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) Akcie spoločností vo vzťahu k personálu za posledný mesiac (áno / nie)

2) Akcie spoločností vo vzťahu k personálu v poslednom mesiaci (fakt v%)

3) Obávať

4) Najväčšie problémy, ktorým čelí moja krajina

5) Aké vlastnosti a schopnosti používajú dobrí vodcovia pri budovaní úspešných tímov?

6) Google. Faktory, ktoré ovplyvňujú efektívnosť tímu

7) Hlavné priority uchádzačov o zamestnanie

8) Čo robí šéfa skvelým vodcom?

9) Čo robí ľudí úspešnými v práci?

10) Ste pripravení na diaľku dostávať menej mzdy za prácu?

11) Existuje ageizmus?

12) Ageizmus v kariére

13) Ageizmus v živote

14) Príčiny ageizmu

15) Dôvody, prečo sa ľudia vzdávajú (Anna Vital)

16) Dôverovať (#WVS)

17) Prieskum o šťastí v Oxforde

18) Psychologický blahobyt

19) Kde by bola vaša ďalšia najzaujímavejšia príležitosť?

20) Čo urobíte tento týždeň, aby ste sa starali o svoje duševné zdravie?

21) Žijem premýšľam o svojej minulosti, prítomnosti alebo budúcnosti

22) Meritokracia

23) Umelá inteligencia a koniec civilizácie

24) Prečo ľudia odkladajú?

25) Rodové rozdiely v budovaní sebavedomia (IFD Allensbach)

26) Xing.com Hodnotenie kultúry

27) „Päť dysfunkcií tímu Patricka Lencioniho“

28) Empatia je ...

29) Čo je nevyhnutné pre IT špecialistov pri výbere ponuky práce?

30) Prečo ľudia odolávajú zmenám (od Siobhán McHale)

31) Ako regulujete svoje emócie? (Autor: Nawal Mustafa M.A.)

32) 21 zručností, ktoré vám platia navždy (od Jeremiáša Teo / 赵汉昇)

33) Skutočná sloboda je ...

34) 12 spôsobov, ako vybudovať dôveru s ostatnými (Justin Wright)

35) Charakteristiky talentovaného zamestnanca (Inštitút riadenia talentov)

36) 10 kľúčov k motivácii vášho tímu

37) Algebra svedomia (Vladimír Lefebvre)

38) Tri odlišné možnosti budúcnosti (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.

