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.

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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.

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ChartsKorrelatioun

VUCA

Kritescher Wäert vun der Korrelatioun souguer gemaach

Normal Verdeelung, vum William Sighty Goesset (Student) r = 0.0312

Net normal Verdeelung, vum Spärman r = 0.0013

Weisen nëmme Resultater > r = 0.0312

Verdeelung	Net normal	Net normal	Net normal	Normelle	Normelle	Normelle	Normelle	Normelle
All Froen All Froen Meng gréissten Angscht ass
Meng gréissten Angscht ass
Answer 1	-	Schwaach positiv 0.0518	Schwaach positiv 0.0230	Schwaach negativ -0.0198	Schwaach positiv 0.0959	Schwaach positiv 0.0410	Schwaach negativ -0.0153	Schwaach negativ -0.1558
Answer 2	-	Schwaach positiv 0.0169	Schwaach negativ -0.0057	Schwaach negativ -0.0399	Schwaach positiv 0.0642	Schwaach positiv 0.0523	Schwaach positiv 0.0121	Schwaach negativ -0.0962
Answer 3	-	Schwaach negativ -0.0047	Schwaach negativ -0.0068	Schwaach negativ -0.0436	Schwaach negativ -0.0398	Schwaach positiv 0.0492	Schwaach positiv 0.0753	Schwaach negativ -0.0272
Answer 4	-	Schwaach positiv 0.0436	Schwaach positiv 0.0324	Schwaach negativ -0.0275	Schwaach positiv 0.0163	Schwaach positiv 0.0420	Schwaach positiv 0.0246	Schwaach negativ -0.1052
Answer 5	-	Schwaach positiv 0.0195	Schwaach positiv 0.1248	Schwaach positiv 0.0110	Schwaach positiv 0.0765	Schwaach negativ -0.0001	Schwaach negativ -0.0162	Schwaach negativ -0.1749
Answer 6	-	Schwaach negativ -0.0019	Schwaach positiv 0.0042	Schwaach negativ -0.0610	Schwaach negativ -0.0073	Schwaach positiv 0.0242	Schwaach positiv 0.0841	Schwaach negativ -0.0374
Answer 7	-	Schwaach positiv 0.0095	Schwaach positiv 0.0322	Schwaach negativ -0.0629	Schwaach negativ -0.0296	Schwaach positiv 0.0513	Schwaach positiv 0.0685	Schwaach negativ -0.0532
Answer 8	-	Schwaach positiv 0.0644	Schwaach positiv 0.0695	Schwaach negativ -0.0243	Schwaach positiv 0.0100	Schwaach positiv 0.0412	Schwaach positiv 0.0159	Schwaach negativ -0.1342
Answer 9	-	Schwaach positiv 0.0750	Schwaach positiv 0.1581	Schwaach positiv 0.0071	Schwaach positiv 0.0612	Schwaach negativ -0.0043	Schwaach negativ -0.0523	Schwaach negativ -0.1827
Answer 10	-	Schwaach positiv 0.0758	Schwaach positiv 0.0605	Schwaach negativ -0.0134	Schwaach positiv 0.0235	Schwaach positiv 0.0354	Schwaach negativ -0.0116	Schwaach negativ -0.1329
Answer 11	-	Schwaach positiv 0.0624	Schwaach positiv 0.0509	Schwaach negativ -0.0083	Schwaach positiv 0.0086	Schwaach positiv 0.0312	Schwaach positiv 0.0209	Schwaach negativ -0.1276
Answer 12	-	Schwaach positiv 0.0405	Schwaach positiv 0.0914	Schwaach negativ -0.0317	Schwaach positiv 0.0337	Schwaach positiv 0.0336	Schwaach positiv 0.0245	Schwaach negativ -0.1527
Answer 13	-	Schwaach positiv 0.0695	Schwaach positiv 0.0941	Schwaach negativ -0.0377	Schwaach positiv 0.0296	Schwaach positiv 0.0437	Schwaach positiv 0.0115	Schwaach negativ -0.1631
Answer 14	-	Schwaach positiv 0.0819	Schwaach positiv 0.0870	Schwaach negativ -0.0003	Schwaach negativ -0.0136	Schwaach positiv 0.0081	Schwaach positiv 0.0112	Schwaach negativ -0.1213
Answer 15	-	Schwaach positiv 0.0576	Schwaach positiv 0.1210	Schwaach negativ -0.0311	Schwaach positiv 0.0085	Schwaach negativ -0.0126	Schwaach positiv 0.0241	Schwaach negativ -0.1153
Answer 16	-	Schwaach positiv 0.0708	Schwaach positiv 0.0204	Schwaach negativ -0.0379	Schwaach negativ -0.0409	Schwaach positiv 0.0752	Schwaach positiv 0.0148	Schwaach negativ -0.0744

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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

Valerii Kosenko

Produit Besëtzer SaaS SDTEST®

De Valerii gouf 1993 als Sozialpädagog-Psycholog qualifizéiert an huet zënterhier säi Wëssen an der Projektmanagement applizéiert.
De Valerii krut e Masterstudium an d'Qualifikatioun vum Projet a Programmmanager am Joer 2013. Während sengem Masterprogramm huet hie sech mam Project Roadmap (GPM Deutsche Gesellschaft für Projektmanagement e. V.) a Spiral Dynamics vertraut.
Valerii ass den Auteur fir d'Onsécherheet vun der V.U.C.A. Konzept mat Spiral Dynamik a mathematesch Statistiken an der Psychologie, an 38 international Ëmfroen.

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