studied tests of executive functions are

described in TABLE 14.2.

TA B LE 1 4 . 2 Tests of Executive Functions

Test name Procedure Scoring Functions

sampled

Wisconsin Card

Sorting Test (WCST)

(Weigl, 1941; Heaton

et al., 1993)

Sort cards into piles on

the basis of the number,

color, or shape of

symbols on card face.

Every 10 cards, discover

Errors in sorting;

perseveration in

old sorting rule

after rule change

Task

switching and

abstract

reasoning

Test name Procedure Scoring Functions

sampled

and shift to a new

sorting rule (see Figure

14.21).

Controlled Oral

Word Association

Test (COWAT) (also

known as the Verbal

Fluency Task;

Benton and

Hamsher, 1976)

Say as many words as

possible that start with a

specific letter (F, A, or

S), in 60 seconds.

Total number of

unique words

uttered for all

three starting

letters

Verbal

fluency,

updating,

working

memory

Stroop Test of ColorWord Interference

(Stroop, 1935;

MacLeod, 1991)

Read aloud as quickly as

possible color names

that are printed in the

congruent color (e.g.,

BLUE) or incongruent

color (e.g., BLUE).

Time and total

errors

Response

inhibition

We have touched on some frontal lobe functions in earlier chapters—

things like movement control, working memory, language,

psychopathology—but this mass of cortex also underlies other, more

mysterious intellectual characteristics. Perhaps it reflects a bit of

vanity about our species, but the large size of our frontal lobes—

about one-third of the entire cortical surface (FIGURE 14.20A)—

also led to the long-standing view that the frontal cortex is the seat of

intelligence and abstract thinking. The remarkable story of Phineas

Gage, one of the most famous case studies in the history of

neuroscience, underscores the subtlety and complexity of behaviors

governed by the frontal lobes. Like Gage, people with discrete frontal

lesions express various unusual emotional, motor, and cognitive

changes. Widespread frontal damage may be associated with a

persistent strange apathy, broken by bouts of euphoria (an exalted

sense of well-being). Ordinary social conventions are readily cast

aside by impulsive behavior. Concern for the past or the future may

be absent, and forgetfulness is shown in many tasks requiring

sustained attention. In fact, some people with frontal damage even

forget their own warnings to “remember.” However, standard IQ test

performance often shows only slight changes after prefrontal injury

or stroke.

FIGU R E 1 4 . 2 0 The Prefrontal Cortex View larger image

On the basis of both structure and function, researchers distinguish

between several major divisions of the human frontal lobes. The

posterior portion of the frontal cortex includes motor and premotor

regions (see Chapter 5). The anterior portion, usually referred to as

prefrontal cortex, is immensely interconnected with the rest of the

brain (Fuster, 1990; Mega and Cummings, 1994). It was prefrontal

cortex that was surgically disrupted in frontal lobotomy—the

notorious, now-discredited treatment for psychiatric disorders that

we discussed in Chapter 12. As shown in FIGURE 14.20B,

neuroscientists subdivide prefrontal cortex into multiple specific

regions on both anatomical and functional grounds; these regions

include the dorsolateral, ventrolateral, ventromedial, and

orbitofrontal cortex (Catani, 2019).

Dorsolateral prefrontal cortex is closely associated with executive

control, as it is crucial for working memory (holding information in

mind while using it to solve problems) and task switching. People

with lesions that include the dorsolateral prefrontal cortex may thus

struggle with top-down conscious switching from one task to a new

one, as in the Wisconsin Card Sorting Test (FIGURE 14.21), and

tend to perseverate (continue beyond a reasonable degree) in any

activity (B. Milner, 1963; Alvarez and Emory, 2006). Similarly,

lesions in the frontal lobes may cause motor perseveration—

repeating a simple movement over and over again—despite

diminished overall levels of spontaneous motor activity. Along with

movement of the head and eyes, facial expression of emotions may

be greatly reduced. People with prefrontal lesions often have an

inability to plan future acts and use foresight, as in the famous case

of Phineas Gage, who survived an accident that extensively damaged

his orbitofrontal and ventromedial prefrontal cortex. Their social

skills may decline, especially the ability to inhibit inappropriate

behaviors, and they may be unable to stay focused on any but shortterm projects. They may agonize over even simple decisions. Some of

the clinical features of damage to the subdivisions of prefrontal

cortex are summarized in TABLE 14.3.

