PREDICTIVE PROGRAMMING - THE SERIES - EPISODE O...

The conspiracists also often argue that, although governments already have the solution to the problem they created in the first place, they deliberately wait for the right time to implement the solution in order to cause the most damage to people's ability to think for themselves. Some believers even claim that predictive programming is actually a highly advanced form of AI used for the psychological conditioning of the masses. Needless to say, these types of arguments are filled with logical fallacies, doctored footage and documents, outright lies, and a complete lack of proper scientific research or rigor. So, why do some people still believe them

Psychologists and researchers consider incidents of predictive programming as either coincidences or likely scenarios based on real research. For example, it is not that difficult to imagine a scenario where an airplane is used as a weapon and write a book or movie with this as a plot, so when a similar event occurs in real life, conspiracists claim the earlier book or movie was actually a prediction. This tendency to view events as more predictable than they really are is called hindsight bias and is a common psychological response to a traumatic event.

A belief in predictive programming may also result from framing bias. This is when someone makes a decision or forms a belief because of the specific way information is presented to them, rather than on the objective facts. Many people come to a belief in conspiracy theories through listening, reading, or watching influencers or media personalities that they have come to trust because they seem familiar, rather than because they have been demonstrated to be truthful. Whereas, if such ideas are presented in a different way or by someone they do not trust, the believer may be more likely to come to a different conclusion about the information.

Some reports also suggest that people may believe in predictive programming due to pareidolia, a generalized term for seeing patterns in random data. Face pareidolia, where people see faces in random objects or patterns of light and shadow is a common phenomenon. Some common examples are seeing a likeness of Jesus in a piece of toast or an image of a bird in a cloud. Once considered a symptom of psychosis, it actually arises from an error in visual perception.

Let's start by saying that predictive programming is not a scientific theory, even remotely. It is a complex of ideas developed by groups of people who believe, among other things, that the world is run by a totalitarian government of \"lizardmen\".

Is it true that the current Ebola virus outbreak was predicted in an Oct. 19, 1997, episode of the animated TV series \"The Simpsons\" An email says that this was before the public was even aware that Ebola hemorrhagic fever existed.

\"Is there something we are not being told Is it by pure chance & coincidence that the Simpsons would do predictive programming There has been numerous predictions from 'The Simpsons,' which revealed futuristic events which came to pass. What of the Ebola recurrence in 2014 Is it planned or is it just by natural means Ebola was not in the 90s, but it happened long ago in the mid 70's ... But 'The Simpsons' [revealed] in late '90s the Ebola virus ... Interesting right\"

In this episode we will be discussing the theory of predictive programming and how it has some how meshed with popular culture...it may even be in our books, films and tv shows. How many coincidences can there be until it is no longer a coincidence Or is it all just an elaborate conspiracy theory made up by a few Check out our website:

Conspiracy theorists have said that most examples of predictive programming can be found in cartoons. Examples of this are shows like Johnny Bravo which predicted 9/11 months before it happened and episodes of Family Guy which predicted the Boston Bombings before they happened.

As it turns out, there's a whole theory about the power of media to predict and prepare us for the future. It's called predictive programming, and it encompasses not just terrorist attacks but new technologies and the existence of aliens.

Coined by conspiracist Alan Watt, predictive programming is the theory that ideas, situations and new technologies are carefully written into movies, TV shows and books to groom the general population into accepting societal changes. Examples include the pilot episode of \"The Lone Gunmen,\" where a hijacked plane was flown into the World Trade Center as a false flag attack; \"The Dark Knight Rises,\" which features a map of Gotham where one of the marked locations is Sandy Hook; and an episode of \"Family Guy\" in which Peter Griffin drives through the Boston Marathon, released only a few months before the bombing at the 2013 Boston Marathon.

