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) Gníomhartha cuideachtaí maidir le pearsanra le mí anuas (tá / níl)

2) Gníomhartha cuideachtaí maidir le pearsanra le mí an mhí dheiridh (fírinne i%)

3) Eagla

4) Na fadhbanna is mó atá os comhair mo thíre

5) Cad iad na cáilíochtaí agus na cumais a úsáideann ceannairí maithe agus foirne rathúla á dtógáil agat?

6) Google. Fachtóirí a mbíonn tionchar acu ar fheabhas foirne

7) Príomhthosaíochtaí lucht cuardaigh poist

8) Cad a dhéanann ceannaire iontach mar cheannaire?

9) Cad a dhéanann daoine ag éirí go maith ag an obair?

10) An bhfuil tú réidh le níos lú pá a fháil chun obair go cianda?

11) An bhfuil aoiseachas ann?

12) Aoiseachas i ngairm

13) Aoiseachas sa saol

14) Cúiseanna le haoiseachas

15) Cúiseanna a thugann daoine suas (de réir Anna REALIAL)

16) Iontaobhas (#WVS)

17) Suirbhé Sonas Oxford

18) Folláine síceolaíoch

19) Cá mbeadh an chéad deis is spreagúla agat?

20) Cad a dhéanfaidh tú an tseachtain seo chun aire a thabhairt do do shláinte mheabhrach?

21) Tá mé i mo chónaí ag smaoineamh ar mo chuid ama, i láthair nó sa todhchaí

22) Fiúntas daonlathas

23) Faisnéis shaorga agus deireadh na sibhialtachta

24) Cén fáth a gcuireann daoine moill ar?

25) Difríocht inscne maidir le féinmhuinín a thógáil (IFD Allensbach)

26) Xing.com Measúnú Cultúir

27) Patrick Lencioni "The Five Dysfunctions of a Team"

28) Is ionbhá ...

29) Cad atá riachtanach do speisialtóirí TF maidir le tairiscint poist a roghnú?

30) Cén fáth a gcuireann daoine in aghaidh an athraithe (le Siobhán McHale)

31) Conas a rialaíonn tú do chuid mothúchán? (le Nawal Mustafa M.A.)

32) 21 scileanna a íocann tú go deo (le Jeremiah Teo / 赵汉昇)

33) Tá fíor -shaoirse ...

34) 12 Bealaí chun muinín a thógáil le daoine eile (le Justin Wright)

35) Saintréithe fostaí cumasach (ag Institiúid Bainistíochta Talent)

36) 10 eochracha chun d’fhoireann a spreagadh

37) Ailgéabar coinsiasa le Vladimir Lefebvre

38) Trí Fhéidearthacht ar Leith sa Todhchaí (leis an 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.

