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 = 0.0331

Distribusi non normal, oleh Spearman r = 0.0013

Hanya menunjukkan hasil > r = 0.0331

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 0.0563	Positif lemah 0.0317	Negatif lemah -0.0161	Positif lemah 0.0907	Positif lemah 0.0298	Negatif lemah -0.0126	Negatif lemah -0.1537
Answer 2	-	Positif lemah 0.0216	Positif lemah 0.0002	Negatif lemah -0.0458	Positif lemah 0.0654	Positif lemah 0.0445	Positif lemah 0.0124	Negatif lemah -0.0937
Answer 3	-	Negatif lemah -0.0035	Negatif lemah -0.0111	Negatif lemah -0.0421	Negatif lemah -0.0456	Positif lemah 0.0466	Positif lemah 0.0786	Negatif lemah -0.0201
Answer 4	-	Positif lemah 0.0435	Positif lemah 0.0353	Negatif lemah -0.0181	Positif lemah 0.0145	Positif lemah 0.0301	Positif lemah 0.0197	Negatif lemah -0.0979
Answer 5	-	Positif lemah 0.0299	Positif lemah 0.1279	Positif lemah 0.0136	Positif lemah 0.0730	Negatif lemah -0.0007	Negatif lemah -0.0207	Negatif lemah -0.1746
Answer 6	-	Negatif lemah -0.0004	Positif lemah 0.0082	Negatif lemah -0.0629	Negatif lemah -0.0078	Positif lemah 0.0193	Positif lemah 0.0830	Negatif lemah -0.0318
Answer 7	-	Positif lemah 0.0122	Positif lemah 0.0381	Negatif lemah -0.0686	Negatif lemah -0.0242	Positif lemah 0.0471	Positif lemah 0.0636	Negatif lemah -0.0513
Answer 8	-	Positif lemah 0.0698	Positif lemah 0.0849	Negatif lemah -0.0321	Positif lemah 0.0146	Positif lemah 0.0345	Positif lemah 0.0130	Negatif lemah -0.1368
Answer 9	-	Positif lemah 0.0665	Positif lemah 0.1674	Positif lemah 0.0092	Positif lemah 0.0691	Negatif lemah -0.0128	Negatif lemah -0.0528	Negatif lemah -0.1812
Answer 10	-	Positif lemah 0.0778	Positif lemah 0.0755	Negatif lemah -0.0180	Positif lemah 0.0231	Positif lemah 0.0346	Negatif lemah -0.0146	Negatif lemah -0.1298
Answer 11	-	Positif lemah 0.0584	Positif lemah 0.0524	Negatif lemah -0.0096	Positif lemah 0.0081	Positif lemah 0.0199	Positif lemah 0.0318	Negatif lemah -0.1197
Answer 12	-	Positif lemah 0.0380	Positif lemah 0.1042	Negatif lemah -0.0352	Positif lemah 0.0357	Positif lemah 0.0254	Positif lemah 0.0286	Negatif lemah -0.1515
Answer 13	-	Positif lemah 0.0644	Positif lemah 0.1057	Negatif lemah -0.0448	Positif lemah 0.0268	Positif lemah 0.0416	Positif lemah 0.0169	Negatif lemah -0.1600
Answer 14	-	Positif lemah 0.0717	Positif lemah 0.1026	Negatif lemah -0.0006	Negatif lemah -0.0089	Negatif lemah -0.0012	Positif lemah 0.0080	Negatif lemah -0.1168
Answer 15	-	Positif lemah 0.0549	Positif lemah 0.1375	Negatif lemah -0.0420	Positif lemah 0.0178	Negatif lemah -0.0160	Positif lemah 0.0216	Negatif lemah -0.1180
Answer 16	-	Positif lemah 0.0591	Positif lemah 0.0273	Negatif lemah -0.0386	Negatif lemah -0.0399	Positif lemah 0.0653	Positif lemah 0.0282	Negatif lemah -0.0708

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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