Universidad de los Andes Facultad de Ciencias Sociales
Departamento de Psicología
SIMILARITY AND CAUSALITY IN SLIPPERY SLOPE ARGUMENTS
Trabajo de grado para optar al título de PSICÓLOGA
Luz Ángela Carvajal Villalobos
Bajo la dirección de William Jiménez-Leal, PhD. Bogotá, D.C., Noviembre de 2014
Tabla de Contenido
Introducción………..……….5
Metodología……….11
Materiales……….11
Procedimiento y Participantes……….…………...14
Resultados………16
Discusión……….25
Lista de Tablas
Table 1.Topics and possible consequences for the causality questionnaire……...…….13 Table 2. Standardized distance between every pair of psychoactive drugs
based on similarity ratings………...………17 Table 3. Standardized distances between every pair of situations of violence
against women based on similarity ratings………...…18 Table 4. Standardized distance between every pair of psychoactive drugs
based on causality ratings………...18
Table 5. Standardized distances between every pair of situations of violence
against women based on causality ratings………...19 Table 6. Average of strength rating for each argument………..….19
Table 7. Correlation coefficients of distance between components and strength
h of the argument……….………20
Table 8. Multiple linear regressions with argument strength as the dependent
Lista de Figuras
Figure 1. Facsimile of the similarity questionnaire as presented to the
participants…………..……….12
Figure 2. Facsimile of the causality questionnaire as presented to participants.……….13 Figure 3. Facsimile of a SSA as presented to the participants………14 Figure 4. Flowchart showing the two stages of the study and their outcomes…………16 Figure 5. Distances between situations of violence against women based
on their similarity ratings………...………..18
Figure 6. Distances between psychoactive drugs against women based
on their similarity ratings………...………..18 Figure 7. Correlation between strength of the argument and distance
between its components……….………..22
Figure 8. Correlation between strength of the argument and the mean of
Similarity and Causality in Slippery Slope Arguments
It’s November and the year is 2014. Marco Fidel Ramírez, a Bogotá city councillor,
reacted on twitter to the approval of the first debate in the Colombian Senate about medical cannabis. His tweet reads “Medical cannabis Dr. Benedetti? We will soon hear about ‘diet’ cyanide. No to marijuana legalization.” (Ramírez, 2014). There are many
authors who have talked about the importance of the study of argumentation, in particular Slippery Slope Arguments (SSA), but nothing makes it more relevant than the fact that it is still being used in political contexts to favor certain positions. This work aims to reach better comprehension on the cognitive mechanisms at work when evaluating a SSA, specifically similarity and causality assessments.
SSA are hard to define due to the content variety they may express (Corner, Hahn & Oaksford, 2011). Also, as an argumentative structure, it can be studied from different fields, such as philosophy or psychology (van Eemeren, Grootendorst & Henkemans, 1996). For instance, SSA can be defined as consequentialist arguments, similar to argumentum ad consequentiam, because they reference an undesirable output that follows certain decision. The undesirable output is shown as a future consequence of accepting some proposal in the present. That consequence is generally an “unsubstantiated negative consequence that is carried to an extreme” as van Eemeren,
Grootendorst & Snoeck Henkemans (1996, p. 69) put it. The negative effect could nonetheless be avoided through the use of strong limits and the correct application of norms and laws (Volokh, 2003), but the person delivering a SSA does not usually consider that.
Corner, Hahn and Oaksford (2006) proposed four components for a SSA: An initial proposal (A), an undesirable result (B), the belief that allowing A will lead to the reevaluation and approval of B and the rejection of A based on this belief. SSA are
normally considered fallacies since the conclusion so reached is not supported by evidence (Lopez de la Vieja, 2007), nor is it valid (den Hartogh, 1998) Its conclusion is not supported by the premises (Eemeren (2001), even though it appears to be so due to its argumentative structure (López de la Vieja, 2007). On the other hand, different approaches like Corner et al.’s (2006) reject the categorical denial of SSA’s validity,
considering that it might be possible for a prediction from a SSA to become true, in which case it would ratify its usefulness as a persuasion strategy. In spite of this debate, SSA continue to be useful tools in debate on a personal and social level.
