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) Accións das empresas en relación co persoal no último mes (si / non)

2) Accións de empresas en relación ao persoal no último mes (feito en%)

3) Medos

4) Maiores problemas aos que se enfronta o meu país

5) Que calidades e habilidades usan os bos líderes á hora de construír equipos de éxito?

6) Google. Factores que afectan á eficacia do equipo

7) As principais prioridades dos demandantes de emprego

8) Que fai dun xefe un gran líder?

9) Que fai que a xente teña éxito no traballo?

10) ¿Estás preparado para recibir menos pagos para traballar de forma remota?

11) Existe o idade?

12) Idade na carreira

13) Idade na vida

14) Causas do idade

15) Razóns polas que a xente renuncia (de Anna Vital)

16) Confía (#WVS)

17) Enquisa de felicidade de Oxford

18) Benestar psicolóxico

19) Onde sería a túa próxima oportunidade máis emocionante?

20) Que farás esta semana para coidar a túa saúde mental?

21) Vivo pensando no meu pasado, presente ou futuro

22) Meritocracia

23) Intelixencia artificial e o final da civilización

24) Por que a xente se procrastina?

25) Diferenza de xénero na construción de autoconfianza (IFD Allensbach)

26) Xing.com Avaliación da cultura

27) As cinco disfuncións dun equipo de Patrick Lencioni

28) A empatía é ...

29) Que é esencial para os especialistas en TI na elección dunha oferta de traballo?

30) Por que a xente se resiste ao cambio (de Siobhán McHale)

31) Como regulas as túas emocións? (de Nawal Mustafa M.A.)

32) 21 habilidades que che pagan para sempre (de Jeremiah Teo / 赵汉昇)

33) A liberdade real é ...

34) 12 xeitos de crear confianza cos demais (de Justin Wright)

35) Características dun empregado de talento (por Talent Management Institute)

36) 10 claves para motivar ao teu equipo

37) Álxebra da conciencia (por Vladimir Lefebvre)

38) Tres posibilidades distintas do futuro (pola doutora 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.

Medos

Charts Correlación

VUCA

O valor crítico do coeficiente de correlación

Distribución normal, de William Sealy Gosset (estudante) r = 0.033

Distribución non normal, por Spearman r = 0.0013

Mostrar só resultados > r = 0.033

Distribución	Non normal	Non normal	Non normal	Normal	Normal	Normal	Normal	Normal
Todas as preguntas Todas as preguntas O meu maior medo é
O meu maior medo é
Answer 1	-	Débil positivo 0.0558	Débil positivo 0.0311	Débil negativo -0.0169	Débil positivo 0.0917	Débil positivo 0.0304	Débil negativo -0.0128	Débil negativo -0.1541
Answer 2	-	Débil positivo 0.0229	Débil negativo -0.0006	Débil negativo -0.0443	Débil positivo 0.0632	Débil positivo 0.0453	Débil positivo 0.0130	Débil negativo -0.0942
Answer 3	-	Débil negativo -0.0032	Débil negativo -0.0122	Débil negativo -0.0413	Débil negativo -0.0464	Débil positivo 0.0469	Débil positivo 0.0786	Débil negativo -0.0196
Answer 4	-	Débil positivo 0.0437	Débil positivo 0.0345	Débil negativo -0.0196	Débil positivo 0.0152	Débil positivo 0.0307	Débil positivo 0.0204	Débil negativo -0.0981
Answer 5	-	Débil positivo 0.0303	Débil positivo 0.1280	Débil positivo 0.0134	Débil positivo 0.0733	Débil negativo -0.0005	Débil negativo -0.0203	Débil negativo -0.1759
Answer 6	-	Débil negativo -0.0003	Débil positivo 0.0082	Débil negativo -0.0630	Débil negativo -0.0082	Débil positivo 0.0195	Débil positivo 0.0830	Débil negativo -0.0315
Answer 7	-	Débil positivo 0.0124	Débil positivo 0.0382	Débil negativo -0.0694	Débil negativo -0.0241	Débil positivo 0.0473	Débil positivo 0.0641	Débil negativo -0.0514
Answer 8	-	Débil positivo 0.0696	Débil positivo 0.0850	Débil negativo -0.0333	Débil positivo 0.0150	Débil positivo 0.0346	Débil positivo 0.0134	Débil negativo -0.1364
Answer 9	-	Débil positivo 0.0667	Débil positivo 0.1676	Débil positivo 0.0077	Débil positivo 0.0694	Débil negativo -0.0128	Débil negativo -0.0517	Débil negativo -0.1817
Answer 10	-	Débil positivo 0.0780	Débil positivo 0.0754	Débil negativo -0.0211	Débil positivo 0.0249	Débil positivo 0.0347	Débil negativo -0.0132	Débil negativo -0.1303
Answer 11	-	Débil positivo 0.0579	Débil positivo 0.0528	Débil negativo -0.0090	Débil positivo 0.0083	Débil positivo 0.0201	Débil positivo 0.0308	Débil negativo -0.1198
Answer 12	-	Débil positivo 0.0389	Débil positivo 0.1036	Débil negativo -0.0362	Débil positivo 0.0359	Débil positivo 0.0255	Débil positivo 0.0297	Débil negativo -0.1521
Answer 13	-	Débil positivo 0.0645	Débil positivo 0.1041	Débil negativo -0.0438	Débil positivo 0.0262	Débil positivo 0.0423	Débil positivo 0.0174	Débil negativo -0.1603
Answer 14	-	Débil positivo 0.0710	Débil positivo 0.1022	Débil negativo -0.0015	Débil negativo -0.0085	Débil negativo -0.0006	Débil positivo 0.0087	Débil negativo -0.1169
Answer 15	-	Débil positivo 0.0555	Débil positivo 0.1365	Débil negativo -0.0429	Débil positivo 0.0179	Débil negativo -0.0158	Débil positivo 0.0223	Débil negativo -0.1178
Answer 16	-	Débil positivo 0.0591	Débil positivo 0.0271	Débil negativo -0.0384	Débil negativo -0.0401	Débil positivo 0.0655	Débil positivo 0.0283	Débil negativo -0.0709

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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

Proxecto de propietario de produtos Saas Pet Project Sdtest®

Valerii foi cualificado como pedagogo social-psicólogo en 1993 e desde entón aplicou os seus coñecementos na xestión de proxectos.
Valerii obtivo un máster e a cualificación do xestor de proxectos e do programa en 2013. Durante o seu programa de máster, familiarizouse coa folla de ruta do proxecto (GPM Deutsche Gesellschaft Für ProjektManagement E. V.) e dinámica espiral.
Valerii realizou varias probas de dinámica en espiral e utilizou os seus coñecementos e experiencia para adaptar a versión actual de SDTest.
Valerii é o autor de explorar a incerteza do V.U.C.A. Concepto empregando dinámicas en espiral e estatísticas matemáticas en psicoloxía, máis de 20 enquisas internacionais.

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