What makes you different subtitulada

Thanks for the information!! Personally, I like to make mnemonic devices to help me remember terms. Both would work! This blog definitely made me think about how limited our memory is and how we can strengthen it. I found this blog very interesting because this always happens to me. I never understood who knew why that happened. I always just thought it was easier to remember song lyrics because it has a good rhythm to it and you can remember it by that.

So when I would study, I would try and make a song. But it never worked. It is interesting to find out the real reason why that happens. I always figured that song lyrics were stored in a different part of the brain than information we learn in school, but I guess not. It would have been interesting if you looked into how things click into cement memory even if we only heard the song a few times.

Is this because we only use one part of our brain to store memories? Thanks for the interesting post I really enjoyed it! I really relate to this because I use songs I know to remember information. For example, when I was in the third grade I had to memorize all the rivers in the United States,so I made it link with a song I know. Sometimes I even make up my own beat. It really helps me to remember information. I really wonder though if there is a way to open up our brains more that way we can forget some of the past we remember and make room for the new.

I forget all kinds of things all the time, and now I even developed a habit — I assume I lose something for sure every time I leave a place, and think what I have lost. I am glad that I am not the only one who forgets about things.

But by the time hip-hop crept into the s, violent overtones were the norm in rap songs, drawing the ire of political pundits and activists who were appalled by the content featured in the culture's songs. That decade would see hip-hop continuously come under fire due to controversial lyrics. The critics perceived the lines as promoting violent acts and criminality under the guise of "keeping it real.

Fans and the greater public have become somewhat desensitized from the harsh realities laid on wax and now categorize it as closer to entertainment than a testimonial. Besides, who doesn't like a little bit of hardcore rap from time to time?

With that in mind, XXL highlights 50 of the most violent lyrics we've come across throughout the years. One of the most widely known phenomena in psychology is the mere exposure effect, a phenomenon where repeated exposure to a non-aversive stimulus increases preference for it [ 20 — 22 ].

One implication of this principle for the present question is that simpler, more repetitive lyrics as these pieces essentially have this effect baked into them and thus may tend to be preferred all other things being equal. Further, songs with more repetitive lyrics may enjoy certain advantages in terms of information transmission as they are easier to remember [ 23 ] and likely easier to transmit with fidelity [ 24 — 26 ].

Why might pop songs become lyrically simpler in times when more new songs are produced? Theory and research from diverse literatures suggest that songs with simpler lyrics might be especially successful when there are more new songs to choose from. First, humans are cognitive misers.

People have limited information-processing capacities [ 28 ], and are known to conserve mental resources [ 29 ]. Consequently, humans often use shortcuts in decision-making [ 30 , 31 ]. Thus, when there are more products to be evaluated, people may increasingly prefer simpler products as they may require less mental effort to engage with.

The mere exposure effect might also have a greater influence on decision making in such contexts as well, given that it too can be thought of as a heuristic or even instinctive evaluation. Further, across real-world studies and in-laboratory experiments, when people are confronted with a greater number of options to choose from, they are more likely to choose simpler, less cognitively demanding products [ 34 ].

Taken together, this work suggests that pop songs on average might become lyrical simpler in times when people are exposed to greater amounts of new songs and that success of such songs might be more strongly linked to lyrical simplicity in such times. We also do so while including a number cultural and ecological control variables, as prior work demonstrates that well-studied ecological features, such as resource levels, pathogen threat, and sources of external threat e. For example, both resource scarcity and pathogen prevalence have been associated with conformity, innovation, and creativity in prior work [ 35 , 39 , 40 ].

We gathered data from 14, songs that entered the Billboard Hot charts spanning the period from the charts inception to The Billboard Hot tracks the most popular songs each week based on music sales, radio airplay, and internet streaming. To operationalize lyrical complexity vs. Further, song lyrics are tractable to work with when using an automated compression algorithm. We used a variant of the established LZ77 compression algorithm.

In brief, the LZ77 algorithm works by finding repeated substrings and replacing them with 'match' objects pointing back to the string's previous occurrence. A match is encoded as a tuple D , L , with D being the distance to the substring's previous occurrence, and L being its length.

We treated these matches as costing 3 bytes. This way, a repeated string only leads to space savings if it is of at least length 4, and longer repetitions lead to greater relative savings. The compression ratios of songs in our dataset i. Most of the byte savings when compressing song lyrics arise from large, multi-line sections most importantly the chorus, and chorus-like hooks. Another significant contributor are multi-word phrases, which may be repeated in variations across different lines for poetic effect e.