Corner et al. (2006) proposed a Bayesian mechanism to understand SSA, which brings in a normative framework for explaining the credibility attribution to these arguments. According to this mechanism, people assimilate the information given to them by a SSA into their own knowledge and belief system, thus assigning the predicted outcome (B) a certain probability and utility. This Bayesian analysis offers an explanation for variation observed in the acceptability of arguments with different content but the same structure. These authors propose that while evaluating a SSA, people use an estimation of the conditional probability of B given A, according to a re-arrangement of the category to which A and B belong, in order to put A and B in the same category. This re-categorization of elements can be based on similarity judgments. Similarity is a notion with a long history in cognitive science and has been understood in several ways. Jiménez-Leal and Gaviria (2013) summarize three main models to understand similarity: Geometrical, featural and alignment models. The geometrical models conceive similarity in terms of spatial dimensions, based on the statistical technique of multi-dimensional scaling. According to this, objects can be located in a space defined by dimensions calculated from similarity judgments. This placing allows the calculation of distance between any pair of elements (Shepard, 1962).
The feature-based models start from the idea that judging similarity between two objects implies an ideal comparison between the set of features that make up each object where similarity is estimated depending on each feature’s relevance and its common
presence. Finally, alignment models assume a structural mapping mechanism in which each object’s structure is aligned and adjusted as best as possible in order to define
similarity between the two objects.
As alternative to Corner’s et al (2011) proposal, Jiménez-Leal and Gaviria
(2013) claimed that the processes of re-categorization that take place during the evaluation of SSA are based on processes of causal categorization. The categorization and introduction of causal models grant the integration of categorization approaches based on dimensions and on features, which leads to the recognition of causal categorization as the basis of similarity judgments. The idea of causal similarity has its basis on the proposal that people create representations of the causal mechanisms that connect two objects (Rehder, 2003). Accordingly, knowledge about causes and consequences of different features and objects is compared in order to create a causal model of categorization, which is then used to assess a SSA.
Jiménez-Leal and Gaviria (2013) explored the relation between similarity and strength of the arguments, proposing causality as a key factor to explain the link between similarity and arguments’ strength. To accomplish this, they asked 120
participants to assess two SSA in which the approval of a substance –either a new fertilizer or a drug– was under consideration, with the additional factor of the risk of a second substance being allowed as a consequence of the first approval. Participants were handed a matrix comparing both substances –the one pending for approval and the possible sequel–, showing how many common features they shared and the presence or absence of a causal trait, previously chosen as relevant in a pilot study. Following this,
an argument like “If we accept Soilex [substance A], then we will have to accept Polenoy [substance B]. Therefore we shouldn’t accept Soilex” was presented and
participants were asked to rate how convincing it was. The results showed that people are sensitive to causal information, which was demonstrated by the lower argument strength when the causal feature was absent.
As a follow up, in 2013 the Cognition and Learning research group of the University of los Andes created and applied an instrument to evaluate the effects of similarity and causality on SSA. Again with fertilizers and drugs, a SSA with a comparison matrix containing a variable number of common traits and the absence or presence of a causal trait was presented to the 237 participants, who were subsequently asked to rate each SSA. For this study, the experimenters systematically varied the absolute number of features a pair of substances shared, as well as the presence/absence of a causally relevant feature. The results did not fully support the causal hypothesis, showing that number of common features had a bigger effect on the argument rate that the presence of the causal trait. A non-conclusive tendency towards argument strength in the presence of the common trait suggests that this subject might be relevant to reach a better understanding of the reasoning behind SSA.
This work is concerned with the role of similarity and causal relations among parts of a SSA in the strength of the argument. A significant difference from the studies mentioned above is that they had a fixed amount of information available, from which they established artificial categories. To guarantee a better ecological validity in this study, the similarity and causality ratings are set by the participants themselves, instead of being defined a priori. Strength of the argument is defined here as the credibility of an argument and its potential to be accepted. The main question posed is: Is there an effect of similarity and common causal properties between the objects presented in the
premises and the conclusion on the perception of strength of a SSA? If so, how can that effect be described? A related question explored here is about the link between the credibility of a SSA and previous knowledge: How do similarity judgments in SSA interact with prior knowledge?
