- The Devil's Deception (Talbis Iblis)
- The Devil’s Deception of the Salafi-Deobandi Partnership
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Why was Eve deceived? To beguile; to cheat. To cut off from expectation; to frustrate or disappoint; 4. To take from; to rob. Download Sermon with PRO. Browse All Media Related Media. Talk about it Nobody has commented yet. Be the first! Join the discussion. Sign in to leave a comment. Your Viewing History Browse All.
Free Sermon Outlines Theme-based sermon outlines for your church. Online Sermon Editor Free for pastors and preachers. Premium Series Kits. Devices Of The Devil-Deception. Add a note optional. Having trouble logging into your account? Product Description The Remembrance Of Death And The Afterlife If a person had no other distress or anguish before them except death, this would be sufficient to disturb their livelihood.
Death is Imam Ibn Al-Qayyim Al-Jawziyyah Hikmah Publications From the plots of the Shaytan and his traps which he uses to influence those who possess only a small share of knowledge, intlellect, and Customers Also Viewed. The subject of good and evils is one which provokes much controversy. The reason for that controversy is that people fail to understand the true meaning of life. That is because, with very few Out of stock. It is commonplace to find many Muslims involved in misused of the tongue, without even recognizing its inherently evil consequences.
Indeed the enmity between man and Shaytaan is old, commencing from the time that Adam alayhi as-salaam was created — from the time he was ordered to prostrate to him. Shaytaan refused, become The Prophet peace be upon him said, ' There is no trial from the time of Adam's a. However, interpersonal interactions are constituted by more than the isolated behaviors of the interlocutors. Interlocutors spontaneously entrain their bodily movement, breathing and turn-taking, re-use each words, and develop shared routines as they interact [ 13 ].
Interpersonal behavioral coordination has been found to be ubiquitous and has been argued to serve a range of communicative functions, such as forging shared understanding and creating a sense of social rapport [ 14 — 16 ]. Nevertheless, little empirical research has been done on whether less savory contexts, such as deceptive and conflictual interactions, might also involve and modulate behavioral coordination.
The goal of this paper is to bridge this gap by systematically investigating interpersonal behavioral coordination within naturalistic open-ended deceptive and truthful conversations. We examine conversational deception across unscripted extended conversations where varied, but experimentally controlled, goals exist. This is a divergence from previous deception research where a small number of potential partners delivered instructions in a prescribed manner, and in which the deceiver knows that the partner is looking for signs of guilt.
Although this can have notable advantages in terms of content control and simulating forensic contexts, it potentially limits the dynamical mechanisms that are central to the emergence of shared signals in open-ended and unconstrained conversations—the very contexts in which everyday deception predominantly occurs [ 17 ]. We situate our work within a synergistic view of behavioral coordination [ 3 , 18 ].
In this way, the analysis is conducted at the level of the dyad and not reduced to the behavior of a single participant c. Coordinated behaviors do not need to be isomorphic and occur close in time, as would be the case for behavioral mimicry [ 14 ]; rather, they can be distributed and loosely coupled across various local and global temporal scales [ 22 — 25 ]. The analysis of such coordination requires the use of unique statistical methods that capture time-evolving interdependent behaviors, including windowed lagged cross correlation and cross recurrence quantification analysis, two methods employed in the current study.
Crucially, the synergistic approach argues that temporal patterns of low-level, continuous, and spontaneous behavioral coordination work in concert with more intentional higher-level processes, so that behavioral coordination is shaped by the goals and context of the interaction c. We thus evaluate the coordination of complementary behavioral channels—continuous head movements and speech rate—across different conversational contexts.
Head movements between conversational partners tend to nonlinearly interact in closely aligned windows of time and are amenable to evaluation as locally coupled sequences of shared activity across minimal delays in time. Speech rate is a complementary measure to movement in that it allows us to examine the more global properties of coordination. Speech occurs in largely non-overlapping turns between interlocutors: when one interlocutor speaks, most often the other does not.
Moreover, coordination of speech rate does not necessarily have to occur over contiguous sequences, but can have temporally extended influences. Increases of speech rate by one partner early in a conversation can be echoed by the interlocutor later in the conversation. Additionally, we expect both behaviors, head movements and speech rate, to become coordinated due to their importance in signaling communicative functions of shared attention, active participation, and cooperation [ 27 — 30 ].
