Basic concep tual and theor et ical issues
In the previ ous chapter, we provided a loose defi n i tion of atten tion as the selec tion or prior it isa tion for processing of certain categor ies of inform a tion. We can subdivide atten tional processes into the two broad func tions attrib uted to atten tion: selec tion and intens ive processing . The inform a tion- processing system must select which stimuli are to be processed most extens ively, and which are permit ted to control response and action. Also, when a mental activ ity is partic- u larly demand ing or import ant, the system must confi g ure itself to maxim ise effi ciency of processing, possibly at the expense of more peri pheral activ it ies.
Selective and intens ive aspects of atten tion may have to func tion in concert, as when listen ing to a tele phone call on a noisy line. These two atten tional func- tions corres pond to one of the great axes of theor et ical debate among atten tional psycho lo gists. On the one hand, theor ies of select ive atten tion tend to emphas ise the import ance of detailed know ledge about the struc ture or archi tec ture of the inform a tion- processing system. Armed with a circuit diagram of the mind, we can attempt to diagnose the points at which inform a tion is selec ted for entry into conscious ness or for control of action. On the other hand, theor ies of demand ing task perform ance, such as simul tan eous perform ance of two or more tasks, emphas ise the overall capa city or resources of the mind, which may be suppor ted by a variety of specifi c processing struc tures.
In the next part of this chapter, we review four broad approaches to atten tion, each provid ing a differ ent perspect ive on the relat ive import ance of the system archi tec ture, and of general processing capa city. First, we consider the search for an atten tional bottle neck: at which points is the system forced to discard inform- a tion? Second, we consider capa city models in detail. Third, we look at dual- level models of atten tion, which distin guish qual it at ively differ ent domains of processing, subject to differ ent processing constraints. Fourth, we consider recent connec tion ist approaches to atten tion. In the next chapter, we consider how
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18 Attention: Basic issues
complex self- know ledge such as Beck’s (1967) schemata may infl u ence select ive atten tion, and we review studies of the selec tion of emotional inform a tion.
The search for a bottle neck Early and late selec tion
Much research on select ive atten tion assumes that the external envir on ment may be divided into chan nels from which inform a tion may be received. The text book example (e.g. Wickens, 1992) is the many instru ments and displays to which an aircraft pilot must attend. Each one is a distinct channel, and the pilot must develop a sampling strategy which controls how frequently each one is monitored. In every day life, chan nels may be defi ned not just by differ ent loca tions in space, but by other distinc tions import ant to the indi vidual. For example, each person parti cip at ing in a conver sa tion may be seen as a distinct channel supply ing visual and audit ory input. The basic problem for select ive atten tion theory is to discover how people are able to process incom ing stim u lus char ac ter ist ics so as to select some chan nels for full processing of inform a tion, and to ignore or process super fi cially other chan nels.
The tradi tional theor et ical dicho tomy in select ive atten tion distin guishes early from late selec tion. Early selec tion (Broadbent, 1958) proposed a select ive fi lter located after initial percep tual analysis, which could be set so that only stimuli possess ing a partic u lar attrib ute or feature were selec ted for further analysis.
Features are simple phys ical prop er ties such as colour, spatial loca tion or pitch. In contrast, late selec tion theor ies (Deutsch & Deutsch, 1963) proposed that all stimuli were fully analysed, with selec tion taking place only when a response was selec ted.
Studies of the famous “shad ow ing” task soon disposed of the original fi lter theory.
This task requires the subject to repeat out loud a spoken message played through head phones. In studies of selec tion, differ ent messages are played to the two ears, and the subject must shadow one. Since “ear” consti tutes a feature, fi lter theory predicts that shad ow ing one ear precludes detailed analysis of the message presen ted at the unat ten ded ear. In fact, several studies of shad ow ing (e.g. Treisman, 1960) showed that subjects often follow a mean ing ful message which switches from ear to ear, imply ing that the supposedly unat ten ded message is in fact analysed for semantic content, contrary to fi lter theory. Another diffi culty for tradi tional fi lter theory is the role of percep tual group ing: it is harder to select an item for processing if it forms a Gestalt confi g ur a tion together with distract ing items, than if the stim- u lus and distract ors form separ ate Gestalts (Prinzmetal, 1981). It has been argued that the selec tion is not geared towards indi vidual stim u lus features, but towards objects, which are natur ally percep tu ally grouped (Duncan, 1984).
