Which research method allows cause and effect explanations?

Ability to Think About Cause and Effect in Social Situations

Social events contain complex cause-and-effect relationships. One event may be both the cause and the result of another event. Therefore, one should be able to associate the consequences with the correct causes and should be able to think about the nature of the complex relationships in a flexible manner. People who are successful in problem solving are able to consider the cause-and-effect relationships within social events despite their complex nature.

This complex cognitive ability is important for the development of interpersonal problem-solving skills. People who can identify the interrelated causes and effects concerning social events are successful at solving social problems in real life. As one can imagine, the ability to think about cause and effect in social relationships renders an individual able to anticipate possible positive and negative consequences.

Inferring causation in experimental research

While it is difficult to establish cause-and-effect relationships conclusively with any research design, ‘true experiments’ offer the greatest potential of any design for inferring causal relationships. This is due to their careful control of experimental conditions, and to the practice of randomisation ensuring groups equivalent in composition.

To infer causation, a researcher needs to be able to eliminate alternative explanations (rival hypotheses), that the fact that observed changes in the experimental group may be due to factors other than the independent/treatment variable.

Some typical rival hypotheses include:

Selection effect − if subjects are permitted to select their own treatment condition (experimental or control), groups will not be equivalent. This alternative explanation can be overcome by the practice of randomisation.

Maturation − any naturally occurring process within persons which could account for the observed change.

History − any event to which subjects are exposed around the time of the experiment, which could account for observed differences between subjects.

Instrumentation − any change in measurement instrument or procedures from one application of a treatment to another.

Mortality − subjects dropping out from a study.

Inferring causation in experimental research

While it is difficult to establish cause-and-effect relationships conclusively with any research design, laboratory experiments offer the greatest potential for inferring causal relationships. This is due to their careful control of experimental conditions, and to the practice of randomisation ensuring that groups are equivalent in composition. To infer causation, a researcher needs to be able to eliminate alternative explanations. In experimental research, some of the main alternative explanations are:

Selection effect. If participants can select their own treatment condition (experimental or control), groups will not be equivalent. Randomisation addresses this alternative explanation.

Maturation. Any naturally occurring process within persons that could account for the observed change.

History. Any event to which subjects are exposed around the time of the experiment, which could account for observed differences between subjects.

Instrumentation. Any change in measurement instrument or procedures from one application of a treatment to another.

Mortality. Participants dropping out from a study.

Substantiation of the Health Claim

To determine the extent to which a cause-and-effect relationship is established between consumption of the food/constituent and claimed effect, the EFSA NDA Panel takes into consideration all of the evidence from pertinent studies. The overall strength, consistency, and biological plausibility of this evidence is assessed, as well as the quality of individual studies and their applicability to the target group and the conditions of use proposed.

Health claim substantiation is mainly based on human intervention studies conducted in the target population using the food and ingredient at the intended dose. This is especially important for claims based on new scientific knowledge (Article 13.5) intended for children’s health or claims associated with reducing a risk factor for disease (Article 14). Double-blinded, randomized, placebo-controlled intervention studies are of primary importance to substantiate cause-and-effect relationships for other essential nutrients. In addition, other studies are considered in the evaluation process (eg, randomized noncontrolled studies, controlled nonrandomized studies, observational studies, etc.), but evidence derived from them is considered of second-order importance. In the EU regulatory framework, the population group for which the health claims on foods are intended is, in principle, the general (healthy) population or specific subgroups thereof (eg, elderly people, physically active subjects, or pregnant women). The NDA Panel considers, on a case-by-case basis, the extent to which it is established that extrapolation from other study groups to the target group is biologically justified (EFSA, 2011a, 2016). For example, data from irritable bowel syndrome patients have been accepted in the context of claims related to reduction of intestinal discomfort. However, data on patients with joint-osteoarthritis complaints were not considered acceptable to substantiate a claim on maintaining normal joints. The admissibility of applications for claims that specify target groups other than the general (healthy) population (disease) and/or under medication (eg, under antibiotic treatment) should first be checked by the EC and Member States (EFSA, 2016). Animal or in vitro studies may provide supportive evidence and mechanistic data, but they are not enough to substantiate a claim per se.

In the EU this scientific evaluation process constitutes the pillar for the authorization of claims on foods by risk managers of the EC and Member States. Thus a list of permitted claims and their specific conditions of use has been created and is available at the EU Register (http://ec.europa.eu/nuhclaims), which constitutes valuable examples for future applications.