A great deal of research has also shown that people unintentionally align and coordinate their speech during communication [ 35 — 37 ], where doing so serves to index feelings of closeness and attitude similarity [ 38 — 40 ]. The focus on examining deception during disagreement and agreement conversations allows us to explore how behavioral coordination might change as a result of a change in high-level conversational goals. Typically, interpersonal behavioral coordination is thought to enable shared mental and action representations [ 41 , 15 ], as well as index general positive social outcomes, such as increased liking and rapport, blurred self-other boundaries, and enhanced altruistic behavior and cooperation [ 16 , 42 — 43 ].
It is assumed that when these shared informational and affiliative processes are disrupted, as in disagreement, decreased and less stable behavioral coordination will follow [ 44 — 45 ].
Thus, we predict that when deception is not a factor, agreement will show greater behavioral coordination than disagreement conversations. It becomes more of an open question as to how behavioral coordination will be expressed when deception is introduced. At one level, deceivers must continue the normal work of collaborating with their conversational partners to establish shared meaning, but at the same time, they have to navigate a number of cognitive challenges associated with deception: i.
As a consequence, deception, like disagreement, can be thought of as being disruptive. Moreover, in a situation where a conversation involves both deception and disagreement, a situation of maximum disruption, behavioral coordination might be most impaired. But there is also an alternative hypothesis to consider—one based on the aforementioned synergistic view. Rather than being an indiscriminate index of cognitive load or rapport, behavioral coordination may instead arise from strategic, adaptive conversational goals that override these factors [ 3 , 18 ].
From this perspective, behavioral coordination serves multiple functions that depend on unique contextual demands.
The Devil's Deception (Talbis Iblis)
In deception, a particularly important demand, at least for the deceiver, is in managing appearances of believability to avoid violations of social norms [ 50 ]. In turn, this greater attunement to the other, particularly in extended interpersonal interaction marked by an open channel of reciprocal involvement, could result in increased and more stable coordination. As a result, behavioral coordination in these conversations, rather than being most impaired, will instead be most pronounced. There is some support for this prediction based on recent work by [ 52 ].
In their study, behavioral coordination between deceivers and a confederate partner was assessed by a group of human raters who rated, amongst other measures of behavior, a general "gestalt" of perceived synchrony. Critically, impressions of behavioral coordination were highest in deception during a conflictual versus neutral phase of the interaction. Participants were recruited to have two 8-minute conversations about political and social topics that typically engender strong opinions.
Written informed consent was obtained from every participant prior to the study, and the procedure was approved by the local Ethics Review Board University of California Merced. All participants were compensated with extra course credit for participation. Participants were led to separate private rooms where they completed a item questionnaire developed by [ 45 ].
These items required participants to provide a one- to two-sentence rationale to support their true opinion on abortion, universal health care, gay marriage, marijuana legalization, death penalty, political party affiliation, war in the middle east, legal drinking age, taxing rich Americans, and financial aid criteria. For each item, the strength of their opinion was also recorded on a 4-point Likert scale. The responses were used to optimally select topics that participants agreed or disagreed on e.
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In the rare occasions where this selection criterion could not be met, participants were excused from further participation. Participants were then led to a large common room where they stood face-to-face at a distance of approximately 6 feet and no less than 3 feet given bounded regions marked on the floor.
The experimenter provided instructions depending on the experimental conditions for the conversation: agreement vs. The other participant hereafter "naive" was given a short questionnaire to assess general emotional state and was asked to wait in the room until the experimenter returned. The naive participant was also told that a problem with the audio equipment had to be addressed and the wait might be a few minutes. The experimenter then entered the private room of the DA, who was informed that she had been selected to discuss a topic with the naive participant by taking an opinion opposite of her own true beliefs.
In the disagreement condition, we chose the topic in which participants had originally shared a similar opinion. For example, if the DA supported marijuana legalization, she had to now argue against legalization with a partner who truly supported it. In this way, the conversation involved ostensible disagreement with one partner providing information that is known to be false to a partner who is unaware of the true state of affairs. Conversely, in the agreement condition, we chose the topic in which participants had originally given dissimilar opinions.
