Pith. sign in

REVIEW 4 major objections 6 minor 50 references

Five decades of US, UK, German and Dutch music charts show that cultural processes are accelerating

T0 review · 4 major / 6 minor · reviewed 2026-08-14 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read According to five decades of US, UK, German, and Dutch album charts, the time an album stays in the top 40 has roughly halved, and the paper reads this as evidence that cultural processes are accelerating.

desk verdict A genuinely new empirical result on accelerating chart dynamics, but the theory section has a real error and the chart-rule confound is not fully addressed. read the letter →

arxiv 1908.10694 v1 pith:AFKSGW4I submitted 2019-08-26 physics.soc-ph nlin.AO

Lukas Schneider , Claudius Gros This is my paper · ORCID
classification physics.soc-phnlin.AO
keywords musicchartsculturalaccelerationalbumlifetimedistributionpowerlawlog-normalself-organizedcriticalityWeber-Fechnerchartdiversity
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Analyzing weekly album charts from the US, UK, Germany, and the Netherlands over roughly five decades, the paper argues that the statistics of chart success have changed in ways that point to an acceleration of cultural processes. The top-40 mean album lifetime in the US and Germany fell by about a factor of two since 1990, number-one albums shifted from climbing for weeks to entering at the top or not at all, and the distribution of album lifetimes moved from log-normal toward a power law with an exponent near 2. The paper also proposes an information-theoretic explanation: maximum-entropy reasoning combined with the Weber-Fechner law of logarithmic perception predicts power laws, and a log-normal arises when individual time horizons matter; the observed shift suggests personal time horizons have faded in relevance as music is bought instantly online. If correct, the same acceleration may apply to opinion formation more broadly, with potential consequences for representative democracy.

What carries the argument

The central objects are the weekly album lifetime $n_w$ and the fitted distribution $P(n_w)\sim \exp(-a\ln n_w - b \ln^2 n_w)$, where $b>0$ gives a log-normal and $b\to 0$ a power law $P(n_w)\sim 1/n_w^a$; the paper tracks $a\to 2$ and $b\to 0$ over successive five-year periods. The other key quantity is the relative inner mobility $M_I = \langle (R(t-1)-R(t))/R\rangle$ with $R=\max(R(t),R(t-1))$, a weekly rank-decay rate. The explanatory machinery is maximum-entropy reasoning under Weber-Fechner logarithmic discounting: entropy maximization with a fixed mean gives an exponential in perceived time, which becomes a power law in physical time; allowing the constraint's weight to vary across individuals gives a log-normal. Decoupling of decisions from personal time horizons drives $b\to 0$.

What would settle it

Recompute the top-40 lifetime distribution and mean lifetime separately within each fixed-methodology era, for example the pre-1991 Billboard charts, the 1991–2014 sales era, and the post-2014 streaming era, and check whether the decline in mean lifetime and the drift of the fit parameter $b$ toward zero persist inside each era; if the changes appear only at the rule-change boundaries, the acceleration claim fails.

Watch

Extended reading notes

Core claim

The paper's central claim is that the long-term evolution of four national album charts constitutes quantitative evidence for an acceleration of cultural processes. Over the past five decades the average number of weeks an album remains in the top 40 has roughly halved in the US and Germany; the share of number-one albums that enter at the top has risen from near zero to the large majority; and the distribution of album lifetimes has turned from a log-normal curve into a near power law with exponent approaching two. The paper interprets the power-law endpoint as a self-organized critical state, and notes that these charts allow observation of the critical state while it is still forming rather than only after it has been reached. A further claim is that sales-based and airplay-based charts differ statistically, and that including streaming in chart algorithms reduces chart diversity and inner mobility.

Load-bearing premise

The central conclusion assumes the secular changes in chart statistics reflect real changes in cultural consumption, not the repeated changes in chart compilation rules (electronic sales tracking in 1991, multi-metric consumption in 2014/15, and streaming inclusion in 2014–2017).

