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) Actions of companies in relation to personnel in the last month (yes / no) 2) Actions of companies in relation to personnel in the last month (fact in %) 3) Fears 4) Biggest problems facing my country 5) What qualities and abilities do good leaders use when building successful teams? 6) Google. Factors that impact team effectiveness 7) The main priorities of job seekers 8) What makes a boss a great leader? 9) What makes people successful at work? 10) Are you ready to receive less pay to work remotely? 12) Ageism in career 13) Ageism in life 14) Ageism’s causes 15) Reasons why people give up (by Anna Vital) 16) TRUST (by WVS) 18) Psychological Wellbeing (by Carol D. Ryff) 19) Where would be your next most exciting opportunity? 20) What will you do this week to look after your mental health? 21) I live thinking about my past, present or future 22) Meritocracy 23) A.I. and the end of civilization 24) Why do people procrastinate? 25) Gender difference in building self-confidence (IFD Allensbach) 26) Xing.com culture assessment 27) Patrick Lencioni's "The Five Dysfunctions of a Team" 28) Empathy is... 29) What is essential for IT specialists in choosing a job offer? 30) Why People Resist Change (by Siobhán McHale) 31) How Do You Regulate Your Emotions? (by Nawal Mustafa M.A.) 32) 21 skills that pay you forever (by Jeremiah Teo / 赵汉昇) 34) 12 ways to build trust with others (by Justin Wright) 35) Characteristics of a talented employee (by Talent Management Institute) 36) 10 Keys to Motivating Your Team 37) Algebra of Conscience (by Vladimir Lefebvre) 38) Three Distinct Possibilities of the Future (by Dr. Clare W. Graves)
| Distribution | NonNormal | NonNormal | NonNormal | Normal | Normal | Normal | Normal | Normal |
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All questions
All questions
My greatest fears are
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| My greatest fears are | ||||||||
| Answer 1 | - | Weak positive 0.0562 | Weak positive 0.0311 | Weak negative -0.0164 | Weak positive 0.0903 | Weak positive 0.0301 | Weak negative -0.0120 | Weak negative -0.1534 |
| Answer 2 | - | Weak positive 0.0217 | Weak positive 0.0011 | Weak negative -0.0455 | Weak positive 0.0660 | Weak positive 0.0440 | Weak positive 0.0117 | Weak negative -0.0942 |
| Answer 3 | - | Weak negative -0.0034 | Weak negative -0.0104 | Weak negative -0.0419 | Weak negative -0.0451 | Weak positive 0.0462 | Weak positive 0.0780 | Weak negative -0.0204 |
| Answer 4 | - | Weak positive 0.0436 | Weak positive 0.0362 | Weak negative -0.0177 | Weak positive 0.0150 | Weak positive 0.0296 | Weak positive 0.0189 | Weak negative -0.0984 |
| Answer 5 | - | Weak positive 0.0298 | Weak positive 0.1270 | Weak positive 0.0133 | Weak positive 0.0724 | Weak negative -0.0002 | Weak negative -0.0199 | Weak negative -0.1742 |
| Answer 6 | - | Weak negative -0.0003 | Weak positive 0.0089 | Weak negative -0.0627 | Weak negative -0.0074 | Weak positive 0.0190 | Weak positive 0.0825 | Weak negative -0.0321 |
| Answer 7 | - | Weak positive 0.0123 | Weak positive 0.0388 | Weak negative -0.0684 | Weak negative -0.0238 | Weak positive 0.0468 | Weak positive 0.0631 | Weak negative -0.0517 |
| Answer 8 | - | Weak positive 0.0699 | Weak positive 0.0857 | Weak negative -0.0318 | Weak positive 0.0150 | Weak positive 0.0341 | Weak positive 0.0125 | Weak negative -0.1372 |
| Answer 9 | - | Weak positive 0.0666 | Weak positive 0.1681 | Weak positive 0.0094 | Weak positive 0.0694 | Weak negative -0.0131 | Weak negative -0.0533 | Weak negative -0.1815 |
| Answer 10 | - | Weak positive 0.0776 | Weak positive 0.0744 | Weak negative -0.0185 | Weak positive 0.0224 | Weak positive 0.0352 | Weak negative -0.0135 | Weak negative -0.1293 |
| Answer 11 | - | Weak positive 0.0585 | Weak positive 0.0531 | Weak negative -0.0094 | Weak positive 0.0086 | Weak positive 0.0195 | Weak positive 0.0313 | Weak negative -0.1200 |
| Answer 12 | - | Weak positive 0.0378 | Weak positive 0.1030 | Weak negative -0.0357 | Weak positive 0.0350 | Weak positive 0.0261 | Weak positive 0.0297 | Weak negative -0.1510 |
| Answer 13 | - | Weak positive 0.0642 | Weak positive 0.1044 | Weak negative -0.0454 | Weak positive 0.0259 | Weak positive 0.0424 | Weak positive 0.0183 | Weak negative -0.1595 |
| Answer 14 | - | Weak positive 0.0718 | Weak positive 0.1034 | Weak negative -0.0003 | Weak negative -0.0085 | Weak negative -0.0016 | Weak positive 0.0074 | Weak negative -0.1172 |
| Answer 15 | - | Weak positive 0.0550 | Weak positive 0.1382 | Weak negative -0.0418 | Weak positive 0.0181 | Weak negative -0.0163 | Weak positive 0.0211 | Weak negative -0.1183 |
| Answer 16 | - | Weak positive 0.0591 | Weak positive 0.0276 | Weak negative -0.0384 | Weak negative -0.0397 | Weak positive 0.0651 | Weak positive 0.0280 | Weak negative -0.0710 |