The primary objective of the study was to document device activity and performance in patients with a Class I indication seen in daily practice. In order to assess SST capability to detect and categorize device-based episodes, sensitivity, specificity, positive- and negative predictive values were computed based on the number device-detected, discriminated and categorized VTAs, ATP therapies, and rescue shocks. Computation of episodes considered both number of episodes and number of patients having experienced one or more of such episodes during the follow-up period. The secondary objective was to report medical outcomes in these patients: adjudicated symptomatic events; hospitalizations (all-cause, cardiac, and arrhythmia-related), deaths (all-cause, cardiac), and severe adverse events including serious adverse device effects (SADE). Finally, in addition to symptoms registered at any of the follow-up visits, patients were encouraged to consult or to inform the follow-up center if they had symptoms suggestive of arrhythmia episodes such as syncope, pre-syncope, palpitations or shock. An expert board of two experienced investigators and one external expert, not involved in the trial, analyzed these events. Symptomatic events were adjudicated and classified as VTA or not by the expert board, using the device-recorded data.

Based on our research, we rate FALSE the claim The Simpsons predicted the monkeypox outbreak. The creators of the series did not predict the spread of monkeypox in 2022 in the USA. The two images are from different episodes and do not depict monkeypox.

Objectives: The purpose of this study was to determine whether incorporation of a 2-part artificial intelligence (AI) filter can improve the positive predictive value (PPV) of implantable loop recorder (ILR)-detected atrial fibrillation (AF) episodes.

Conclusions: Despite currently available ILR programming options, designed to maximize PPV in a given population, false-positive AF episodes remain common. An AI-based solution may significantly reduce the time and effort needed to adjudicate these false-positive events.

The excerpt comes from episode 14 in season 2 of the American series \"Dead Zone\". The main actor, Anthony Michael Hall, plays a man who went into a coma after being injured in a car accident. When he wakes up six months later, he can see the future through strange visions.

Many theorists allege that the contents of fictional media, in a process called \"predictive programming,\" are manipulated to reference planned false flags, technological innovations, social changes, and other future events.[77] These references are understood to be a conditioning and brainwashing tool, such that the public becomes more accepting of these events than they would be otherwise.[77][78]

Television (TV) programming attracts ever-growing audiences and dominates the cultural zeitgeist. Viewership and social media engagement have become standard indices of programming success. However, accurately predicting individual episode success or future show performance using traditional metrics remains a challenge. Here we examine whether TV viewership and Twitter activity can be predicted using electroencephalography (EEG) measures, which are less affected by reporting biases and which are commonly associated with different cognitive processes. 331 participants watched an hour-long episode from one of nine prime-time shows (36 participants per episode). Three frequency-based measures were extracted: fronto-central alpha/beta asymmetry (indexing approach motivation), fronto-central alpha/theta power (indexing attention), and fronto-central theta/gamma power (indexing memory processing). All three EEG measures and the composite EEG score significantly correlated across episode segments with the two behavioral measures of TV viewership and Twitter volume. EEG measures explained more variance than either of the behavioral metrics and mediated the relationship between the two. Attentional focus was integral for both audience retention and Twitter activity, while emotional motivation was specifically linked with social engagement and program segments with high TV viewership. These findings highlight the viability of using EEG measures to predict success of TV programming and identify cognitive processes that contribute to audience engagement with television shows.

Early-warning signals (EWS) have been successfully employed to predict transitions in research fields such as biology, ecology, and psychiatry. The predictive properties of EWS might aid in foreseeing transitions in mood episodes (i.e. recurrent episodes of mania and depression) in bipolar disorder (BD) patients. We analyzed actigraphy data assessed during normal daily life to investigate the feasibility of using EWS to predict mood transitions in bipolar patients. Actigraphy data of 15 patients diagnosed with BD Type I collected continuously for 180 days were used. Our final sample included eight patients that experienced a mood episode, three manic episodes and five depressed episodes. Actigraphy data derived generic EWS (variance and kurtosis) and context-driven EWS (autocorrelation at lag-720) were used to determine if these were associated to upcoming bipolar episodes. Spectral analysis was used to predict changes in the periodicity of the sleep/wake cycle. The study procedures were pre-registered. Results indicated that in seven out of eight patients at least one of the EWS did show a significant change-up till four weeks before episode onset. For the generic EWS the direction of change was always in the expected direction, whereas for the context-driven EWS the observed effect was often in the direction opposite of what was expected. The actigraphy data derived EWS and spectral analysis showed promise for the prediction of upcoming transitions in mood episodes in bipolar patients. Further studies into false positive rates are suggested to improve effectiveness for EWS to identify upcoming bipolar episode onsets. 59ce067264

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