Editorial extensions

If this is right

  • If the acceleration is real, the yearly number of number-one albums has grown from about a dozen in the 1980s to about 40 today, approaching the maximum of 52 per year.
  • An album that does not enter at or near the top now has little chance of ever reaching number one; the average climb time has fallen to under a week in the US, Germany, and the Netherlands.
  • The inner-mobility data imply a weekly rank-decay time that shortened by roughly a factor of three between 1990 and 2010 for the US, German, and Dutch charts.
  • Charts compiled from sales show self-organization toward power-law lifetimes, while charts based substantially on airplay do not follow the same lifetime-distribution pattern.
  • Including streaming in chart algorithms reduces chart diversity and inner mobility, an adverse effect on the number of albums that reach the charts.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The time-horizon decoupling mechanism implies a testable generalization: other cultural consumption measures that allow instant purchase, such as book sales, movie box-office runs, or streaming playlist retention, should show the same log-normal-to-power-law shift as frictions to purchase disappear.
  • Because the paper does not model the chart-rule changes, the cleanest test of the acceleration claim would be to recompute all statistics within epochs of fixed methodology; the paper's own continuity series, the sales-only Billboard Top Album Sales, makes part of that test possible.
  • The political extension the paper sketches would become more than suggestive if analogous shortening were found in opinion polls or news-attention cycles; those data would decide whether the music-chart acceleration is a cultural curiosity or a general social phenomenon.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 6 minor

Summary. The paper analyzes five decades of US, UK, German and Dutch album charts, using chart diversity, top-40 album lifetimes, entry/exit ranks of number-one albums, and a relative inner-mobility statistic. It reports that chart diversity increased, mean album lifetimes fell by roughly a factor of two, number-one albums increasingly entered at the top rather than climbing, and the lifetime distribution evolved from log-normal to a power law with exponent approaching two. The authors interpret these changes as evidence for accelerating cultural processes. They then propose an information-theoretic model in which maximum-entropy production under the Weber–Fechner law yields power laws, and a Gaussian distribution of individual time-horizon preferences yields log-normals; the observed evolution is attributed to the fading relevance of personal time horizons. The paper also discusses implications for political opinion dynamics and democratic stability.

Significance. If the empirical trends survive controls for chart-methodology changes, the paper would be a valuable contribution to the literature on social acceleration and to empirical studies of self-organized criticality. The multi-country design, the long observation window, and the use of multiple chart statistics are genuine strengths, and the paper is candid about some of its limitations. However, the central empirical claim depends on treating documented changes in chart compilation rules as negligible, and the theoretical derivation in Section 3 contains a mathematical error. At present the manuscript is not technically sound enough for publication, although the main issues appear addressable within the scope of a revision.

