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:

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2) Ayyukan kamfanoni dangane da ma'aikata a watan da ya gabata (gaskiya a cikin%)

3) Tsoro

4) Manyan matsaloli suna fuskantar ƙasata

5) Wadanne halaye da kasuwar suna yin amfani da shugabannin kirki yayin da gina kungiyoyi masu nasara?

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10) Shin kana shirye ka karɓi ƙasa da biyan kuɗi don yin aiki tukuru?

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13) Adireshin A cikin Rayuwa

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22) Malitocrate

23) Hankali da kuma ƙarshen wayewar kai

24) Me yasa mutane suke yin sujada?

25) Bambancin jinsi a cikin Gina Kai (IFD Allensbach)

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27) Patrick Lenconii's "Dysfunctions na kungiyar"

28) Tausasawa shine ...

29) Menene mahimmanci ga ƙwararrun ƙwararru wajen zabar tayin aiki?

30) Me yasa mutane ke tsayayya da canji (by siiyhán Mchale)

31) Taya zaka tsara motsin zuciyar ka? (by Nawal Mustafa M.A.)

32) 0 Kwarewar da ta biya ku har abada (by Irmiya Teo / 赵汉昇)

33) 'Yanci na gaske shine ...

34) Hanyoyi 12 don gyara aminci tare da wasu (ta Justin Wright)

35) Halayen ma'aikaci mai kwarewa (ta Cibiyar kula da Kwarewa)

36) 10 makullin don motsa ƙungiyar ku

37) Algebra of Conscience (na Vladimir Lefebvre)

38) Rarraba Dama Uku Na Gaba (na 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.

Tsoro

Charts Hulda

VUCA

M darajar da hulda coefficient

Rarraba al'ada, ta William Gubetes (Dalibi) r = 0.0335

Rarraba ba rarraba ba, da Spearman r = 0.0014

Nuna kawai sakamako > r = 0.0335

Rarrabuwa	Da ba al'ada ba	Da ba al'ada ba	Da ba al'ada ba	Na al'ada	Na al'ada	Na al'ada	Na al'ada	Na al'ada
Duk Tambayoyi Duk Tambayoyi Mafi girma tsoro shine
Mafi girma tsoro shine
Answer 1	-	Rauni kyau 0.0521	Rauni kyau 0.0294	Rauni korau -0.0147	Rauni kyau 0.0885	Rauni kyau 0.0316	Rauni korau -0.0110	Rauni korau -0.1513
Answer 2	-	Rauni kyau 0.0213	Rauni kyau 0.0013	Rauni korau -0.0432	Rauni kyau 0.0618	Rauni kyau 0.0453	Rauni kyau 0.0103	Rauni korau -0.0918
Answer 3	-	Rauni korau -0.0042	Rauni korau -0.0116	Rauni korau -0.0406	Rauni korau -0.0477	Rauni kyau 0.0487	Rauni kyau 0.0767	Rauni korau -0.0191
Answer 4	-	Rauni kyau 0.0421	Rauni kyau 0.0350	Rauni korau -0.0115	Rauni kyau 0.0112	Rauni kyau 0.0307	Rauni kyau 0.0175	Rauni korau -0.0980
Answer 5	-	Rauni kyau 0.0288	Rauni kyau 0.1272	Rauni kyau 0.0146	Rauni kyau 0.0697	Rauni kyau 0.0037	Rauni korau -0.0215	Rauni korau -0.1746
Answer 6	-	Rauni korau -0.0001	Rauni kyau 0.0042	Rauni korau -0.0607	Rauni korau -0.0115	Rauni kyau 0.0231	Rauni kyau 0.0826	Rauni korau -0.0309
Answer 7	-	Rauni kyau 0.0117	Rauni kyau 0.0372	Rauni korau -0.0653	Rauni korau -0.0283	Rauni kyau 0.0495	Rauni kyau 0.0626	Rauni korau -0.0505
Answer 8	-	Rauni kyau 0.0658	Rauni kyau 0.0830	Rauni korau -0.0310	Rauni kyau 0.0139	Rauni kyau 0.0334	Rauni kyau 0.0134	Rauni korau -0.1322
Answer 9	-	Rauni kyau 0.0660	Rauni kyau 0.1658	Rauni kyau 0.0051	Rauni kyau 0.0691	Rauni korau -0.0093	Rauni korau -0.0498	Rauni korau -0.1820
Answer 10	-	Rauni kyau 0.0758	Rauni kyau 0.0724	Rauni korau -0.0173	Rauni kyau 0.0236	Rauni kyau 0.0312	Rauni korau -0.0115	Rauni korau -0.1263
Answer 11	-	Rauni kyau 0.0577	Rauni kyau 0.0544	Rauni korau -0.0075	Rauni kyau 0.0082	Rauni kyau 0.0185	Rauni kyau 0.0293	Rauni korau -0.1190
Answer 12	-	Rauni kyau 0.0376	Rauni kyau 0.1007	Rauni korau -0.0342	Rauni kyau 0.0296	Rauni kyau 0.0273	Rauni kyau 0.0341	Rauni korau -0.1500
Answer 13	-	Rauni kyau 0.0627	Rauni kyau 0.1017	Rauni korau -0.0443	Rauni kyau 0.0248	Rauni kyau 0.0434	Rauni kyau 0.0189	Rauni korau -0.1576
Answer 14	-	Rauni kyau 0.0732	Rauni kyau 0.1036	Rauni kyau 0.0048	Rauni korau -0.0105	Rauni korau -0.0039	Rauni kyau 0.0041	Rauni korau -0.1157
Answer 15	-	Rauni kyau 0.0539	Rauni kyau 0.1381	Rauni korau -0.0424	Rauni kyau 0.0163	Rauni korau -0.0147	Rauni kyau 0.0216	Rauni korau -0.1173
Answer 16	-	Rauni kyau 0.0590	Rauni kyau 0.0274	Rauni korau -0.0375	Rauni korau -0.0429	Rauni kyau 0.0687	Rauni kyau 0.0253	Rauni korau -0.0698

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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

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