The Effect of Stress on Cognitive Structuring

Abstract

In this article, we outline a model of the factors involved in the relationship between stress and cognitive structuring. More specifically, we propose that the desire for certainty, the need for cognitive structure, and perceived efficacy at satisfying one’s epistemic needs intervene in the effect exerted by stress on cognitive structuring. We further suggest expanding the model to account for aspects of general information processing and to encompass the effect of various trait-like characteristics on the cognitive response to stress. We also offer an account of the possible effects of extreme stress on the model’s components.

Keywords

stress certainty need for structure efficacy at fulfilling epistemic needs cognitive structuring

Ample research demonstrates strong covariance between level of stress and cognitive structuring (Hancock & Vasmatzidis, 2003; Reich, Zautra, & Potter, 2001; Webster, Richter, & Kruglanski, 1996). Cognitive structuring is defined as “the creation and use of abstract mental representations (e.g., schemata, prototypes, scripts, attitudes, and stereotypes)―representations that are simplified generalizations of previous experience” (Neuberg & Newsom, 1993, p. 113). Cognitive structuring is often associated with “top down” holistic and rapid processing, with crudely differentiated categories and stereotypical thinking, premature black-and-white-type solutions, and over-simplified dichotomizations. Cognitive structuring is relatively automatic, effort-free, and faster than “bottom-up” piecemeal processing (Shiffrin & Schneider, 1977). The latter involves vigilant behavior, consisting of a systematic and effortful search for relevant information and its assimilation (Driscoll, Hamilton, & Sorrentino, 1991; Janis & Mann, 1977). In the process of cognitive structuring, inconsistent or irrelevant information is filtered out (Fiske & Linville, 1980; Koriat, Lichtenstein, & Fischhoff, 1980) and previously stored information is used (Fiske & Linville, 1980). It has been suggested that assimilating new information to preexisting categories is the cognitively easier default option when the person has no reason to discredit the existing categorization (Fiske, 1993; Neuberg & Newsom, 1993). The heuristic processing instances—such as premature closure of decision alternatives, a restricted use of relevant cues, cruder categories, more errors on cognitive tasks, and more schematic or stereotyped, found very often under stress judgments (e.g., Eysenck, 1982; Hancock, 1986; Keinan, Friedland, & Arad, 1991; Keinan, Friedland, & Even-Haim, 2000; Mikulincer, Kedem, & Paz, 1990; Svenson & Maule, 1993; Szalma & Hancock, 2011)—can be viewed as manifestation of cognitive structuring.

The aim of this article is to suggest a cognitive motivational model to account for the relationships between stress and cognitive structuring. To this end, we first examine the main existing theoretical accounts for the effect of stress on cognitive structuring. Then we present a model that may resolve the shortcomings of the existing models. We suggest that two types of mechanisms intervene the relationship between stress and cognitive structuring. The first has to do with coping with stress and the motivational implications of this coping (the desire for certainty). The second derives from a conceptualization suggested by Bar-Tal and colleagues (Bar-Tal, 1994a, 1994b; Bar-Tal, Kishon-Rabin, & Tabak, 1997; Bar-Tal & Kossowska, 2010; Bar-Tal, Raviv, & Spitzer, 1999). The basis for this conceptualization is epistemic needs and efficacy at fulfilling these needs. Specifically, we suggest that coping with stress requires achieving control and that one prerequisite of control is a sense of certainty. Furthermore, uncertainty may affect stress and coping directly and not only via the sense of control. Certainty is an essential component of people’s attempts to reduce the effect of psychological stress. To achieve certainty, in turn, requires cognitive structuring. Finally, we propose that cognitive structuring is a function of the combination of a person’s epistemic need (his or her need for cognitive structure [NCS]) and the person’s efficacy at fulfilling that need.

Capacity Resource Theory and Its Drawbacks

Several explanations have been proposed for the detrimental effect of stress on cognitive structuring. Easterbrook (1959), for example, proposed that emotional arousal reduces the range of cues that an individual uses. However, most of these explanations are partial, insufficiently developed, or relate to certain types of cognitive functioning only (Easterbrook, 1959; Janis & Mann, 1977).

A more comprehensive explanation, widely cited in the literature, derives from Capacity Resource Theory (CRT), which posits that stressor identification and appraisal, emotional and physiological stress reactions, and coping efforts all have cognitive representations that take up cognitive capacity (Mandler, 1993). Given that attentional resources are limited (Eysenck, 1982), their consumption by stress leaves less attention for task performance. To overcome the overload, people shift to the effortless and less resource-taxing approach of cognitive structuring, and this shift results in deficient performance (Svenson & Maule, 1993). Similarly, Kahneman (1973) has argued that the amount of cognitive capacity available has been assumed to be limited and a function of arousal level. Eysenck and Calvo (1992) also postulated that worries caused by anxiety reduce the storage and processing capacity of the working memory system available for a concurrent task. Furthermore, the allocation of resources for coping with stress and its outcomes lead people to adopt simplifying and superficial decision-making strategies that are not cognitively taxing (Friedland, Keinan, & Tytium, 1999; Hancock, Ross, & Szalma, 2007; Janis & Mann, 1977; Keinan et al., 1991). It follows from such CRT-derived explanations, therefore, that stress causes cognitive structuring.

Although its explanations are appealing, CRT has not escaped criticism. The theory has been criticized for being incompatible with the structure of the mind and brain (for a review, see Wells & Matthews, 1994). Furthermore, most experimental paradigms that purport to validate CRT in fact fail to do so and are not immune to alternative explanations of their findings (Navon & Miller, 1987). Although many other studies have validated the CRT (e.g., Pendry & Macrae, 1999), evidence suggests that people react in different and even contradictory ways to those predicted by the theory (Braunstein-Bercovitz, Dimentman-Ashkenazi, & Lubow, 2001; Chajut & Algom, 2003). Braunstein-Bercovitz et al. (2001), for example, demonstrated that stress may be associated with higher sensitivity to irrelevant information that implies an increase rather than decrease in the amount of processed information.

Some studies have shown that people may react to stress by making more commission errors (Domes, Heinrichs, Rimmele, Reichwald, & Hautzinger, 2004), by discriminating less between relevant and nonrelevant information, by lower and selective attention in general (Keinan, Friedland, Kahneman, & Roth, 1999; Muraven, Tice, & Baumeister, 1998), by less stereotyping (Gilbert & Hixon, 1991), and by increased reaction time (Ashcraft & Kirk, 2001). Moreover, whereas several kinds of stressors (e.g., heat, noise, fatigue, time pressure, and pain) have been demonstrated to decrease cognitive performance (Hancock & Vasmatzidis, 2003; Reich et al., 2001; Webster et al., 1996), others have had no effect or have even improved performance (Helton, Matthews, & Warm, 2009). Some studies have found that stress disturbed local processing more than global processing (van der Linden & Eling, 2006) whereas in other studies the opposite trend has emerged (Smith, 1985).

By showing that stress may sometimes increase rather than decrease effortful and nonschematic processing, these results challenge CRT’s postulates. Moreover, the CRT-based explanation for the stress–cognitive structuring link does not address individual differences in cognitive structuring behavior under stress, despite evidence showing that personality traits, needs, and skills that are not related to cognitive capacity can moderate the effect of stress on cognitive processing (Heaton & Kruglanski, 1991; Keinan et al., 2000).

Given CRT’s shortcomings, a revised model of the effect of stress on cognitive structuring is needed, and this article puts forward such a cognitive motivational model. According to this model, the style of information processing adopted under stress may be viewed as controllable and guided by personal motivation rather than as a relatively involuntary and automatic recourse. The model accounts for differences between individuals in cognitive structuring under stress and sets out the mechanisms that regulate cognitive structuring.

The Cognitive Motivational Model

The cognitive motivational model (see Figure 1) proceeds from the premises that coping with stress requires achieving control and that a prerequisite for control is a sense of certainty. Achieving certainty, in turn, requires cognitive structuring. We propose that cognitive structuring depends on the individual’s epistemic needs and his or her efficacy at meeting those needs (Bar-Tal, 1994a, 1994b, 2010; Bar-Tal et al., 1997; Bar-Tal et al., 1999; Bar-Tal & Kossowska, 2010). We now elaborate each of the model’s component elements.