major comments (4)
  1. [§3(b), Eqs (3.4)-(3.5)] The central claim that the observed trends reflect cultural acceleration assumes that the chart rankings are comparable across five decades, but the manuscript itself documents major rule changes that are not modeled or subtracted. The 1991 SoundScan change, the 2010 catalog-album inclusion, the 2014/15 Billboard multi-metric switch, and the streaming inclusions in the Netherlands (2014), UK (2016), and Germany (2017) could each shorten measured lifetimes, raise diversity, and increase top entries. For the UK, German, and Dutch charts only the post-streaming metric is available, so there is no like-for-like control. The statement in §2 that the 2014/15 update "affected the chart statistics profoundly" and the concession in §3(c) that "we cannot rule out that other drivings may cause the observed changes" directly undermine the strong conclusion in the abstract. I request a breakpoint analysis around each rule change, a sensitivity analysis restricted to the sales-based Billboard Top Album Sales metric for the full period, and explicit tests of whether the reported trends remain when streaming-era data are excluded.
  2. [§2(b), Figs 3 and 4] The marginalization integral in Eq. (3.4) is evaluated incorrectly. For Gaussian p(h) ∝ exp(−(h−h̄)²/(2σ_h²)) with h̄ set to zero, the integral over h of exp(−(a+κh)s − h²/(2σ_h²)) gives exp(−as + (κσ_h)²s²/2), i.e. a positive quadratic term, not exp(−as − bs²). Consequently Eq. (3.5) does not yield a log-normal distribution after the Weber–Fechner transform; it yields a distribution that grows as exp(+b ln² S), which is not normalizable. This is a load-bearing error because the paper's theoretical explanation of the log-normal-to-power-law transition rests on this derivation. The authors should correct the sign and normalization, or explicitly introduce a different mechanism (for example, a constraint on the variance of s) that produces the claimed log-normal.
  3. [Abstract and §3(c)] The claim that the lifetime distribution evolves from log-normal to a power law is supported only by quadratic fits to binned log-log histograms, with no error bars, no confidence intervals, and no model comparison. The fitted coefficient b in Fig. 4 is reported without uncertainty, and for the German charts the trend is acknowledged to be "less clear". Without bootstrap confidence intervals, goodness-of-fit tests, and a formal comparison between log-normal and power-law models (for example, by AIC or likelihood-ratio tests), the reader cannot assess whether the change in b is statistically meaningful or an artifact of binning and sample-size changes. This is central to the paper's most novel empirical claim and should be quantified.
  4. [Conclusion, §5] The abstract states that the fading relevance of personal time horizons "may be causing" the observed changes, but no direct measure of time horizons is provided, and the paper itself concedes in §3(c) that the arguments are "at present only circumstantial." The data are consistent with many explanations, including changes in marketing, chart methodology, and consumption technology. The causal language should be softened to "consistent with" or "suggests," and the interpretation should be explicitly framed as a hypothesis rather than an empirical finding.
minor comments (6)
  1. [Data Accessibility] The caption contains a typo: "20014/15" should be "2014/15."
  2. [§2(a)] The Data Accessibility statement refers to references 25–28, but the chart data sources are references 20–23; the numbering should be corrected.
  3. [§2(e)] The approximation w̄ ≈ 1/d should be stated more carefully; it assumes a fixed number of slots and ignores that an album can appear in multiple years, so the inverse-diversity relation is only a rough guide.
  4. [§2(b)] There is a duplicated word in the text: "the the" in the sentence describing the inner mobility trends.
  5. [§3(b)] The sentence listing the starting years for the four charts ("since 1963/1967/1978/1979, and respectively since 1963/1982/1993/1990") is confusing and should be rewritten as a table or a clearer enumeration.
  6. [§2(f)] The statement that setting h̄ to zero can be done "without loss of generality" is only correct if the mean is absorbed into the Lagrange multiplier a; this should be stated explicitly to avoid confusion about the role of h̄.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the acceleration trends are direct measurements of chart data; the theoretical model is post hoc but not used as an independent prediction.

full rationale

The empirical findings (chart diversity, album lifetimes, entry ranks, inner mobility) are computed directly from public chart data. The relation d = Na/Ns and w-bar ≈ 1/d is an accounting identity, but the paper does not present it as independent evidence and separately measures top-40 lifetimes from consecutive weekly listings. The information-theoretic derivation in Sec. 3 produces the same two-parameter functional family as the empirical fit in Eq. (2.1), so it is flexible and non-predictive rather than an independent confirmation; the authors explicitly call the time-horizon interpretation a postulate and concede 'we cannot rule out that other drivings may cause the observed changes' (Sec. 3c). Self-citations (refs 16, 24, 41) support background concepts and the speculative political-instability aside, but the central acceleration claim rests on chart statistics, not on these citations. No equation is defined in terms of its target, no fitted parameter is renamed as a prediction, and no uniqueness theorem is imported from the authors' prior work. The documented chart-rule changes (SoundScan 1991, streaming inclusions 2014-2017) are a validity threat to the causal interpretation, but they are not a circularity in the derivation chain.

Assumptions & free parameters 4 free parameters · 5 assumptions · 1 invented entities

The empirical analysis rests on fitted shape parameters a and b; the explanatory theory adds a latent preference variable h with free parameters κ and σ_h and postulates Weber-Fechner discounting and maximum entropy. None of the theory parameters are fixed by independent data, and the derivation contains a sign error. The main load-bearing assumptions are the chart-data methodology and the interpretation of power laws as self-organized criticality.

