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.

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Here are reports of polls which SDTEST^® makes:

1) Tindakan syarikat berhubung dengan kakitangan pada bulan lalu (ya / tidak)

2) Tindakan syarikat berhubung dengan kakitangan pada bulan lepas (fakta dalam%)

3) Ketakutan

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11) Adakah umur wujud?

12) Ageism dalam kerjaya

13) Umur dalam hidup

14) Punca umur

15) Sebab Mengapa Orang Menyerah (oleh Anna Vital)

16) Kepercayaan (#WVS)

17) Kajian Kebahagiaan Oxford

18) Kesejahteraan psikologi

19) Di manakah peluang paling menarik seterusnya?

20) Apa yang akan anda lakukan minggu ini untuk menjaga kesihatan mental anda?

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35) Ciri -ciri pekerja berbakat (oleh Institut Pengurusan Bakat)

36) 10 kunci untuk memotivasi pasukan anda

37) Algebra of Conscience (oleh Vladimir Lefebvre)

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40)

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

carta Korelasi

VUCA

Nilai kritikal pekali korelasi

Pengagihan Normal, oleh William Sealy Gosset (Pelajar) r = 0.0315

Pengedaran tidak normal, oleh Spearman r = 0.0013

Tunjukkan hanya hasil > r = 0.0315

Pengedaran	Tidak normal	Tidak normal	Tidak normal	Biasa	Biasa	Biasa	Biasa	Biasa
Semua soalan Semua soalan Ketakutan terbesar saya adalah
Ketakutan terbesar saya adalah
Answer 1	-	Lemah positif 0.0543	Lemah positif 0.0273	Lemah negatif -0.0190	Lemah positif 0.0935	Lemah positif 0.0385	Lemah negatif -0.0161	Lemah negatif -0.1550
Answer 2	-	Lemah positif 0.0175	Lemah negatif -0.0076	Lemah negatif -0.0388	Lemah positif 0.0638	Lemah positif 0.0496	Lemah positif 0.0135	Lemah negatif -0.0956
Answer 3	-	Lemah negatif -0.0015	Lemah negatif -0.0091	Lemah negatif -0.0472	Lemah negatif -0.0424	Lemah positif 0.0499	Lemah positif 0.0762	Lemah negatif -0.0221
Answer 4	-	Lemah positif 0.0450	Lemah positif 0.0322	Lemah negatif -0.0266	Lemah positif 0.0171	Lemah positif 0.0375	Lemah positif 0.0257	Lemah negatif -0.1041
Answer 5	-	Lemah positif 0.0263	Lemah positif 0.1264	Lemah positif 0.0099	Lemah positif 0.0744	Lemah positif 0.0015	Lemah negatif -0.0157	Lemah negatif -0.1765
Answer 6	-	Lemah positif 0.0007	Lemah positif 0.0049	Lemah negatif -0.0626	Lemah negatif -0.0111	Lemah positif 0.0263	Lemah positif 0.0853	Lemah negatif -0.0360
Answer 7	-	Lemah positif 0.0119	Lemah positif 0.0323	Lemah negatif -0.0680	Lemah negatif -0.0311	Lemah positif 0.0535	Lemah positif 0.0701	Lemah negatif -0.0523
Answer 8	-	Lemah positif 0.0653	Lemah positif 0.0710	Lemah negatif -0.0284	Lemah positif 0.0134	Lemah positif 0.0393	Lemah positif 0.0167	Lemah negatif -0.1337
Answer 9	-	Lemah positif 0.0774	Lemah positif 0.1611	Lemah positif 0.0055	Lemah positif 0.0601	Lemah negatif -0.0074	Lemah negatif -0.0486	Lemah negatif -0.1816
Answer 10	-	Lemah positif 0.0763	Lemah positif 0.0633	Lemah negatif -0.0124	Lemah positif 0.0267	Lemah positif 0.0355	Lemah negatif -0.0106	Lemah negatif -0.1330
Answer 11	-	Lemah positif 0.0633	Lemah positif 0.0516	Lemah negatif -0.0083	Lemah positif 0.0095	Lemah positif 0.0269	Lemah positif 0.0254	Lemah negatif -0.1273
Answer 12	-	Lemah positif 0.0435	Lemah positif 0.0911	Lemah negatif -0.0333	Lemah positif 0.0326	Lemah positif 0.0356	Lemah positif 0.0264	Lemah negatif -0.1527
Answer 13	-	Lemah positif 0.0715	Lemah positif 0.0923	Lemah negatif -0.0386	Lemah positif 0.0277	Lemah positif 0.0442	Lemah positif 0.0154	Lemah negatif -0.1632
Answer 14	-	Lemah positif 0.0816	Lemah positif 0.0879	Lemah negatif -0.0058	Lemah negatif -0.0120	Lemah positif 0.0073	Lemah positif 0.0149	Lemah negatif -0.1202
Answer 15	-	Lemah positif 0.0548	Lemah positif 0.1236	Lemah negatif -0.0354	Lemah positif 0.0119	Lemah negatif -0.0137	Lemah positif 0.0269	Lemah negatif -0.1157
Answer 16	-	Lemah positif 0.0714	Lemah positif 0.0218	Lemah negatif -0.0386	Lemah negatif -0.0390	Lemah positif 0.0742	Lemah positif 0.0186	Lemah negatif -0.0766

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

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

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

2023.10.13

FearpersonqualitiesprojectorganizationalstructureRACIresponsibilitymatrixCritical ChainProject Managementfocus factorJiraempathyleadersbossGermanyChinaPolicyUkraineRussiawarvolatilityuncertaintycomplexityambiguityVUCArelocatejobproblemcountryreasongive upobjectivekeyresultmathematicalpsychologyMBTIHR metricsstandardDEIcorrelationriskscoringmodelGame TheoryPrisoner's Dilemma

Valerii Kosenko

Pemilik Produk SaaS SDTEST®

Valerii telah layak sebagai pedagogue-psikologi sosial pada tahun 1993 dan sejak itu telah menggunakan pengetahuannya dalam pengurusan projek.
Valerii memperoleh ijazah Sarjana dan kelayakan pengurus projek dan program pada tahun 2013. Semasa program Sarjananya, beliau mengenali Pelan Hala Tuju Projek (GPM Deutsche Gesellschaft für Projektmanagement e. V.) dan Spiral Dynamics.
Valerii ialah pengarang meneroka ketidakpastian V.U.C.A. konsep menggunakan Spiral Dynamics dan statistik matematik dalam psikologi, dan 38 tinjauan antarabangsa.

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