Contemporary early selec tion theor ies
Early selec tion theor ies have evolved to accom mod ate such fi nd ings, exem pli fi ed best by a series of models proposed by Treisman. Her fi rst depar ture from the
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The search for a bottle neck 19
original fi lter theory was to propose that the percep tual fi lter has only an atten u- at ing effect (Treisman, 1964). Most of the encod ings of the element ary features asso ci ated with an unat ten ded input are discarded but a few features may pass through the fi lter. In the case of a strongly expec ted stim u lus, these features may be suffi cient to trigger conscious recog ni tion, as when a person’s name “breaks through” into aware ness from the unat ten ded ear in a shad ow ing exper i ment.
Further devel op ments of the theory primar ily concern visual selec tion. Treisman and Souther (1985) and Treisman and Gormican (1988) proposed two stages or domains of processing. The fi rst stage is pass ively driven by stim u lus input, whereas the second stage is asso ci ated with “top- down” processing, stim u lus analysis depend ent on the person’s know ledge and expect a tions (which may or may not be access ible to conscious ness). Early stim u lus- driven processing, oper- at ing in paral lel, gener ates a series of “maps” of the visual fi eld, each coding the spatial posi tion of features such as colours, lines of specifi ed orient a tion and so on.
Hence, search for a single feature is partic u larly fast, and unaf fected by the pres- ence of other, distract ing features. Treisman (1988) also discusses feature inhib i- tion processes which may serve to focus atten tion on percep tu ally grouped stim u lus elements. Search for conjunc tions of features requires serial search through maps combined to produce percepts of whole, conjoined objects, and so is slower, and subject to distractor effects. Conjunction search requires alloc a tion of atten- tion to the corres pond ing loca tions of the various feature maps, to identify which features are asso ci ated with the same percept. The end- product of fi rst- stage processing is the construc tion of an epis odic “object fi le” encod ing the spatial confi g ur a tion of features asso ci ated with each percep tual object, which can be further analysed by the second, serial stage of processing. Unattended stimuli simply fail to gener ate an object fi le. Top- down processes serve to control the focus of spatial atten tion, and to elim in ate feature conjunc tions incom pat ible with expect a tion, though Treisman (1988) suggests that the process of feature conjunc tion itself is insens it ive to top- down control. Cave and Wolfe (1990) have developed a modi fi c a tion of Treisman’s (1988) feature integ ra tion theory which offers a differ ent view of top- down control. According to their guided search model, top- down processing gener ates a map coding the simil ar it ies between the features actu ally present and the “target” feature expec ted on the basis of prior know ledge. A combin a tion of inform a tion from bottom- up and top- down processing directs the later, serial processing stage to the most prom ising spatial loca tions for fi nding the desired target.
Contemporary late selec tion theor ies
Late selec tion theor ies (e.g. Duncan, 1980) make a similar distinc tion between early, paral lel pre- attent ive processing, and a limited- capa city system oper at ing later in processing. The essen tial differ ence is that, accord ing to late selec tion theory, stim u lus attrib utes are extens ively analysed and assigned to objects pre- attent ively. Hence, the system is not select ing which inputs are fully analysed, but
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20 Attention: Basic issues
which inputs control response. The simplest evid ence for this view is provided by studies of dual- task perform ance, showing that the level of inter fer ence between the tasks depends mainly on the simil ar ity of response (McLeod, 1977). Late selec tion theor ists also claim that, contrary to early selec tion theory, repres ent a- tions of “unat ten ded” stimuli are not simply lost, but may continue to infl u ence processing. Studies of “negat ive priming” show that target processing is slowed if the target is related to the supposedly ignored distractor of the previ ous trial (Tipper & Driver, 1988). Duncan and Humphreys (1989) have proposed perhaps the most detailed contem por ary late selec tion model. In contrast to the Treisman (e.g. 1988) theory, object fi les, or struc tural units, are compiled by pre- attent ive processing. Structural units compete for access to a limited- capa city visual short- term memory, depend ing on their relat ive atten tional strengths or weights, which are frequently infl u enced by an advance specifi c a tion or “template” of expec ted or import ant inform a tion. The 3–4 most strongly weighted units enter a limited- capa city, visual short- term memory store, at which point they reach conscious aware ness and may control the subsequent response.