Both similarity and the causal link between two elements are expected to be good predictors of a SSA’s strength. This means that the ‘closer’ two elements are,
according to their similarity and causal relation rating, the easier it is to put forward a convincing SSA that suggests that the acceptance of one of them will lead to the necessary acceptance of the other. Causal relation is expected to have a more important effect, supporting the idea that two elements with a close causal relation are more likely to compose a persuasive SSA (Jiménez-Leal & Gaviria, 2013). It is expected that causal information will have a greater role than similarity information in argument acceptance. The relation between causality and similarity should arise in the presence of prior knowledge: it is known that perceived argument strength is strongly associated with prior beliefs (Paglieri & Castelfranchi, 2005).
The study and understanding of the cognitive mechanisms that underlie the perception and assessment of a SSA is relevant because those arguments show up on contemporary political debates in which important decisions with national-wide effects are being made and also in day-to-day conversation. In current debates like same-sex marriage or euthanasia the advocates of the statu quo often use SSA to defend their position (Cahill, 2005). Recognizing these arguments is a first step on the way to deeper and cleaner debates, but it is important for psychology to go beyond and try to understand how these arguments convince people. A better comprehension of how SSA work would open the path for an analysis of the manipulations of language and information that happen in order to alter public opinion and that would be a tool to call
these manipulations out. Besides, understanding of the SSA and their assessment would help the understanding of the conceptual representation of the world and its causal structure in the human mind (Jiménez-Leal & Gaviria, 2013).
Two main topics were selected for the similarity and causality evaluation: Psychoactive drugs and violence against women. Psychoactive drugs were selected because as shown by the Bogotá city councilor example, it is easy to recur to a SSA regarding this subject, and there are changes on regulations and legal status of drugs around the globe, so the debate is very much alive (United Nations Office on Drugs and Crime, 2014). Also, it has clearly limited elements (different substances) that make it easy for comparisons to be presented to participants. There is both the popular belief and statistical evidence that there is a ‘gateway effect’ between different drugs, meaning
that the use of one can lead to try another one (Wagner & Anthony, 2002), which creates a link between different psychoactive drugs.
Violence against women is a subject in which there is yet to define a clear line between what’s legal or socially accepted and what is not. For example, considering that ‘catcalling’ is a cross-culturally widespread practice (Walkowitz, 1998), it is surprising
how many nations have failed to create legal definitions and strict strategies to control it (DLA Piper, 2014). The idea of establishing similarities between different types of violence against women comes from the notion that there is a spectrum of violence against women rooted on the same principles of discrimination and female undermining that ranges from street harassment (Kissling, 1991) to other types of sexual violence like unwanted physical contact (Holmes & Holmes, 2002), physical violence or feminicide (Crowell & Burgess, 1996).
Method
Materials
This study was implemented in two stages: The first one was to collect data on similarity and causality ratings. With these ratings, a set of arguments was constructed, which was then assessed in the second stage. Participants in both stages filled an online questionnaire developed by the researcher. In the first stage the sample was randomly assigned to fill either the causality or the similarity questionnaire. With the information from the first couple of questionnaires, a set of 14 arguments was composed associating pairs of elements from the first stage according to their similarity and causal relation rates using a typical SSA structure. With those arguments, a third questionnaire was built and all the participants were asked to answer it, giving thus the strength ratings for every SSA.
There were six elements selected for the relations and comparisons from each topic (psychoactive drugs and violence against women). Alcohol, marijuana, cocaine, LSD, ecstasy and heroin were the substances. On the other side, workplace discrimination, street harassment, unwanted touching in public transportation, sexual assault, physical violence and feminicide were the selected situations for violence against women.
The similarity questionnaire asked “How alike do you think _____ and _____ are?” once for each possible pair for each topic. The quoted question was asked 15
times for each topic, one for every possible pair between the six mentioned elements. In front of every question there was a sliding bar that allowed the participants to rate the similarity from 1 to 10, 1 meaning ‘completely different’ and 10 being ‘completely alike’. This gave a total of 15 similarity ratings for each topic, associating every element
Figure 1. Facsimile of the similarity questionnaire as presented to the participants.
