Theoretical issues
Theoretical accounts of affect ive bias typic ally make two kinds of propos i tion.
The fi rst concerns categor ies of tasks or subjects which show or do not show affect- congru ent bias. It might be argued that bias is stronger in select ive atten- tion than in percep tion, or that clin ical patients show stronger bias than anxious normals. Observations of this kind have fairly direct implic a tions for theory. For example, Williams and co- workers’ (1988) iden ti fi c a tion of anxiety with bias in pre- attent ive processing and depres sion with bias in post- attent ive elab or a tion is in part a direct extra pol a tion from their reading of the relev ant data. A scien tifi c- ally valu able theory like that of Williams et al. does more than just redescribe the data though. The second kind of theor et ical propos i tion goes beyond the data to some degree in intro du cing concepts such as pre- attent ive processing, which are not directly observ able, and make sense only within a wider concep tual frame- work for under stand ing atten tional phenom ena. In prin ciple, answer ing ques- tions such as whether anxiety affects bias in memory is relat ively straight for ward, and issues of this kind were dealt with in the previ ous chapter. Deciding whether anxiety effects are pre- attent ive is more diffi cult, because the criteria for estab- lish ing a pre- attent ive effect are them selves uncer tain and subject to debate. In this chapter, we tackle these more prob lem atic theor et ical issues. This is partly a matter of match ing predic tions from theory against data, and partly a matter of assess ing the valid ity of the impli cit or expli cit criteria used by theor ists for decid ing whether effects are pre- attent ive, uncon scious, auto matic and so on.
We consider two major theor et ical approaches which are partic u larly apt for explain ing atten tional phenom ena. The fi rst is network theory, exem pli fi ed by Bower’s (1981) network model, in which bias is attrib uted to the states of activ a- tion of nodes in the network. The second is inform a tion- processing theory, exem pli fi ed by Williams and co- workers’ (1988) model, in which stim u lus input under goes a series of stages of processing, with differ ent types of bias located at
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84 Affective bias in attention
pre- attent ive and post- attent ive processing stages. This theory defi nes pre- attent ive processing as the stage follow ing stim u lus input at which stim u lus elements are processed auto mat ic ally and in paral lel, prior to stra tegic elab or a tion of stimuli activ ated and entered into conscious ness by pre- attent ive processing.
Williams et al. appear to concep tu al ise pre- attent ive processing as auto matic, and post- attent ive processing as controlled, but, as discussed in Chapter 2, these two processing char ac ter ist ics are not neces sar ily identical, and we shall consider them separ ately.
Network models of affect ive bias: Bower (1981; 1987)
The essence of Bower’s (1981) original network model was that emotions may be repres en ted by discrete network nodes or units just as propos i tions and events may be in conven tional cognit ive psycho logy (e.g. Anderson & Bower, 1973).
Emotion nodes may be activ ated either by appro pri ate external inputs, or through activ a tion of network nodes asso ci at ively linked with the emotion, such as the nodes repres ent ing the memory of an unhappy event. Once activ ated, emotion nodes infl u ence the course of future inform a tion- processing through the spread ing of activ a tion to asso ci ated nodes. The general predic tion is that emotional states prime processing congru ent with the emotion. Nodes asso ci ated with the emotion node become weakly activ ated, though prob ably not to the extent of alter ing conscious aware ness, so that they are more readily activ ated by stim u lus input, or by inputs from other nodes in the course of processing. Bower (1981) describes three distinct effects of this kind. The fi rst is mood state- depend ent retrieval (MSD), as previ ously described. At encod ing, nodes for the mater ial to be remembered become asso ci ated with nodes for contex tual features, includ ing the person’s emotional state. When retrieval takes place in the same emotional state, the emotion node partly activ ates, or primes, the nodes for the mater ial remembered, render ing it more or less access ible. The second is mood- congru ent retrieval (MC). There are stable asso ci at ive links between emotion nodes and nodes for affect ively valenced concepts or events. Hence a depressed mood tends to activ ate nodes for unpleas ant concepts and sad events in the person’s life, again increas ing the ease with which they can be recalled. Third, a similar priming mech an ism is predicted to cause mood- congru ence in various addi tional cognit ive processes, such as gener at ing free asso ci ates, inter pret ing pictures and people, and percep tion and select ive atten tion.