Figure 1.

The cognitive motivational model

Stress Increases the Desire for Control

Sense of control has been defined as “an individual’s beliefs, at a given point in time, in his or her ability to effect a change, in a desired direction, on the environment” (Greenberger & Strasser, 1986, p. 165). Most theorists of stress and coping emphasize the causal role of a sense of control in the creation of stress and effective coping (Carver, Scheier, & Weintraub, 1989; Hobfoll, 1998; Lazarus & Folkman, 1984; Miller, 1990; Rosenbaum, 1983). For example, Lazarus and Folkman (1984) suggested that generalized beliefs about control influence both primary and secondary appraisals that, in their turn, determine the level of stress experienced. Threat appraisals (primary appraisal) are more likely when expectations for control in a given situation are lower than wished. By contrast, a greater sense of control reduces the perception of threat and induces a more optimistic appraisal of coping resources and options (secondary appraisal). A similar argument can be made to relate sense of control to coping. Coping can be characterized as an attempt to control threat appraisals by either controlling the distressing emotion (emotion-focused coping) or by managing the problem causing the distress (problem-focused coping). The lower a person’s confidence that coping efforts can reduce distress (i.e., the lower their sense of control), the less they will try to cope.

Whereas previous theoretical postulates have centered on the effect of sense of control on stress and coping, we wish to reverse that relationship and highlight the effect of stress and coping on the sense of control. We suggest that stress, by causing individuals to appraise a situation as threatening and appraise coping resources as less adequate, increases people’s desire for control. Keinan and colleagues (Friedland & Keinan, 1991; Friedland, Keinan, & Regev, 1992; Keinan, 1994, 2002; Keinan & Sivan, 2001) provide empirical support for this hypothesis. On the assumption that stress undermines the sense of control and thus endangers the perception of controllability, Friedland et al. (1992) demonstrated that highly stressed individuals, in contrast to those under less stress, prefer forms of gambling that heighten the perception of controllability. Similarly, Keinan and Sivan (2001) demonstrated that (a) people in high-stress conditions make more causal attributions than those in low-stress situations and (b) the effect of stress is more profound for participants with a high desire for control than for those with a low desire. Finally, on the hypothesis that superstitious beliefs may constitute a means to achieve control in uncontrollable situations (cf. Dudley, 1999), Keinan (1994) demonstrated that people under high-stress conditions (living in areas exposed to missile attack during the Gulf War) used magical thinking more frequently than people in low-stress conditions (residents living in areas not exposed to missile attacks). In sum, people under stress seem to experience loss of control and act in a variety of ways to regain a sense of control.

Stress Increases the Desire for Certainty

Stress and the motivation to cope with it increase the desire for certainty. The potential of uncertainty—a sense of insufficient knowledge about current or future events—as a stressor has long been recognized (Epstein, 1972; Greco & Roger, 2003; Kagan & Bar-Tal, 2008; Lazarus & Folkman, 1984). For example, Epstein (1972) suggested that uncertainty under stress may trigger anxiety. Katz and Wykes (1985) demonstrated that when participants do not have control over an aversive shock, they perceive predictable shocks (certainty) as less aversive than unpredictable ones (uncertainty). They also showed that autonomic indices of arousal were lower during the preshock signal for the predictable condition than during the equivalent period for the unpredictable condition. Similar results have been obtained by Greco and Roger (2003). Thus, the increased desire for certainty under stress may be related to the individual’s attempt to decrease the additional stress caused by the uncertainty.

The Relationship Between Control and Certainty

The ability to control one’s environment gives people certainty in their future, and vice versa. When the ability to influence or control events falls, uncertainty climbs. For example, Averill (1973) concluded from an extensive research review that a sense of behavioral control may be integrally linked to a greater sense of certainty. The relationship between control and certainty underlies coping theories such as those of Krohne’s (1986) and Miller’s (1990) (for a review see, Krohne, 1992). Miller’s monitoring–blunting hypothesis (1990) suggests that stress increases the desire for control and that control may be achieved via two cognitive strategies, one of which is monitoring (attempts to avoid uncertainty) and the other is blunting (reducing anxiety by avoiding threatening information).

In this context, Keinan (1994) demonstrated that tolerance of ambiguity moderates the relationship between psychological stress and magical thinking. People reacted to the loss of control caused by psychological stress with magical thinking in an effort to regain a sense of control, but people with high tolerance of ambiguity did not, demonstrating that people who are less bothered by uncertainty are less motivated to restore a sense of control. Case, Fitness, Cairns, and Stevenson (2004) obtained similar results.

To recapitulate, the cognitive motivational model proposes that stress increases the desire for certainty either by increasing the desire for control, which in turn increases the desire for certainty, or by directly increasing the motivation to increase certainty or reduce uncertainty.

The Role of Cognitive Structuring in Achieving Certainty

Achieving certainty involves two complementary processes—piecemeal information processing and cognitive structuring. Although, sometimes the two processes are characterized as discrete, we think that the two processes are better viewed as two extremes of one continuum rather than two discrete processes because people cannot achieve certainty without cognitive structuring. Even when information is processed piecemeal, information has to be structured at some point in the process. This postulate is consistent with Bunder’s (1962) conviction regarding the decisive role of cognitive structuring in achieving certainty. A similar position is advanced by the Lay Epistemological Theory (Kruglanski, Dechesne, Orehek, & Pierro, 2009), which asserts that epistemic “freezing” (a parallel concept to cognitive structuring) is essential for achieving certainty. Indeed, research findings validate the role of cognitive structuring in achieving certainty (Bar-Tal, 1994c; Mayseles & Kruglanski, 1987; Mishel, 1997).

Factors Affecting Cognitive Structuring

We have established, then, that certainty requires cognitive structuring. Understanding the factors that influence cognitive structuring may therefore explain how people achieve certainty. According to Bar-Tal’s cognitive motivational model of cognitive structuring (Bar-Tal, 1994a, 2010; Bar-Tal et al., 1997; Bar-Tal & Guinote, 2002; Bar-Tal & Kossowska, 2010; Kossowska & Bar-Tal, in press), cognitive structuring is affected by the interaction between the NCS and efficacy at satisfying this need (EFEN).¹

NCS

The motivational factor that predisposes people striving for certainty to deploy either piecemeal or cognitive structuring has long been a focus of psychological research (Bunder, 1962; Epstein, 1994; Kruglanski & Webster, 1996; Neuberg & Newsom, 1993). Theoretical elaborations may differ, but the common assumption is that the cognitive processes used by high-NCS individuals to reduce uncertainty are, typically, category based, nonsystematic, and heuristic, whereas low-NCS individuals prefer to reduce uncertainty by using piecemeal or individuating processes. Studies on NCS have demonstrated its effect on a wide variety of phenomena related to cognitive structuring, for example, sense of certainty, stereotyping, the use of cognitive biases, and primacy and recency effects (for review, see Epstein, 1994; Kruglanski & Webster, 1996; Neuberg & Newsom, 1993).

Both theory and empirical research into epistemic needs are based on the one imperative assumption that as cognitive structuring is so effortless (i.e., effortless to use, not necessarily to create), people can use it whenever they are motivated to do so (Fiske, 1993). Bar-Tal and colleagues (Bar-Tal, 1994a, 1994b, 2010; Bar-Tal et al., 1997; Bar-Tal et al., 1999) have challenged this assumption as an oversimplistic matching of need and efficacy. They suggest that NCS cannot alone determine the realization of cognitive structuring. Rather, they proposed that people wish to perceive themselves as capable of achieving cognitive structuring in light of their level of NCS. Some high-NCS individuals may regard themselves as unable to achieve certainty using cognitive structuring even when it is available to them. Similarly, low-NCS individuals may believe that they cannot achieve certainty using piecemeal processing. In other words, the fact that some people prefer to reduce their uncertainty by cognitive structuring does not necessarily mean that they feel capable of doing so. Similarly, the desire to reduce uncertainty by means of piecemeal processing may or may not be accompanied by the confidence that one has the skills to do so. People differ both in their efficacy at satisfying their epistemic needs (EFENs) and in the extent of the correspondence between their EFEN and NCS. EFEN thus signifies the extent to which individuals believe they are able to implement the information processing strategy (cognitive structuring or piecemeal) that is consistent with their level of NCS.