V. Memory Suppression—Forgetting Versus Extinction, and Latent Inhibition

Associative conditioning instantiates a memory for a cause-and-effect relationship between the CS and the US, that is, it establishes a memory that the CS reliably predicts the upcoming occurrence of the US. In the natural setting, knowledge of a predictable relationship between environmental stimuli has great adaptive advantage. However, consider the situation in which the CS-US relationship changes such that the CS no longer reliably predicts the US. An animal would then be responding inappropriately to the CS, for example fleeing when unnecessary or worse, yet not fleeing because it thought the CS indicated a non-threatening situation. Clearly these types of behaviors would not be advantageous. These considerations bring up the issue of the necessity of memory systems to be able to “undo” a previously learned association. This undoing of associative memory can take one of two forms: forgetting and extinction.

Forgetting is the dissipation over time of a previously-formed memory. Forgetting conceptually can be due to a failure in the fidelity of the memory storage process, or a failure of recall mechanisms such that they can no longer trigger effective retrieval of the memory.

Extinction is the specific override of a prior memory in response to a new set of contingencies. In the context of associative conditioning, extinction is triggered by the situation that the CS no longer predicts the US. In the laboratory setting, extinction of learned associations is generally triggered by repetitive presentation of the CS without a subsequent presentation of the US. Over time, with repeated CS-alone presentations, the prior CS-US association is extinguished.

Learned associations generally are quite robust, and it is not unusual for extinction of an association to require many more CS-alone presentations than were required for the initial learning of the original CS-US contingency.

As was emphasized previously in this book, extinction is not forgetting. Nor is it a manifestation of an obliteration of the previously learned association. Experimentally, this can be demonstrated by delivering a “reminder” presentation of a CS-US pairing to a fully-extinguished animal. Generally, after a single reminder trial the CS-US association comes back as a fully manifest, robust response. In other words, a single CS-US pairing is sufficient to drive reinstatement of the original conditioned response. This observation emphasizes the fact that the prior CS-US association is a latent memory still stored in the CNS. However, extinction training leads to the original memory being overridden, and not expressed.

The existence of extinction as a unique cognitive process illustrates that memory recall or retrieval is not simply a passive process that does not impact the CNS. Rather, recall in many instances also sets in motion its own set of processes that allow the capacity for relearning of new contingencies alongside the previously learned ones. As has already been pointed out, the necessity for memories to be reconsolidated after retrieval (see Box 9) also illustrates the complex and unique events set in motion during and after a memory recall episode.

Box 9

Reconsolidation of memories

What if every time you recalled a memory you made that memory subject to erasure? A frightening thought, certainly. The idea also seems somewhat at odds with our perception of consistency in our own memories—recalling them seems to make them stronger, not weaker. Nevertheless, recent provocative studies have suggested that every time we recall a specific memory, we make it necessary for that memory to be re-established. The word used to describe this attribute of memory is “reconsolidation” in reference to the well-known attribute that long-term memories, when initially formed, are labile and subject to disruption over a period of hours. It appears that previously established long-term memories also are subject to disruption, specifically during that period immediately after each time they are recollected.

The most definitive recent experiment concerning memory reconsolidation was performed by Karim Nader and Glenn Schafe in Joe LeDoux’s laboratory (8), although important work in this area has also been performed by the laboratories of Susan Sara, Yadin Dudai, and Alcino Silva, among others. Nader et al. studied memory reconsolidation using cued fear conditioning in rats which, as we discussed in this chapter, is an amygdala-dependent process. Basically, Nader et al. found that when an animal is re-exposed to a conditioned stimulus (an auditory cue in this case), which of course elicits recollection of a prior CS-US pairing, restorage of that memory can be disrupted by inhibiting protein synthesis in the amygdala. The same memory is impervious to an equivalent period of protein synthesis inhibition as long as the animal is not stimulated to recall the CS-US pairing during that time. The implication of these studies is that reactivated memories must be put back into long-term storage via a protein synthesis-dependent process similar to that used during the initial consolidation period; hence, the use of the term reconsolidation.

There are of course a great number of questions raised by these studies. Is the reconsolidation mechanism identical to the initial consolidation mechanism? Are all long-term memories subject to reconsolidation after each recollection, or is this mechanism restricted to particular brain areas or memory types? Might disruption of this process contribute to memory pathologies such as aging-related memory loss? Could pharmacologic means be used as a therapeutic intervention in “pathologic” memory such as post-traumatic stress disorder? Future studies will hopefully lead to new insights into these and other questions concerning this fascinating phenomenon.

Finally, there is one additional example of a suppressive form of memory that has already been discussed in an earlier chapter, latent inhibition. Latent inhibition refers to the capacity of prior experience to suppress (inhibit) new learning. The “latent” in latent inhibition refers to the attribute that the process is experimentally not observable or demonstrable until one observes a failure of learning in a subsequent test. Regarding associative conditioning paradigms, latent inhibition is generated by repeated presentations of CS alone without a subsequent contingent US presentation. The repeated CS-alone presentations diminish or completely block the subsequent capability of the animal to learn a CS-US association when they experience CS-US training trials.