Comparison of models for selec tion
At present, no decis ive resol u tion of the early–late selec tion contro versy is possible. The liter at ure on atten tion is replete with tech nical argu ments on the valid ity of the various pieces of evid ence. Diffi culties for the Treisman and Souther (1985) early selec tion model include a variety of studies suggest ing that feature and conjunc tion search may not be controlled by qual it at ively differ ent processes (Duncan & Humphreys, 1989), and that selec tion by single phys ical features is not always partic u larly easy (Allport, 1980). Conversely, it is claimed that dual- task inter fer ence is only partially explained by inter fer ence between similar responses (Pashler, 1989), and that negat ive priming may be an arti fact of distractor processing taking place after the target has been success fully selec ted (Yantis & Johnston, 1990). Johnston and Dark (1985) and Allport (1989) provide reviews broadly support ive of early and late selec tion, respect ively. Perhaps more import ant is the agree ment across theor ies of several general prin ciples, which tends to blur the early–late distinc tion in some respects. These prin ciples include the distinc tion between the early paral lel processing stage and later capa city- limited processing, which corres pond to distinct domains of “pre- attent ive” and
“post- attent ive” processing. There is also a consensus that select ive atten tion is primar ily oriented towards the selec tion of objects, rather than isol ated stim u lus attrib utes. It is widely believed, too, that processing is guided by detailed specifi c- a tion of the object to be selec ted which oper ates through biasing the bottom- up func tion ing of the early processing stage, so that non- targets may be rejec ted prior to entry into the later, limited-capa city domain (Cave & Wolfe, 1990;
Duncan & Humphreys, 1989). The nature of the repres ent a tion of rejec ted non- targets becomes more an import ant detail than a funda mental point of prin ciple.
As Duncan (1985) has pointed out, it may be more useful to invest ig ate how
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Capacity models of atten tion 21
inform a tion is used at differ ent stages of processing, than to ask where stimuli are
“iden ti fi ed” in some all- or- none fashion. If so, there appears to be some degree of conver gence between early and late selec tion theory.
In addi tion, the stage of processing at which selec tion takes place may vary. As an example, consider the spot light meta phor for visual atten tion. Within the area illu min ated by the spot light, late selec tion oper ates, as shown by evid ence that confl ict ing, spatially contigu ous letter stimuli gener ate inter fer ence at the motor response level (Coles et al., 1985). Late selec tion may be confi ned to elements common to a single percep tual object: it is currently some what unclear whether processing of unat ten ded objects within the atten tional spot light can gener ate inter fer ence ( Johnston & Dark, 1985). However, stim u lus processing outside the spot light is largely restric ted to simple phys ical features ( Johnston & Dark, 1985), and early selec tion by loca tion oper ates very effi ciently when condi tions are favour able for estab lish ing a fi xed spatial focus (Yantis & Johnston, 1990). The most detailed model of this kind is that of Eriksen and Yeh (1985). They propose that spatial atten tion oper ates like a zoom- lens, so that the person can expand the area of space fully atten ded to, at the cost of a loss of “resolv ing power” or effi - ciency of processing. Stimuli asso ci ated with confl ict ing responses will gener ate inter fer ence only within the area of focal atten tion (Eriksen & Schulz, 1979).