Efficacy¹ at satisfying epistemic needs (EFENs)

The idea behind EFEN is that people’s life experiences and situational factors engender in them expectations as to their ability to choose a path to certainty that matches their level of NCS. In other words, they differ in their perceived ability to realize their epistemic needs. When they expect to be able to satisfy their epistemic need (high EFEN), they tend to use piecemeal processing if their NCS is low and cognitive structuring if it is high. Put differently, high-EFEN people will use cognitive structuring or piecemeal processing, respectively, as their level of NCS predicts. However, when people expect to fail in achieving certainty using the process consistent with their level of NCS, they may revert to a process inconsistent with their NCS. Specifically, the model suggests that low EFEN leads high-NCS individuals to “fall back” on a more effortful bottom-up process and low-NCS individuals to “fall back” on effortless processing.

The idea that a high NCS may be associated with effortful information search rather than with effortless, category based, heuristic processing, seems counterintuitive. Nevertheless, Trumbo (1999) demonstrated that self-efficacy at decision making is positively associated with both heuristic and systematic processing. Participants with lower self-efficacy, on the other hand, tend to avoid using heuristic processing. This pattern can also be identified, theoretically, in Janis and Mann’s (1977) “hypervigilant” decision makers. These are persons who suffer from extreme uncertainty and who, consequently, shift rapidly and unsystematically between alternatives, paying attention indiscriminately to both relevant and irrelevant items of information. In the authors’ terminology, this pattern represents an ideal type (in the sociological sense). Strongly motivated to reach a clear-cut decision (high NCS) but lacking the confidence to use cognitive structuring (low EFEN), they engage in an unsystematic disorganized information search that exposes them to even more nonstructural information and greater uncertainty. Another example of this behavior pattern involves clinical and nonclinical obsessive-compulsive checkers. These are people who cannot tolerate uncertainty and ambiguity (high NCS; Gallagher, South, & Oltmanns, 2003) and who, even when faced with clear and unambiguous evidence, still suffer uncertainty (Dar, Rish, Hermesh, Fux, & Taub, 2000). Obsessive compulsives, for all their high NCS, do not use the cognitive structures clearly available to them to achieve certainty. However, low recourse to cognitive structuring does not, of itself, imply high recourse to piecemeal processing.

High-NCS/low-EFEN persons (low use of cognitive structuring) differ from low-NCS/high-EFEN persons (high use of piecemeal processing) in several ways. First, the former are less sensitive to information relevancy and so attend to and examine information more than the latter. Clark and Purdon (1993) suggested that, whereas in normal cognitive processing irrelevant thoughts are simply ignored, individuals with obsessive-compulsive disorders do pay them attention (for similar findings, see Kaplan et al., 2006). Along these lines, Kossowska and Bar-Tal (in press) demonstrated that for low-EFEN individuals, there is positive correlation between their levels of NCS and their recall of irrelevant information. This relationship was opposite in high efficacy at fulfilling need for closure individuals.

The difference between low-NCS/high EFEN and high NCS/low EFEN individuals relates also to the different level of control felt by the two groups. According to Bar-Tal (1994a, 1994b), perceived control is affected by one’s level of EFEN. Even if both low-NCS/high-EFEN and high-NCS/low-EFEN persons experience uncertainty (because of the low use of cognitive structuring typical of both), the former feel more in control (this feeling is so central to cognitive functioning in general that it spills over into other life experiences).

The third combination of NCS and EFEN in the model proposed here is high NCS/high EFEN. When EFEN is high, NCS and level of cognitive structure are positively correlated, so that high NCS/high EFEN generates high use of cognitive structuring (exactly as low NCS/high EFEN results in low use of cognitive structuring and high use of piecemeal processing).

The fourth combination is low NCS/low EFEN, a less surprising pairing than high NCS/low EFEN because it is easier to conceive of people who find it difficult to use piecemeal processes even when motivated to do so. Many studies have shown that resource depletion or task difficulty make it harder to satisfy the desire for accuracy (low NCS; Ford & Kruglanski, 1995). Furthermore, Bohner, Rank, Reinhard, Einwiller, and Erb (1998) demonstrated that a person’s belief about efficacy is enough on its own to moderate the effect of the desire for accuracy on effortful processing. Likewise, under the model proposed here, it is not essential to assume that resource depletion explains why low-NCS individuals avoid piecemeal processing: one’s perception or belief about it is quite enough to produce the effect.

However, in the model proposed here, although low NCS/low EFEN individuals tend to use effortless cognitive structuring processing, the result will not necessarily be the same as when high NCS/high EFEN individuals use it. The difference between the two groups can best be described using Dickman’s (1990) distinction between functional and dysfunctional impulsivities. Dickman defined impulsivity as the tendency to deliberate less before acting than most people of equal ability, and distinguished between functional and dysfunctional impulsivities. Functional impulsivity is the tendency to act with relatively little forethought when rapid response is required and/or there is little cost to error. It can be equated to using cognitive structuring when that is indeed the strategy required. Impulsivity becomes dysfunctional when it is not the appropriate strategy, when it brings trouble, or when piecemeal processing would be more effective than cognitive structuring. In terms of our model, high functional impulsivity implies cognitive structuring when NCS is high (i.e., high EFEN) and high dysfunctional impulsivity implies high cognitive structuring when NCS is low (low EFEN). Thus, although both groups achieve certainty easily (because of their extensive use of cognitive structuring), only for the high NCS/high EFEN group does certainty comes together with a sense of great control.

The four NCS-EFEN pairings thus predict that NCS and EFEN exert a disordinal interaction effect on the use of cognitive structuring and sense of certainty. Under high EFEN, the level of NCS will be positively correlated with level of cognitive structuring use and certainty, but under low EFEN, the level of NCS will be negatively correlated with these two variables. Several studies endorse the above prediction (Bar-Tal, 1994a, 1994b, 2010; Bar-Tal et al., 1997; Bar-Tal & Guinote, 2002; Bar-Tal & Kossowska, 2010; Kossowska & Bar-Tal, in press; Muluk, 2010; Otten & Bar-Tal, 2002). Study 1 of Bar-Tal et al. (1997), for example, demonstrated that, for participants with low EFEN, cognitive structuring decreases as NCS rises, whereas for high-EFEN participants, it increases as NCS rises. These results were replicated by the remaining studies in this investigation under a range of operationalizations of the independent and dependent variables. Study 1 also validated the hypothesis that low NCS/high EFEN (vigilance) predicts high piecemeal processing use, whereas high NCS/low EFEN (hypervigilance) predicts effortful processing, as implied by a low use of cognitive structuring. The researchers also found that inconsistent information was recalled better by vigilant participants (because of its diagnostic value), while irrelevant information was recalled better by hypervigilant participants. The fact that these results were obtained when cognitive structure (schema) was available to participants implies that the difficulty of the high NCS/low EFEN to use cognitive structuring does not reflect merely their difficulty either to construct abstract categories or to assimilate information to preexisting categories, but the difficulty to use the cognitive structures even when it is available to them.

The Moderating Effect of EFEN and NCS on the Relationship Between Stress and Cognitive Structuring

Given the role of cognitive structuring in achieving the certainty essential to coping with stress, and given the effect of NCS and EFEN on cognitive structuring, the present model proposes a three-way interaction among stress, NCS, and EFEN. Four studies have been carried out to demonstrate that EFEN and NCS moderate the relationship between stress and the cognitive reaction to it (see Bar-Tal et al., 1999). In these studies, the independent variable (stress), the dependent variable (cognitive structuring measured by self-rated decision conflict and difficulty, the filtering out of schema-inconsistent information, and categorization), and the moderating variables (NCS and EFEN) were all operationalized in different ways, but all four studies confirmed a significant three-way interaction among stress, EFEN, and NCS. Their results showed that for high-EFEN participants, the higher their NCS, the more stress increased their use of cognitive structuring and effortless processing (i.e., they used cruder categories, preferred schema-consistent information, etc.). In contrast, for low-EFEN participants, higher NCS was associated with a positive correlation between stress and effortful information processing (i.e., participants preferred schema-inconsistent information and used more refined categories, and stress had a stronger effect in making decisions more difficult and more time-consuming).