Obávať

grafy Korelácia

VUCA

Kritická hodnota korelačného koeficientu

Normálne rozdelenie, od Williama Sealyho Gosset (študent) r = 0.033

Normálne rozdelenie, Spearman r = 0.0013

Zobraziť iba výsledky > r = 0.033

Distribúcia	Nekonečný	Nekonečný	Nekonečný	Normálny	Normálny	Normálny	Normálny	Normálny
Všetky otázky Všetky otázky Môj najväčší strach je
Môj najväčší strach je
Answer 1	-	Slabo pozitívne 0.0567	Slabo pozitívne 0.0317	Slabý negatívny -0.0161	Slabo pozitívne 0.0910	Slabo pozitívne 0.0295	Slabý negatívny -0.0121	Slabý negatívny -0.1545
Answer 2	-	Slabo pozitívne 0.0228	Slabý negatívny -0.0002	Slabý negatívny -0.0450	Slabo pozitívne 0.0639	Slabo pozitívne 0.0444	Slabo pozitívne 0.0133	Slabý negatívny -0.0938
Answer 3	-	Slabý negatívny -0.0028	Slabý negatívny -0.0120	Slabý negatívny -0.0410	Slabý negatívny -0.0470	Slabo pozitívne 0.0467	Slabo pozitívne 0.0790	Slabý negatívny -0.0198
Answer 4	-	Slabo pozitívne 0.0443	Slabo pozitívne 0.0349	Slabý negatívny -0.0188	Slabo pozitívne 0.0144	Slabo pozitívne 0.0301	Slabo pozitívne 0.0209	Slabý negatívny -0.0984
Answer 5	-	Slabo pozitívne 0.0305	Slabo pozitívne 0.1282	Slabo pozitívne 0.0136	Slabo pozitívne 0.0734	Slabý negatívny -0.0013	Slabý negatívny -0.0200	Slabý negatívny -0.1758
Answer 6	-	Slabo pozitívne 8.40E-5	Slabo pozitívne 0.0083	Slabý negatívny -0.0622	Slabý negatívny -0.0089	Slabo pozitívne 0.0194	Slabo pozitívne 0.0832	Slabý negatívny -0.0318
Answer 7	-	Slabo pozitívne 0.0130	Slabo pozitívne 0.0382	Slabý negatívny -0.0683	Slabý negatívny -0.0250	Slabo pozitívne 0.0470	Slabo pozitívne 0.0644	Slabý negatívny -0.0518
Answer 8	-	Slabo pozitívne 0.0702	Slabo pozitívne 0.0849	Slabý negatívny -0.0322	Slabo pozitívne 0.0141	Slabo pozitívne 0.0345	Slabo pozitívne 0.0136	Slabý negatívny -0.1369
Answer 9	-	Slabo pozitívne 0.0672	Slabo pozitívne 0.1676	Slabo pozitívne 0.0087	Slabo pozitívne 0.0689	Slabý negatívny -0.0131	Slabý negatívny -0.0515	Slabý negatívny -0.1820
Answer 10	-	Slabo pozitívne 0.0786	Slabo pozitívne 0.0755	Slabý negatívny -0.0199	Slabo pozitívne 0.0241	Slabo pozitívne 0.0343	Slabý negatívny -0.0129	Slabý negatívny -0.1307
Answer 11	-	Slabo pozitívne 0.0582	Slabo pozitívne 0.0533	Slabý negatívny -0.0091	Slabo pozitívne 0.0080	Slabo pozitívne 0.0196	Slabo pozitívne 0.0313	Slabý negatívny -0.1199
Answer 12	-	Slabo pozitívne 0.0394	Slabo pozitívne 0.1038	Slabý negatívny -0.0353	Slabo pozitívne 0.0352	Slabo pozitívne 0.0251	Slabo pozitívne 0.0300	Slabý negatívny -0.1524
Answer 13	-	Slabo pozitívne 0.0648	Slabo pozitívne 0.1049	Slabý negatívny -0.0443	Slabo pozitívne 0.0262	Slabo pozitívne 0.0417	Slabo pozitívne 0.0179	Slabý negatívny -0.1604
Answer 14	-	Slabo pozitívne 0.0716	Slabo pozitívne 0.1022	Slabý negatívny -0.0002	Slabý negatívny -0.0094	Slabý negatívny -0.0010	Slabo pozitívne 0.0089	Slabý negatívny -0.1173
Answer 15	-	Slabo pozitívne 0.0560	Slabo pozitívne 0.1366	Slabý negatívny -0.0419	Slabo pozitívne 0.0172	Slabý negatívny -0.0161	Slabo pozitívne 0.0225	Slabý negatívny -0.1182
Answer 16	-	Slabo pozitívne 0.0593	Slabo pozitívne 0.0274	Slabý negatívny -0.0384	Slabý negatívny -0.0403	Slabo pozitívne 0.0653	Slabo pozitívne 0.0285	Slabý negatívny -0.0710

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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

Majiteľ produktu SaaS Pet Project Sdtest®

Valerii bol v roku 1993 kvalifikovaný ako sociálny pedagógový psychológ a odvtedy uplatňoval svoje znalosti v oblasti projektového riadenia.
Valerii získal magisterský titul a kvalifikáciu projektu a programového manažéra v roku 2013. Počas magisterského programu sa zoznámil s projektovým plánom (GPM Deutsche Gesellschaft Für Projektmanagement e. V.) a dynamikou špirály.
Valerii absolvoval rôzne testy špirálovej dynamiky a využil svoje vedomosti a skúsenosti na prispôsobenie súčasnej verzie SDTEST.
Valerii je autorom skúmania neistoty V.U.C.A. Koncept využívajúci špirálovú dynamiku a matematickú štatistiku v psychológii, viac ako 20 medzinárodných prieskumov prieskumov.

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