FIGU R E 1 4 . 2 1 The Wisconsin Card Sorting Test (WCST) View larger image

Phineas Gage Phineas P. Gage was a sober, polite, and capable member of a rail-laying

crew, responsible for placing the charges used to blast rock from new rail beds. That’s Gage

on the left, holding a meter-long steel tamping rod. Perhaps the images on the right can

help you guess why there appears to be something wrong with the left side of his face. In a

horrific accident in 1848, a premature detonation blew that rod right through Gage’s skull,

on the trajectory shown in red, severely damaging both frontal lobes, especially in the

orbitofrontal regions. Amazingly, Gage could speak shortly after the accident, and he walked

up the stairs to a doctor’s office, although no one expected him to live (Macmillan and Lena,

2010). In fact, Gage survived another 12 years, but he was definitely a changed man, so

rude and aimless, and his powers of attention so badly impaired, “that his friends and

acquaintances said that he was ‘no longer Gage.’” The historical account of Gage’s case,

and anatomical reconstruction of his injury, are consistent with modern cases of people with

prefrontal damage that includes orbitofrontal cortex (Wallis, 2007; de Schotten et al., 2015).

View larger image

TA B LE 1 4 . 3 Regional Prefrontal Syndromes

Prefrontal

damage type

Syndrome

type

Characteristics

Dorsolateral Dysexecutive Diminished judgment, planning, insight, and

temporal organization; reduced cognitive focus;

motor-programming deficits (possibly including

aphasia and apraxia); diminished self-care

Orbitofrontal Disinhibited Stimulus-driven behavior; diminished social insight;

distractibility; emotional lability

Mediofrontal Apathetic Diminished spontaneity; diminished verbal output;

diminished motor behavior; urinary incontinence;

lower-extremity weakness and sensory loss;

diminished spontaneous prosody; increased

response latency

In monkeys, distinct populations of orbitofrontal cortical neurons

become especially active when the animal has to make an uncertain

decision that may provide a reward (Matsumoto et al., 2022),

indicating that orbitofrontal cortex controls reward-directed

behaviors. In general, orbitofrontal cortex—and especially the

ventromedial prefrontal cortex—seems important for learning about

rewarding behaviors, and it is believed to play a special role in

anticipating the values of different choices, along with other

operations that guide decisions about how to behave (Hiser and

Koenigs, 2018; Knudsen and Wallis, 2022). In humans performing

tasks in which some stimuli have more reward value than others, the

level of activation in prefrontal cortex correlates with how rewarding

the stimulus is (Gottfried et al., 2003; Kokmotou et al., 2017). This

relationship seems to be a significant factor in gambling behavior

and, more generally, is important for our decision-making processes,

as we discuss next.

We make decisions using a frontal

network that weighs risk and benefit

The waiter has brought over the dessert trolley, and it’s decision

time: do you go with the certain delight of the chocolate cake, or do

you succumb to the glistening allure of the sticky toffee pudding? Or,

do you allow yourself only a cup of black coffee, for the sake of your

waistline? What happens in the brain when we make everyday

decisions?

In the lab, researchers usually evaluate decision-making by using

monetary rewards (instead of desserts, darn it) because money is

convenient: you can vary how much money is at stake, how great a

reward is offered, and so on, to accurately gauge how we really make

economic decisions. These studies show that most of us are very

averse to loss and risk: we are more sensitive to losing a certain

amount of money than we are to gaining that amount. In other

words, losing $20 makes us feel a lot worse than gaining $20 makes

us feel good. From a strictly logical point of view, the value of money,

whether lost or gained, should be exactly the same. Our tendency to

overemphasize loss is just one of several ways in which people fail to

act rationally in the marketplace.

Neuroeconomics is the study of brain mechanisms at work during

economic decision-making, and our attention to environmental

factors and evaluation of rewards has a tremendous impact on these

decisions. In general, findings from human and animal research

suggest that two main systems underlie decision processes (Kable

and Glimcher, 2009). The first, consisting of the orbitofrontal and

ventromedial prefrontal cortex (including the anterior cingulate

cortex) plus the dopamine-based reward system of the brain (see

Chapter 3), is a valuation system, a network that ranks choices on

the basis of their perceived worth and potential reward (Clairis and

Pessiglione, 2022; Ballesta et al., 2020). Impressively, using an

optogenetic technique (see Chapter 2) to selectively activate neurons

that express dopamine receptor D in the nucleus accumbens—a

central forebrain component of the brain’s reward system—can

instantaneously turn a risk-preferring rat into a risk-averse rat

(Zalocusky et al., 2016)! Presumably, the activated cells cause the

valuation system to devalue the reward relative to risk; a similar

dopamine-dependent process appears to participate in human

monetary decisions too (Ojala et al., 2018).

The second system involves dorsolateral prefrontal cortex, dorsal

anterior cingulate cortex, and parietal regions (like the LIP or IPS

discussed earlier in the chapter), and it is thought to be a choice

system, sifting through the valuated alternatives and producing the

conscious decision.

