depression

How Mood and felt Energy are related to thought variability and speed

There is a recent article by Pronin and Jacobs, on the relationship between mood, thought speed and experience of ‘mental motion’ that builds up on their previous work.

Let us see how they describe thought speed and variability and what their hypothesis is:

1. The principle of thought speed. Fast thinking, which involves many thoughts per unit time, generally produces positive affect. Slow thinking, which involves few thoughts per unit time, generally produces less positive affect. At the extremes of thought speed, racing thoughts can elicit feelings of mania, and sluggish thoughts can elicit feelings of depression.

2. The principle of thought variability. Varied thinking generally produces positive affect, whereas repetitive thinking generally produces negative affect. This principle is derived in part from the speed principle: when thoughts are repetitive, thought speed (thoughts per unit time) diminishes. At its extremes, repetitive thinking can elicit feelings of depression (or anxiety), and varied thinking can elicit feelings of mania (or reverie).

Let me clarify at the outset that they are aware of the effects of though speed on variability and vice versa; as well as the effects of mood on felt energy and vice versa; thus they know that one can confound the other. Another angle they consider is the relationship between thought speed/variability i.e the form of thought and the contents of thought (whether having emotional salience or neutral) and investigated whether the effects of speed and variability were confounded with though content; they found negative evidence for this inetrcationist view.

Let me also clarify that I differ slightly (based on my interpreation of their data) from their original hypothesis, in the sense that I believe that their data shows that speed affects felt energy and variability affects affect and that the effects of speed on mood may be mediated by the effect of speed on felt energy and similarly the effect of variability on felt energy may be mediated by its effects on mood.

Thus my claim is that:

  1. Thought speed leads to more felt energy. Extremes of ‘racing thoughts’ leads to the manic feeling of being very energetic (when accompanied with positive mood, this may give rise to feelings of grandiosity- I have the energy to achieve anything), while also may lead to anxiety states (when accompanied with negative affect) in which one cannot really suppress a negative chain of thoughts – one following the other in fast succession, regarding the object of ones anxiety. The counterpart to this the state where thoughts come slowly (writer’s block etc) and when accompanied with negative affect, this can easily be viewed as depression.
  2. Thought variability leads to more positive affect: Extremes of ‘tangential thoughts’ leads to the manic feeling of being in a good mood (when accompanied with high energy , this manifest as feelings of euphoria); while the same tangential thoughts when accompanied by low felt energy may actually be felt as serenity/ calmness/ reverie. The counterpart to this is the state of thoughts that are stuck in a rut – when accompanied with low energy this leads to feelings of depression and sadness.

Thus, to put simply : there are two dimensions one needs to take care of – mood (thought variability) x energy (thought speed) and high and low extremes on these dimensions are all opposites of their counterpart.

Before we move on, I’ll let the authors present their other two claims too:

3. The combination principle. Fast, varied thinking prompts elation; slow, repetitive thinking prompts dejection. When speed and variability oppose each other, such that one is low and the other high, individuals’ affective experience will depend on factors including which one of the two factors is more extreme. The psychological state elicited by such combinations can vary apart from its valence, as shown in Figure 1. For example, repetitive thinking can elicit feelings of anxiety rather than depression if that repetitive thinking is rapid. Notably, anxious states generally are more energetic than depressive states. Moreover, just as fast-moving physical objects possess more energy than do identical slower objects, fast thinking involves more energy (e.g., greater wakefulness, arousal, and feelings of energy) than does slow thinking.

4. The content independence principle. Effects of thought speed and variability are independent of the specific nature of thought content. Powerful affective states such as depression and anxiety have been traced to irrational and dysfunctional cognitions (e.g., Beck, 1976). According to the independence principle, effects of mental motion on mood do not require any particular type of thought content.

They review a number of factors and studies that all point to a causal link between thought speed and energy and between thought variability and mood. More importantly they show the independent effects of though speed and variability from the effects of thought content on mood. I’ll not go into the details of the studies and experiments they performed, as their article is available freely online and one can read for oneself (it makes for excellent reading); suffice it to say that I believe they are on the right track and have evidence to back their claims.

