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) اقدامات شرکت ها در رابطه با پرسنل در ماه گذشته (بله / خیر)

2) اقدامات شرکت ها در رابطه با پرسنل در ماه گذشته (واقعیت در٪)

3) ترس

4) بزرگترین مشکلات پیش روی کشور من

5) رهبران خوب هنگام ساختن تیم های موفق از چه خصوصیات و توانایی هایی استفاده می کنند؟

6) گوگل. عواملی که بر اثربخشی تیم تأثیر می گذارد

7) اولویت های اصلی افراد متقاضی کار

8) چه چیزی رئیس را به یک رهبر بزرگ تبدیل می کند؟

9) چه چیزی باعث موفقیت افراد در کار می شود؟

10) آیا شما آماده دریافت دستمزد کمتری برای کار از راه دور هستید؟

11) آیا سن گرایی وجود دارد؟

12) سن گرایی در حرفه

13) سن گرایی در زندگی

14) علل سن گرایی

15) دلایلی که مردم تسلیم می شوند (توسط آنا ویتال)

16) اعتماد (#WVS)

17) بررسی خوشبختی آکسفورد

18) سلامت روانی

19) جالب ترین فرصت بعدی شما کجا خواهد بود؟

20) در این هفته چه کاری انجام خواهید داد تا از سلامت روانی خود مراقبت کنید؟

21) من زندگی می کنم در مورد گذشته ، حال یا آینده ام

22) شایسته سالاری

23) هوش مصنوعی و پایان تمدن

24) چرا مردم به تعویق می افتند؟

25) تفاوت جنسیتی در ایجاد اعتماد به نفس (IFD Allensbach)

26) ارزیابی فرهنگ Xing.com

27) پنج اختلال عملکرد یک تیم پاتریک لنسیونی

28) همدلی است ...

29) چه چیزی برای متخصصان فناوری اطلاعات در انتخاب پیشنهاد شغلی ضروری است؟

30) چرا مردم در برابر تغییر مقاومت می کنند (توسط Siobhán Mchale)

31) چگونه احساسات خود را تنظیم می کنید؟ (توسط Nawal Mustafa M.A.)

32) 21 مهارتی که برای همیشه به شما می پردازد (توسط ارمیا Teo / 赵汉昇)

33) آزادی واقعی ...

34) 12 راه برای ایجاد اعتماد با دیگران (توسط جاستین رایت)

35) ویژگی های یک کارمند با استعداد (توسط موسسه مدیریت استعداد)

36) 10 کلید برای ایجاد انگیزه در تیم خود

37) جبر وجدان (نویسنده ولادیمیر لوفور)

38) سه احتمال متمایز آینده (توسط دکتر کلر دبلیو گریوز)

39) اقداماتی برای ایجاد اعتماد به نفس تزلزل ناپذیر (نوشته سورن سامرچیان)

40)

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.

ترس

نمودارهمبستگی

VUCA

نوع همبستگی	خطی (پیرسون) خطی (پیرسون) غیر خطی (Spearman)
مقدار بحرانی ضریب همبستگی	توزیع عادی ، توسط ویلیام سیلی گوست (دانشجو) توزیع عادی ، توسط ویلیام سیلی گوست (دانشجو) توزیع غیر عادی ، توسط Spearman	فقط نتایج را نشان می دهد > r = 0.0297

