Mathematical Psychology

This project investigates mathematical psychology's historical and philosophical foundations to clarify its distinguishing characteristics and relationships to adjacent fields. Through gathering primary sources, histories, and interviews with researchers, author Prof. Colin Allen - University of Pittsburgh [1, 2, 3] and his students Osman Attah, Brendan Fleig-Goldstein, Mara McGuire, and Dzintra Ullis have identified three central questions:

What makes the use of mathematics in mathematical psychology reasonably effective, in contrast to other sciences like physics-inspired mathematical biology or symbolic cognitive science?
How does the mathematical approach in mathematical psychology differ from other branches of psychology, like psychophysics and psychometrics?
What is the appropriate relationship of mathematical psychology to cognitive science, given diverging perspectives on aligning with this field?

Preliminary findings emphasize data-driven modeling, skepticism of cognitive science alignments, and early reliance on computation. They will further probe the interplay with cognitive neuroscience and contrast rational-analysis approaches. By elucidating the motivating perspectives and objectives of different eras in mathematical psychology's development, they aim to understand its past and inform constructive dialogue on its philosophical foundations and future directions. This project intends to provide a conceptual roadmap for the field through integrated history and philosophy of science.

The Project: Integrating History and Philosophy of Mathematical Psychology

This project aims to integrate historical and philosophical perspectives to elucidate the foundations of mathematical psychology. As Norwood Hanson stated, history without philosophy is blind, while philosophy without history is empty. The goal is to find a middle ground between the contextual focus of history and the conceptual focus of philosophy.

The team acknowledges that all historical accounts are imperfect, but some can provide valuable insights. The history of mathematical psychology is difficult to tell without centering on the influential Stanford group. Tracing academic lineages and key events includes part of the picture, but more context is needed to fully understand the field's development.

The project draws on diverse sources, including research interviews, retrospective articles, formal histories, and online materials. More interviews and research will further flesh out the historical and philosophical foundations. While incomplete, the current analysis aims to identify important themes, contrasts, and questions that shaped mathematical psychology's evolution. Ultimately, the goal is an integrated historical and conceptual roadmap to inform contemporary perspectives on the field's identity and future directions.

The Rise of Mathematical Psychology

The history of efforts to mathematize psychology traces back to the quantitative imperative stemming from the Galilean scientific revolution. This imprinted the notion that proper science requires mathematics, leading to "physics envy" in other disciplines like psychology.

Many early psychologists argued psychology needed to become mathematical to be scientific. However, mathematizing psychology faced complications absent in the physical sciences. Objects in psychology were not readily present as quantifiable, provoking heated debates on whether psychometric and psychophysical measurements were meaningful.

Nonetheless, the desire to develop mathematical psychology persisted. Different approaches grappled with determining the appropriate role of mathematics in relation to psychological experiments and data. For example, Herbart favored starting with mathematics to ensure accuracy, while Fechner insisted experiments must come first to ground mathematics.

Tensions remain between data-driven versus theory-driven mathematization of psychology. Contemporary perspectives range from psychometric and psychophysical stances that foreground data to measurement-theoretical and computational approaches that emphasize formal models.

Elucidating how psychologists negotiated to apply mathematical methods to an apparently resistant subject matter helps reveal the evolving role and place of mathematics in psychology. This historical interplay shaped the emergence of mathematical psychology as a field.

The Distinctive Mathematical Approach of Mathematical Psychology

What sets mathematical psychology apart from other branches of psychology in its use of mathematics?

Several key aspects stand out:

Advocating quantitative methods broadly. Mathematical psychology emerged partly to push psychology to embrace quantitative modeling and mathematics beyond basic statistics.
Drawing from diverse mathematical tools. With greater training in mathematics, mathematical psychologists utilize more advanced and varied mathematical techniques like topology and differential geometry.
Linking models and experiments. Mathematical psychologists emphasize tightly connecting experimental design and statistical analysis, with experiments created to test specific models.
Favoring theoretical models. Mathematical psychology incorporates "pure" mathematical results and prefers analytic, hand-fitted models over data-driven computer models.
Seeking general, cumulative theory. Unlike just describing data, mathematical psychology aspires to abstract, general theory supported across experiments, cumulative progress in models, and mathematical insight into psychological mechanisms.

So while not unique to mathematical psychology, these key elements help characterize how its use of mathematics diverges from adjacent fields like psychophysics and psychometrics. Mathematical psychology carved out an identity embracing quantitative methods but also theoretical depth and broad generalization.

Situating Mathematical Psychology Relative to Cognitive Science

What is the appropriate perspective on mathematical psychology's relationship to cognitive psychology and cognitive science? While connected historically and conceptually, essential distinctions exist.

Mathematical psychology draws from diverse disciplines that are also influential in cognitive science, like computer science, psychology, linguistics, and neuroscience. However, mathematical psychology appears more skeptical of alignments with cognitive science.

For example, cognitive science prominently adopted the computer as a model of the human mind, while mathematical psychology focused more narrowly on computers as modeling tools.