Instructions were also given to the DA not to reveal her actual beliefs to the naive participant thus, the DA was instructed to lie , and that lying successfully in this way is indicative of skilled argumentative abilities. The DA was given three minutes to consider various talking points to be used in the upcoming conversation. Participants were then brought back into the main common room and again stood face-to-face at a comfortable distance.
At this point both participants were told which topic they would discuss e. For those in the disagreement condition, participants were also told that they should attempt to convince the other of their opinion. For those in the agreement condition, participants were told that they should discuss the merits of their shared opinion in order to prepare for a hypothetical debate with a team that holds the opposite view. The importance of staying on topic was also stressed. After conversing for eight minutes, participants were instructed to return to their original separate rooms to complete a three-item questionnaire to gauge interpersonal rapport after conversing cf.
Participants were then brought back into the main common room for a second conversation where they honestly discussed a topic in which both agreed or disagreed depending on their assigned condition. After this conversation, participants returned to separate rooms to complete the questionnaire set and were finally debriefed. Participants were also asked whether they suspected anything unusual about the conversations. No naive participant reported suspicion of deception.
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Written informed consent was obtained from every participant prior to the study, who were also aware that video and audio recordings of their behavior would be collected and thus they were not completely anonymous to researchers during and after data collection. All procedures were approved by the local Ethics Review Board. Twelve pairs had to be discarded due to technical problems e. This resulted in 22 pairs for the disagreement, and 24 for the agreement condition. The final number of dyads analyzed 46 is similar to other studies conducted in this area e.
Dyads were largely mixed-sex and female mixed: 20; female-female: 22; male-male: 4. No dyads reported knowing each other well prior to the interaction. During each interaction, the speech and movements of participants were recorded with lapel microphones attached to each participant and connected to a Canon HD Vixia camcorder. The camcorder was placed approximately 15 feet from the participants to capture a side view of the interaction. Audio was recorded as separate channels and synched to the video. The video and audio streams were analyzed using automated computational techniques, capturing a time series of head movements and speech rate.
The Devil’s Deception of the Salafi-Deobandi Partnership
We then compared the time series from each dyad, for each modality, to derive modality-specific measures of interpersonal coordination. These movements are undifferentiated in that, rather than head movements as isolated and discrete events, they are treated as a continuous, gestalt-like signal of rhythmic change, also known as motion energy flows [ 55 ]. These are extracted with a technique similar to that of [ 55 ], and recently implemented by [ 56 ] but extended here to focus on targeted body regions.
This method takes advantage of the RGB values encoded by each pixel three color values, ranging from 0 to in each video frame. When people move, RGB values will change from frame to frame across corresponding pixels and remain static for pixels capturing the background. As movements become more pronounced, more pixels will be affected.
We then compute the absolute summed difference across the pixel changes across every 5th consecutive frame i. This is plotted as a time series where the y-axis represents the absolute summed difference values please see Fig 1 for a systematic walk-through of this method. The result is a continuous signal of movement displacement that captures the duration and extent of movement change. Top panel Example of movement change across two sequential points in time, targeting head movements gray boxes.
Middle Panel Pixels that change from frame-to-frame are converted to a white dot for visualization purposes, producing motion energy flows. Bottom panel The number of pixels that change from frame-to-frame, repeated over the length of the video, are converted into a time series that captures degree of movement displacement for each participant. In the next step, we apply a windowed lagged cross correlation WLCC technique to derive measurements of shared behaviors. WLCC has recently been used in a number of analyses to assess how two time series change together on a moment-by-moment basis [ 25 , 34 ], and is robust against statistical assumptions that are problematic for more traditional analyses, such as assumptions of stationarity i.
WLCC computes cross correlations within small moving windows of time ten seconds , and then aggregates these windows to produce an overall measure of similarity. These aggregated points are then plotted, producing, for example, the WLCC profiles reported in the Results section. This region corresponds to near-simultaneous shared activity. Each of these lags corresponds to the immediate responsiveness of one partner relative to the other. Given that the positive and negative lags correspond to differences in who follows who, the measurement over positive lags is hereafter referred to as "DAFollows ms" and the measurement over negative lags is hereafter referred to as "NaiveFollows ms.