The causality questionnaire was presented as a matrix that linked every element of the topic –drugs or situations of violence against women– (rows) with six possible consequences (columns) as shown in figure 2. Table 1 shows the six possible consequences for each topic. The participants had to answer with a number from 1 to 10 according to the attributed probability of each element to cause each possible consequence, giving a total of 36 causal probability ratings for each topic.
Table 1.
Topics and possible consequences for the causality questionnaire.
Psychoactive drugs Violence against women
Feeling of wellbeing Transitory emotional discomfort
Dependence Resentment towards the perpetrator
Withdrawal syndrome Psychological trauma Poor personal relationships Helplessness
Loss of academic or work opportunities Detriment of physical abilities Participation in illegal activities Death
Figure 2. Facsimile of the causality questionnaire as presented to participants.
Based on the given ratings, the two closest and two most distant pairs, of the 15 possible for each topic were selected for the creation of the arguments. According to this, the two most and least similar pairs of situations of violence against women and drugs, along with the most and least causally related pairs were identified. This led to a
total of 16 pairs, with two repeated pairs, ending with a total of 14 –seven related to drugs and seven related to violence against women–, resulting in 14 SSA to be assessed. All the arguments followed the same structure: “If we admit __(A)__, that will necessarily lead to the admission of __(B)__. Therefore, we shouldn’t admit __(A)__”.
In front of every argument there was a sliding bar to rate the argument ranging from 0 to 10, 0 meaning ‘It’s not convincing at all’ and 10 being ‘It’s totally convincing’.
Figure 3. Facsimile of a SSA as presented to the participants.
Procedure and Participants
The instruments were developed and distributed through Qualtrics, via word of mouth in the first stage and in the second stage via email with the address supplied in the first survey. A considerable portion of the sample was students from an undergraduate psychology class of the Universidad de los Andes, who received class credit in exchange for their participation. None of the other participants received any kind of reward. Personal data and survey answers of every participant were kept private and confidential. A research proposal for this work was reviewed and approved by the Ethics Committee of the Psychology Department at Universidad de los Andes.
Two online surveys were created for the first stage, both of them started with a brief informed consent, explaining the basics of the study and the participant’s rights.
After that there was a demographics section, asking for an ID, gender, age, profession and a contact email to reach the participants when the second stage’s questionnaire was ready. Then the randomly assigned half of the participants who got the similarity survey and the other half, with the causality survey assigned answered their correspondent questions. There was no prior information on which of the two surveys the participant would respond. Finally, after the similarity or causality questions, all participants were asked for their knowledge and personal opinion on each of the topics presented. The knowledge question had a sliding bar with which participants could rate their knowledge from 1 to 10, 1 being ‘No knowledge at all’ and 10 being ‘Total knowledge’. The opinion question also used a sliding bar ranging from 1 to 10 in which 1 was ‘No damaging at all’ and 10 was ‘Completely damaging’.
In this first stage there were a total of 157 participants, from them only 109 finished the questionnaire, and those were the participants whose answers were considered for the study. The sample was 57.8% female and 42.2% male, between the ages of 18 and 61 years old, with a mean value of 26.8.
Every participant from the first stage received an email with a link to complete the second survey, and a few days later, those who had not answered it, received a reminder. The survey consisted of a question for an ID –in order to associate the results from both stages– and the arguments. Figure 4 shows a flowchart describing the procedure.
95 surveys were collected in the second part. Two of the subjects could not be identified neither with their provided ID nor email, so their information is unavailable. The final sample of 93 participants also had an age range from 18 to 61 years old, with a mean of 26.9, and the proportion of female was 58% against a 42% of male participants.
These indicators suggest that the samples from both stages are equivalent demographically.
Figure 4. Flowchart showing the two stages of the study and their outcomes.