This completes our presentation of our model’s main constructs. Next, we would like to emphasize three major characteristics of the cognitive motivational model that distinguishes it from previous theoretical attempts to explain the relationship between stress and cognitive structuring.

The Cognitive Motivational Model Accounts for Different Types of Cognitive Response to Stress

As noted earlier, cognitive structuring is not the only possible response to stress. Four categories of cognitive processing are possible: vigilance, cognitive structuring, dysfunctional impulsivity, and hypervigilance. To date, most studies that have examined the effect of stress on cognitive performance have assumed, implicitly or explicitly, that the ability to react to stress with vigilance is explained by the “vigilant” persons’ better coping abilities and their experiencing lower stress. Hancock and Vasmatzidis (2003) claimed this most explicitly by arguing that workers’ cognitive performance (vigilant processing) is a more sensitive measure of their state of stress than even a direct measure of their reported stress.

The model put forward here proposes that sustained vigilance under stress is not necessarily the fruit of lower stress. In our model, the groups that exhibit the most cognitive structuring (over fast responses, higher rates of omission errors, and stereotypic responses, etc.) are the high-NCS/high-EFEN and the low-NCS/low-EFEN groups, which are also the groups that feel the least stress (Bar-Tal et al., 1999). Supporting evidence for the idea that the cognitive structuring following stress may decrease the experienced load comes from Webster, Kruglanski, and Pattison (1997, Study 2), who demonstrated that participants under stress (operationalized as ambient noise) reported that a cognitive task required less thought and was easier to complete than did participants performing under less stressful conditions. Participants under stress also reported higher confidence in their judgment than participants under less stress. According to our interpretation, the participants in Webster et al.’s study had both NCS and EFEN high, and therefore, under stress were able to achieve certainty faster using cognitive structuring.

The model proposed here not only predicts which persons will maintain vigilance under stress but also the pattern of mistakes that will probably characterize nonvigilant responses. The Janis and Mann (1977) hypervigilance construct has been widely adopted. But some have made it contain the whole range of flawed decision-making processes and products, including instances that clearly belong to cognitive structuring (see Baradell & Klein, 1993; Keinan, 1987). Johnston, Driskell, and Salas (1997), for example, stated: “In contrast to vigilant decision making, a hypervigilant pattern of decision making is characterized by (a) a nonsystematic or selective information search, (b) consideration of limited alternatives, (c) rapid evaluation of data, and (d) selection of a solution without extensive review or reappraisal” (p. 614). Our model distinguishes between those who respond with omission errors, rapid response, heuristic and stereotypic thinking (cognitive structuring) and those who respond with higher rates of commission errors, slower reaction time, insufficient information filtering, attention to irrelevant and inconsistent information, and the preservation and disorderly examination of available choices (hypervigilance). Moreover, the above distinctions may also shed new light on the mystery of the effect of stress on speed-accuracy trade-off (MacKay, 1982). It may explain when and why reduction in speed does not increase accuracy, or when reduction in accuracy does not increase speed.

The Role of Motivation

The second significant characteristic of the present model is its heavy reliance on cognitive motivational factors to explain the impact of stress on cognitive processing. That is, rather than basing the explanation on hypothetical inherent shortcomings of human cognition, we suggest a conception that emphasizes the motivational bases of the phenomenon. The idea that the style of information processing under stress is controllable and guided by personal motivation rather than by relatively involuntary and automatic processes is not new (e.g., Hockey, 1986; Kruglanski, 1989). Kruglanski and colleagues (Ford & Kruglanski, 1995; Kruglanski et al., 2009; Kruglanski & Webster, 1996) claimed that stress in general, and cognitive busyness in particular, increases motivation for closure. They argued that the more that stress or cognitive load make information-processing effortful, the higher the cost of that processing becomes. As a result, people will try to cut back their cognitive effort and look instead for swift closure, for instance, by resorting to preexisting knowledge structures. However, our approach differs in at least two major respects from Kruglanski’s Lay Epistemology Theory. First, we propose that stress affects a person’s motivation for certainty directly rather than their desire for cognitive structuring, and second, and more importantly, that efficacy at satisfying epistemic needs moderates the effect of NCS on information processing. It is important to note that the efficacy component of our model does not necessarily represent objective limitations of the individuals, but rather their perception of the resources available to them and/or their chances of achieving their epistemic goal.

Another theory that includes a motivational component is the attentional control theory, a recent theory concerning the relationship between stress and general cognitive functioning (Eysenck, Derakshan, Santos, & Calvo, 2007). According to attentional control theory, worry is the main ingredient of the anxiety that decreases cognitive processing. Worry has two effects. Worrisome thoughts consume the limited attentional resources of working memory, which are therefore less available for concurrent task processing. The motivational component in the theory consists of the idea that worry also increases motivation to minimize the aversive anxiety state. The theory also proposes that performance impairments can be compensated for by an increased use of processing resources. Thus, the theory makes a distinction between effectiveness and efficiency. Effectiveness, it says, refers to the quality of task performance, whereas efficiency refers to the relationship between the effectiveness of performance and the effort or resources spent in task performance. According to the theory, anxiety impairs efficiency more than it does effectiveness. Similarly, Hockey’s (1997) cognitive-energetical framework also differentiates between effectiveness and efficiency. This framework maintains that effectiveness in performing primary tasks may be protected from decrements under stress by the recruitment of further resources. However, avoiding performance decrements comes at the expense of decreased efficiency due to psychophysiological activation and the use of less efficient strategies.

The cognitive motivational model links up with some of the ideas proposed by attentional control theory and the cognitive-energetical framework. Worrisome thoughts, the model suggests, can be seen as a direct product of uncertainty or an indirect product of the reduced sense of control that is the result of uncertainty. The model thus emphasizes the effect of stress on motivation by shifting the focus to the desire for certainty. Furthermore, the NCS × EFEN interaction helps to distinguish between when efficiency is impaired mainly due to making additional effort (as in the case of low NCS-high EFEN) and when it is due to impaired performance effectiveness (as in the case of low NCS-low EFEN).

Another similarity between the cognitive motivational model and the attentional control theory is not only in epistemological factors involved in the process but also in the attention invested in psychological factors. Thus, while the attentional control theory explains the function of the worrisome thoughts, the cognitive motivational model explains how the effect of stress on cognitive processing is integral to the cognitive aspects of stress without relying on unrelated concepts, such as the limited capacity of cognitive systems. Its primary explanatory factors derive from the stress process and are relevant to the psychoemotional outcome of this process (Bar-Tal et al., 1999). Thus, for example, the model explains how the greater use of cognitive structuring by high-NCS individuals is associated with lower uncertainty, higher control, and less anxiety (i.e., it can reflect effective coping). The model also predicts situations where the greater use of cognitive structuring under stress may produce higher certainty but a lower sense of control (dysfunctional impulsivity; low NCS /low EFEN).

The Role of Personality Traits in the Process

The strong emphasis on motivation in our theory suggests that personality traits may play an important role in the cognitive response to stress. Dispositions such as anxiety (Bolmont, Bouquet, & Thullier, 2001), Type A (Perry & Laurie, 1992), locus of control (Judge & Bono, 2001), self-focus (Baradell & Klein, 1993), desire for control (Keinan, 2002; Keinan & Sivan, 2001), field dependence (Bernardi, 2003), and NCS (Ford & Kruglanski, 1995) moderate the stress–cognition relationship. The same is true for more general and basic personality constructs, such as extraversion and conscientiousness (Rose, Murphy, Byard, & Nikzad, 2002).