Additionally, mathematical psychology seems to take a more critical stance towards purely simulation-based modeling in cognitive science, instead emphasizing iterative modeling tightly linked to experimentation.

Overall, mathematical psychology exhibits significant overlap with cognitive science but strongly asserts its distinct mathematical orientation and modeling perspectives. Elucidating this complex relationship remains an ongoing project, but preliminary analysis suggests mathematical psychology intentionally diverged from cognitive science in its formative development.

This establishes mathematical psychology's separate identity while retaining connections to adjacent disciplines at the intersection of mathematics, psychology, and computation.

Looking Ahead: Open Questions and Future Research

This historical and conceptual analysis of mathematical psychology's foundations has illuminated key themes, contrasts, and questions that shaped the field's development. Further research can build on these preliminary findings.

Additional work is needed to flesh out the fuller intellectual, social, and political context driving the evolution of mathematical psychology. Examining the influences and reactions of key figures will provide a richer picture.

Ongoing investigation can probe whether the identified tensions and contrasts represent historical artifacts or still animate contemporary debates. Do mathematical psychologists today grapple with similar questions on the role of mathematics and modeling?

Further analysis should also elucidate the nature of the purported bidirectional relationship between modeling and experimentation in mathematical psychology. As well, clarifying the diversity of perspectives on goals like generality, abstraction, and cumulative theory-building would be valuable.

Finally, this research aims to spur discussion on philosophical issues such as realism, pluralism, and progress in mathematical psychology models. Is the accuracy and truth value of models an important consideration or mainly beside the point? And where is the field headed - towards greater verisimilitude or an indefinite balancing of complexity and abstraction?

By spurring reflection on this conceptual foundation, this historical and integrative analysis hopes to provide a roadmap to inform constructive dialogue on mathematical psychology's identity and future trajectory.

The SDTEST^®

The SDTEST^® is a simple and fun tool to uncover our unique motivational values that use mathematical psychology of varying complexity.

The SDTEST^® helps us better understand ourselves and others on this lifelong path of self-discovery.

Here are reports of polls which SDTEST^® makes:

1) Tindakan perusahaan sehubungan dengan personel dalam sebulan terakhir (ya / tidak)

2) Tindakan perusahaan sehubungan dengan personel pada bulan lalu (fakta dalam%)

3) Ketakutan

4) Masalah terbesar yang dihadapi negara saya

5) Kualitas dan kemampuan apa yang digunakan pemimpin yang baik saat membangun tim yang sukses?

6) Google. Faktor -faktor yang memengaruhi efektivitas tim

7) Prioritas utama pencari kerja

8) Apa yang membuat bos pemimpin yang hebat?

9) Apa yang membuat orang sukses di tempat kerja?

10) Apakah Anda siap menerima lebih sedikit gaji untuk bekerja dari jarak jauh?

11) Apakah Ageism ada?

12) Usia dalam karier

13) Usia dalam hidup

14) Penyebab Ageism

15) Alasan mengapa orang menyerah (oleh Anna Vital)

16) MEMERCAYAI (#WVS)

17) Survei Kebahagiaan Oxford

18) Kesejahteraan psikologis

19) Di mana peluang paling menarik Anda berikutnya?

20) Apa yang akan Anda lakukan minggu ini untuk menjaga kesehatan mental Anda?

21) Saya hidup berpikir tentang masa lalu, masa kini atau masa depan saya

22) Meritokrasi

23) Kecerdasan buatan dan akhir peradaban

24) Mengapa orang menunda -nunda?

25) Perbedaan gender dalam membangun kepercayaan diri (IFD Allensbach)

26) Xing.com Penilaian Budaya

27) Patrick Lencioni "The Five Disfunctions of a Team"

28) Empati adalah ...

29) Apa yang penting bagi spesialis TI dalam memilih tawaran pekerjaan?

30) Mengapa orang menolak perubahan (oleh Siobhán McHale)

31) Bagaimana Anda mengatur emosi Anda? (oleh Nawal Mustafa M.A.)

32) 21 Keterampilan yang Membayar Anda Selamanya (oleh Jeremiah Teo / 赵汉昇)

33) Kebebasan nyata adalah ...

34) 12 cara untuk membangun kepercayaan dengan orang lain (oleh Justin Wright)

35) Karakteristik karyawan yang berbakat (oleh Talent Management Institute)

36) 10 kunci untuk memotivasi tim Anda

37) Aljabar Hati Nurani (oleh Vladimir Lefebvre)

38) Tiga Kemungkinan Berbeda di Masa Depan (oleh Dr. Clare W. Graves)

Below you can read an abridged version of the results of our VUCA poll “Fears“. The full version of the results is available for free in the FAQ section after login or registration.

