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) Aksies van maatskappye in verhouding tot personeel die afgelope maand (ja / nee)

2) Aksies van maatskappye met betrekking tot personeel in die laaste maand (feit in%)

3) Vrese

4) Grootste probleme wat my land in die gesig staar

5) Watter eienskappe en vermoëns gebruik goeie leiers as hulle suksesvolle spanne bou?

6) Google. Faktore wat die effektiwiteit van die span beïnvloed

7) Die belangrikste prioriteite van werksoekers

8) Wat maak 'n baas 'n wonderlike leier?

9) Wat maak mense suksesvol by die werk?

10) Is u gereed om minder betaal te ontvang om op afstand te werk?

11) Bestaan ouderdomsisme?

12) Ouderdom in loopbaan

13) Ouderdom in die lewe

14) Oorsake van ouderdomsisme

15) Redes waarom mense opgee (deur Anna Vital)

16) VERTROUE (#WVS)

17) Oxford Happiness Survey

18) Sielkundige welstand

19) Waar sou u volgende mees opwindende geleentheid wees?

20) Wat sal u hierdie week doen om na u geestesgesondheid om te sien?

21) Ek leef nadink oor my verlede, hede of toekoms

22) Meritokrasie

23) Kunsmatige intelligensie en die einde van die beskawing

24) Waarom stel mense uit?

25) Geslagsverskil in die bou van selfvertroue (IFD Allensbach)

26) Xing.com -kultuurassessering

27) Patrick Lencioni se "The Five Disfunctions of a Team"

28) Empatie is ...

29) Wat is noodsaaklik vir IT -spesialiste om 'n werkaanbod te kies?

30) Waarom mense weerstand bied teen verandering (deur Siobhán McHale)

31) Hoe reguleer u u emosies? (deur Nawal Mustafa M.A.)

32) 21 Vaardighede wat u vir ewig betaal (deur Jeremiah Teo / 赵汉昇)

33) Regte vryheid is ...

34) 12 maniere om vertroue met ander op te bou (deur Justin Wright)

35) Eienskappe van 'n talentvolle werknemer (deur Talent Management Institute)

36) 10 sleutels om u span te motiveer

37) Algebra van die gewete (deur Vladimir Lefebvre)

38) Drie duidelike moontlikhede van die toekoms (deur 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.

Vrese

Kaarte Korrelasie

VUCA

Kritieke waarde van die korrelasiekoëffisiënt

Normale verspreiding, deur William Sealy Gosset (student) r = 0.033

Nie normale verspreiding, deur Spearman r = 0.0013

Toon slegs resultate > r = 0.033

Verspreiding	Nie normaal nie	Nie normaal nie	Nie normaal nie	Normaal	Normaal	Normaal	Normaal	Normaal
Alle vrae Alle vrae My grootste vrees is
My grootste vrees is
Answer 1	-	Swak positief 0.0532	Swak positief 0.0292	Swak negatief -0.0175	Swak positief 0.0919	Swak positief 0.0301	Swak negatief -0.0114	Swak negatief -0.1523
Answer 2	-	Swak positief 0.0208	Swak negatief -0.0014	Swak negatief -0.0431	Swak positief 0.0641	Swak positief 0.0449	Swak positief 0.0130	Swak negatief -0.0931
Answer 3	-	Swak negatief -0.0053	Swak negatief -0.0130	Swak negatief -0.0406	Swak negatief -0.0456	Swak positief 0.0474	Swak positief 0.0793	Swak negatief -0.0204
Answer 4	-	Swak positief 0.0427	Swak positief 0.0328	Swak negatief -0.0200	Swak positief 0.0158	Swak positief 0.0306	Swak positief 0.0217	Swak negatief -0.0980
Answer 5	-	Swak positief 0.0255	Swak positief 0.1255	Swak positief 0.0143	Swak positief 0.0732	Swak negatief -0.0019	Swak negatief -0.0196	Swak negatief -0.1747
Answer 6	-	Swak negatief -0.0027	Swak positief 0.0074	Swak negatief -0.0629	Swak negatief -0.0074	Swak positief 0.0199	Swak positief 0.0835	Swak negatief -0.0324
Answer 7	-	Swak positief 0.0110	Swak positief 0.0371	Swak negatief -0.0688	Swak negatief -0.0227	Swak positief 0.0471	Swak positief 0.0650	Swak negatief -0.0523
Answer 8	-	Swak positief 0.0693	Swak positief 0.0825	Swak negatief -0.0321	Swak positief 0.0139	Swak positief 0.0351	Swak positief 0.0147	Swak negatief -0.1369
Answer 9	-	Swak positief 0.0643	Swak positief 0.1659	Swak positief 0.0082	Swak positief 0.0699	Swak negatief -0.0136	Swak negatief -0.0513	Swak negatief -0.1826
Answer 10	-	Swak positief 0.0760	Swak positief 0.0730	Swak negatief -0.0219	Swak positief 0.0254	Swak positief 0.0318	Swak negatief -0.0138	Swak negatief -0.1318
Answer 11	-	Swak positief 0.0571	Swak positief 0.0514	Swak negatief -0.0099	Swak positief 0.0077	Swak positief 0.0206	Swak positief 0.0308	Swak negatief -0.1211
Answer 12	-	Swak positief 0.0373	Swak positief 0.1013	Swak negatief -0.0357	Swak positief 0.0357	Swak positief 0.0243	Swak positief 0.0296	Swak negatief -0.1524
Answer 13	-	Swak positief 0.0621	Swak positief 0.1036	Swak negatief -0.0438	Swak positief 0.0273	Swak positief 0.0414	Swak positief 0.0176	Swak negatief -0.1608
Answer 14	-	Swak positief 0.0703	Swak positief 0.1007	Swak positief 9.54E-5	Swak negatief -0.0088	Swak negatief -0.0011	Swak positief 0.0084	Swak negatief -0.1174
Answer 15	-	Swak positief 0.0554	Swak positief 0.1349	Swak negatief -0.0418	Swak positief 0.0179	Swak negatief -0.0165	Swak positief 0.0219	Swak negatief -0.1181
Answer 16	-	Swak positief 0.0581	Swak positief 0.0255	Swak negatief -0.0388	Swak negatief -0.0407	Swak positief 0.0654	Swak positief 0.0283	Swak negatief -0.0714

Uitvoer na MS Excel

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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

Produk -eienaar SaaS Pet Project SdTest®

Valerii is in 1993 as 'n maatskaplike pedagoge-sielkundige gekwalifiseer en het sedertdien sy kennis in projekbestuur toegepas.
Valerii het in 2013 'n meestersgraad en die kwalifikasie van die projek- en programbestuurder verwerf. Tydens sy meestersprogram het hy vertroud geraak met Project Roadmap (GPM Deutsche Gesellschaft Für Projektmanagement e. V.) en Spiral Dynamics.
Valerii het verskillende spiraaldinamika -toetse afgelê en sy kennis en ervaring gebruik om die huidige weergawe van SDTest aan te pas.
Valerii is die skrywer van die verkenning van die onsekerheid van die V.U.C.A. Konsep met behulp van spiraaldinamika en wiskundige statistieke in sielkunde, meer as 20 internasionale peilings.

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