What are the implications of this:

The speed and repetition of thoughts, we suggest, could be manipulated in order to alter and alleviate some of the mood and energy symptoms of mental disorders. The slow and repetitive aspects of depressive thinking, for example, seem to contribute to the disorder’s affective symptoms (e.g., Ianzito et al., 1974; Judd et al., 1994; Nolen-Hoeksema, 1991; Philipp et al., 1991; Segerstrom et al., 2000). Thus, techniques that are effective in speeding cognition and in breaking the cycle of repetitive thought may be useful in improving the mood and energy levels of depressed patients. The potential of this sort of treatment is suggested by Pronin and Wegner’s (2006) study, in which speeding participants’ cognitions led to improved mood and energy, even when those cognitions were negative, self-referential, and decidedly depressing. It also is suggested by Gortner et al.’s (2006) finding that an expressive writing manipulation that decreased rumination (even while inducing thoughts about an upsetting experience) rendered recurrent depression less likely.

There also is some evidence suggesting that speeding up even low-level cognition may improve mood in clinically depressed patients. In one experiment, Teasdale and Rezin (1978) instructed depressed participants to repeat aloud one of four letters of the alphabet (A, B, C, or D) presented in random order every 1, 2, or 4 s. They found that those participants required to repeat the letters at the fastest rate experienced the most reduction in depressed mood. Similar techniques could be tested for the treatment of other mental illnesses. For example, manipulations might be designed to decrease the mental motion of manic patients, perhaps by introducing repetitive and slow cognitive stimuli. Or, in the case of anxiety disorders, it would be worthwhile to test interventions aimed at inducing slow and varied thought (as opposed to the fast and repetitive thought characteristic of anxiety). The potential effectiveness of such interventions is supported by the fact that mindfulness meditation, which involves slow but varied thinking, can lessen anxiety, stress, and arousal.

 hat tip: Ulterior Motives

ResearchBlogging.org
Pronin, E., & Jacobs, E. (2008). Thought Speed, Mood, and the Experience of Mental Motion Perspectives on Psychological Science, 3 (6), 461-485 DOI: 10.1111/j.1745-6924.2008.00091.x
Pronin, E., & Wegner, D. (2006). Manic Thinking: Independent Effects of Thought Speed and Thought Content on Mood Psychological Science, 17 (9), 807-813 DOI: 10.1111/j.1467-9280.2006.01786.x

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Low Mood and Risk Aversion: a poor State outcome?

Daniel Nettle, writes an article in Journal Of Theoretical Biology about the evolution of low mood states. Before I get to his central thesis, let us review what he reviews:

Low mood describes a temporary emotional and physiological state in humans, typically characterised by fatigue, loss of motivation and interest, anhedonia (loss of pleasure in previously pleasurable activities), pessimism about future actions, locomotor retardation, and other symptoms such as crying.

This paper focuses on a central triad of symptoms which are common across many types of low mood, namely anhedonia, fatigue and pessimism. Theorists have argued that, whereas their opposites facilitate novel and risky behavioural projects. These symptoms function to reduce risk-taking. They do this, proximately, by making the potential payoffs seem insufficiently rewarding (anhedonia), the energy required seem too great (fatigue), or the probability of success seem insufficiently high (pessimism). An evolutionary hypothesis for why low mood has these features, then, is that is adaptive to avoid risky behaviours when one is in a relatively poor current state, since one would not be able to bear the costs of unsuccessful risky endeavors at such times .