The compression may make use of repeated individual words, or even sub-word units that repeat perhaps incidentally , but their contribution to the overall compressibility is low.

Higher compression scores signify more repetition and therefore higher simplicity. A score of 0 means no compression was possible e. We computed mean compressibility for each year based on all songs that entered the Hot charts in a given year for which we were able to scrape lyrics — Some of the theoretical positions we draw on to evaluate possible reasons for changes in lyrical complexity suggest that more compressible songs may be more likely to be successful.

To evaluate this proposition, we additionally gathered data on the highest position of each song in the sample achieved on the Billboard charts. In the spirit of the multiverse analyses [ 43 ], we used three separate indicators to assess the amount of new music to which people are likely exposed in a given year.

For each year — we computed the total number of songs which made the Hot chart, the number of musical releases per year according to Discogs Discogs. We assessed a range of well-studied socioecological factors e. Resource scarcity has been linked to greater conformity [ 39 ] and cross-temporal work has found that greater resource levels are linked to more innovation and creative output [ 40 ] and less conformity [ 44 , 45 ]. Higher levels of infectious disease have also been linked to more conformity [ 46 , 47 ], traditionalism [ 48 ], and tight social norms [ 35 , 49 ].

External threats , due to climate or war, have also been linked to more traditional outlooks and tight social norms [ 49 ], which might similarly bear on trends in lyrical simplicity. We thus included publicly accessible data indexing these factors GDP per capita, GDP growth, unemployment, pathogen prevalence, climatic stress, and participation of the US in major armed conflicts. The data used in our analyses covered the years — We also explore the possible impact of other socioecological factors that might plausibly affect lyrical simplicity.

One might speculate that immigration could drive increases in lyrical simplicity. For example, simpler lyrics in American pop songs might be linked to shifts in the amount of people for whom English may not be a first language. To assess the possibility that a rise in simpler English lyrics might be linked to shifts in the amount of people for whom English may not be a first language, we used data on the number of green cards issued from the Department of Homeland Security as a marker of immigration.

To assess possibilities linked to ethnic fractionalization, we used data on ethnic fractionalization from the US Census Bureau. Research on the consequences of residential mobility also suggests that perhaps this variable might also affect lyrical trends.

Previous studies have linked residential mobility to greater susceptibility to the mere exposure effect and greater preference for familiar cultural products [ 52 ]; thus, it may be that mobility is also linked to temporal variations in lyrical complexity of pop songs.

At the same time, a simpler variable might also be driving this effect. Perhaps products that succeed with a larger audience are merely simpler, akin to a lowest common denominator effect. Because the U. Thus, we also gathered data on the total size of the US population from macrotrends. Prior work has found conservatives show a preference for simple and unambiguous art, speech patterns, and literature [ 53 — 57 ] though see also Conway et al.

Thus, one might suspect that possible changes in conservatism could be driving lyrical simplicity. Somewhat similarly, other evidence suggests that cross-cultural differences in aesthetic preferences and expression are linked to orientations toward collectivism [ 59 , 60 ]. Thus, we also gathered data on indicators of conservative ideology , operationalized conservatism as the average percent of annual survey respondents in Gallup polls identifying as conservative, and we included as an index of cultural level collectivism based on frequency of collectivism related words in the Google Ngrams American English corpus [ 45 ].

It provides a conservative estimate, which is preferred because time series data is rarely normally distributed. In the initial step, we examined zero-order relationships between each of the three indices of available novel song choices and average lyrical compressibility of popular songs. Next, we created a composite index of novel song choices and assessed the robustness of the hypothesized link between amount of novel song choices and average lyrical compressibility of popular songs by controlling for a host of ecological, socioecological, and cultural factors that might plausibly influence cultural level success for simplicity vs.

Our chief analyses focused on a set of corrective analyses, in which we controlled for the possibly spurious nature of the relationship between our key time series due to temporal autocorrelation. Given the range of possibilities of correcting for temporal autocorrelation, we opted to perform three different types of analyses that correct or account for the possibility that observed relationships might be spurious as a function of autocorrelation in the time series.

First, we computed adjusted significance thresholds based on the Tiokhin-Hruschka procedure [ 62 ]. Second, we detrended our novel song production and lyrical compressibility time series by residualizing for year and assessed the correlation between our detrended variables.

