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) 先月の人事に関する企業の行動（はい /いいえ）

2) 先月の職員に関連した企業の行動（％の場合）

3) 恐ろしい

4) 私の国が直面している最大の問題

5) 成功したチームを構築する際に、優れたリーダーがどのような資質と能力を使用していますか？

6) グーグル。チームの効率性に影響を与える要因

7) 求職者の主な優先事項

8) 何が上司を偉大なリーダーにしているのですか？

9) 何が人々を仕事で成功させるのですか？

10) リモートで仕事をするために少ない給料を受け取る準備はできていますか？

15) 人々があきらめる理由（アンナビタルによる）

16) 信頼 (#WVS)

17) オックスフォード幸福調査

18) 心理的幸福

19) あなたの次の最もエキサイティングな機会はどこにありますか？

20) あなたはあなたの精神的健康の世話をするために今週何をしますか？

21) 私は自分の過去、現在、または未来について考えて住んでいます

22) 功績

23) 人工知能と文明の終わり

24) なぜ人々は先延ばしになるのですか？

25) 自信の構築における性差（ifd allensbach）

26) xing.com文化評価

27) パトリック・レンシオーニの「チームの5つの機能不全」

28) 共感は...

29) ITスペシャリストが求人を選択するのに不可欠なことは何ですか？

30) 人々が変化に抵抗する理由（SiobhánMChaleによる）

31) 感情をどのように調節しますか？（Nawal Mustafa M.A.

32) あなたに永遠に支払う21スキル（エレミヤ・テオ /赵汉昇）

33) 本当の自由は...

34) 他の人との信頼を築くための12の方法（ジャスティンライトによる）

35) 才能のある従業員の特徴（Talent Management Instituteによる）

36) チームをやる気にさせるための10のキー

37) 良心の代数 (ウラジミール・ルフェーブル著)

38) 未来の 3 つの異なる可能性 (クレア W. グレイブス博士著)

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.