One of the great strengths of the Bower (1981) paper was that it set out a range of falsifi able predic tions, which subsequent research has duly tested. Bower (1987) expresses consid er able pess im ism about the success of such tests, and states, with unusual candour, that the theory is “badly in need of repairs—or in need of a replace ment theory” (p. 454). In some respects, the evid ence reviewed suggests that this is an excess ively pess im istic view. Ucros’ (1989) meta- analyses suggest that many of the fail ures to replic ate MC and MSD may be attrib uted to meth- od o lo gical factors. Likewise, we have seen that the Stroop test shows reli able
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Network models of affect ive bias 85
atten tional bias across a range of emotional disorders, although, as Bower (1987) states, induced moods do not give reli able effects. Anxiety effects on other atten- tional tasks are broadly consist ent with the model. The most serious problem is the general failure to fi nd mood- congru ence in simple percep tual and encod ing tasks, in both mood- induc tion studies, and in anxious and depressed patients.
Tasks of this kind are relat ively easily modelled in terms of network activ a tion processes (e.g. McClelland & Rumelhart, 1981), and it is hard to explain the absence of effects, partic u larly when more complex eval u ation tasks do show mood- congru ence. Similarly, the strength of effects on memory seems to increase with the need for active processing of the mater ial, when a simple priming model would predict the oppos ite. Another general diffi culty, at least for studies of anxiety, is the failure of state anxiety differ ences to explain the cognit ive differ ences between patients and controls. There appears to be more to anxiety disorders than just over- activ a tion of an anxiety node.
A number of other, more specifi c criti cisms may be made. Forgas and Bower (1987) describe evid ence that posit ive moods have more robust effects on judge- ment than negat ive moods, that semantic simil ar ity between the mood source and the judge mental target does not neces sar ily contrib ute to bias, and that percep tions of the self are more strongly affected than percep tions of others. Asymmetry between posit ive and negat ive moods is also found in memory studies (Singer &
Salovey, 1988). In the context of memory research, Williams et al. (1988) point out that the network model ignores import ant retrieval processes. The response of network theor ists to prob lems of this kind has been to elab or ate the Bower (1981) model to accom mod ate higher- level cognit ive and social infl u ences on affect. For example, asso ci at ive links between an emotion and a stim u lus may form only if the person caus ally relates their emotional reac tion to the occur rence of the stim u lus (Bower, 1987). Bower and Cohen’s (1982) black board model postu lates a working memory or “black board” which integ rates emotional inform a tion from a variety of sources. It allows the strength of emotion to be modi fi ed by inter pret a tional rules so that the person’s emotional response is (at least approx im ately) socially appro pri ate. These rules may be applied either auto mat ic ally or through delib er ate reas on ing. In prin ciple, such a model can account for some of the diffi culties noted. For example, mood asym metry may be caused by the applic a tion of rules concerned with regu lat ing and controlling negat ive moods. As Singer and Salovey (1988) state, people are gener ally motiv ated to “repair” unpleas ant moods through a variety of cognit ive strategies. We could also argue that clin ical patients are char- ac ter ised by malad apt ive inter pret a tional rules as well as abnor mal affect, partic u- larly with regard to self- percep tions, so that state mood meas ures are not a reli able guide to the emotion- related cogni tions of patients. The general disad vant age of the black board model is that it is diffi cult to falsify, since a new “emotional inter- pret a tion rule” can always be invoked to account for awkward data. It then becomes more a general frame work than a test able theory (Williams et al., 1988).