Results
The first stage of data collection for this study resulted in 30 similarity ratings and 72 causality ratings, as described in figure 4. The purpose was to interpret these data in order to be able to compare the ratings for each pair of elements and identify how close they were in terms of both causality and similarity. With this in mind, all the ratings were arranged and analysis were performed until there were two distance matrices for
each topic, one with the results from the causality questionnaire and the other with the results from the similarity questionnaire.
The similarity results (Analysis A) were organized in a matrix with columns and rows composed by the six elements being related. That matrix was then analyzed using the software R, with the function smacofSym (smacof library), which ran a multidimensional analysis with the purpose of identifying how many dimensions could best explain the data behavior. This analysis showed that a model with two dimensions has the best fit with the data, with a stress reduction better than any other addition of dimensions, compared with the unidimensional model. After knowing that the bidimensional model was the best, using the function MdsDiss, a distance matrix was created. This distance matrix shows, according to the bidimensional model, the distances between each pair of elements. Below are shown the resulting distances from the distance matrices for violence and drugs and a graphic representation of those distances. The pairs with the lowest and highest distance values are highlighted.
Table 2
Standardized distance between every pair of psychoactive drugs based on similarity ratings
Substances Alcohol Marijuana Cocaine Heroin LSD Marijuana 0.640
Cocaine 0.637 0.610
Heroin 1 0.926 0.376
LSD 0.963 0.622 0.403 0.424
Ecstasy 0.721 0.897 0.306 0.382 0.652
Figure 5 provides visual proof of the findings shown in Table 3: Feminicide and street harassment is the most distant pair, followed by sexual assault and workplace discrimination. Physical violence and sexual assault are the closest elements, followed by physical violence and feminicide. Likewise, Figure 6 shows that, corresponding with
Table 2, alcohol and heroin are the most distant drugs, followed by alcohol and LSD.
Cocaine and ecstasy are the closest drugs, followed by cocaine and heroin.
Figure 5. Distances between situations of violence against women based on their similarity ratings.
Figure 6. Distances between psychoactive drugs against women based on their similarity ratings.
Table 3
Standardized distances between every pair of situations of violence against women based on similarity ratings
Situations Workplace discrimination Street harassment Unwanted touching in public transportation Sexual assault Physical violence
Street harassment 0.666 Unwanted
touching in public transportation
0.846 0.496
Sexual assault 0.950 0.930 0.501
Physical violence 0.746 0.809 0.471 0.205
Feminicide 0.710 1 0.754 0.433 0.298
The causality ratings analysis (Analysis B) proceeded differently. First, using Microsoft Excel, the collection of individual ratings for every consequence was correlated among pairs. This means that the assigned probability for causation of a consequence (e.g. helplessness) by an element (e.g. sexual assault) was correlated with the probability for the same consequence, by a different element (e.g. street harassment). This was repeated until there were six correlation coefficients (one for each possible consequence) for each of the possible pair of elements. Then the mean of those six coefficients was calculated, leaving a total of 15 (one for each pair) causality estimations.
That number signifies how related were the probabilities for possible consequences among each pair of elements, how alike are they were to cause the same outcome, thus it was considered as an indicator of how those two elements were causally related. Then a matrix connecting every element with themselves and each other was built, and the causality ratings for each pair were places in their corresponding cells. Finally, using the function Dist in R, Euclidean distances were calculated from those indicators. Those distances are shown in the tables below. The pairs with lowest and highest distance values are highlighted.
Table 4
Standardized distance between every pair of psychoactive drugs based on causality ratings.
Substances Alcohol Marijuana Cocaine Heroin LSD Marijuana 0.706
Cocaine 0.798 0.697
Heroin 0.829 0.937 0.581
LSD 0.935 0.680 0.750 0.872
Ecstasy 0.941 0.712 0.766 1 0.533
Table 5
Standardized distances between every pair of situations of violence against women based on causality ratings
Situations Workplace discrimination Street harassment Unwanted touching in public transportation Sexual assault Physical violence
Street harassment 0.499 Unwanted
touching in public transportation
0.465 0.397
Sexual assault 0.776 0.785 0.691
Physical violence 0.594 0.737 0.680 0.696
Feminicide 0.902 0.977 1 0.805 0.822
The two pairs with the highest and lowest distance values from each table (highlighted) were selected. The structure of the argument was decided considering the ‘damaging’ rating collected in the first survey (Analysis C), so that the least damaging
element was the element in consideration, and the most damaging the unwanted consequence in the SSA. To give an example: “If we admit workplace discrimination, that will necessarily lead to the admission of unwanted touching in public transportation. Therefore, we shouldn’t admit workplace discrimination”.