Finally, for central univariate and multivariate analyses, we used an automated auto-regressive integrated moving average forecasting model auto. ARIMA to assess the relationship between novel song choices and lyrical compressibility [ 63 ]. This technique involves a machine learning algorithm that tests a number of different possible models which vary in autoregressive components, differencing, and moving average components, as well as whether they include an exogenous predictor.

Additionally, we used auto. ARIMA to generate a forecast for future patterns of lyrical compressibility — For multivariate analyses we entered multiple predictors of lyrical compressibility over time. To avoid multicollinearity and overfitting and due to limited number of units at the yearly level of analysis , we first aggregated covariance scores attributed to additional socioecological and cultural factors see Table 1 by performing a principal component analysis on these covariates and saving component scores for further multivariate time series analyses.

Other covariates Climatic Stress, Unemployment, Conservatism, Collectivism showed very weak loadings. Next, we entered both yearly music production scores and covariate-PCA scores as independent predictors of lyrical compressibility, simultaneously accounting for the time series structure in the data. As Fig 1 indicates, mean lyrical compressibility i.

Hot songs, Discog music releases, and Wikipedia song entries were highly correlated,. To avoid multicollinearity, we used component scores for further analyses. Although several ecological dimensions were associated with changes in average lyrical compressibility over time see Table 1 , these relationships were often in the opposite direction that prior research or theorizing would suggest. For example, there were significant negative correlations between GDP per capita and pathogen prevalence and average lyrical compressibility.

Further, our two cultural variables were either unrelated to lyrical compressibility conservatism or correlated in the opposite of the predicted direction collectivism. We did observe theoretically sensible relationships between compressibility and residential mobility, immigration, ethnic fractionalization, and population size. However, when controlling for the potentially confounding effect of temporal auto-correlation by residualizing out the effect of year, only three of these relationships are statistically significant, and only the relationship between pathogen prevalence and average lyrical complexity remains in a theoretically sensible direction see Table 1.

Further, it remained significant when controlling separately for each of the 12 specified control variables,. Full correlations between these variables are presented in S1 Fig. As an alternative method for dealing with autocorrelation, we also detrended the time series by residualizing out the linear impact of year.

Given the time series nature of our data, another way to test the hypothesized link between amount of new songs available and average compressibility of these songs while also addressing the issue of autocorrelation can involve an automated ARIMA algorithm auto. This machine-learning algorithm inspects the time-series data to fit the optimal forecasting function.

The auto-regressive AR p component refers to the use of past values in the regression equation for the series Y. The auto-regressive parameter p specifies the number of lags used in the model.

A moving average MA q component represents the error of the model as a combination of previous error terms e t. The order q determines the number of terms to include in the model. ARIMA models are well-suited for long-term time series, such as the historic patterns in the present data. The automated algorithm within the forecast package searches through combinations of order parameters and picks the set that optimizes model fit criteria, comparing Akaike information criteria AIC or Bayesian information criteria BIC of respective models.

Notably, the automated forecasting approach allows us to specify an exogenous predictor such as novel song choices, such that the automated function can evaluate the extent to which this exogenous predictor improves the fit above and beyond the decomposition of the time-series of the dependent variable.

In other words, the automated function provides a conservative way to see whether an exogenous predictor such as the novel song choices index improves accuracy in forecasts of the lyrical compressibility.

If the final model selected by auto. ARIMA includes our putative exogenous variable in this case amount of novel song choices , then this suggests that this variable helps the model to achieve optimal fit to the data. We also ran an alternative set of auto. ARIMA analyses where we set novel song choices as the dependent variable and average lyrical compressibility as an exogenous predictor.

ARIMA analysis on the residuals. In another set of control analyses, we performed an auto. By comparing the magnitude of the effect from this first principal component which was chiefly driven by ecological variables and music production index, we can assess the relative contribution of the music production index via-a-vis other socio-ecological covariates.

Moreover, the effect of music production on lyrical compressibility was stronger than other feasible covariates explored in the present dataset. In exploratory analyses we evaluated how lyrical compressibility is associated with song success, and whether this relationship was stronger in time periods when more novel music was produced.

Preliminary auto. ARIMA analyses on the yearly aggregate data indicated that a model with no auto-regressive components but a linear trend would show the best model fit.

Therefore, in the first multi-level model we included year as a proxy for a linear trend as well as compressibility X year interaction as predictors of song success.