free parameters (4)
  • lifetime distribution exponent a (per 5-year period) = not tabulated; approaches ~2 over time (Fig. 4)
    Fitted to the log-normal/power-law form (2.1) for each 5-year bin; the trend b→0 is read off these fits.
  • lifetime distribution curvature b (per 5-year period) = not tabulated; tends to 0 (Fig. 4)
    Fitted curvature parameter in (2.1); b→0 indicates power-law behavior.
  • hidden-variable coupling κ
    Coupling strength between album lifetime and individual preference variable h in Eq (3.3); not estimated from data.
  • preference spread σ_h
    Standard deviation of the Gaussian hidden preference distribution, Eqs (3.4)-(3.5); not estimated.
assumptions (5)
  • domain assumption Weber-Fechner law: neural representations of stimuli, numbers, and time scale logarithmically.
    Used in Eq (3.1) to convert exponential distributions in perceived magnitude into power laws in physical magnitude; cited from psychophysics refs [17-19].
  • ad hoc to paper Human activities produce maximum-entropy distributions.
    Central postulate of Section 3; not proven and not independently verified.
  • ad hoc to paper Individual preferences (hidden variable h) are Gaussian distributed.
    Assumed in Eq (3.4) to obtain a Gaussian marginal; no empirical basis given.
  • domain assumption Chart statistics are a valid proxy for cultural and opinion-formation processes.
    Underlies the jump from music charts to cultural acceleration and political opinion dynamics in Sections 4-5.
  • ad hoc to paper A power-law lifetime distribution indicates a self-organized critical state.
    Interpretation in Section 2(b); drawn from SOC literature but not tested with SOC diagnostics.
invented entities (1)
  • hidden variable h (individual time-horizon or preference coupling)
    purpose: Explains the transition from log-normal to power-law lifetime distributions via a distribution of Lagrange multipliers in the maximum-entropy model.
    No direct measurement or falsifiable prediction is attached to h; it is a latent construct. The claimed Gaussian marginal also rests on the sign-error-prone Eqs (3.4)-(3.5).

how reviews work

0 comments
Cite this review

Pith. "Pith review of Five decades of US, UK, German and Dutch music charts show that cultural processes are accelerating." pith.science (2026). https://pith.science/paper/AFKSGW4I

@misc{pith2026190810694,
  author       = {Pith},
  title        = {Pith review of: Five decades of US, UK, German and Dutch music charts show that cultural processes are accelerating},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/AFKSGW4I}},
  note         = {Machine review of arXiv:1908.10694}
}
read the original abstract

Analyzing the timeline of US, UK, German and Dutch music charts, we find that the evolution of album lifetimes and of the size of weekly rank changes provide evidence for an acceleration of cultural processes. For most of the past five decades number one albums needed more than a month to climb to the top, nowadays an album is in contrast top ranked either from the start, or not at all. Over the last three decades, the number of top-listed albums increased as a consequence from roughly a dozen per year to about 40. The distribution of album lifetimes evolved during the last decades from a log-normal distribution to a powerlaw, a profound change. Presenting an information-theoretical approach to human activities, we suggest that the fading relevance of personal time horizons may be causing this phenomenon. Furthermore we find that sales and airplay based charts differ statistically and that the inclusion of streaming affects chart diversity adversely. We point out in addition that opinion dynamics may accelerate not only in cultural domains, as found here, but also in other settings, in particular in politics, where it could have far reaching consequences.

Figures

Figures reproduced from arXiv: 1908.10694 by the authors.

