Panimalay/Balabag/Mathematical Psychology

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) Mga aksyon sa mga kompanya nga may kalabutan sa mga kawani sa miaging bulan (oo / dili)

2) Mga aksyon sa mga kompanya nga may kalabutan sa mga kawani sa miaging bulan (kamatuoran sa%)

3) Gikahadlokan

4) Labing dako nga mga problema nga giatubang sa akong nasud

5) Unsang mga hiyas ug katakos ang gigamit sa maayong mga lider sa pagtukod sa malampuson nga mga koponan?

6) Google. Mga hinungdan nga nakaapekto sa tim nga pagkaayo

7) Ang nag-unang mga prayoridad sa mga nangita sa trabaho

8) Unsa man ang naghimo sa usa ka boss nga usa ka maayo nga lider?

9) Unsa man ang naghimo sa mga tawo nga malampuson sa trabaho?

10) Andam ka ba nga makadawat gamay nga bayad aron magtrabaho nga layo?

11) Adunay ba ang edad?

12) Ageism sa karera

13) Ageism sa kinabuhi

14) Mga Hinungdan sa Ageismo

15) Mga hinungdan ngano nga ang mga tawo mihunong (ni Anna hinungdanon)

16) Mosalig (#WVS)

17) Surbey sa Kalipay sa Oxford

18) Psychological Wellbeing

19) Asa ang imong sunod nga labing kulbahinam nga oportunidad?

20) Unsa man ang imong buhaton karong semanaha aron maatiman ang imong kahimsog sa pangisip?

21) Nagpuyo ko nga naghunahuna sa akong nangagi, karon o sa umaabot

22) Meritocracy

23) Artipisyal nga salabutan ug ang katapusan sa sibilisasyon

24) Ngano nga ang mga tawo nag-procrastinate?

25) Pagkalainlain sa Gender sa Pagtukod sa Kaugalingon nga Pagsalig (IFD Allensbach)

26) Xing.com Pag-asayn sa Kultura

27) Patrick Lencioni's "Ang Lima nga Dysfunction sa usa ka Team"

28) Empatiya ...

29) Unsa ang hinungdanon alang sa mga espesyalista sa pagpili sa usa ka tanyag sa trabaho?

30) Ngano nga Batok sa mga Tawo ang Pagbag-o (ni Siobhán MCHALE)

31) Giunsa nimo pag-regulate ang imong emosyon? (ni Nawal Mustafa M.a.)

32) 21 Mga Kahanas nga Nagabayad Kanimo sa Kahangturan (ni Jeremiah Teo / 赵汉昇)

33) Tinuod nga Kagawasan ang ...

34) 12 Mga Paagi Aron Mabuhat ang Pagsalig sa Uban (Ni Justin Wright)

35) Mga Kinaiya sa usa ka Talento nga Empleyado (pinaagi sa Talent Management Institute)

36) 10 mga yawi sa pagdasig sa imong team

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.

