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:
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.
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 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.
What sets mathematical psychology apart from other branches of psychology in its use of mathematics?
Several key aspects stand out:
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.
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.
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.
1) Aktionen von Unternehmen in Bezug auf Personal im letzten Monat (Ja / Nein) 2) Aktionen von Unternehmen in Bezug auf das Personal im letzten Monat (Tatsache in%) 3) Ängste 4) Größte Probleme mit meinem Land 5) Welche Eigenschaften und Fähigkeiten nutzen gute Führungskräfte beim Aufbau erfolgreicher Teams? 6) Google. Faktoren, die sich auf die Teamwirksamkeit auswirken 7) Die Hauptprioritäten von Arbeitssuchenden 8) Was macht einen Chef zu einem großartigen Anführer? 9) Was macht die Menschen bei der Arbeit erfolgreich? 10) Sind Sie bereit, weniger Bezahlung für die Arbeit aus der Ferne zu erhalten? 13) AGEUSM IM DIFE 15) Gründe, warum Menschen aufgeben (von Anna Vital) 16) VERTRAUEN (#WVS) 19) Wo wäre Ihre nächste aufregendste Gelegenheit? 20) Was werden Sie diese Woche tun, um sich um Ihre geistige Gesundheit zu kümmern? 21) Ich lebe über meine Vergangenheit, Gegenwart oder Zukunft nach 22) Meritokratie 23) Künstliche Intelligenz und das Ende der Zivilisation 24) Warum zögern die Menschen? 25) Geschlechtsunterschied beim Aufbau von Selbstvertrauen (IFD Allensbach) 27) Patrick Lencionis "Die fünf Funktionsstörungen eines Teams" 28) Empathie ist ... 29) Was ist für IT -Spezialisten für die Auswahl eines Stellenangebots wichtig? 30) Warum Menschen Veränderungen widerstehen (von Siobhán McHale) 31) Wie regulieren Sie Ihre Emotionen? (von Nawal Mustafa M.A.) 32) 21 Fähigkeiten, die Sie für immer bezahlen (von Jeremiah Teo / 赵汉昇) 34) 12 Möglichkeiten, Vertrauen mit anderen aufzubauen (von Justin Wright) 35) Merkmale eines talentierten Mitarbeiters (vom Talent Management Institute) 36) 10 Schlüssel, um Ihr Team zu motivieren 37) Algebra des Gewissens (von Vladimir Lefebvre) 38) Drei verschiedene Möglichkeiten der Zukunft (von Dr. Clare W. Graves)
| Verteilung | Nonnormal | Nonnormal | Nonnormal | Normal | Normal | Normal | Normal | Normal |
|
Alle Fragen
Alle Fragen
Meine größte Angst ist
| ||||||||
| Meine größte Angst ist | ||||||||
| Answer 1 | - | Schwach positiv 0.0563 | Schwach positiv 0.0317 | Schwach negativ -0.0161 | Schwach positiv 0.0907 | Schwach positiv 0.0298 | Schwach negativ -0.0126 | Schwach negativ -0.1537 |
| Answer 2 | - | Schwach positiv 0.0216 | Schwach positiv 0.0002 | Schwach negativ -0.0458 | Schwach positiv 0.0654 | Schwach positiv 0.0445 | Schwach positiv 0.0124 | Schwach negativ -0.0937 |
| Answer 3 | - | Schwach negativ -0.0035 | Schwach negativ -0.0111 | Schwach negativ -0.0421 | Schwach negativ -0.0456 | Schwach positiv 0.0466 | Schwach positiv 0.0786 | Schwach negativ -0.0201 |
| Answer 4 | - | Schwach positiv 0.0435 | Schwach positiv 0.0353 | Schwach negativ -0.0181 | Schwach positiv 0.0145 | Schwach positiv 0.0301 | Schwach positiv 0.0197 | Schwach negativ -0.0979 |
| Answer 5 | - | Schwach positiv 0.0299 | Schwach positiv 0.1279 | Schwach positiv 0.0136 | Schwach positiv 0.0730 | Schwach negativ -0.0007 | Schwach negativ -0.0207 | Schwach negativ -0.1746 |
| Answer 6 | - | Schwach negativ -0.0004 | Schwach positiv 0.0082 | Schwach negativ -0.0629 | Schwach negativ -0.0078 | Schwach positiv 0.0193 | Schwach positiv 0.0830 | Schwach negativ -0.0318 |
| Answer 7 | - | Schwach positiv 0.0122 | Schwach positiv 0.0381 | Schwach negativ -0.0686 | Schwach negativ -0.0242 | Schwach positiv 0.0471 | Schwach positiv 0.0636 | Schwach negativ -0.0513 |
| Answer 8 | - | Schwach positiv 0.0698 | Schwach positiv 0.0849 | Schwach negativ -0.0321 | Schwach positiv 0.0146 | Schwach positiv 0.0345 | Schwach positiv 0.0130 | Schwach negativ -0.1368 |
| Answer 9 | - | Schwach positiv 0.0665 | Schwach positiv 0.1674 | Schwach positiv 0.0092 | Schwach positiv 0.0691 | Schwach negativ -0.0128 | Schwach negativ -0.0528 | Schwach negativ -0.1812 |
| Answer 10 | - | Schwach positiv 0.0778 | Schwach positiv 0.0755 | Schwach negativ -0.0180 | Schwach positiv 0.0231 | Schwach positiv 0.0346 | Schwach negativ -0.0146 | Schwach negativ -0.1298 |
| Answer 11 | - | Schwach positiv 0.0584 | Schwach positiv 0.0524 | Schwach negativ -0.0096 | Schwach positiv 0.0081 | Schwach positiv 0.0199 | Schwach positiv 0.0318 | Schwach negativ -0.1197 |
| Answer 12 | - | Schwach positiv 0.0380 | Schwach positiv 0.1042 | Schwach negativ -0.0352 | Schwach positiv 0.0357 | Schwach positiv 0.0254 | Schwach positiv 0.0286 | Schwach negativ -0.1515 |
| Answer 13 | - | Schwach positiv 0.0644 | Schwach positiv 0.1057 | Schwach negativ -0.0448 | Schwach positiv 0.0268 | Schwach positiv 0.0416 | Schwach positiv 0.0169 | Schwach negativ -0.1600 |
| Answer 14 | - | Schwach positiv 0.0717 | Schwach positiv 0.1026 | Schwach negativ -0.0006 | Schwach negativ -0.0089 | Schwach negativ -0.0012 | Schwach positiv 0.0080 | Schwach negativ -0.1168 |
| Answer 15 | - | Schwach positiv 0.0549 | Schwach positiv 0.1375 | Schwach negativ -0.0420 | Schwach positiv 0.0178 | Schwach negativ -0.0160 | Schwach positiv 0.0216 | Schwach negativ -0.1180 |
| Answer 16 | - | Schwach positiv 0.0591 | Schwach positiv 0.0273 | Schwach negativ -0.0386 | Schwach negativ -0.0399 | Schwach positiv 0.0653 | Schwach positiv 0.0282 | Schwach negativ -0.0708 |