Figure 1
Figure 1. Chart diversity. The evolution of the chart diversity, which is defined as the fraction d=Na/Ns of the number of distinct albums Na listed in a given year and the number Ns of slots available. Lines are thin for periods for which less than 100 chart positions are available, and dashed once streaming was included. For a top 100 chart and 52 weeks per year there are Ns=100·52 slots. One observes that the chart diversi… view at source ↗
Figure 2
Figure 2. Album lifetime. The top 40 mean lifetime, namely the number of consecutive weeks an album is listed on the average among the top 40. The data has been pooled for trailing 5-year periods. The algorithms used for the compilation of the individual charts have been adjusted over time, mostly in minor ways. A major update occurred for the Billboard charts in 2014/15, when the traditional sales-based ranking was substitut… view at source ↗
Figure 3
Figure 3. Lifetime distribution. On a basis ten log-log plot, the top 40 lifetime distribution, namely the distribution of the number of weeks a given album is listed among the top 40 on the Billboard chart. The data (circles) has been pooled for successive 5-year periods and fitted quadratically (lines), compare (2.1). One observes that the lifetime distribution evolves over the years from a log-normal distribution towards a… view at source ↗
Figures from the paper (5 more)
Figure 4
Figure 4. Figure 4: Coefficients of the lifetime distribution fits. The time evolution of the fit parameters a and b for the top 40 album lifetimes (circles), which correspond to the number of weeks nw an album is listed among the top 40. The lifetime distribution has been fitted by exp(−…
Figure 5
Figure 5. Figure 5: Number one albums. Left: The probability Pone that a number one album started as such. The fraction of albums managing to reach the top when starting form a lower entry position is 1 − Pone. Right: The average number of weeks number one albums did need to reach the top…
Figure 6
Figure 6. Figure 6: Entry & Exit distributions. For the top 100 Billboard album charts the distribution of entry (blue) and exit ranks (orange), average over five years periods. The width of the violin-charts measures the respective probabilities. Also included are the mean entry and exit…
Figure 7
Figure 7. Figure 7: Inner Mobility. On a year by year basis the relative inner mobility MI of albums in the respective music charts, as defined by (2.2). Lines are thin for periods with less than 100 chart positions and dashed once streaming was included. Shown are the average weekly rank…
Figure 8
Figure 8. Figure 8: Single lifetimes. On a basis ten log-log plot, the top 40 lifetime distribution for the Billboard single charts, which are based in part on airplay data. Included are, as for [PITH_FULL_IMAGE:figures/full_fig_p011_8.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

50 extracted references · 49 canonical work pages

  1. [1]

    2015 The evolution of popular music: USA 1960–2010

    Mauch M, MacCallum RM, Levy M, Leroi AM. 2015 The evolution of popular music: USA 1960–2010. Royal Society Open Science 2, 150081

    work page 2015

  2. [2]

    2018 Race, gender, and the Billboard Top 40 charts between 1997 and 2007

    Lafrance M, Scheibling C, Burns L, Durr J. 2018 Race, gender, and the Billboard Top 40 charts between 1997 and 2007. Popular Music and Society 41, 522–538

    work page 2018

  3. [3]

    2016 Objectification in popular music lyrics: An examination of gender and genre differences

    Flynn MA, Craig CM, Anderson CN, Holody KJ. 2016 Objectification in popular music lyrics: An examination of gender and genre differences. Sex roles 75, 164–176

    work page 2016

  4. [4]

    2016 From fewer blockbusters by more superstars to more blockbusters by fewer superstars: How technological innovation has impacted convergence on the music chart

    Ordanini A, Nunes JC. 2016 From fewer blockbusters by more superstars to more blockbusters by fewer superstars: How technological innovation has impacted convergence on the music chart. International Journal of Research in Marketing 33, 297–313

    work page 2016

  5. [5]

    2017 A study of pop songs based on the billboard corpus

    Nishina Y. 2017 A study of pop songs based on the billboard corpus. International Journal of Language and Linguistics 4, 125–134

    work page 2017

  6. [6]

    2015 The power of repetition: repetitive lyrics in a song increase processing fluency and drive market success

    Nunes JC, Ordanini A, Valsesia F. 2015 The power of repetition: repetitive lyrics in a song increase processing fluency and drive market success. Journal of Consumer Psychology 25, 187– 199

    work page 2015

  7. [7]

    2011 A cultural globalization of popular music? American, Dutch, French, and German popular music charts (1965 to 2006)

    Achterberg P , Heilbron J, Houtman D, Aupers S. 2011 A cultural globalization of popular music? American, Dutch, French, and German popular music charts (1965 to 2006). American behavioral scientist 55, 589–608

    work page 2011

  8. [8]

    2018 Musical trends and predictability of success in contemporary songs in and out of the top charts

    Interiano M, Kazemi K, Wang L, Yang J, Yu Z, Komarova NL. 2018 Musical trends and predictability of success in contemporary songs in and out of the top charts. Royal Society Open Science 5, 171274

    work page 2018

Show all 50 references
  1. [9]

    2017 Significance and popularity in music production

    Monechi B, Gravino P , Servedio VD, Tria F, Loreto V . 2017 Significance and popularity in music production. Royal Society Open Science 4, 170433

    2017

  2. [10]

    2013 Social acceleration: A new theory of modernity

    Rosa H. 2013 Social acceleration: A new theory of modernity. Columbia University Press

    2013

  3. [11]

