Figure 183

Bower's semantic network theory. The ovals represent nodes or units within the network. Adapted from Bower (1981).

• Activation from an activated node spreads to related nodes. This assumption is crucial, because it means that activation of an emotion node (e.g., sadness) leads to activation of emotion-related nodes or concepts (e.g., loss; despair) in the semantic network.

• "Consciouness" consists of a network of nodes activated above some threshold value.

These assumptions lead to the following hypotheses:

• Mood-state-dependent recall: recall is best when the mood at recall matches that at the time of learning.

• Mood congruity: emotionally toned information is learned best when there is correspondence between its affective value and the learner's current mood state.

• Thought congruity: an individual's free associations, interpretations, thoughts, and judgements tend to be thematically congruent with his or her mood state.

• Mood intensity: increases in intensity of mood cause increases in the activation of associated nodes in the associative network.

How exactly do the four hypotheses relate to the six theoretical assumptions? So far as mood-state-dependent recall is concerned, associations are formed at the time of learning between the activated nodes representing the to-be-remembered items and the emotion node or nodes activated because of the participant's mood state. At the time of recall, the mood state at that time leads to activation of the appropriate emotion node. Activation then spreads from that emotion node to the various nodes associated with it. If there is a match between the mood state at learning and at recall, then this increases activation of the nodes of to-be-remembered items, and leads to enhanced recall. However, the associative links between the to-be-remembered stimulus material and the relevant emotion node are likely to be relatively weak. As a result, mood-state-dependent effects are likely to be greater when the memory test is a difficult one offering few retrieval cues (e.g., free recall) than when it provides strong retrieval cues (e.g., recognition memory).

Mood-state-dependent effects are also predicted by other theories. According to Tulving's encoding specificity principle (see Chapter 6), the success of recall or recognition depends on the extent to which the information available at the time of retrieval matches the information stored in memory. If information about the mood state at the time of learning is stored in memory, then being in the same mood state at the time of retrieval increases this information matching. Theoretically, this should increase both recall and recognition.

Thought congruity occurs for two reasons. First, the current mood state leads to activation of the corresponding emotion node. Second, activation spreads from that emotion node to other, associated related nodes, which will tend to contain information emotionally congruent with the activated emotion node.

Mood congruity occurs when people in a good mood learn and remember emotionally positive material better than those in a bad mood, whereas the opposite is the true for emotionally negative material. According to Gilligan and Bower (1984), mood congruity depends on the fact that emotionally loaded information tends to be associated more strongly with its congruent emotion node than with any other emotion node. For example, those nodes containing information about sadness-provoking events and experiences are associatively linked to the emotion node for sadness (see Figure 18.3). To-be-remembered material that is congruent with the current mood state links up with this associative network of similar information. This leads to extensive or elaborative encoding of the to-be-remembered material. As we saw in Chapter 6, elaborative encoding is generally associated with superior long-term memory.

One might assume that the effects described here would become stronger as the intensity of the current mood increases. The reason is that the spread of activation from the activated emotion node to other related nodes would increase in line with the intensity with which emotion was experienced. However, a very sad mood may lead to a focus on internal information relating to failure, fatigue, and so on, and this may inhibit processing of all kinds of external stimuli whether or not they are congruent with the sad mood state.

Mood states

It is hard to ensure that participants are in the appropriate mood state. One method is to try to induce the required mood state under laboratory conditions, and another is to make use of naturally occurring mood states (e.g., in patients with mood disorders).

The most popular mood-induction approach is based on the procedure introduced by Velten (1968). Participants read a set of sentences designed to induce increasingly intense feelings of elation or depression. Participants typically report that their mood has altered as expected, but they may simply be responding as they believe the experimenter wants them to. A further problem is that this mood-induction procedure usually produces a blend of several mood states rather than just the desired one (Polivy, 1981).

Bower (e.g., Bower, Gilligan, & Monteiro, 1981; Bower & Mayer, 1985) has used hypnosis combined with self-generated imagery. When in the hypnotic state, participants are asked to think of images of a past happy or sad emotional experience, using those images to produce the appropriate mood state. This approach produces strong and long-lasting moods. However, it is necessary to use participants who score highly on tests of hypnotic susceptibility, and it may be unwise to generalise from such participants to other people.

Bower's network theory is clearly oversimplified. Emotions or moods and cognitive concepts are both represented as nodes within a semantic network. In reality, however, moods and cognitions are very different. For example, moods tend to change slowly in intensity, whereas cognitions tend to be all-or-none, and there is often rapid change from one cognition to another. As Power and Dalgleish (1997, p. 74) pertinently remarked, "A theory that gives emotion the same status as individual words or concepts is theoretically confused."