Neuroeconomics research is confirming that the prefrontal cortex

normally inhibits impulsive decision-making as a way to avoid loss

(Tom et al., 2007; Muhlert and Lawrence, 2015). As people are faced

2

with more and more uncertainty, the prefrontal cortex becomes more

and more active (Hsu et al., 2005; Huettel et al., 2006), and the

dorsal cingulate cortex may improve decisions by delaying action

until full processing of a complex decision can be completed (Sheth

et al., 2012; Heilbronner and Hayden, 2016). Likewise, when people

have made wrong, costly decisions that they regret, thereby changing

their future decision-making—called the sunk cost fallacy—activity

increases in a network including the amygdala, cingulate, and

orbitofrontal cortex (FIGURE 14.22) (Coricelli et al., 2005; Haller

and Schwabe, 2014), reflecting the person’s growing aversion to loss.

FIGU R E 1 4 . 2 2 A Costly Decision View larger image

We may never have a full understanding of the deepest secrets of

consciousness, or an answer to the question of whether we actually

make decisions based on the free will that our brain seems to

perceive as an element of consciousness (Haggard, 2017). But that

doesn’t prevent us from marveling that our consciousness has

become so self-aware that it can study itself to a high degree. Perhaps

it’s best to allow ourselves at least one or two mysteries, if only for

the sake of art. Would life seem as rich if we could predict other

people’s behavior, or even our own, with perfect accuracy?

How’s It Going?

1. Define consciousness (or at least try!).

2. Discuss unconsciousness in states like deep coma. Is

this unconsciousness the inverse of consciousness?

3. Contrast the easy and hard problems of consciousness,

giving examples of each. What are qualia?

4. Name the main subdivisions of the prefrontal cortex.

How do they differ in function?

5. What are some of the main symptoms of frontal lobe

lesions?

6. What are the two main neural systems that are thought

to operate in the process of decision-making, as

identified in neuroeconomics research?

FOOD FOR THOUGHT

Emerging technology—high-resolution cortical recording and AIbased image reconstruction, for example—may soon allow us to

record and play back the sensory content of peoples’ thoughts,

dreams, and memories. How will this affect our lives, both pro

and con?

RECOMMENDED READING

Buzsáki, G. (2021). The Brain from Inside Out. New York, NY:

Oxford University Press.

D’Esposito, M., and Grafman, J. H. (Eds.). (2019). The Frontal

Lobes (Handbook of Clinical Neurology, Volume 163).

Cambridge, MA: Elsevier.

Gazzaniga, M. S. (2018). The Consciousness Instinct:

Unraveling the Mystery of How the Brain Makes the Mind. New

York, NY: Farrar, Straus and Giroux.

Glimcher, P. W., and Fehr, E. (2013). Neuroeconomics: Decision

Making and the Brain (2nd ed.). San Diego, CA: Academic

Press.

Goldberg, E. (2017). Executive Functions in Health and

Disease. New York, NY: Academic Press.

Harley, T. A. (2021). The Science of Consciousness. Cambridge,

UK: Cambridge University Press.

Hopfinger, J. B., and Slotnick, S. (Eds.). (2021). The Cognitive

Neuroscience of Attention: Current Debates and Research.

Abingdon-on-Thames, UK: Routledge.

Koch, C. (2020). The Feeling of Life Itself: Why Consciousness

Is Widespread but Can’t Be Computed. Cambridge, MA: MIT

Press.

Laureys, S., and Tononi, G. (Eds.). (2015). The Neurology of

Consciousness: Cognitive Neuroscience and Neuropathology

(2nd ed.). New York, NY: Academic Press.

Nobre, K., and Kastner, S. (2018). The Oxford Handbook of

Attention. Oxford, UK: Oxford University Press.

Owen, A. (2017). Into the Gray Zone: A Neuroscientist Explores

the Border between Life and Death. New York, NY: Scribner.

Sapolski, R. M. (2023). Determined: A Science of Life Without

Free Will. New York, NY: Penguin.

VISUAL SUMMARY

You should be able to relate each summary to the adjacent

illustration, including structures and processes. The online

version of this Visual Summary includes links to figures,

animations, and activities that will help you consolidate the

material.

Visual Summary Chapter 14 View larger image

LIST OF KEY TERMS

attention

attentional bottleneck

attentional spotlight

attention deficit hyperactivity disorder (ADHD)

auditory N1 effect

binding problem

Bálint’s syndrome

cocktail party effect

cognitively impenetrable

conjunction search

consciousness

covert attention

default mode network

divided-attention tasks

easy problem of consciousness

event-related potential (ERP)

executive function

feature search

free will

frontal eye field (FEF)

hard problem of consciousness

hemispatial neglect

inattentional blindness

inhibition of return

intraparietal sulcus (IPS)

lateral intraparietal area

Neuroeconomics

overt attention

P3 effect

perceptual load

peripheral spatial cuing

perseverate

prefrontal cortex

pulvinar nucleus

qualia

reaction time

reflexive attention

shadowing

simultagnosia

spatial resolution

superior colliculus

sustained-attention tasks

symbolic cuing

temporal resolution

temporoparietal junction (TPJ)

vigilance

visual P1 effect

voluntary attention