توزیع	غیر عادی	غیر عادی	غیر عادی	طبیعی	طبیعی	طبیعی	طبیعی	طبیعی
تمام س questions الات تمام س questions الات بزرگترین ترس من این است
بزرگترین ترس من این است
Answer 1	-	مثبت ضعیف 0.0476	مثبت ضعیف 0.0211	منفی ضعیف -0.0240	مثبت ضعیف 0.0979	مثبت ضعیف 0.0334	منفی ضعیف -0.0079	منفی ضعیف -0.1479
Answer 2	-	مثبت ضعیف 0.0211	مثبت ضعیف 0.0016	منفی ضعیف -0.0378	مثبت ضعیف 0.0596	مثبت ضعیف 0.0449	مثبت ضعیف 0.0126	منفی ضعیف -0.0960
Answer 3	-	منفی ضعیف -0.0007	مثبت ضعیف 0.0027	منفی ضعیف -0.0447	منفی ضعیف -0.0462	مثبت ضعیف 0.0438	مثبت ضعیف 0.0768	منفی ضعیف -0.0248
Answer 4	-	مثبت ضعیف 0.0417	مثبت ضعیف 0.0306	منفی ضعیف -0.0296	مثبت ضعیف 0.0168	مثبت ضعیف 0.0357	مثبت ضعیف 0.0258	منفی ضعیف -0.0969
Answer 5	-	مثبت ضعیف 0.0196	مثبت ضعیف 0.1276	مثبت ضعیف 0.0039	مثبت ضعیف 0.0821	منفی ضعیف -2.01E-5	منفی ضعیف -0.0078	منفی ضعیف -0.1813
Answer 6	-	مثبت ضعیف 0.0056	مثبت ضعیف 0.0165	منفی ضعیف -0.0616	منفی ضعیف -0.0138	مثبت ضعیف 0.0148	مثبت ضعیف 0.0885	منفی ضعیف -0.0379
Answer 7	-	مثبت ضعیف 0.0129	مثبت ضعیف 0.0443	منفی ضعیف -0.0684	منفی ضعیف -0.0384	مثبت ضعیف 0.0432	مثبت ضعیف 0.0768	منفی ضعیف -0.0507
Answer 8	-	مثبت ضعیف 0.0589	مثبت ضعیف 0.0839	منفی ضعیف -0.0273	مثبت ضعیف 0.0101	مثبت ضعیف 0.0361	مثبت ضعیف 0.0200	منفی ضعیف -0.1376
Answer 9	-	مثبت ضعیف 0.0701	مثبت ضعیف 0.1600	مثبت ضعیف 7.34E-5	مثبت ضعیف 0.0596	منفی ضعیف -0.0079	منفی ضعیف -0.0451	منفی ضعیف -0.1753
Answer 10	-	مثبت ضعیف 0.0746	مثبت ضعیف 0.0580	منفی ضعیف -0.0136	مثبت ضعیف 0.0220	مثبت ضعیف 0.0387	منفی ضعیف -0.0047	منفی ضعیف -0.1316
Answer 11	-	مثبت ضعیف 0.0600	مثبت ضعیف 0.0546	منفی ضعیف -0.0040	مثبت ضعیف 0.0093	مثبت ضعیف 0.0235	مثبت ضعیف 0.0222	منفی ضعیف -0.1257
Answer 12	-	مثبت ضعیف 0.0383	مثبت ضعیف 0.1035	منفی ضعیف -0.0370	مثبت ضعیف 0.0348	مثبت ضعیف 0.0270	مثبت ضعیف 0.0284	منفی ضعیف -0.1516
Answer 13	-	مثبت ضعیف 0.0717	مثبت ضعیف 0.1001	منفی ضعیف -0.0354	مثبت ضعیف 0.0235	مثبت ضعیف 0.0332	مثبت ضعیف 0.0182	منفی ضعیف -0.1602
Answer 14	-	مثبت ضعیف 0.0837	مثبت ضعیف 0.0912	منفی ضعیف -0.0060	منفی ضعیف -0.0159	مثبت ضعیف 0.0033	مثبت ضعیف 0.0127	منفی ضعیف -0.1153
Answer 15	-	مثبت ضعیف 0.0540	مثبت ضعیف 0.1271	منفی ضعیف -0.0282	مثبت ضعیف 0.0108	منفی ضعیف -0.0219	مثبت ضعیف 0.0275	منفی ضعیف -0.1176
Answer 16	-	مثبت ضعیف 0.0663	مثبت ضعیف 0.0287	منفی ضعیف -0.0357	منفی ضعیف -0.0471	مثبت ضعیف 0.0649	مثبت ضعیف 0.0201	منفی ضعیف -0.0664

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

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

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

2023.10.13

FearpersonqualitiesprojectorganizationalstructureRACIresponsibilitymatrixCritical ChainProject Managementfocus factorJiraempathyleadersbossGermanyChinaPolicyUkraineRussiawarvolatilityuncertaintycomplexityambiguityVUCArelocatejobproblemcountryreasongive upobjectivekeyresultmathematicalpsychologyMBTIHR metricsstandardDEIcorrelationriskscoringmodelGame TheoryPrisoner's Dilemma

والری

مالک محصول SaaS SDTEST®

والری در سال 1993 به عنوان یک معلم اجتماعی-روانشناس صلاحیت یافت و از آن زمان دانش خود را در مدیریت پروژه به کار گرفته است.
والری در سال 2013 مدرک کارشناسی ارشد و صلاحیت مدیر پروژه و برنامه را دریافت کرد. در طول دوره کارشناسی ارشد خود، با Project Roadmap (GPM Deutsche Gesellschaft für Projektmanagement e. V.) و Spiral Dynamics آشنا شد.
والری نویسنده کتاب بررسی عدم قطعیت V.U.C.A است. مفهوم با استفاده از دینامیک مارپیچی و آمار ریاضی در روانشناسی و 38 نظرسنجی بین المللی

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