About minutes of open-ended conversational dialogue were collected: two eight-minute conversations from each of the 46 dyads. Within the Praat environment—a widely used speech analysis computer program [ 57 ]—a team of four human "taggers" were trained to mark the beginning and end of each spoken utterance by the use of auditory and visual cues e. In cases where separate stereo channels were not available and conversations had to be transcribed across a mono channel, taggers relied mostly on audio feedback although the single waveform was visible; this situation applied to 12 conversations due to experimenter error in setting up recording equipment.
We employed utterance boundaries to precisely extract fundamental frequency Hz and intensity dB using Praat and correcting for octave jumps and other artifacts. Voiced peaks in intensity were automatically isolated and employed as proxies for vowel onsets, according to the procedure in [ 58 ]. In order to assess shared dynamics between the interlocutors, we could not rely on a simple count of average syllable count per minute, we instead needed a continuous time series displaying changes of pace over time.
Therefore, we used 5-second windows with a ms slide to generate time-series of estimated syllables per minute at 3Hz. In other words, we estimated how many syllables would have been generated if the speaker had maintained that rhythm for a full minute multiplying the number of syllables in the 5 second window by Then we shifted the window forward of ms and repeated. This procedure was validated for other interval time series in [ 59 ]. We thus achieved continuous uniformly sampled time-series of estimated syllables per minute, analogous to the movement displacement time-series.
To derive measures of global coordination, we cannot use WLCC because it requires the events analyzed to co-occur in time or at fixed lags. Instead, we employed Cross Recurrence Quantification Analysis CRQA , a nonlinear and more flexible analog of cross correlation that quantifies shared dynamics between time series.
CRQA employs the Takens theorem [ 60 ] to reconstruct the phase space in which the two time series move. In other words, CRQA identifies all possible combination of states in which the two time series can be e. A speaking 4 syllables per second, while B 5 per second; and on to all other possible combinations of values. CRQA then maps the trajectory of the time series at all possible lags within such phase space, isolating those instances in which the two speakers present similar speech rate dynamics. By reconstructing the possible states of the two systems and assessing the points in time in which they visit similar states, CRQA quantifies how often the two systems display similar patterns of change, and how complex the structure of the entrainment between their trajectories is.
This analysis of entrainment across all possible lags enabled us to analyze coordination in speech rate time series that presented a turn-taking structure. CRQA was originally designed to explore how two systems come to share similar dynamics in a common state space. It has been applied to many types of biological and physical systems, where an earlier state of one signal, as a state attractor, can influence the states of another signal removed in time.
In terms of two people talking, the earlier speech rate of one participant can have an influence on the other later in the interaction. CRQA thus assesses coordination that is not necessarily limited to contiguous sequences of behavior, and quantifies its global properties, such as temporal extension and flexibility. CRQA was then used to assess how similar the general dynamics of speech rate were across interlocutors: do we observe similar values of speech rate across interlocutors? Are sequences of speech rate produced by A re-used later on by B independently of how much later?
And so on. In particular, CRQA produces different indexes of cross recurrence, which we used to quantify different properties of speech rate coordination:. Amount of coordination : defined as the percentage of single values that mutually recur are present across the entirety of both time series recurrence rate, RR. The higher the amount of coordination, the more the interlocutors will display similar speech rate values, though not necessarily at the same time or displaying the same fine temporal dynamics.
Stability of coordination , articulated in: the percentage of values that do not recur in isolation, but form sequences of contiguous repeated values DET ; average length of sequences repeated across time-series L ; length of longest repeated sequence LMAX ; and average distance between repetitions T2. As detailed above, CRQA analyzes coordination across all possible time lags, therefore, a stable repeated sequence might involve individual speech rate sequences from distant utterances.
What matters is that speech rate values that are contiguous in the first speaker are also contiguous in the second speaker, be that at an earlier or later point. Complexity of coordination : defined as low if all repeated sequences are of the same length, high if repeated sequences vary in length entropy, ENTR , thus suggesting that coordination is flexible and not mechanical imitation.
The higher the complexity of coordination, the more diversity we observe in the repeated patterns across interlocutors: sometimes the shared sequence is only a ms long, perhaps pertaining to short bouts of backchanneling, sometimes it stretches across much longer periods, as full conversational moves are matched across interlocutors. These indexes enable us to assess the structure of coordination in terms of whether interlocutors share a similar speech rate, but also in how this similarity is structured in time: just maintaining the same range of values or repeating highly articulated sequences for long stretches of time.