More recently, Bower (1992) has proposed that emotions may activ ate not just isol ated semantic concepts but rule- based action plans which have proved useful
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86 Affective bias in attention
in similar previ ous situ ations. We shall argue in Chapter 12 that the action plan concept is better suited to explain ing the exper i mental data on atten tional bias than the original asso ci at ive network model. However, Bower’s (1992) formu la- tion of the concept is too general to lend itself to predic tion of atten tional bias. In partic u lar, he does not specify the extent to which action plans are auto matic or controlled. As discussed in Chapter 2, rule- based processing systems may meet the oper a tional criteria for either type of processing (Ackerman, 1988), and theor et ical discrim in a tion of the two modes of control is essen tial for explain ing atten tional phenom ena (Norman & Shallice, 1985).
A network model for clin ical depres sion: Ingram (1984)
Ingram (1984) has elab or ated network theory specifi c ally to explain inform a tion processing in clin ical depres sion. Like Bower (1981), Ingram sees depres sion as asso ci ated with activ a tion of a depres sion node, caused, in general, by appraisal of life events asso ci ated with loss. Ingram extends the theory by consid er ing also the main ten ance of depres sion. He suggests that the depres sion node becomes asso ci at- ively linked in a loss- asso ci ated network with nodes repres ent ing recent events and cogni tions related to prior epis odes of depres sion. Activation may gener ate a
“cognit ive loop” (Clark & Isen, 1982), whereby activ a tion spreads through the network and feeds back into the depres sion node, main tain ing its activ a tion. In non- depress ives, the activ a tion level of the network decays over time, so that the person only exper i ences a mood, of short dura tion. In clin ical depress ives, there are various exacer bat ing factors which tend to prevent decay of network activ a- tion. For example, if the loss- asso ci ated network is partic u larly large and inter- con nec ted, neutral events may be appraised as depress ing (see Teasdale, 1988, for related points). Recycling of activ a tion through the network is described as auto- matic, with the proviso that it gener ates conscious cogni tions which demand atten tion and engage atten tional capa city. Ingram also emphas ises the import ance of voli tional control in inter rupt ing or modi fy ing the recyc ling cogni tions. The Ingram (1984) model accounts straight for wardly for the effects of depres sion on memory, since recyc ling serves to elab or ate the repres ent a tion of the mater ial in memory. It also explains the import ance of self- refer ence and active processing of the mater ial, since both will tend to strengthen asso ci ations with the loss- asso ci ated network. Qualitative differ ences between indi vidu als in the complex ity of the loss- asso ci ated network explain why mood states are not neces sar ily equi val ent to traits or clin ical disorders. Qualitative aspects of network func tion may also account for asym met ries between posit ive and negat ive mood effects. Isen (e.g. 1990) suggests that inform a tion asso ci ated with posit ive affect is repres en ted as a more extens ive network, leading to stronger effects of posit ive moods, and perform ance enhance ment on tasks related to network complex ity, such as creativ ity test perform ance.
Application of the model to atten tional studies is more diffi cult, though it can reas on ably explain greater distrac tion by stimuli likely to be activ ated by the
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Information- processing models of bias 87
network, as in the Stroop test. It remains unclear why depress ives do not reli ably show stronger facil it a tion of processing unpleas ant stimuli in simple percep tual tasks. There is also a theor et ical diffi culty in describ ing “auto matic” recyc ling as partly access ible to conscious ness, and atten tion- demand ing. On the face of it, the process described by Ingram might better be seen as partially auto mat ised, at an inter me di ate stage of a continuum of auto mati city. It would appear fairly simple to extend the model to anxiety, with worry ing serving to recycle activ a- tion through a network asso ci ated with threats in general or one partic u lar threat.
Current status of network models
In conclu sion, Bower’s (1981) network theory is of limited use in predict ing which types of inform a tion- processing task are most sens it ive to affect ive bias. It also fails to explain why trait and state emotion may have distinct effects on processing. We have seen that a more soph ist ic ated network model such as Ingram’s (1984) may be able to serve these purposes. The two main stum bling blocks appear to be distin guish ing the roles of auto matic spread ing activ a tion and controlled or strategy- driven processing, and explain ing the weak ness of affect ive bias effects on low- level encod ing. Moreover, patients and controls may differ in network prop er ties other than chronic activ a tion of emotion. Ingram (1984) rightly draws atten tion to the like li hood of differ ences in the strength and extent of excit at ory links between emotion and other nodes. As Matthews and Harley (1993) have shown, there are a variety of specifi c network para met ers which may account for indi vidual and group differ ences in inform a tion processing, includ ing para met ers govern ing rates of decay of activ a tion, level of random noise in the network, and strengths of connec tions between differ ent sets of units. The effects of differ ent para met ers can only be distin guished by integ rat ing simu la tion and exper i mental studies, which has yet to be done for affect ive bias.