Table 6
Average of strength rating for each argument.
Argument Strength
Alcohol Heroin 2.17
Alcohol LSD 2.31
Cocaine Heroin 4.96
Workplace discrimination unwanted touching 3.04 Workplace discrimination sexual assault 3.32
Ecstasy cocaine 4.45
Ecstasy Heroin 4.41
LSD ecstasy 4.94
Unwanted touching feminicide 4.52
Marijuana Heroin 3.10
Street harassment feminicide 3.45
Street harassment unwanted touching 4.90
Physical violence feminicide 7.20
Physical violence sexual assault 7.43
Table 6 gives a first look into the assessment of SSA, but does not allow the comparison of distances between the components of the argument and strength. So, to test the hypothesis, distance and strength were correlated. First it was with all the data integrated, and then the data were divided by topic (drugs and violence) and source of the argument (either the similarity survey or the causality one). The results of those five correlations are shown in table 7.
Table 7
Correlation coefficients of distance between components and strength of the argument
Section of data Correlation coefficient p-value
Integrated -0.728 0.000
Similarity -0.881 0.001
Causality -0.293 0.240
Drugs -0.743 0.017
Violence -0.733 0.019
The first thing to note is that there is no significant difference between the correlation coefficient and their p-values between the data from drugs and violence arguments. This suggests that in this case the content of the arguments was not a determining variable, and that participants performed the argument evaluations based on other types on information. This also suggests that data from both topics are comparable.
Figure 7. Correlation between strength of the argument and distance between its components.
In general, from the information given by the correlation with integrated data, it can be said similarity, represented by distance, is in fact an important factor. According to the correlation coefficient, the greater the distance between two elements, the lower the SSA is rated. It is also worth noting that there is a remarkable difference in the correlation coefficient when arguments from similarity and causality ratings are viewed separately. The correlation between distance and strength is strong and significant in the SSA created using elements from the similarity survey, but is weak and non-significant in the SSA made from causality ratings. This suggests that only similarity plays an important role in the assessment of SSA. To further explore these results, multiple linear regressions were run. This analysis makes it possible to test the prediction about similarity and causality affecting the strength of the argument and also the effect of personal beliefs and previous knowledge. Table 8 shows the results of the regressions made with the data integrated and partitioned by source. Figures 7 and 8 show the relation between strength of the argument and both distance and opinion, the significant relations with the integrated data according to the regression analysis.
These results show that distance is a good predictor variable for the integrated data. This means that the closer the two parts of the argument are, the significantly higher strength rating the argument gets, as shown in Figure 7. This effect is also apparent when considering the elements rated by similarity and not those rated by causality. This means that two elements that are alike make up a strong and convincing SSA, but not two elements that have a close causal relation. Hence, similarity and not causality is a relevant relation considered when evaluating a SSA.
Table 8
Multiple linear regressions with argument strength as the dependent variable and distance, opinion and knowledge as independent variables
Data F P r2 Independent
Variable T P
Integrated 8.111 0.003 0.587 Distance -3.202 0.007** Knowledge -0.088 0.931 Opinion 2.248 0.044* Integrated 4.896 0.015 0.565 Distance -2.918 0.015*
with Knowledge 0.462 0.654
Categories Opinion 1.807 0.100
Questionnaire 0.776 0.455
Topic 0.843 0.419
Causality 0.728 0.586 -0.131 Distance -0.965 0.389
Subset Knowledge -1.150 0.314
Opinion 0.333 0.756 Similarity 30.86 0.003 0.927 Distance -5.293 0.006**
Subset Knowledge 2.972 0.041*
Opinion 0.932 0.403
Significance codes: ** 0.01, *0.05
Previous knowledge is a significant predictor of argument strength, but only when considering the similarity data. In that case, previous knowledge has a significant and positive effect, meaning that people who reported having a higher knowledge about the topic, gave the SSA a higher rate. This goes against the prediction that people with more knowledge would be less sensitive to argument structure. Moreover, personal opinion was a good predictor of argument strength but only when the integrated data was being examined, as can be seen in Figure 8. The results show that people who thought the elements were highly damaging, gave a higher rating to the argument’s strength. This is understandable because a consequence with negative attributions is more likely to be avoided.