    2017 Slowing down modernity: A critique

    Vostal F. 2017 Slowing down modernity: A critique. Time & Society p. 0961463X17702163

    2017

  4. [12]

    2004 Liberal democracy and the social acceleration of time

    Scheuerman WE. 2004 Liberal democracy and the social acceleration of time. JHU Press

    2004

  5. [13]

    2014 A question of time: Responsive and responsible democratic politics

    Goetz KH. 2014 A question of time: Responsive and responsible democratic politics. West European Politics 37, 379–399

    2014

  6. [14]

    2013 Acceleration in working life: An empirical test of a sociological framework

    Ulferts H, Korunka C, Kubicek B. 2013 Acceleration in working life: An empirical test of a sociological framework. Time & Society 22, 161–185

    2013

  7. [15]

    Private communication

    Rosa H. Private communication

  8. [16]

    2014 Power laws and self-organized criticality in theory and nature

    Markovi´ c D, Gros C. 2014 Power laws and self-organized criticality in theory and nature. Physics Reports 536, 41–74

    2014

  9. [17]

    1924 The visual discrimination of intensity and the Weber-Fechner law.The Journal of general physiology 7, 235–267

    Hecht S. 1924 The visual discrimination of intensity and the Weber-Fechner law.The Journal of general physiology 7, 235–267

    1924

  10. [18]

    2003 The neural basis of the Weber–Fechner law: a logarithmic mental number line

    Dehaene S. 2003 The neural basis of the Weber–Fechner law: a logarithmic mental number line. Trends in cognitive sciences7, 145–147

    2003

  11. [19]

    2018 Memory as Perception of the Past: Compressed Time inMind and Brain

    Howard MW. 2018 Memory as Perception of the Past: Compressed Time inMind and Brain. Trends in cognitive sciences22, 124–136

    2018

  12. [20]

    http://www.billboard.com/

    Billboard Charts. http://www.billboard.com/

  13. [21]

    http://www.officialcharts.com

    Official Charts. http://www.officialcharts.com

  14. [22]

    http://www.offiziellecharts.de

    Official German Charts. http://www.offiziellecharts.de

  15. [23]

    http://dutchcharts.nl

    Dutch Charts. http://dutchcharts.nl

  16. [24]

    2012 Neuropsychological constraints to human data production on a global scale

    Gros C, Kaczor G, Markovi´ c D. 2012 Neuropsychological constraints to human data production on a global scale. The European Physical Journal B 85, 28

    2012

  17. [25]

    1987 Self-organized criticality: An explanation of the 1/f noise

    Bak P , Tang C, Wiesenfeld K. 1987 Self-organized criticality: An explanation of the 1/f noise. Physical review letters 59, 381

    1987

  18. [26]

    2002 Evolution of the social network of scientific collaborations

    Barabâsi AL, Jeong H, Néda Z, Ravasz E, Schubert A, Vicsek T. 2002 Evolution of the social network of scientific collaborations. Physica A: Statistical mechanics and its applications311, 590– 614

    2002

  19. [27]

    2018 Unidirectional random growth with resetting

    Biro TS, Neda Z. 2018 Unidirectional random growth with resetting. Physica A: Statistical Mechanics and its Applications 499, 335–361

    2018

  20. [28]

    2015 Complex and adaptive dynamical systems: A primer

    Gros C. 2015 Complex and adaptive dynamical systems: A primer. Springer. 18rsos.royalsocietypublishing.org R. Soc. open sci. 0000000

    2015

  21. [29]

    2014 The log-dynamic brain: how skewed distributions affect network operations

    Buzsáki G, Mizuseki K. 2014 The log-dynamic brain: how skewed distributions affect network operations. Nature Reviews Neuroscience 15, 264

    2014

  22. [30]

    2003 Coding of cognitive magnitude: Compressed scaling of numerical information in the primate prefrontal cortex

    Nieder A, Miller EK. 2003 Coding of cognitive magnitude: Compressed scaling of numerical information in the primate prefrontal cortex. Neuron 37, 149–157

    2003

  23. [31]

    2002 Representation of the quantity of visual items in the primate prefrontal cortex

    Nieder A, Freedman DJ, Miller EK. 2002 Representation of the quantity of visual items in the primate prefrontal cortex. Science 297, 1708–1711