Beck's schema theory

Beck (1976) put forward a different theoretical approach to that of Bower, and this approach was developed by Beck and Clark (1988). The essence of this approach is that some individuals have greater vulnerability than others to developing depressive or anxiety disorders. Such vulnerability depends on the formation in early life of certain schemas or organised knowledge structures (see Chapter 9). According to Beck and Clark (1988, p. 26):

The schematic organisation of the clinically depressed individual is dominated by an overwhelming negativity. A negative cognitive trait is evident in the depressed person's view of the self, word and future. In contrast the maladaptive schemas in the anxious patient involve perceived physical or psychological threat to one's personal domain as well as an exaggerated sense of vulnerability.

Beck and Clark (1988) assumed that schemas influence most cognitive processes such as attention, perception, learning, and retrieval of information. Schemas produce processing biases in which the processing of schema-consistent or emotionally congruent information is favoured. Thus, individuals with anxiety-related schemas should selectively process threatening information, and those with depressive schemas should selectively process emotionally negative information. While Beck and Clark (1988) emphasised the role of schemas in producing processing biases, they claimed that schemas would only become active and influence processing when the individual is an anxious or depressed state.

Beck's schema theory was originally intended to provide a framework for understanding clinical anxiety and depression. However, it can readily be applied to personality research. For example, Eysenck (1992, 1997) argued that normal individuals high in trait anxiety possess danger or vulnerability schemas leading them to favour the processing of threat-related information, especially when they are feeling anxious.


The notion that some individuals have schemas that predispose them towards clinical anxiety or depression is a valuable one. However, it has proved hard to show that such schemas play a causal role in the development of anxiety disorders or depression. Some weaknesses in Beck's approach were identified by Eysenck (1997, pp. 95-96):

First, the central theoretical construct of 'schema' is amorphous [vague], and often seems to mean little more than 'belief'. Second, the evidence for the existence of specific schemas is often based on a circular argument. Behavioural evidence of a cognitive bias in anxious patients is used to infer the presence of a schema, and then that schema is used to 'explain' the observed cognitive bias. In other words, there is generally no direct or independent evidence of the existence of a schema.

Comparison of approaches

On the face of it, Bower's network theory and Beck's schema theory are very different. For example, the emphasis within network theory is on the transient effects of mood on information processing via low-level processes in long-term memory, whereas the focus in schema theory is on the semi-permanent effects of schemas on information processing via high-level processes in long-term memory However, as MacLeod (1990, p. 15) pointed out, the two theories or models make parallel predictions concerning the relationship between emotion and cognition .Both the schema model and the network model of emotion and cognition predict the existence of pervasive processing biases, associated with both anxiety and depression, affecting the encoding, comprehension and retrieval of emotionally valenced [loaded] information. Such biases should operate to favour consistently the processing of emotionally congruent information.

Williams et al. (1988, 1997)

Williams et al. (1988) focused on the effects of anxiety and depression on emotional processing. Their starting point was the distinction between priming and elaboration originally proposed by Graf and Mandler (1984). Priming is an automatic process in which a stimulus word produces activation of its various components in long-term memory, whereas elaboration is a later strategic process involving the activation of related concepts. According to the theory, anxious individuals show initial priming of threat-related stimuli, and so have an attentional bias towards threat. In contrast, depressed individuals show elaboration of threat-related stimuli, and so have a memory bias in which they find it easier to retrieve threatening than neutral material.

Some of the main predictions made by Williams et al. (1988) concern the effects of anxiety and depression on explicit and implicit memory. Explicit memory involves conscious recollection of past events, and presumably involves elaborative processes. In contrast, implicit memory does not involve conscious recollection, and may depend mainly on priming or automatic processes (see Chapter 7). Depressed individuals should show an explicit memory bias favouring retrieval of threatening material, whereas anxious individuals should show an implicit memory bias for threatening material.

Williams et al. (1997) developed their previous theory in various ways. They argued that the different functions of anxiety and depression have implications for information processing. Anxiety has the function of anticipating danger. As a result, it is "associated with a tendency to give priority to processing threatening stimuli; the encoding involved is predominantly perceptual rather than conceptual in nature" (Williams et al., 1997, p. 307). In contrast, if depression involves the replacement of failed goals, "then the conceptual processing of internally generated material related to failure or loss may be more relevant to this function than perceptual vigilance" (Williams et al., 1997, p. 315).

Williams et al. (1997) made use of Roediger's (1990) distinction between perceptual and conceptual processes (see Chapter 7). Perceptual processes are essentially data-driven processes, and are typically involved in basic attentional processes and in implicit memory. In contrast, conceptual processes are top-down processes, and are typically involved in explicit memory. Suppose we assume that anxiety facilitates the perceptual processing of threat-related stimuli, whereas depression facilitates the conceptual processing of threatening information. This would lead to the prediction of an implicit memory bias associated with anxiety and an explicit memory bias associated with depression.

Three theoretical approaches to the effects of personality traits and mood states on emotional processing. Adapted from Rusting (1998).

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