All analyses were also calculated using the CRP toolbox in Matlab a. For further details on the methods see [ 61 , 62 ]. In order to ensure that the levels of coordination observed in movement and speech rate were due to the actual interaction and not simply to the constraints of the task standing in a room facing another person during a conversation , we compared real pairs of interlocutors with virtual pairs.
These virtual pairs were artificially constructed by juxtaposing two interlocutors from different conversations. Doing so breaks up the perceptual and temporal dependencies between partners, but still preserves the general pattern of behavior. If synchrony were found here, this would undermine the claim that coordination emerges from the real-time dynamics of interaction.
The virtual pairs control baseline was originally introduced by [ 63 ].
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To confirm that our manipulation of agreement and disagreement conversations were perceived by conversational partners as engendering more or less rapport, we asked three questions after each conversation. A higher absolute difference score would indicate a more divergent opinion, whereas a lower absolute difference score indicates a more shared opinion.
These were examined via two diverse analytical approaches. The first is with mixed effect modeling that is best suited for identifying differences while simultaneously taking into account within-participant in our case, within-dyad idiosyncrasies. There is a limitation of such modeling in that it remains unclear whether the results might generalize across dyads to any dyad.
Or is it possible to individuate speech rate thresholds that indicate the general likelihood of deception in any dyad? These are crucial questions for the study of deception and motivate our second analytical approach. We use a cross-validation technique with training and tests sets that assesses the possibility of creating a model of deceptive cues from one set of dyads training , in order to assess deception in a second set of never-seen-before dyads. These analyses are elaborated upon further in the following two sections.
All comparisons reported here were evaluated using a linear mixed-effects model framework from the lme4 module within the R statistical package [ 64 ]. Agreement; coded as Truth; coded as 0. Female-Male interactions were entered as fixed-effect predictors. In addition, Dyad 46 and Topic 10 were entered as random effects including random slopes for Conflict and Veracity. Given the limited amount of degrees of freedom in the data, we only looked at interactions between Conflict and Veracity. In cases where the models could not converge due to the complexity of the random effect structure, we removed effects one-by-one until convergence was achieved, simultaneously ensuring via likelihood ratio tests that the simpler model did not statistically vary from the more complex model in terms of variance captured.
For all models we report an overall measure of captured variance, coefficients of the predictors, their standard error, and p -values for each of the factors in the model. Captured variance is reported as Marginal R 2 Rm 2 —variance explained by fixed factors alone—and Conditional R 2 R 2 —variance explained by fixed and random factors together—and computed using the MuMIn R statistical package [ 65 ].
To find deceptive and disagreement cues that might generalize across dyads, we also assessed the unique contribution of multiple behavioral variables in predicting outcomes of interest, retaining only those that contribute unique sources of variance in making predictions. Thus, if movement and speech channels all reflect the same underlying dynamics, then only a small number of variables is needed. For these analyses, we employed a 5-fold cross-validated feature selection and multiple regression models [ 66 , 67 ], using the Statistics, Bioinformatics, and MICP toolboxes in Matlab a.
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Such a large number of independent variables run the risk of overfitting the data when drawing predictions. To address this, we used a common algorithm to select a parsimonious subset of features tailored to each dependent variable: ElasticNet feature selection [ 68 ]. ElasticNet assesses the correlation between variables and selects the minimal subset preserving the overall variance of the dataset. We begin by splitting the dataset into five subsets with each dyad belonging to only one subset.
Each subset then becomes the testing set to optimize the features selected by ElasticNet on the other four subsets. Per each dependent variable, we thus employed the relevant optimal variable set in a multiple logistic regression model, maintaining the 5-fold cross-validated procedure. The 5-fold cross-validation ensures generalizability of the results: the model is fit to four fifths of the dyads and the statistical significance of the regression models and their effect sizes are only calculated on the remaining fifth.
The statistical accuracy of the logistic regression models was then balanced using variational Bayesian inference, which conservatively compensates for missing data, individual variability along the dyads, and makes sure sensitivity and specificity are at comparable level [ 69 ]. Finally, given the random nature of the fold-split, the process was repeated times to assess reliability of the results, reported as mean and confidence intervals across all runs.
As shown in the Fig 2 WLCC profile, there is little visual evidence of coordination between Veracity and Conflict across any lag series, which was statistically confirmed through linear mixed effects models.