    2002

  24. [32]

    2005 Loss of self-control in intertemporal choice may be attributable to logarithmic time-perception

    Takahashi T. 2005 Loss of self-control in intertemporal choice may be attributable to logarithmic time-perception. Medical hypotheses 65, 691–693

    2005

  25. [33]

    1999 Emergence of scaling in random networks

    Barabási AL, Albert R. 1999 Emergence of scaling in random networks. science 286, 509–512

    1999

  26. [34]

    2009 Guided self-organization

    Prokopenko M. 2009 Guided self-organization

    2009

  27. [35]

    2014 Generating functionals for guided self-organization

    Gros C. 2014 Generating functionals for guided self-organization. In Guided Self-Organization: Inception , pp. 53–66. Springer

    2014

  28. [36]

    2013 Generating functionals for autonomous latching dynamics in attractor relict networks

    Linkerhand M, Gros C. 2013 Generating functionals for autonomous latching dynamics in attractor relict networks. Scientific reports 3, 2042

    2013

  29. [37]

    2014 Generating functionals for computational intelligence: the Fisher information as an objective function for self-limiting Hebbian learning rules

    Echeveste R, Gros C. 2014 Generating functionals for computational intelligence: the Fisher information as an objective function for self-limiting Hebbian learning rules. Frontiers in Robotics and AI 1, 1

    2014

  30. [38]

    2015 Analysis of genetic and non-genetic factors influencing timing and time perception

    Bartholomew AJ, Meck WH, Cirulli ET. 2015 Analysis of genetic and non-genetic factors influencing timing and time perception. PLoS One 10, e0143873

    2015

  31. [39]

    2016 Statistics for the behavioral sciences

    Gravetter FJ, Wallnau LB. 2016 Statistics for the behavioral sciences. Cengage Learning

    2016

  32. [40]

    2013 Public Opinion in the US States: 1956 to 2010

    Enns PK, Koch J. 2013 Public Opinion in the US States: 1956 to 2010. State Politics & Policy Quarterly 13, 349–372

    2013

  33. [41]

    2017 Entrenched time delays versus accelerating opinion dynamics: Are advanced democracies inherently unstable?

    Gros C. 2017 Entrenched time delays versus accelerating opinion dynamics: Are advanced democracies inherently unstable?. The European Physical Journal B 90, 223

    2017

  34. [42]

    2015 Politics and popular culture

    Street J, Inthorn S, Scott M. 2015 Politics and popular culture. In From entertainment to citizenship , . Manchester University Press

    2015

  35. [43]

    2005 The speed of global flows and the pace of democratic politics

    Rosa H. 2005 The speed of global flows and the pace of democratic politics. New Political Science 27, 445–459

    2005

  36. [44]

    2015 Is democracy in decline?

    Plattner MF. 2015 Is democracy in decline?. Journal of Democracy 26, 5–10

    2015

  37. [45]

    2015 Crisis and transition, but not decline

    Schmitter PC. 2015 Crisis and transition, but not decline. Journal of Democracy 26, 32–44

    2015

  38. [46]

    2018 How to lose a constitutional democracy

    Huq A, Ginsburg T. 2018 How to lose a constitutional democracy. UCLA L. Rev. 65, 78

    2018

  39. [47]

    2018 Quantifying reputation and success in art

    Fraiberger SP , Sinatra R, Resch M, Riedl C, Barabási AL. 2018 Quantifying reputation and success in art. Science 362, 825–829

    2018

  40. [48]

    2018 Hot streaks in artistic, cultural, and scientific careers

    Liu L, Wang Y, Sinatra R, Giles CL, Song C, Wang D. 2018 Hot streaks in artistic, cultural, and scientific careers. Nature 559, 396

    2018

  41. [49]

    2009 Billboard 200 Undergoes Makeover

    Trust G. 2009 Billboard 200 Undergoes Makeover. Billboard Magazine 11/17

    2009

  42. [50]

    2014 Billboard 200 Makeover: Album Chart to Incorporate Streams & Track Sales

    Staff B. 2014 Billboard 200 Makeover: Album Chart to Incorporate Streams & Track Sales. Billboard Magazine 11/19

    2014

Pith tools

Reviewed August 14, 2026 · model on record in the stance chip above.