As there are no empirical evidence or even theoretical claims that all these personality traits directly affect an individual’s capacity or resources, their effect is explained here in terms of their association with either stress and/or coping ability, or directly with cognitive structuring (Rose et al., 2002). For example, it is relatively easy to see how trait anxiety relates to how stress is experienced, because (a) trait anxiety makes a situation seem more threatening (Williams, Watts, MacLeod, & Mathews, 1997) and (b) appraising a situation as threatening induces stress (Lazarus & Folkman, 1984). Indeed, the moderating effect of trait anxiety on the association between stress and performance has been well established (Wofford & Goodwin, 2002). Similarly, self-focus is associated with higher awareness of emotional states (McFarland & Buehler, 1998) and thus may accentuate both the experience of stress (Mor & Winquist, 2002) and the reaction to it (Panayiotou & Vrona, 1998).

The model laid out here does not deny that personality traits moderate the stress–cognitive structuring relationship via their impact on either the experience of stress or cognitive performance. It does posit, however, an additional path by which this influence may be explained, namely, by its effect on the mediating/moderating factors put forward in our theory (desire for certainty, NCS, and EFEN). For example, given that self-focus increases the motivation to reduce discrepancies within the self (Duval & Wicklund, 1972) and that inconsistency is an obstacle to validating a given premise with certainty (Kruglanski, 1989), we may conclude that self-focus increases the desire for certainty. In addition, the greater intolerance of discrepancy associated with self-focus may represent a greater intolerance of ambiguity (higher NCS), which in turn makes self-focus potentially relevant to the stress–cognitive performance relationship by (a) amplifying the effect of the interaction between NCS and EFEN (if self-focus increases the desire for certainty), (b) increasing the use of cognitive structuring (if self-focus increases an individual’s NCS and the individual possesses a sufficiently high EFEN), or (c) hypervigilance (if self-focus increases the individual’s NCS but the individual does not possess sufficient EFEN).

A similar analysis can be applied to another general personality trait, neuroticism, which is related to Gray’s Behavioral Inhibition System (BIS; Avila & Parcet, 2001). Gray (1982) proposed the existence of a BIS located in the septohippocampal system, whose function is to tag certain stimuli as “important” (i.e., to select and categorize stimuli and so facilitate their analysis). According to Gray, a hyperreactive BIS is associated with less ability to ignore irrelevant information and focus attention on selected and essential situational characteristics. Bar-Tal et al. (1997) proposed that BIS is related to the Ability to Achieve Cognitive Structure (AACS), making it reasonable to assume that BIS is related to both actual and the perceived ability to fulfill epistemic needs (EFEN). In terms of our model, neuroticism may moderate the relationship between stress and cognitive processing via its effect on EFEN: Neuroticism is associated with decreased EFEN. Under stress, such an effect causes hypervigilance and this is most likely to happen to individuals who have high NCS.

Possible Exceptions to the Moderating Effect of EFEN and NCS on the Relationship Between Stress and Cognitive Structuring

The model we propose focuses on the moderating effect of EFEN and NCS on the relationship between stress and information processing. However, the relationship between extreme stress and cognitive processing may go in other directions too. One possibility is that extreme stress will reduce EFEN. It is possible that EFEN is not only a trait-like characteristic but also manipulable by situational variables. For instance, Bar-Tal and Kossowska (2011) recently demonstrated that positive mood increases the consistency between participants’ NCS and their cognitive structuring behavior. The authors suggested that a positive mood increases participants’ efficacy at using the cognitive processing behavior consistent with their level of NCS. They also suggested that a strong negative mood has the opposite effect of reducing participants’ EFEN. Thus, positive mood may increase individuals’ efficacy at satisfying the epistemic need that in turn helps them to achieve sense of control and reducing the experienced stress (Ong, Bergeman, & Bisconti, 2006). At the same time, positive mood may increase heuristic processing that helps people filter out anxiety-evoking cognitions and intrusive thoughts (Tugade & Fredrickson, 2004). However, in the case of low-NCS individuals, positive mood may increase piecemeal processing (Fredrickson & Branigan, 2005). Yet, the effect of positive mood on the sense of control of low-NCS individuals may be similar to its effects on high-NCS individuals.

Another example of state-like effect on EFEN can be drawn from the learned helplessness training (LHT) paradigm (Kofta & Sedek, 1989). Kofta and Sedek argued that LHT impairs the ability to use cognitive structures. However, Bar-Tal demonstrated that LHT is not necessarily associated with less use of cognitive structuring but rather with less efficacy at satisfying epistemic needs (low EFEN; see Otten & Bar-Tal, 2002). We suggest that stress may reduce EFEN in an analogous manner by reducing people’s sense of control. Under stress, the result of secondary appraisals, which accentuate one’s inadequacy to cope with severe stressors, may be generalized to one’s efficacy at satisfying epistemic needs. Extreme stress may thus generate a simple interaction between stress and NCS rather than the three-way interaction described earlier.

A further possibility is that stress, as well as affecting the motivation for certainty, may also impact on a person’s NCS, or at least on its saliency. Several theorists suggest that the major mediating process between stress and cognitive performance is the effect of stress on NCS (Kruglanski, 1989; Kruglanski & Webster, 1996). Although the lack of empirical evidence makes it impossible to decide whether and to what extent stress impacts on the desire for certainty or directly impacts on the level of NCS, it is again possible that the effect of stress on NCS may generate a two-way interaction (Stress × EFEN), rather than the three-way interaction mentioned earlier. The idea that extreme stress generates a simple interaction rather than a three-way interaction corresponds with predictions made by the dynamic model of stress and sustained attention (Hancock & Warm, 1989). The model predicts lower variability in coping behaviors under extreme stress. Hancock and Warm proposed that under extreme stress, when there is a reduction of available attentional capacity and an increase in physiological strain, the number of avenues open to achieve desired goals decreases and there is a tendency toward stereotypical behavior.

The existence of other factors that may affect NCS or EFEN may imply, however, that a higher order interaction will be found. For example, Klein (2008) showed that experienced decision makers use their expertise in a specific way to make tough decisions under difficult conditions. These people generally look for the first workable solution to the problem rather than trying to find the best possible solution, yet they also use mental simulation to imagine how that solution would play out within the context of the specific situation. That is, expertise in this case seems to affect NCS and EFEN. Similarly, other factors such as mood (Bar-Tal & Kossowska, 2011), resource depletion, or aging (Kossowska, Jasko, Bar-Tal, & Szastok, 2012) can be shown to have a direct effect on NCS or EFEN. In other words, even under extreme stress, it is possible that a higher order interaction would be needed to explain the effect of stress on cognitive structuring.

Conclusions

We have put forward a new model of the processes and factors involved in the cognitive response to stress, a model that avoids the drawbacks of previous conceptualizations of stress and cognitive processes. We propose that the cognitive response to stress is triggered by an increased desire for certainty. Achieving certainty requires cognitive structuring, and how and to what degree this structuring is achieved is determined by the NCS of people under stress and their EFENs. Our model proposes, therefore, that the constructs that mediate and moderate the effect of stress on cognitive processing are (a) motivation (the desire for certainty) and (b) two variables that influence cognitive structuring—NCS and EFEN.

We further suggest that certain factors that affect the stress–cognitive structuring relationship can be explained in terms of the concepts employed by the proposed model (stress, coping, desire for certainty, perceived control, NCS, EFEN, and cognitive structuring). The model also accounts for the possible effects of extreme stress on its component variables. These effects may explain the circumstances under which lower level effects (simple interactions or even main effects) may be found, instead of the three-way interaction the model predicts in other cases. These predictions should fuel future research into the interrelationships between personality, motivation, and cognitive structuring, as well as into more general features of information processing under stress.