Yantis and Johnston (1990) propose that people are fl ex ible in their atten tional strategies, select ing early or late depend ing on task demands and strategy. People may adopt a late selec tion strategy either because of diffi culties in making use of feat ural inform a tion ( Johnston & Heinz, 1978), or because task require ments favour late selec tion, as in the case of divid ing atten tion across two tasks. There may also be multiple selec tion points within the same task. Pashler (1989) discrim- in ates two quite separ ate bottle necks in divided atten tion, one asso ci ated with visual percep tual processing, and one with queuing of responses for selec tion.
Capacity models of atten tion The capa city meta phor
The idea of limited inform a tion- processing capa city is one of the most appeal ing but prob lem atic unify ing concepts in atten tional theory. It is also one of the most misun der stood. There is no doubt that the neural basis of atten tion dictates capa- city limit a tions. What is at issue is whether we can identify limit a tions of the processing system as a whole, as opposed to limit a tions of its indi vidual constitu ent parts, whether these are char ac ter ised as cell assem blies, or as element ary processes.
Several rather differ ent defi n i tions of capa city, in this non- local sense, have been proposed. Early attempts (e.g. Broadbent, 1958) were inspired by the digital computer to see capa city resid ing in a central, general- purpose processor, with an upper limit to the rate at which serial- processing oper a tions could be performed.
A more subtle variant on this theme (Moray, 1967; Navon & Gopher, 1979) was to see capa city as a set of specifi c processing resources, such as processing networks,
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22 Attention: Basic issues
memory space, commu nic a tions chan nels and so on, which could be alloc ated fl ex ibly accord ing to processing demands, under the control of an exec ut ive resource manager. This model is compat ible with a wide variety of processing archi tec tures. A further defi n i tion (Wickens, 1980) is to equate capa city with a meta phor ical supply of energy or fuel for processing. As with an elec trical circuit, loss of power leads to a gradual degrad a tion in output from the powered compon- ents. The crit ical feature of this defi n i tion is that capa city is some thing addi tional to the inform a tion- processing machinery; ener gisa tion is non- local , in that it is not simply a matter of the response to processing load vari ation of each indi vidual processing unit. A rather differ ent approach has been provided by Townsend and Ashby (1980), who suggest an oper a tional defi n i tion: capa city refers to the effect on perform ance of changes in processing load. At a micro level, capa city limit a- tions may be a direct result of system archi tec ture (and so of limited theor et ical interest). The capa city of a serial processor will simply refl ect the time taken to process a single input. However, the defi n i tion is equally applic able at the macro level, when the under ly ing system archi tec ture is unknown. Capacity is a conveni ent way of describ ing the load- response char ac ter ist ics of the system, which may or may not be asso ci ated with non- local ener gisa tion of resource alloc a tion processes.
Resource theor ies
The most infl u en tial form al isa tion of capa city theory is Norman and Bobrow’s (1975) resource theory. They describe a hypo thet ical Performance–Resource Function (PRF), a graph relat ing resources to perform ance. The key point is that the gradi ent of the curve may vary, such that perform ance may vary more or less with changes in resource avail ab il ity. Processes may be either resource- limited, or data- limited, in which case perform ance depends on the quality of signal or memory data, and is unaf fected by changes in the supply of resources. A process may be resource- limited along some parts of the PRF but data- limited along others. The PRF is not directly observ able, because we cannot measure resources and perform ance inde pend ently. Resource theory can only be tested by making indir ect infer ences about the shape of the PRF in single- and dual- task perform- ance. Dual- task methods have been most widely used. The basic logic is that resource- limited perform ance on one task should be sens it ive to the quant ity of resources diver ted to a second task. The simple demon stra tion of dual- task inter- fer ence is inad equate, since it may refl ect changes in data limit a tions gener ated by diffi culties in combin ing the tasks, termed the cost of concur rence (Wickens, 1984).
For example, visual monit or ing of two widely separ ated loca tions is diffi cult because we cannot fi xate both simul tan eously. Dual- task inter fer ence must show diffi culty- sens it iv ity , so that the amount of inter fer ence increases with the diffi culty of the inter fer ing task (Wickens, 1980). The most thor ough way of invest ig at ing resource usage in dual- task perform ance is by construct ing a Performance Operating Characteristic (POC: Wickens, 1984). Subjects perform a pair of