Literature DB >> 15116981

How a cognitive psychologist came to seek universal laws.

Roger N Shepard1.   

Abstract

My early fascination with geometry and physics and, later, with perception and imagination inspired a hope that fundamental phenomena of psychology, like those of physics, might approximate universal laws. Ensuing research led me to the following candidates, formulated in terms of distances along shortest paths in abstract representational spaces: Generalization probability decreases exponentially and discrimination time reciprocally with distance. Time to determine the identity of shapes and, provisionally, relation between musical tones or keys increases linearly with distance. Invariance of the laws is achieved by constructing the representational spaces from psychological rather than physical data (using multidimensional scaling) and from considerations of geometry, group theory, and symmetry. Universality of the laws is suggested by their behavioral approximation in cognitively advanced species and by theoretical considerations of optimality. Just possibly, not only physics but also psychology can aspire to laws that ultimately reflect mathematical constraints, such as those of group theory and symmetry, and, so, are both universal and nonarbitrary.

Mesh:

Year:  2004        PMID: 15116981     DOI: 10.3758/bf03206455

Source DB:  PubMed          Journal:  Psychon Bull Rev        ISSN: 1069-9384


  57 in total

1.  On the possible psychophysical laws.

Authors:  R D LUCE
Journal:  Psychol Rev       Date:  1959-03       Impact factor: 8.934

2.  Stimulus generalization in the learning of classifications.

Authors:  R N SHEPARD; J J CHANG
Journal:  J Exp Psychol       Date:  1963-01

3.  The selection of strategies in cue learning.

Authors:  F RESTLE
Journal:  Psychol Rev       Date:  1962-07       Impact factor: 8.934

4.  Is numerical comparison digital? Analogical and symbolic effects in two-digit number comparison.

Authors:  S Dehaene; E Dupoux; J Mehler
Journal:  J Exp Psychol Hum Percept Perform       Date:  1990-08       Impact factor: 3.332

Review 5.  Time, our lost dimension: toward a new theory of perception, attention, and memory.

Authors:  M R Jones
Journal:  Psychol Rev       Date:  1976-09       Impact factor: 8.934

6.  Shape, orientation, and apparent rotational motion.

Authors:  J E Farrell; R N Shepard
Journal:  J Exp Psychol Hum Percept Perform       Date:  1981-04       Impact factor: 3.332

7.  Geometrical approximations to the structure of musical pitch.

Authors:  R N Shepard
Journal:  Psychol Rev       Date:  1982-07       Impact factor: 8.934

8.  Hypothesis behavior by humans during discrimination learning.

Authors:  M Levine
Journal:  J Exp Psychol       Date:  1966-03

9.  Time required for judgements of numerical inequality.

Authors:  R S Moyer; T K Landauer
Journal:  Nature       Date:  1967-09-30       Impact factor: 49.962

10.  Commentary: Extensional Versus Intuitive Reasoning: The Conjunction Fallacy in Probability Judgment.

Authors:  Peter Lewinski
Journal:  Front Psychol       Date:  2015-11-25
View more
  6 in total

1.  The versatility of SpAM: a fast, efficient, spatial method of data collection for multidimensional scaling.

Authors:  Michael C Hout; Stephen D Goldinger; Ryan W Ferguson
Journal:  J Exp Psychol Gen       Date:  2012-07-02

Review 2.  Using multidimensional scaling to quantify similarity in visual search and beyond.

Authors:  Michael C Hout; Hayward J Godwin; Gemma Fitzsimmons; Arryn Robbins; Tamaryn Menneer; Stephen D Goldinger
Journal:  Atten Percept Psychophys       Date:  2016-01       Impact factor: 2.199

3.  Differences in Sequential Eye Movement Behavior between Taiwanese and American Viewers.

Authors:  Yen-Ju Lee; Harold H Greene; Chia W Tsai; Yu J Chou
Journal:  Front Psychol       Date:  2016-05-20

4.  Physical Time Within Human Time.

Authors:  Ronald P Gruber; Richard A Block; Carlos Montemayor
Journal:  Front Psychol       Date:  2022-03-30

5.  Similarity of cortical activity patterns predicts generalization behavior.

Authors:  Crystal T Engineer; Claudia A Perez; Ryan S Carraway; Kevin Q Chang; Jarod L Roland; Andrew M Sloan; Michael P Kilgard
Journal:  PLoS One       Date:  2013-10-16       Impact factor: 3.240

6.  Deep learning can be used to train naïve, nonprofessional observers to detect diagnostic visual patterns of certain cancers in mammograms: a proof-of-principle study.

Authors:  Jay Hegdé
Journal:  J Med Imaging (Bellingham)       Date:  2020-02-04
  6 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.