Figure 8. Correlation between strength of the argument and the mean of the opinion ratings of both parts of the argument.
A model predicting the strength rate of an argument that takes into consideration the similarity relation between its components and the participant’s opinions and belief
works accurately with the data and accounts for 92.7% of the variability of the responses. The main inference that can be drawn from these results is that similarity has an important effect in the evaluation of SSA, and at a lesser extent, previous knowledge and personal opinion. Also, it can be said that, against the predictions proposed initially, causal relation did not appear to be related to argument strength.
Discussion
This study was designed to explore the relation of causality and similarity with SSA. Participants were asked to give causality or similarity rates to pairs of elements and then assess how convincing a SSA composed by those pairs of elements was. The expected result was a strong effect of both similarity and causality over the strength of the argument, especially causality, as it has been conceived as the base of similarity judgments themselves. The results did not conform to this prediction, and only similarity showed an effect on the argument’s strength.
This is coherent with the non-published findings of the Cognition and Learning research group, which also found a stronger effect of common traits (similarity) than of a causal trait on the evaluation of SSA. In contrast, Jiménez-Leal and Gaviria (2013) reported that people consider causal information when judging a SSA. However, both studies had a different methodology concerning how to measure both similarity and causal relation, using a trait model instead of a geometrical model as did this study. The geometrical model favors a more flexible estimation of the closeness of the elements, compared to the trait model.
There is actually an inherent problem to this type of study, and is the difficulty of quantifying a relation like similarity or causality. It is hard to think of a direct way to measure the causal relation of two elements. While it is easy to answer ‘how alike x and
y are?’, it might be a little more intricate to consider the causal mechanisms connecting two elements and giving them a causal relation. This is why the causal relation had to be extracted from other measures, while similarity was assessed directly. This could be the reason why there is such a strong effect of similarity but not causality in this study.
There could also be a more conceptual misunderstanding of the general situation. If causal information is what defines similarity estimation, as Jiménez-Leal and Gaviria (2013) proposed, maybe the idea of understanding one independently from the other is not feasible. There could be an enhancement of the understanding of this subject if the relation between causality and similarity were explored on detail and a common mechanism or strategy could be found among them.
There could also be a shift of perspective from similarity and causality between the two elements of a SSA to the analogy than can be established between the elements. There are many people interested in the use of analogy in argumentation, working from different fields (Walton, 2014). Analogies can be relevant in the context of SSA, as an
analogy can be proposed connecting the situation of allowing something with the future hypothetical situation of allowing something else. There can be a shift of severity in the implications of each situation, but both scenarios could be associated and used in a SSA (Hofstadter, 2001). This would change the study object toward structural and superficial similarity between situations (Blanchette & Dunbar, 2000), suggesting a new approach that emphasizes the role of analogy in SSA.
Summing up, the results of this study show that similarity does play a relevant role in the evaluation of a SSA, and that causal relation does not seem to do the same. Both personal opinion and previous knowledge play a smaller role, but can have an effect in the assessment of a SSA. This finding can be applied in the analysis of political discourse, in an attempt to uncover fallacious arguments proclaimed by orators in order to persuade the public. Additionally, being aware of the importance that knowledge can have in the evaluation of a SSA, it would be relevant to use education and information as a tool to create a skeptical public, aware of the power of argumentative structures over content.
These findings and applications could be expanded in the future by bringing a new and more direct and effective way of measuring causal relations, shifting perspectives on the study of SSA and opening up the subject to other cognitive processes that could be part of the SSA appraisal. Finally, examining more closely the particular knowledge and ideological position of the people evaluating a SSA could also bring a better understanding of what makes a convincing SSA.
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