恐ろしい

チャート相関

VUCA

相関係数の臨界値

ウィリアム・シーリー・ゴセット（学生）による正規分布 r = 0.033

スピアマンによる非正規分布 r = 0.0013

結果のみを表示します > r = 0.033

分布	非正常	非正常	非正常	普通	普通	普通	普通	普通
すべての質問すべての質問私の最大の恐れは
私の最大の恐れは
Answer 1	-	弱いポジティブ 0.0536	弱いポジティブ 0.0297	弱いネガティブ -0.0174	弱いポジティブ 0.0910	弱いポジティブ 0.0303	弱いネガティブ -0.0114	弱いネガティブ -0.1522
Answer 2	-	弱いポジティブ 0.0197	弱いネガティブ -0.0002	弱いネガティブ -0.0449	弱いポジティブ 0.0661	弱いポジティブ 0.0446	弱いポジティブ 0.0121	弱いネガティブ -0.0926
Answer 3	-	弱いネガティブ -0.0055	弱いネガティブ -0.0120	弱いネガティブ -0.0413	弱いネガティブ -0.0450	弱いポジティブ 0.0473	弱いポジティブ 0.0789	弱いネガティブ -0.0205
Answer 4	-	弱いポジティブ 0.0427	弱いポジティブ 0.0339	弱いネガティブ -0.0192	弱いポジティブ 0.0153	弱いポジティブ 0.0305	弱いポジティブ 0.0210	弱いネガティブ -0.0983
Answer 5	-	弱いポジティブ 0.0252	弱いポジティブ 0.1256	弱いポジティブ 0.0135	弱いポジティブ 0.0733	弱いネガティブ -0.0016	弱いネガティブ -0.0198	弱いネガティブ -0.1742
Answer 6	-	弱いネガティブ -0.0024	弱いポジティブ 0.0078	弱いネガティブ -0.0633	弱いネガティブ -0.0069	弱いポジティブ 0.0198	弱いポジティブ 0.0835	弱いネガティブ -0.0330
Answer 7	-	弱いポジティブ 0.0112	弱いポジティブ 0.0375	弱いネガティブ -0.0688	弱いネガティブ -0.0225	弱いポジティブ 0.0471	弱いポジティブ 0.0647	弱いネガティブ -0.0528
Answer 8	-	弱いポジティブ 0.0698	弱いポジティブ 0.0830	弱いネガティブ -0.0314	弱いポジティブ 0.0137	弱いポジティブ 0.0350	弱いポジティブ 0.0144	弱いネガティブ -0.1376
Answer 9	-	弱いポジティブ 0.0644	弱いポジティブ 0.1664	弱いポジティブ 0.0083	弱いポジティブ 0.0702	弱いネガティブ -0.0136	弱いネガティブ -0.0517	弱いネガティブ -0.1830
Answer 10	-	弱いポジティブ 0.0760	弱いポジティブ 0.0738	弱いネガティブ -0.0199	弱いポジティブ 0.0242	弱いポジティブ 0.0319	弱いネガティブ -0.0149	弱いネガティブ -0.1320
Answer 11	-	弱いポジティブ 0.0580	弱いポジティブ 0.0514	弱いネガティブ -0.0111	弱いポジティブ 0.0076	弱いポジティブ 0.0206	弱いポジティブ 0.0318	弱いネガティブ -0.1213
Answer 12	-	弱いポジティブ 0.0367	弱いポジティブ 0.1023	弱いネガティブ -0.0355	弱いポジティブ 0.0359	弱いポジティブ 0.0245	弱いポジティブ 0.0286	弱いネガティブ -0.1524
Answer 13	-	弱いポジティブ 0.0619	弱いポジティブ 0.1049	弱いネガティブ -0.0451	弱いポジティブ 0.0281	弱いポジティブ 0.0412	弱いポジティブ 0.0173	弱いネガティブ -0.1609
Answer 14	-	弱いポジティブ 0.0707	弱いポジティブ 0.1011	弱いポジティブ 7.75E-5	弱いネガティブ -0.0083	弱いネガティブ -0.0012	弱いポジティブ 0.0083	弱いネガティブ -0.1182
Answer 15	-	弱いポジティブ 0.0549	弱いポジティブ 0.1358	弱いネガティブ -0.0409	弱いポジティブ 0.0176	弱いネガティブ -0.0163	弱いポジティブ 0.0208	弱いネガティブ -0.1180
Answer 16	-	弱いポジティブ 0.0582	弱いポジティブ 0.0259	弱いネガティブ -0.0395	弱いネガティブ -0.0403	弱いポジティブ 0.0652	弱いポジティブ 0.0283	弱いネガティブ -0.0717

MS Excelへのエクスポート

この機能は、独自のVUCA投票で利用できるようになります

You can not only just create your poll in the 関税 «V.U.C.A投票デザイナー» (with a unique link and your logo) but also you can earn money by selling its results in the 関税 «投票ショップ», 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 関税 «マイSDT»

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

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

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

2023.10.13

Valerii Kosenko

プロダクトオーナーSaaS PetProjectSdtest®

Valeriiは、1993年に社会教育教師の心理学者としての資格があり、その後、プロジェクト管理に関する知識を適用しています。
Valeriiは2013年に修士号とプロジェクトおよびプログラムマネージャーの資格を取得しました。修士課程のプログラムで、彼はプロジェクトロードマップ（GPM DeutscheGesellschaftFürProjektmanagemente。V。）とスパイラルダイナミクスに精通しました。
Valeriiはさまざまなスパイラルダイナミクステストを受け、彼の知識と経験を使用して、現在のバージョンのSDTestを適応させました。
Valeriiは、V.U.C.Aの不確実性を調査する著者です。精神学におけるスパイラルダイナミクスと数学統計を使用した概念、20を超える国際的な世論調査。

この投稿にはあります 0 コメント

に返信

返信をキャンセルします

コメントを残してください