Both year and lyrical compressibility were mean-centered prior to analyses. In the second step, we added mean-centered yearly music production index as a second covariate, along with a music production X compressibility interaction. Based on prior auto. ARIMA results, we also included linear effect of year to account for the trend in the chart position. These analyses yield results consistent with the proposition that lyrically simpler songs enjoy greater success in time periods in which more novel song choices are available.

As a final step, we generated a forecast for average lyrical compressibility for four decades after the last data point in our time series.

These forecasts enable a test of this theoretical model against concrete future cultural trends. Using the automated ARIMA algorithm, we also identified the best function for the novel song choices data, which we used to estimate the subsequent 40 data points.

In turn, we used this estimated data in conjunction with the compressibility function to forecast the further development of lyrical compressibility.

Results of this model suggest that lyrical compressibility will continue to increase over the next several decades see Fig 1. Popular music lyrics have recently been used to inform work on the cultural transmission of emotional expression [ 14 , 66 ], as an index of culture-level changes in self- versus other-focus [ 5 ], and as a reflection of cultural mood in respond to economic and social threats [ 18 , 19 ]. But one major trend in popular music lyrics remained underexplored and unexplained—popular music lyrics are coming increasingly simple over time.

We reasoned and found support for the hypothesis that increasing lyrical simplicity is associated with increasing amounts of novel music production. That is, in times when more novel music is produced, popular songs become increasingly lyrically simple. The relationship between mean lyrical compressibility and the amount of novel music produced each year was robust. We observed significant positive associations across three operationalizations of the amount of novel song choices and the average lyrical compressibility of popular songs.

Further, the relationship between amount of novel song choices and average compressibility of popular songs remained significant when including a host of ecological, socioecological, and cultural factors linked to other types of cultural change both in univariate and multivariate analyses.

By and large these other variables were not significantly associated with changes in lyrical simplicity after controlling for the potentially confounding influence of temporal autocorrelation. Of note, we also observed a significant negative association between changes in pathogen prevalence and lyrical simplicity.

This observation suggests a potentially new consequence of infectious disease threat, one that should be explored in more detail in future work.

Importantly, the linkage between amount of new music produced and average compressibility of popular songs also held when accounting for temporal autocorrelation using three distinct methods.

Thus, results suggest that the amount of novel music produced contributes to changes in average lyrical compressibility above and beyond other plausible causes and autoregressive trends in the data. In exploratory analyses, we also found evidence suggesting that success, as indexed by position in the billboard charts, among popular songs was associated with greater lyrical compressibility. Importantly, this effect appeared to be stronger in years when the amount of novel songs produced was higher, providing conceptual confirmation of our key finding.

More novel song choices appear linked to both greater average lyrical compressibility of the body of songs that succeeds i. This finding might parallel ongoing research taking information-theoretic approaches in exploring communicative efficiency in human language [ 67 , 68 ]. Moreover, these more successful i. This observation dovetails with our finding regarding the success of simpler lyrics.

Indeed, the increasingly success of simple lyrics may reflect increasing communicative efficacy. A preference for simpler information in increasingly information-saturated environments might also be consistent with some propositions from cultural evolutionary theory.

One tenet of cumulative cultural evolutionary theory is that human innovation, transmission, and learning increase the amount and quality of cultural information, while also increasing the learnability of this information [ 70 , 25 ].

One way to increase information learnability is via simplicity [ 71 , 72 ], thereby yielding increasingly efficient communication. What does this tell us more broadly about how American culture has changed? It suggests potentially that success of aesthetic complexity at the cultural level may be something that shifts over time. Although this is not the first such demonstration of this phenomenon, to our knowledge this is the first attempt to formally evaluate why such cultural-level preferences may change.

Although we found that our key effect was highly robust, alternative or complementary explanations for the growing success of lyrically simpler songs are still possible. For example, changes in the ways that people consume popular music could perhaps affect lyrical simplicity. Technological innovation e. Relatedly, one might speculate that the success of increasingly simple lyrics might owe to technologically mediated increases in listening to music primarily in the background e.

However, one might easily argue that for generations music has been consumed in this fashion albeit with slightly different technologies—portable radios, car stereos, and portable music players have existed and been widely used for decades. It would be interesting to attempt to assess this question empirically, although we are not currently aware of high-quality time series data relating to how and why people listen to popular music.