Finally, although the present article focuses on the effect of stress on cognitive structuring and it does not directly address the question of the effect of stress on cognitive and noncognitive performance, it is still interesting to examine the implication of the presented model to the explanation of these effects. There is ample research showing that stress may have negative impact on a large variety of cognitive, behavioral, functional, and health-related outcomes (Arora et al., 2010; Segerstrom & Miller, 2004). The most direct application of the present framework to the explanation of the effect of stress on various life aspects is in its emphasis on the role of NCS and EFEN in the stress process. Given the importance of certainty and sense of control in all aspects of the stress process (Lazarus & Folkman, 1984), and the explanations derived from our cognitive motivational model, it is only reasonable to assume that our model may explain much of the variation of the effects of stress on performance found in the literature. Thus, by applying our ideas to Lazarus’ stress theory, we may explain some of the variance of people’s responses to objective stress occurrences and the effectiveness of their coping with the stress. It is possible that many of the variables found to moderate the effect of stress on performance (e.g., social support, sense of coherence, locus of control, hardiness, self-efficacy, intolerance of ambiguity, personal mastery, spirituality, biological sex, Type A behavior pattern, cognitive rigidity, etc.) may be explained in terms of their direct effect on the individuals’ uncertainty or sense of control, or their effect on NCS and EFEN, which in turn affect certainty and sense of control in the situation. For example, Spitzer, Bar-Tal, and Golander (1995) demonstrated that the effect of social support on the stress process can be explained in terms of its effect on the individual’s sense of control. Also, Mishel (1997) suggested that social support decreases uncertainty caused by the state of illness. Bar-Tal (1994c) established that cognitive structuring may affect the perception of the extent of social support and control a person has. Thus, cognitive structuring may have an important role in reducing the effect of stress that in turn may affect the person’s performance. It is, however, important to remember that when high performance requires systematic and effortful cognitive processes, reduced performance manifested in cognitive structuring is sometimes a result of very effective coping. Similarly, maintaining a high level of performance (in vigilance tasks) does not necessarily imply lower stress or better coping. Our model suggests, for example, that, for high-NCS/low-EFEN individuals, highly vigilant behavior may be associated with high stress and feelings of helplessness. It is also important to remember that sometimes better performance requires cognitive structuring rather than systematic processing. For example, performance of tasks by experts is considered superior because of the greater use of cognitive structuring (Gobet & Simon, 1996). Consequently, the greater use of cognitive structuring by experts under stress may be accompanied with a better performance (Arora et al., 2010). Hence, rather than attempting to explain the effect of stress on performance we centered on the explanation of the effect of stress on cognitive structuring.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Notes

References

Arora

Sevdalis

Nestel

Woloshynowych

Darzi

Kneebone

(2010). The impact of stress on surgical performance: A systematic review of literature. Surgery, 147, 318-330.

Ashcraft

M. A.

Kirk

E. P.

(2001). The relationship among working memory, math anxiety, and performance. Journal of Experimental Psychology: General, 130, 224-237.

Averill

J. R.

(1973). Personal control over aversive stimuli and its relationship to stress. Psychological Bulletin, 80, 286-303.

Avila

Parcet

M. A.

(2001). Personality and inhibition deficit in the stop-signal task: The mediating role of Gray’s anxiety and impulsivity. Personality and Individual Differences, 31, 975-986.

Baradell

J. G.

Klein

(1993). Relationship of life stress and body consciousness to hypervigilant decision making. Journal of Personality and Social psychology, 64, 267-273.

Bar-Tal

(1994a). The effect of need and ability to achieve cognitive structure on mundane decision making. European Journal of Personality, 8, 45-58.

Bar-Tal

(1994b). Monitoring, blunting, and the ability to achieve cognitive structure. Anxiety, Stress, & Coping, 6, 265-274.

Bar-Tal

(l994c). Uncertainty and the perception of sufficiency of social support, control and information. Psychological Record, 44, 13-24.

Bar-Tal

(2010). When the need for cognitive structure does not cause heuristic thinking: The moderating effect of perceived ability to achieve cognitive structure. Psychology, 1, 96-105.

10.

Bar-Tal

Guinote

(2002). Who exhibits more stereotypical thinking? The effect of need and ability to achieve cognitive structure on stereotyping. European Journal of Personality, 16, 313-331.

11.

Bar-Tal

Kishon-Rabin

Tabak

(1997). The effect of need and ability to achieve cognitive structure on cognitive structuring. Journal of Personality and Social Psychology, 73, 1158-1176.

12.

Bar-Tal

Kossowska

(2010). The efficacy at fulfilling need for closure: The concept and its measurement. In Villanueva

J. P.

(Ed.), Personality traits: Classification, effects and changes (pp. 47-64). New York, NY: Nova Publishers.

13.

Bar-Tal

Kossowska

(2011). A positive mood enhances efficacy at meeting epistemic needs. Unpublished manuscript, Tel Aviv University, Israel.

14.

Bar-Tal

Raviv

Spitzer

(1999). Individual differences that moderate the effect of stress on information processing. Journal of Personality and Social Psychology, 77, 33-51.

15.

Bernardi

R. A.

(2003). Students’ performance in accounting: Differential effect of field dependence-independence as a learning style. Psychological Reports, 93, 135-142.

16.

Bohner

Rank

Reinhard

M. A.

Einwiller

Erb

H. P.

(1998). Motivational determinants of systematic processing: Expectancy moderates effects of desired confidence on processing effort. European Journal of Social Psychology, 28, 185-206.

17.

Bolmont

Bouquet

Thullier

(2001). Relationship of personality traits with performance in reaction time, psychomotor ability, and mental efficiency during a 31-day simulated climb of Mount Everest in a hypobaric chamber. Perceptual & Motor Skills, 92, 1022-1030.

18.

Braunstein-Bercovitz

Dimentman-Ashkenazi

Lubow

R. E.

(2001). Stress affects the selection of relevant from irrelevant stimuli. Emotion, 1, 182-192.

19.

Bunder

(1962). Intolerance of ambiguity as a personality variable. Journal of Personality, 30, 29-51.

20.

Carver

C. S.

Scheier

M. F.

Weintraub

J. K.

(1989). Assessing coping strategies: A theoretically based approach. Journal of Personality and Social Psychology, 56, 267-283.

21.

Case

T. I.

Fitness

Cairns

D. R.

Stevenson

R. J.

(2004). Coping with uncertainty: Superstitious strategies and secondary control. Journal of Applied Social Psychology, 34, 848-871.

22.

Chajut

Algom

(2003). Selective attention under stress: Implications for theories of social cognition. Journal of Personality and Social Psychology, 85, 231-248.

23.

Clark

D. A.

Purdon

(1993). New perspectives for a cognitive theory of obsessions. Australian Psychologist, 28, 161-167.

24.

Clarkson

J. J.

Hirt

E. R.

Jia

Alexander

M. B.

(2010). When perception is more than reality: The effects of perceived versus actual resource depletion on self-regulatory behavior. Journal of Personality and Social Psychology, 98, 29-46.

25.

Dar

Rish

Hermesh

Fux

Taub

(2000). Realism of confidence in obsessive-compulsive checkers. Journal of Abnormal Psychology, 109, 673-678.

26.

Dickman

S. J.

(1990). Functional and dysfunctional impulsivity: Personality and cognitive correlates. Journal of Personality and Social Psychology, 58, 95-102.

27.

Domes

Heinrichs

Rimmele

Reichwald

Hautzinger

(2004). Acute stress impairs recognition for positive words–association with stress-induced cortisol secretion. Stress: The International Journal on the Biology of Stress, 7, 173-181.

28.

Driscoll

D. M.

Hamilton

D. L.

Sorrentino

R. M.

(1991). Uncertainty-orientation and recall of person-descriptive information. Personality and Social Psychology Bulletin, 17, 494-500.

29.

Dudley

R. T.

(1999). The effect of superstitious belief on performance following an unsolvable problem. Personality and Individual Differences, 26, 1057-1064.

30.

Duval

Wicklund

(1972). A theory of objective self-awareness. New York, NY: Academic Press.

31.

Easterbrook

J. A.

(1959). The effect of emotion on cue utilization and the organization of behavior. Psychological Review, 66, 183-201.

32.

Epstein

(1972). The nature of anxiety with emphasis upon its relationship to expectancy. In Spielberger

C. D.

(Ed.), Anxiety: Current trends in theory and research ( Vol.2, pp. 291-337). New York, NY: Academic Press.