Two Types of Behavior: Intentional Actions and Compensatory Reactions

The two blocked arrows in Figure 3 represent lineal cause and effect relationships that emerge from the underlying circular feedback process. The two blocked arrows represent emergent properties of the whole loop. They are not integral parts of the loop itself. Note that the two blocked arrows point in a counterclockwise direction, which is opposite to the clockwise direction of the feedback loop itself. Closed-loop control systems do not control their input by controlling their output. Nor do environmental disturbances elicit compensatory output by being sensed.

The two types of behavior, the intentional actions and the compensatory reactions, are synergistically coupled. That is, although intentional actions and compensatory reactions are mutually exclusive types of action they are not mutually exclusive actions. On the contrary, they are always found to go hand in hand in any system that controls its own input. For example, the flight path of an airplane is the pilot’s (or autopilot’s) doing only to the degree that the pilot’s (or autopilot’s) reactions automatically offset any would-be aerodynamic disturbances to the intended flight path. Otherwise, he, she (or it) is merely along for the ride.

The intentional actions and automatic reactions represented by the blocked arrows in Figure 3 are both entirely dependent upon, but emergent from, the underlying negative feedback loop. Because negative feedback is the re-afference principle put to good use, control theorists view the re-afference principle in an altogether different light than von Holst’s. Control theorists view re-afference as essential to closed-loop control and, thus, as helpful. Von Holst viewed re-afference as a contamination of afference and, thus, as harmful. Accordingly, von Holst’s efference-copy hypothesis deals with the re-afference principle in a manner that is altogether different from that of the control-theoretic model. In fact, the two models are functionally antithetical: Whereas von Holst’s ideal functional schemata is supposed to rid afference of all re-afference by means of feedback negation, an ideal closed-loop control system rids afference of all ex-afference by means of negative feedback (e.g., in the cruise-control example, the speed of the vehicle does not vary with the terrain). The antithesis is twofold: (a) feedback negation versus negative feedback, and (b) ridding afference of re-afference versus ridding afference of ex-afference.

Conclusion

The psychological autopsy is a powerful tool for the skilled suicidologist. It cannot definitively define cause-and-effect relationships, thus it cannot validly inform an expert that a suicide definitely occurred; rather, it can better inform opinions as to whether a decedent likely completed suicide and provide a better understanding of pathways to the determined manner of death. As such, it informs coroners and medical examiners and the courts which are ultimately the decision-makers.

Information derived from psychological autopsies will necessarily be incomplete and the totality of data sources may serve to create a mosaic that is more impressionistic than factual. The light it shines on its subject is often filtered by the prismatic lenses of many observers. Nevertheless, it illuminates. As an intensive single-case research procedure, the psychological autopsy significantly improves manner of death determinations and offers clues to understand better the state of mind of those who complete suicide.

4.1 IA Models

IA models are computer simulation frameworks that try to describe quantitatively, as much as possible, the cause and effect relationships of a specific issue and of the inter-linkages and interactions among different issues. Current projects in IA modeling build on a tradition started in the early 1970s, by the Club of Rome (Meadows et al. 1972).

The next generation of IA models explicitly addressed environmental issues, such as acidification (Hordijk 1991) and climate change (Rotmans 1990, Nordhaus 1992). Recent overviews of IA modeling activities in the field of climate change can be found in Weyant et al. (1996) and Rotmans and Dowlatabadi (1998). The latter distinguish between macroeconomic-oriented models, which represent relatively simple, parameterized decision-analytic formulations of complex problems, and biosphere-oriented models, which represent a more comprehensive, process-oriented description of a complex problem. Most macroeconomic-oriented models are neoclassical models based on an equilibrium framework, using traditional economic concepts regarding optimization and capital accumulation, largely ignoring environmental dynamics.

Biosphere-oriented models, however, focus on a systems-based description of the geophysical and biogeochemical processes and feedbacks, but do not adequately represent the socioeconomic system. The Dynamic Integrated Climate Economy (DICE) model is a well-known exponent of the macroeconomic-oriented school, whereas the Integrated Model to Assess the Greenhouse Effect (IMAGE) model (Rotmans 1990, Alcamo 1994) is representative for the biosphere-oriented school. Meanwhile, some attempts are underway to combine the best of both worlds, yielding a hybrid of the two categories above. Examples of such hybrid IA models are ICAM (Dowlatabadi and Morgan 1993), Global Change Assessment Model (GCAM) (Edmonds et al. 1994), and TARGETS (Rotmans and de Vries 1997).

IA models have the advantage that they are flexible and rapid simulation tools, which can easily explore interactions, feedback mechanisms, and uncertainties. Above all, they are tools to communicate complex scientific issues to decision makers, disciplinary scientists, stakeholders, and the general public.

Which research method involves cause

Which research method allows cause

In which method of research the cause

Does experimental research show cause