I would like to pause here and note how he has beautifully summed up the low mood symptoms and key features; taking liberty to define using my own framework of Value X Expectancy and distinction between cognitive(‘wanting’) and behavioral (‘liking’) side of things :

  • Anhedonia: behavioral inability to feel rewarded by previously pleasurable activities. Loss of ‘liking’ following the act. Less behavioral Value assigned.
  • Loss of motivation and interest: cognitive inability to look forward to or value previously desired activities. Loss of ‘wanting’ prior to the act. Less cognitive Value assigned.
  • Fatigue: behavioral inability to feel that one can achieve the desired outcome due to feelings that one does not have sufficient energy to carry the act to success. Less behavioral Expectancy assigned.
  • Pessimism: cognitive inability to look forward to or expect good things about the future or that good outcomes are possible. Less cognitive Expectancy assigned.

The reverse conglomeration is found in high mood- High wanting and liking, high energy and outlook. Thus, I agree with Nettle fully that low mood and high mood are defined by these opposed features and also that these features of low and high mood are powerful proximate mechanisms that determine the risk proneness of the individual: by subjectively manipulating the Value and Expectancy associated with an outcome, the high and low mood mediate the risk proneness that an organism would display while assigning a utility to the action. Thus, it is fairly settled: if ultimate goal is to increase risk-prone behavior than the organism should use the proximate mechanism of high mood; if the ultimate goal is to avoid risky behavior, then the organism should display low mood which would proximately help it avoid risky behavior.

Now let me talk about Nettle’s central thesis. It has been previously proposed in literature that low mood (and thus risk-aversion) is due to being in a poor state wherein one can avoid energy expenditure (and thus worsening of situation) by assuming a low profile. Nettle plays the devil’s advocate and argues that an exactly opposite argument can be made that the organism in a poor state needs to indulge in high risk (and high energy) activities to get out of the poor state. Thus, there is no a prior reason as to why one explanation may be more sound than the other. To find out when exactly high risk behavior pay off and when exactly low risk behaviors are more optimal, he develops a model and uses some elementary mathematics to derive some conclusions. He, of course , bases his model on a Preventive focus, whereby the organism tries to minimize getting in a state R , which is sub-threshold. He allows the S(t) to be maximized under the constraint that one does not lose sight of R. I’ll not go into the mathematics, but the results are simple. When there is a lot of difference between R (dreaded state) and S (current state), then the organism adopts a risky behavioral profile. when the R and S are close, he maintains low risk behavior, however when he is in dire circumstances (R and S are very close) then risk proneness again rises to dramatic levels. To quote:

The model predicts that individuals in a good state will be prepared to take relatively large risks, but as their state deteriorates, the maximum riskiness of behaviour that they will choose declines until they become highly risk-averse. However, when their state becomes dire, there is a predicted abrupt shift towards being totally risk-prone. The switch to risk-proneness at the dire end of the state continuum is akin to that found near the point of starvation in the original optimal foraging model from which the current one is derived (Stephens, 1981). The graded shift towards greater preferred risk with improving state is novel to this model, and stems from the stipulation that if the probability of falling into the danger zone in the next time step is minimal, then the potential gain in S at the next time step should be maximised. However, a somewhat similar pattern of risk proneness in a very poor state, risk aversion in an intermediate state, and some risk proneness in a better state, is seen in an optimal-foraging model where the organism has not just to avoid the threshold of starvation, but also to try to attain the threshold of reproduction (McNamara et al., 1991). Thus, the qualitative pattern of results may emerge quite generally from models using different assumptions.

Nettle, then extrapolates the clinical significance from this by proposing that ‘agitated’ / ‘excited’ depression can be explained as when the organism is in dire straits and has thus become risk-prone. He also uses a similar logic for dysphoric mania although I don’t buy that. However, I agree that euphoric mania may just be the other extreme of high mood and more risk proneness and goal achievements; while depression the normal extreme of low mood and adverse circumstances and risk aversion. To me this model ties up certain things we know about life circumstances and the risk profile and mood tone of people and contributes to deepening our understanding.
ResearchBlogging.org
Nettle, D. (2009). An evolutionary model of low mood states Journal of Theoretical Biology, 257 (1), 100-103 DOI: 10.1016/j.jtbi.2008.10.033

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