Gikahadlokan

tsart Correlation

VUCA

Critical bili sa correlation coefficient

Kasagaran nga pag-apod-apod, ni William Sedy Gosset (Estudyante) r = 0.0353

Dili normal nga pag-apod-apod, pinaagi sa Spearman r = 0.0014

Ipakita ang mga resulta > r = 0.0353

Pag-apod-apod	Dili normal	Kasagaran	Dili normal	Kasagaran	Kasagaran	Kasagaran	Kasagaran	Kasagaran
Tanan nga mga pangutana Tanan nga mga pangutana Ang akong labing dako nga kahadlok mao ang
Ang akong labing dako nga kahadlok mao ang
Answer 1	-	Maluya nga positibo 0.0297	Maluya nga positibo 0.0298	Naluya nga negatibo -0.0106	Maluya nga positibo 0.0970	Maluya nga positibo 0.0325	Naluya nga negatibo -0.0019	Naluya nga negatibo -0.1558
Answer 2	-	Maluya nga positibo 0.0188	Maluya nga positibo 0.0076	Naluya nga negatibo -0.0360	Maluya nga positibo 0.0711	Maluya nga positibo 0.0387	Maluya nga positibo 0.0082	Naluya nga negatibo -0.1011
Answer 3	-	Maluya nga positibo 0.0026	Naluya nga negatibo -0.0170	Naluya nga negatibo -0.0443	Naluya nga negatibo -0.0458	Maluya nga positibo 0.0547	Maluya nga positibo 0.0808	Naluya nga negatibo -0.0270
Answer 4	-	Maluya nga positibo 0.0332	Maluya nga positibo 0.0285	Naluya nga negatibo -0.0006	Maluya nga positibo 0.0155	Maluya nga positibo 0.0276	Maluya nga positibo 0.0105	Naluya nga negatibo -0.0917
Answer 5	-	Maluya nga positibo 0.0122	Maluya nga positibo 0.1193	Maluya nga positibo 0.0095	Maluya nga positibo 0.0721	Maluya nga positibo 0.0057	Naluya nga negatibo -0.0083	Naluya nga negatibo -0.1687
Answer 6	-	Maluya nga positibo 0.0044	Maluya nga positibo 0.0005	Naluya nga negatibo -0.0582	Naluya nga negatibo -0.0004	Maluya nga positibo 0.0210	Maluya nga positibo 0.0830	Naluya nga negatibo -0.0418
Answer 7	-	Maluya nga positibo 0.0242	Maluya nga positibo 0.0368	Naluya nga negatibo -0.0521	Naluya nga negatibo -0.0234	Maluya nga positibo 0.0403	Maluya nga positibo 0.0568	Naluya nga negatibo -0.0597
Answer 8	-	Maluya nga positibo 0.0707	Maluya nga positibo 0.0781	Naluya nga negatibo -0.0244	Maluya nga positibo 0.0140	Maluya nga positibo 0.0303	Maluya nga positibo 0.0137	Naluya nga negatibo -0.1334
Answer 9	-	Maluya nga positibo 0.0564	Maluya nga positibo 0.1531	Maluya nga positibo 0.0127	Maluya nga positibo 0.0769	Naluya nga negatibo -0.0136	Naluya nga negatibo -0.0495	Naluya nga negatibo -0.1752
Answer 10	-	Maluya nga positibo 0.0711	Maluya nga positibo 0.0700	Naluya nga negatibo -0.0127	Maluya nga positibo 0.0246	Maluya nga positibo 0.0363	Naluya nga negatibo -0.0156	Naluya nga negatibo -0.1273
Answer 11	-	Maluya nga positibo 0.0542	Maluya nga positibo 0.0488	Maluya nga positibo 0.0086	Maluya nga positibo 0.0078	Maluya nga positibo 0.0162	Maluya nga positibo 0.0315	Naluya nga negatibo -0.1248
Answer 12	-	Maluya nga positibo 0.0281	Maluya nga positibo 0.0929	Naluya nga negatibo -0.0325	Maluya nga positibo 0.0361	Maluya nga positibo 0.0276	Maluya nga positibo 0.0365	Naluya nga negatibo -0.1482
Answer 13	-	Maluya nga positibo 0.0643	Maluya nga positibo 0.0916	Naluya nga negatibo -0.0418	Maluya nga positibo 0.0237	Maluya nga positibo 0.0425	Maluya nga positibo 0.0239	Naluya nga negatibo -0.1558
Answer 14	-	Maluya nga positibo 0.0697	Maluya nga positibo 0.1017	Maluya nga positibo 0.0149	Naluya nga negatibo -0.0062	Naluya nga negatibo -0.0087	Naluya nga negatibo -0.0002	Naluya nga negatibo -0.1161
Answer 15	-	Maluya nga positibo 0.0603	Maluya nga positibo 0.1299	Naluya nga negatibo -0.0379	Maluya nga positibo 0.0163	Naluya nga negatibo -0.0091	Maluya nga positibo 0.0164	Naluya nga negatibo -0.1204
Answer 16	-	Maluya nga positibo 0.0691	Maluya nga positibo 0.0221	Naluya nga negatibo -0.0305	Naluya nga negatibo -0.0515	Maluya nga positibo 0.0750	Maluya nga positibo 0.0187	Naluya nga negatibo -0.0696

Export sa MS Excel

Kini nga pag-andar mahimong magamit sa imong kaugalingon nga mga botohan sa VUCA

You can not only just create your poll in the Taripa «V.U.C.A poll designer» (with a unique link and your logo) but also you can earn money by selling its results in the Taripa «Tindahan sa poll», 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 Taripa «Ang akong SDT»

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

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

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

2023.10.13

Valerii Kesenko

Tag-iya sa Produkto Saas Petsa Project Sdtest®

Ang Valerii kwalipikado isip usa ka sosyal nga pedagogue-psychologist kaniadtong 1993 ug sukad gigamit ang iyang kahibalo sa pagdumala sa proyekto.
Nakuha ni Valerii ang degree sa usa ka master ug ang proyekto sa proyekto ug programa sa Program sa 2013. Sa panahon sa programa sa iyang agalon, nahibal-an niya ang proyekto sa proyekto nga gesellschaft E. Viral Dinnamics.
Gikuha ni Valerii ang lainlaing mga pagsulay sa dinamika sa Spiral ug gigamit ang iyang kahibalo ug kasinatian aron mapahiangay ang karon nga bersyon sa SdTest.
Ang Valerii mao ang tagsulat sa pagsuhid sa kawalay kasiguruhan sa V.U.C.A. Konsepto gamit ang mga dinamikong Spiral ug mga istatistika sa matematika sa Psychology, labaw sa 20 nga internasyonal nga botohan.

Kini nga post adunay 0 Mga Komento

Tubag sa

Kanselahon ang usa ka tubag

Biyai ang Imong Komento