33.

Epstein

(1994). Integration of the cognitive and the psychodynamic unconscious. American Psychologist, 49, 709-724.

34.

Eysenck

M. W.

(1982). Attention and arousal: Cognition and performance. Berlin, Germany: Springer.

35.

Eysenck

M. W.

Calvo

M. G.

(1992). Anxiety and performance: The processing efficiency theory. Cognition & Emotion, 6, 409-434.

36.

Eysenck

M. W.

Derakshan

Santos

Calvo

M. G.

(2007). Anxiety and cognitive performance: Attentional control theory. Emotion, 7, 336-353.

37.

Fiske

S. T.

(1993). Social cognition and social perception. Annual Review of Psychology, 44, 155-194.

38.

Fiske

S. T.

Linville

P. W.

(1980). What does the schema concept buy us? Personality and Social Psychology Bulletin, 6, 543-557.

39.

Ford

T. E.

Kruglanski

A. W.

(1995). Effects of epistemic motivations on the use of accessible constructs in social judgment. Personality and Social Psychology Bulletin, 21, 950-962.

40.

Fredrickson

B. L.

Branigan

(2005). Positive emotions broaden the scope of attention and thought-action repertoires. Cognition & Emotion, 19, 313-332.

41.

Friedland

Keinan

(1991). The effects of stress, ambiguity tolerance, and trait anxiety on the formation of causal relationships. Journal of Research in Personality, 25, 88-107.

42.

Friedland

Keinan

Regev

(1992). Controlling the uncontrollable: Effects of stress on illusory control. Journal of Personality and Social Psychology, 63, 923-931.

43.

Friedland

Keinan

Tytium

(1999). The effects of psychological stress and tolerance of ambiguity on stereotypic attributions. Anxiety, Stress & Coping, 12, 397-410.

44.

Gallagher

N. G.

South

S. C.

Oltmanns

T. F.

(2003). Attentional coping style in obsessive-compulsive personality disorder: A test of the intolerance of uncertainty hypothesis. Personality and Individual Differences, 34, 41-57.

45.

Gilbert

D. T.

Hixon

J. G.

(1991). The trouble of thinking: Activation and application of stereotypic beliefs. Journal of Personality and Social Psychology, 60, 509-517.

46.

Gobet

Simon

H. A.

(1996). The roles of recognition processes and look-ahead search in time-constrained expert problem solving: Evidence from grand-master-level chess. Psychological Science, 7, 52-55.

47.

Gray

J. A.

(1982). The neuropsychology of anxiety: An enquiry into the functions of the septo-hippocampal system. New York, NY: Oxford University Press.

48.

Greco

Roger

(2003). Uncertainty, stress, and health. Personality and Individual Differences, 34, 1057-1068.

49.

Greenberger

Strasser

(1986). Development and application of a model of personal control in organizations. Academy of Management Review, 11, 164-177.

50.

Hancock

P. A.

(1986). Sustained attention under thermal stress. Psychological Bulletin, 99, 263-281.

51.

Hancock

P. A.

Ross

J. M.

Szalma

J. L.

(2007). A meta-analysis of performance response under thermal stressors. Human Factors, 49, 851-877.

52.

Hancock

P. A.

Vasmatzidis

(2003). Effects of heat stress on cognitive performance: The current state of knowledge. International Journal of Hyperthermia, 19, 355-372.

53.

Hancock

P. A.

Warm

J. S.

(1989). A dynamic model of stress and sustained attention. Human Factors, 31, 519-537.

54.

Heaton

A. W.

Kruglanski

A. W.

(1991). Person perception by introverts and extraverts under time pressure: Effects of need for closure. Personality and social Psychology Bulletin, 17, 161-165.

55.

Helton

W. S.

Matthews

Warm

J. S.

(2009). Stress state mediation between environmental variables and performance: The case of noise and vigilance. Acta Psychologica, 130, 204-213.

56.

Hobfoll

S. E.

(1998). Stress, culture, and community: The psychology and philosophy of stress. New York, NY: Plenum.

57.

Hockey

G. R. J.

(1986). A state control theory of adaptation and individual differences in stress management. In Hockey

G. R. J.

Gaillard

A. W. K.

Coles

M. G. H.

(Eds.), Energetics and human information processing (pp. 285-298). Dordrecht, Netherlands: Martinus Nijhoff.

58.

Hockey

G. R. J.

(1997). Compensatory control in the regulation of human performance under stress and high workload: A cognitive-energetical framework. Biological Psychology, 45, 73-93.

59.

Janis

I. L.

Mann

(1977). Decision making: A psychological analysis of conflict, choice and commitment. New York, NY: Free Press.

60.

Johnston

J. H.

Driskell

J. E.

Salas

(1997). Vigilant and hypervigilant decision making. Journal of Applied Psychology, 82, 614-622.

61.

Judge

T. A.

Bono

J. E.

(2001). Relationship of core self-evaluations traits—Self esteem, generalized self-efficacy, locus of control, and emotional stability—With job satisfaction and job performance: A meta-analysis. Journal of Applied Psychology, 86, 80-92.

62.

Kagan

Bar-Tal

(2008). The effect of pre-operative uncertainty and anxiety on short-term recovery after elective arthroplasty. Journal of Clinical Nursing, 17, 576-583.

63.

Kahneman

(1973). Attention and effort. Englewood Cliffs, NJ: Prentice Hall.

64.

Kaplan

Dar

Rosenthal

Hermesh

Fux

Lubow

R. E.

(2006). Obsessive-compulsive disorder patients display enhanced latent inhibition on a visual search task. Behavior Research and Therapy, 44, 1137-1145.

65.

Katz

Wykes

(1985). The psychological differences between temporally predictable and unpredictable stressful events: Evidence for information control theories. Journal of Personality and Social Psychology, 48, 781-790.

66.

Keinan

(1987). Decision making under stress: Scanning of alternatives under controllable and uncontrollable threats. Journal of Personality and Social Psychology, 52, 629-644.

67.

Keinan

(1994). Effects of stress and tolerance of ambiguity on magical thinking. Journal of Personality and Social Psychology, 67, 48-55.

68.

Keinan

(2002). The effects of stress and desire for control on superstitious behavior. Personality and Social Psychology Bulletin, 28, 102-108.

69.

Keinan

Friedland

Arad

(1991). Chunking and integration: Effects of stress on the structuring of information. Cognition & Emotion, 5, 133-145.

70.

Keinan

Friedland

Even-Haim

(2000). The effects of stress and self esteem on social stereotyping. Journal of Social & Clinical Psychology, 19, 206-219.

71.

Keinan

Friedland

Kahneman

Roth

(1999). The effect of stress on the suppression of erroneous competing responses. Anxiety, Stress, & Coping, 12, 455-476.

72.

Keinan

Sivan

(2001). The effects of stress and desire for control on the formation of causal attributions. Journal of Research in Personality, 35, 127-137.

73.

Klein

(2008). Naturalistic decision making. Human Factors, 50, 456-460.

74.

Kofta

Sedek

(1989). Repeated failure: A source of helplessness or a factor irrelevant to its emergence? Journal of Experimental Psychology: General, 118, 3-12.

75.

Koriat

Lichtenstein

Fischhoff

(1980). Reasons for confidence. Journal of Experimental Psychology: Human Learning and Memory, 6, 107-118.

76.

Kossowska

Bar-Tal

(in press). The interaction between the need for closer and efficacy at fulfilling the need for closer as a predictor of judgments and decisions. British Journal of Psychology.

77.

Kossowska

Jasko

Bar-Tal

Szastok

(2012). The relationship between need for closer and memory for schema-related information among younger and older adults. Aging, Neuropsychology, and Cognition, 19, 283-300.

78.

Krohne

H. W.

(1986). Coping with stress: Disposition, strategies, and the problem of measurement. In Appley

M. H.

Trumbull

(Eds.), Dynamics of stress: Physiological, psychological, and social perspectives (pp. 209-234). New York, NY: Plenum.

79.

