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) Ações das empresas em relação ao pessoal no último mês (sim / não)

2) Ações de empresas em relação ao pessoal no último mês (fato em%)

3) Medos

4) Maiores problemas enfrentados pelo meu país

5) Quais qualidades e habilidades os bons líderes usam ao criar equipes de sucesso?

6) Google. Fatores que afetam a eficácia da equipe

7) As principais prioridades dos candidatos a emprego

8) O que faz de um chefe um grande líder?

9) O que faz as pessoas bem -sucedidas no trabalho?

10) Você está pronto para receber menos pagamento para trabalhar remotamente?

11) O envelhecimento existe?

12) Envelhecimento na carreira

13) Envelhecimento na vida

14) Causas do envelhecimento

15) Razões pelas quais as pessoas desistem (de Anna Vital)

16) CONFIAR EM (#WVS)

17) Pesquisa de Felicidade de Oxford

18) Bem -estar psicológico

19) Onde seria sua próxima oportunidade mais emocionante?

20) O que você fará esta semana para cuidar de sua saúde mental?

21) Eu vivo pensando no meu passado, presente ou futuro

22) Meritocracia

23) Inteligência artificial e o fim da civilização

24) Por que as pessoas procrastinam?

25) Diferença de gênero na construção da autoconfiança (Ifd Allensbach)

26) Xing.com avaliação da cultura

27) Patrick Lencioni "As Cinco Disfunções de uma equipe"

28) Empatia é ...

29) O que é essencial para os especialistas em TI na escolha de uma oferta de emprego?

30) Por que as pessoas resistem à mudança (por Siobhán McHale)

31) Como você regular suas emoções? (Por Nawal Mustafa M.A.)

32) 21 Habilidades que pagam para sempre (por Jeremiah Teo / 赵汉昇)

33) A verdadeira liberdade é ...

34) 12 maneiras de construir confiança com outras pessoas (de Justin Wright)

35) Características de um funcionário talentoso (pelo Instituto de Gerenciamento de Talentos)

36) 10 chaves para motivar sua equipe

37) Álgebra da Consciência (por Vladimir Lefebvre)

38) Três possibilidades distintas do futuro (por Dra. 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 Correlação

VUCA

O valor crítico do coeficiente de correlação

Distribuição normal, de William Sealy Gosset (aluno) r = 0.033

Distribuição não normal, por Spearman r = 0.0013

Mostrar apenas resultados > r = 0.033

Distribuição	Não normal	Não normal	Não normal	Normal	Normal	Normal	Normal	Normal
Todas as perguntas Todas as perguntas Meu maior medo é
Meu maior medo é
Answer 1	-	Fraco positivo 0.0559	Fraco positivo 0.0315	Negativo fraco -0.0170	Fraco positivo 0.0920	Fraco positivo 0.0294	Negativo fraco -0.0124	Negativo fraco -0.1539
Answer 2	-	Fraco positivo 0.0229	Negativo fraco -0.0002	Negativo fraco -0.0448	Fraco positivo 0.0636	Fraco positivo 0.0445	Fraco positivo 0.0134	Negativo fraco -0.0939
Answer 3	-	Negativo fraco -0.0032	Negativo fraco -0.0121	Negativo fraco -0.0416	Negativo fraco -0.0462	Fraco positivo 0.0466	Fraco positivo 0.0788	Negativo fraco -0.0195
Answer 4	-	Fraco positivo 0.0438	Fraco positivo 0.0348	Negativo fraco -0.0195	Fraco positivo 0.0153	Fraco positivo 0.0300	Fraco positivo 0.0207	Negativo fraco -0.0980
Answer 5	-	Fraco positivo 0.0304	Fraco positivo 0.1282	Fraco positivo 0.0135	Fraco positivo 0.0734	Negativo fraco -0.0013	Negativo fraco -0.0200	Negativo fraco -0.1757
Answer 6	-	Negativo fraco -0.0002	Fraco positivo 0.0082	Negativo fraco -0.0627	Negativo fraco -0.0083	Fraco positivo 0.0193	Fraco positivo 0.0831	Negativo fraco -0.0315
Answer 7	-	Fraco positivo 0.0126	Fraco positivo 0.0381	Negativo fraco -0.0687	Negativo fraco -0.0243	Fraco positivo 0.0469	Fraco positivo 0.0642	Negativo fraco -0.0515
Answer 8	-	Fraco positivo 0.0698	Fraco positivo 0.0848	Negativo fraco -0.0327	Fraco positivo 0.0148	Fraco positivo 0.0345	Fraco positivo 0.0134	Negativo fraco -0.1365
Answer 9	-	Fraco positivo 0.0668	Fraco positivo 0.1676	Fraco positivo 0.0083	Fraco positivo 0.0693	Negativo fraco -0.0131	Negativo fraco -0.0516	Negativo fraco -0.1818
Answer 10	-	Fraco positivo 0.0782	Fraco positivo 0.0753	Negativo fraco -0.0204	Fraco positivo 0.0247	Fraco positivo 0.0342	Negativo fraco -0.0131	Negativo fraco -0.1304
Answer 11	-	Fraco positivo 0.0578	Fraco positivo 0.0532	Negativo fraco -0.0096	Fraco positivo 0.0087	Fraco positivo 0.0195	Fraco positivo 0.0311	Negativo fraco -0.1196
Answer 12	-	Fraco positivo 0.0390	Fraco positivo 0.1037	Negativo fraco -0.0358	Fraco positivo 0.0358	Fraco positivo 0.0250	Fraco positivo 0.0299	Negativo fraco -0.1520
Answer 13	-	Fraco positivo 0.0644	Fraco positivo 0.1048	Negativo fraco -0.0448	Fraco positivo 0.0268	Fraco positivo 0.0417	Fraco positivo 0.0178	Negativo fraco -0.1600
Answer 14	-	Fraco positivo 0.0712	Fraco positivo 0.1021	Negativo fraco -0.0007	Negativo fraco -0.0088	Negativo fraco -0.0011	Fraco positivo 0.0088	Negativo fraco -0.1169
Answer 15	-	Fraco positivo 0.0557	Fraco positivo 0.1365	Negativo fraco -0.0423	Fraco positivo 0.0177	Negativo fraco -0.0162	Fraco positivo 0.0224	Negativo fraco -0.1179
Answer 16	-	Fraco positivo 0.0591	Fraco positivo 0.0273	Negativo fraco -0.0386	Negativo fraco -0.0400	Fraco positivo 0.0653	Fraco positivo 0.0284	Negativo fraco -0.0708

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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

Proprietário do produto SaaS Pet Project Sdtest®

Valerii foi qualificado como psicólogo social de pedagogo em 1993 e, desde então, aplicou seu conhecimento em gerenciamento de projetos.
Valerii obteve um mestrado e a qualificação do gerente do projeto e do programa em 2013. Durante o programa de mestrado, ele se familiarizou com o Roteiro do Projeto (GPM Deutsche Gesellschaft für projektmanagement e. V.) e dinâmica em espiral.
Valerii fez vários testes de dinâmica em espiral e usou seu conhecimento e experiência para adaptar a versão atual do SDTEST.
Valerii é o autor de explorar a incerteza do V.U.C.A. Conceito usando dinâmica espiral e estatística matemática em psicologia, mais de 20 pesquisas internacionais.

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