Ketakutan

grafik Korelasi

VUCA

Nilai kritis dari koefisien korelasi

Distribusi normal, oleh William Sealy Gosset (siswa) r = 0.033

Distribusi non normal, oleh Spearman r = 0.0013

Hanya menunjukkan hasil > r = 0.033

Distribusi	Tidak normal	Tidak normal	Tidak normal	Normal	Normal	Normal	Normal	Normal
Semua pertanyaan Semua pertanyaan Ketakutan terbesar saya adalah
Ketakutan terbesar saya adalah
Answer 1	-	Positif lemah 0.0532	Positif lemah 0.0292	Negatif lemah -0.0175	Positif lemah 0.0919	Positif lemah 0.0301	Negatif lemah -0.0114	Negatif lemah -0.1523
Answer 2	-	Positif lemah 0.0208	Negatif lemah -0.0014	Negatif lemah -0.0431	Positif lemah 0.0641	Positif lemah 0.0449	Positif lemah 0.0130	Negatif lemah -0.0931
Answer 3	-	Negatif lemah -0.0053	Negatif lemah -0.0130	Negatif lemah -0.0406	Negatif lemah -0.0456	Positif lemah 0.0474	Positif lemah 0.0793	Negatif lemah -0.0204
Answer 4	-	Positif lemah 0.0427	Positif lemah 0.0328	Negatif lemah -0.0200	Positif lemah 0.0158	Positif lemah 0.0306	Positif lemah 0.0217	Negatif lemah -0.0980
Answer 5	-	Positif lemah 0.0255	Positif lemah 0.1255	Positif lemah 0.0143	Positif lemah 0.0732	Negatif lemah -0.0019	Negatif lemah -0.0196	Negatif lemah -0.1747
Answer 6	-	Negatif lemah -0.0027	Positif lemah 0.0074	Negatif lemah -0.0629	Negatif lemah -0.0074	Positif lemah 0.0199	Positif lemah 0.0835	Negatif lemah -0.0324
Answer 7	-	Positif lemah 0.0110	Positif lemah 0.0371	Negatif lemah -0.0688	Negatif lemah -0.0227	Positif lemah 0.0471	Positif lemah 0.0650	Negatif lemah -0.0523
Answer 8	-	Positif lemah 0.0693	Positif lemah 0.0825	Negatif lemah -0.0321	Positif lemah 0.0139	Positif lemah 0.0351	Positif lemah 0.0147	Negatif lemah -0.1369
Answer 9	-	Positif lemah 0.0643	Positif lemah 0.1659	Positif lemah 0.0082	Positif lemah 0.0699	Negatif lemah -0.0136	Negatif lemah -0.0513	Negatif lemah -0.1826
Answer 10	-	Positif lemah 0.0760	Positif lemah 0.0730	Negatif lemah -0.0219	Positif lemah 0.0254	Positif lemah 0.0318	Negatif lemah -0.0138	Negatif lemah -0.1318
Answer 11	-	Positif lemah 0.0571	Positif lemah 0.0514	Negatif lemah -0.0099	Positif lemah 0.0077	Positif lemah 0.0206	Positif lemah 0.0308	Negatif lemah -0.1211
Answer 12	-	Positif lemah 0.0373	Positif lemah 0.1013	Negatif lemah -0.0357	Positif lemah 0.0357	Positif lemah 0.0243	Positif lemah 0.0296	Negatif lemah -0.1524
Answer 13	-	Positif lemah 0.0621	Positif lemah 0.1036	Negatif lemah -0.0438	Positif lemah 0.0273	Positif lemah 0.0414	Positif lemah 0.0176	Negatif lemah -0.1608
Answer 14	-	Positif lemah 0.0703	Positif lemah 0.1007	Positif lemah 9.54E-5	Negatif lemah -0.0088	Negatif lemah -0.0011	Positif lemah 0.0084	Negatif lemah -0.1174
Answer 15	-	Positif lemah 0.0554	Positif lemah 0.1349	Negatif lemah -0.0418	Positif lemah 0.0179	Negatif lemah -0.0165	Positif lemah 0.0219	Negatif lemah -0.1181
Answer 16	-	Positif lemah 0.0581	Positif lemah 0.0255	Negatif lemah -0.0388	Negatif lemah -0.0407	Positif lemah 0.0654	Positif lemah 0.0283	Negatif lemah -0.0714

Ekspor ke MS Excel

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[1] https://twitter.com/wileyprof

[2] https://colinallen.dnsalias.org

[3] https://philpeople.org/profiles/colin-allen

2023.10.13

Valerii Kosenko

Pemilik Produk SaaS Pet Project SDTest®

Valerii memenuhi syarat sebagai ahli pedagog sosial pada tahun 1993 dan sejak itu menerapkan pengetahuannya dalam manajemen proyek.
Valerii memperoleh gelar master dan kualifikasi Proyek dan Program Manajer pada tahun 2013. Selama program masternya, ia menjadi terbiasa dengan Project Roadmap (GPM Deutsche Gesellschaft für ProJektanagement e. V.) dan dinamika spiral.
Valerii mengambil berbagai tes dinamika spiral dan menggunakan pengetahuan dan pengalamannya untuk mengadaptasi versi SDTest saat ini.
Valerii adalah penulis menjelajahi ketidakpastian V.U.C.A. Konsep menggunakan dinamika spiral dan statistik matematika dalam psikologi, lebih dari 20 jajak pendapat internasional.

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