Krohne

H. W.

(Ed.). (1992). Attention and avoidance: Strategies in coping with aversiveness. Toronto, Ontario, Canada: Hogrefe & Huber.

80.

Kruglanski

A. W.

(1989). Lay epistemics and human knowledge: Cognitive and motivational bases. New York, NY: Plenum.

81.

Kruglanski

A. W.

Dechesne

Orehek

Pierro

(2009). Three decades of lay epistemics: The why, how, and who of knowledge formation. European Review of Social Psychology, 20, 146-191.

82.

Kruglanski

A. W.

Webster

(1996). Motivated closing of the mind: “Seizing” and “freezing”. Psychological Review, 103, 263-283.

83.

Lazarus

R. S.

Folkman

(1984). Stress, appraisal, and coping. New York, NY: Springer.

84.

MacKay

D. G.

(1982). The problem of flexibility, fluency, and speed-accuracy trade-off in skilled behavior. Psychological Review, 89, 483-506.

85.

Mandler

(1993). Thought, memory, and learning: Effects of emotional stress. In Goldberger

Breznitz

(Eds.), Handbook of stress: Theoretical and clinical aspects (pp. 40-55). New York, NY: Free Press.

86.

Mayseles

Kruglanski

A. W.

(1987). What makes you so sure? Effects of epistemic motivation on judgmental confidence. Organizational Behavior and Human Decision Processes, 39, 162-183.

87.

McFarland

Buehler

(1998). The impact of negative affect on autobiographical memory: The role of self focused attention to moods. Journal of Personality and Social Psychology, 75, 1424-1440.

88.

Mikulincer

Kedem

Paz

(1990). The impact of trait anxiety and situational stress on the categorization of natural objects. Anxiety Research, 2, 85-101.

89.

Miller

S. M.

(1990). To see or not to see: Cognitive informational styles in coping process. In Rosenbaum

(Ed.), Learned resourcefulness: On coping skills, self-regulation, and adaptive behavior (pp. 95-126). New York, NY: Springer.

90.

Mishel

M. H.

(1997). Uncertainty in acute illness. Annual Review of Nursing Research, 15, 57-80.

91.

Mor

Winquist

(2002). Self-focused attention and negative affect: A meta-analysis. Psychological Bulletin, 128, 638-662.

92.

Muluk

(2010). Intratextual fundamentalism and the desire for simple cognitive structure: The moderating effect of the ability to achieve cognitive structure. Archive for the Psychology of Religion, 32, 217-238.

93.

Muraven

Tice

D. M.

Baumeister

R. F.

(1998). Self-control as a limited resource: Regulatory depletion patterns. Journal of Personality and Social Psychology, 74, 774-789.

94.

Navon

Miller

(1987). Role of outcome conflict in dual-task interference. Journal of Experimental Psychology: Human Perception and Performance, 13, 435-448.

95.

Neuberg

S. L.

Newsom

J. T.

(1993). Personal need for structure: Individual differences in the desire for simple structure. Journal of Personality and Social Psychology, 65, 113-131.

96.

Ong

A. D.

Bergeman

C. S.

Bisconti

T. L.

(2006). Psychological resilience, positive emotions, and successful adaptation to stress in later life. Journal of Personality and Social Psychology, 91, 730-749.

97.

Otten

Bar-Tal

(2002). Self-anchoring in the minimal group paradigm: The impact of need and ability to achieve cognitive structure. Group Processes & Intergroup Relations, 5, 267-284.

98.

Panayiotou

Vrona

S. R.

(1998). Effect of self-focused attention on the startle reflex, heart rate, and memory performance among socially anxious and nonanxious individuals. Psychophysiology, 35, 328-336.

99.

Pendry

L. F.

Macrae

C. N.

(1999). Cognitive load and person memory: The role of perceived group variability. European Journal of Social Psychology, 29, 925-942.

100.

Perry

A. R.

Laurie

C. A.

(1992). Sustained attention and the Type A behavior pattern: The effect of daydreaming on performance. Journal of General Psychology, 119, 217-228.

101.

Reich

J. W.

Zautra

A. J.

Potter

P. T.

(2001). Cognitive structure and the independence of positive and negative affect. Journal of Social & Clinical Psychology, 20, 99-115.

102.

Rose

C. L.

Murphy

L. B.

Byard

Nikzad

(2002). The role of the big five personality factors in vigilance performance and workload. European Journal of Personality, 16, 185-200.

103.

Rosenbaum

(1983). Learned resourcefulness as a behavioral repertoire for the self regulation of internal events: Issues and speculations. In Rosenbaum

Franks

C. M.

Jaffe

(Eds.), Perspectives on behavior therapy in the eighties (pp. 54-73). New York, NY: Springer.

104.

Segerstrom

S. C.

Miller

G. E.

(2004). Psychological stress and the human immune system: A meta-analytic study of 30 years of inquiry. Psychological Bulletin, 130, 601-630.

105.

Shiffrin

R. M.

Schneider

(1977). Controlled and automatic human information processing: II. Perceptual learning, automatic attending, and a general theory. Psychological Review, 84, 127-190.

106.

Smith

A. P.

(1985). The effects of noise on the processing of global shape and local detail. Psychological Research, 47, 103-108.

107.

Spitzer

Bar-Tal

Golander

(1995). Social support: How does it really work? Journal of Advanced Nursing, 22, 850-854.

108.

Svenson

Maule

J. A.

(Eds.). (1993). Time pressure and stress in human judgment and decision making. New York, NY: Plenum.

109.

Szalma

J. L.

Hancock

P. A.

(2011). Noise effect on human performance: A meta-analytic synthesis. Psychological Bulletin, 137, 682-707.

110.

Trumbo

C. W.

(1999). Heuristic systematic information processing and risk judgment. Risk Analysis, 19, 391-400.

111.

Tugade

M. M.

Fredrickson

B. L.

(2004). Resilient individuals use positive emotions to bounce back from negative emotional experiences. Journal of Personality and Social Psychology, 86, 320-333.

112.

van der Linden

Eling

(2006). Mental fatigue disturbs local processing more than global processing. Psychological Research, 70, 395-402.

113.

Webster

D. M.

Kruglanski

A. W.

Pattison

D. S.

(1997). Motivated language use in intergroup contexts: Need for closure effects on the linguistic intergroup bias. Journal of Personality and Social Psychology, 65, 1093-1104.

114.

Webster

D. M.

Richter

Kruglanski

A. W.

(1996). On leaping to conclusions when feeling tired: Mental fatigue effects on impressional primacy. Journal of Experimental Social Psychology, 32, 181-195.

115.

Wells

Matthews

(1994). Attention and emotion: A clinical perspective. Hillsdale, NJ: Lawrence Erlbaum.

116.

Williams

J. M.

Watts

F. N.

MacLeod

Mathews

(1997). Cognitive psychology and emotional disorders (2nd ed.). Chichester, UK: Wiley.

117.

Wofford

J. C.

Goodwin

V. L.

(2002). The linkages of cognitive processes, stress propensity, affect, and strain: Experimental test of a cognitive model of stress response. Personality and Individual Differences, 32, 1413-1430.

The Effect of Stress on Cognitive Structuring

Abstract

Keywords

Capacity Resource Theory and Its Drawbacks

The Cognitive Motivational Model

Stress Increases the Desire for Control

Stress Increases the Desire for Certainty

The Relationship Between Control and Certainty

The Role of Cognitive Structuring in Achieving Certainty

Factors Affecting Cognitive Structuring

NCS

Efficacy 1 at satisfying epistemic needs (EFENs)

The Moderating Effect of EFEN and NCS on the Relationship Between Stress and Cognitive Structuring

The Cognitive Motivational Model Accounts for Different Types of Cognitive Response to Stress

The Role of Motivation

The Role of Personality Traits in the Process

Possible Exceptions to the Moderating Effect of EFEN and NCS on the Relationship Between Stress and Cognitive Structuring

Conclusions

Footnotes

Declaration of Conflicting Interests

Funding

Notes

References

Efficacy¹ at satisfying epistemic needs (EFENs)