Eagla

Charts Comhghaoil

VUCA

Luach criticiúil an chomhéifeacht comhghaoil

Dáileadh Gnáth, le William Sealy Gosset (Mac Léinn) r = 0.033

Dáileadh Neamh -Ghnáth, le Spearman r = 0.0013

Taispeáin torthaí amháin > r = 0.033

Imdháileadh	Neamhghnách	Neamhghnách	Neamhghnách	Gnáth-	Gnáth-	Gnáth-	Gnáth-	Gnáth-
Gach ceist Gach ceist Is é an t-eagla is mó atá agam ná
Is é an t-eagla is mó atá agam ná
Answer 1	-	Dearfach lag 0.0569	Dearfach lag 0.0313	Diúltach lag -0.0161	Dearfach lag 0.0906	Dearfach lag 0.0297	Diúltach lag -0.0118	Diúltach lag -0.1544
Answer 2	-	Dearfach lag 0.0225	Dearfach lag 0.0002	Diúltach lag -0.0450	Dearfach lag 0.0644	Dearfach lag 0.0442	Dearfach lag 0.0128	Diúltach lag -0.0940
Answer 3	-	Diúltach lag -0.0030	Diúltach lag -0.0116	Diúltach lag -0.0411	Diúltach lag -0.0465	Dearfach lag 0.0466	Dearfach lag 0.0786	Diúltach lag -0.0200
Answer 4	-	Dearfach lag 0.0440	Dearfach lag 0.0354	Diúltach lag -0.0189	Dearfach lag 0.0150	Dearfach lag 0.0299	Dearfach lag 0.0204	Diúltach lag -0.0986
Answer 5	-	Dearfach lag 0.0309	Dearfach lag 0.1278	Dearfach lag 0.0137	Dearfach lag 0.0728	Diúltach lag -0.0011	Diúltach lag -0.0195	Diúltach lag -0.1757
Answer 6	-	Diúltach lag -0.0001	Dearfach lag 0.0086	Diúltach lag -0.0623	Diúltach lag -0.0085	Dearfach lag 0.0193	Dearfach lag 0.0829	Diúltach lag -0.0319
Answer 7	-	Dearfach lag 0.0127	Dearfach lag 0.0385	Diúltach lag -0.0683	Diúltach lag -0.0246	Dearfach lag 0.0468	Dearfach lag 0.0640	Diúltach lag -0.0519
Answer 8	-	Dearfach lag 0.0700	Dearfach lag 0.0853	Diúltach lag -0.0322	Dearfach lag 0.0146	Dearfach lag 0.0344	Dearfach lag 0.0132	Diúltach lag -0.1370
Answer 9	-	Dearfach lag 0.0670	Dearfach lag 0.1680	Dearfach lag 0.0087	Dearfach lag 0.0692	Diúltach lag -0.0132	Diúltach lag -0.0518	Diúltach lag -0.1822
Answer 10	-	Dearfach lag 0.0784	Dearfach lag 0.0758	Diúltach lag -0.0199	Dearfach lag 0.0245	Dearfach lag 0.0342	Diúltach lag -0.0133	Diúltach lag -0.1308
Answer 11	-	Dearfach lag 0.0586	Dearfach lag 0.0528	Diúltach lag -0.0091	Dearfach lag 0.0074	Dearfach lag 0.0198	Dearfach lag 0.0318	Diúltach lag -0.1198
Answer 12	-	Dearfach lag 0.0392	Dearfach lag 0.1042	Diúltach lag -0.0353	Dearfach lag 0.0357	Dearfach lag 0.0249	Dearfach lag 0.0297	Diúltach lag -0.1526
Answer 13	-	Dearfach lag 0.0646	Dearfach lag 0.1052	Diúltach lag -0.0444	Dearfach lag 0.0266	Dearfach lag 0.0416	Dearfach lag 0.0176	Diúltach lag -0.1605
Answer 14	-	Dearfach lag 0.0714	Dearfach lag 0.1026	Diúltach lag -0.0002	Diúltach lag -0.0090	Diúltach lag -0.0012	Dearfach lag 0.0086	Diúltach lag -0.1174
Answer 15	-	Dearfach lag 0.0558	Dearfach lag 0.1369	Diúltach lag -0.0419	Dearfach lag 0.0176	Diúltach lag -0.0163	Dearfach lag 0.0222	Diúltach lag -0.1183
Answer 16	-	Dearfach lag 0.0592	Dearfach lag 0.0275	Diúltach lag -0.0384	Diúltach lag -0.0402	Dearfach lag 0.0652	Dearfach lag 0.0283	Diúltach lag -0.0710

Easpórtáil go MS Excel

Beidh an fheidhmiúlacht seo ar fáil i do vótaíochtaí VUCA féin

Go maith

You can not only just create your poll in the Taraife «V.U.C.A dearthóir vótaíocht» (with a unique link and your logo) but also you can earn money by selling its results in the Taraife «Siopa vótaíochta», as already the authors of polls.

If you participated in VUCA polls, you can see your results and compare them with the overall polls results, which are constantly growing, in your personal account after purchasing Taraife «Mo SDT»

[1] https://twitter.com/wileyprof

[2] https://colinallen.dnsalias.org

[3] https://philpeople.org/profiles/colin-allen

2023.10.13

Valerii Kosenko

Úinéir an Táirge SaaS Pet Project Sdtest®

Bhí Valerii cáilithe mar shíceolaí oideolaíoch sóisialta i 1993 agus ó shin i leith chuir sé a chuid eolais i bhfeidhm i mbainistíocht tionscadail.
Fuair Valerii céim mháistreachta agus cáilíocht an tionscadail agus an bhainisteora cláir in 2013. Le linn a chláir mháistir, bhí sé eolach ar threochlár Project (GPM Deutsche Gesellschaft Für Projektmanagement e. V.) agus dinimic Spiral.
Ghlac Valerii tástálacha éagsúla dinimic bíseach agus d'úsáid sé a chuid eolais agus taithí chun an leagan reatha de SDTest a oiriúnú.
Is é Valerii údar iniúchadh a dhéanamh ar neamhchinnteacht an V.U.C.A. Coincheap ag baint úsáide as dinimic bíseach agus staitisticí matamaiticiúla i síceolaíocht, níos mó ná 20 vótaíocht idirnáisiúnta.

Tá an post seo 0 Deir

Freagra ar

Cealaigh freagra

Fág do thrácht