Information- processing models of bias: Williams et al. (1988) The most fully developed altern at ive to network theory is the model of Williams et al. (1988). They distin guish differ ing biases asso ci ated with trait and state depres sion and anxiety, and locate them at differ ent stages within an inform a- tion- processing model of atten tion and memory. A schem atic repres ent a tion of the model is shown in Fig. 5.1. Anxiety effects are pre- attent ive: state anxiety increases the threat value assigned to the stim u lus, whereas trait (and clin ical) anxiety bias subsequent resource alloc a tion. Anxious subjects tend to divert resources to stimuli eval u ated as threat en ing, whereas non- anxious subjects pref- er en tially alloc ate resources to non- threat en ing stimuli. Depression infl u ences processing only after stim u lus iden ti fi c a tion, when atten ded stimuli are elab or at- ively processed; that is, further processing of the rela tion ships between stimuli, and between stimuli and context. State depres sion biases negat ive eval u ations of stimuli, whereas trait/clin ical depres sion facil it ates elab or a tion of negat ive
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88 Affective bias in attention
mater ial, again through a resource alloc a tion process. Because this model is the one which distin guishes most expli citly between differ ent atten tional processes, it deserves special consid er a tion. We also discuss briefl y a some what similar altern at ive model of this kind, Eysenck’s (1992) hyper vi gil ance theory.
There are three broad types of predic tion from the model to be considered.
Two of these are relat ively straight for ward empir ical predic tions, which were examined in detail in the previ ous chapter. The third predic tion concerns the atten tional mech an isms sens it ive to trait and state emotion, and requires more detailed discus sion. First, Williams et al. predict that anxiety and depres sion will affect qual it at ively differ ent tasks. Anxiety should infl u ence percep tion and atten- tion, whereas depres sion effects should be restric ted to tasks requir ing elab or a- tion, partic u larly memory tasks. We have seen previ ously that anxiety and depres sion effects are less distinct than the Williams et al. model predicts. Second, trait and state effects should be distinct, though inter act ing under many circum- stances. Ideally, it should be possible to demon strate double disso ci ations between trait and state effects on suit able tasks. For example, state but not trait anxiety should affect ratings of the threat value of stimuli, whereas trait but not state anxiety should predict alloc a tion of resources to a stim u lus of a subject ive threat value controlled across indi vidu als. In general, evid ence on this aspect of anxiety effects is lacking. We have seen that the predic tion of inter ac tion between trait
FIGURE 5.1 A processing stage model of anxiety and depres sion effects on atten tion (Williams et al., 1988).
STIMULUS
INPUT AFFECTIVE DECISION Threat MECHANISM value?
HIGH LOW
State anxiety may increase output of this system (mimics effect of high threat stimulus input)
RESOURCE ALLOCATION MECHANISM
Towards location of threat Away from location of threat (Perceptual cognitive avoidance)
Trait anxiety may represent permanent tendency to react to input from ADm by direction attention towards or away from location of threat
(a) Representation of how state and trait mood (e.g. anciety) may affect resource allocation at pre-attentive stage.
INPUT from PREATIENTIVE MECHANISMS
AFFECTIVE
DECISION Negativity?
MECHANISM
HIGH LOW
Transient mood (e.g. depression) may increase output of this system (mimics effect of highly negative stimulus)
Resource Allocation Mechanism
Greater elaboration Reduced elaboration (Elaborative cognitive avoidance)
Trait mood may represent permanent tendency to react to input from ADM by directing resources towards or away from item, resulting in facilitated or inhibited elaboration
(b) Representation of how state and trait mood (e.g. depression) may affect resource allocation of elaboration stage.