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Journal articles on the topic 'Categorization (Psychology)'

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Published: 4 June 2021
Last updated: 30 July 2024

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1

Chater, Nick. "Categorization, theories and folk psychology." Behavioral and Brain Sciences 16, no. 1 (March 1993): 37. http://dx.doi.org/10.1017/s0140525x00028739.

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2

Vandierendonck, André. "Cognitive Psychology: Variations on Categorization." Contemporary Psychology: A Journal of Reviews 39, no. 1 (January 1994): 53–54. http://dx.doi.org/10.1037/033811.

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3

Pothos, Emmanuel M., and Nick Chater. "Unsupervised Categorization and Category Learning." Quarterly Journal of Experimental Psychology Section A 58, no. 4 (May 2005): 733–52. http://dx.doi.org/10.1080/02724980443000322.

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When people categorize a set of items in a certain way they often change their perceptions for these items so that they become more compatible with the learned categorization. In two experiments we examined whether such changes are extensive enough to change the unsupervised categorization for the items—that is, the categorization of the items that is considered more intuitive or natural without any learning. In Experiment 1 we directly employed an unsupervised categorization task; in Experiment 2 we collected similarity ratings for the items and inferred unsupervised categorizations using Pothos and Chater's (2002) model of unsupervised categorization. The unsupervised categorization for the items changed to resemble more the learned one when this was specified by the suppression of a stimulus dimension (both experiments), but less so when it was almost specified by the suppression of a stimulus dimension (Experiment 1, nonsignificant trend in Experiment 2). By contrast, no changes in the unsupervised categorization were observed when participants were taught a classification that was specified by a more fine tuning of the relative salience of the two dimensions.
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Hadden, Benjamin W., S. Marie Harvey, Richard A. Settersten, and Christopher R. Agnew. "What Do I Call Us? The Investment Model of Commitment Processes and Changes in Relationship Categorization." Social Psychological and Personality Science 10, no. 2 (March 20, 2018): 235–43. http://dx.doi.org/10.1177/1948550617745115.

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The investment model of commitment has been used to understand relationship maintenance and dissolution across a variety of populations and relationship types. The current study used data from the Project on Partner Dynamics (POPD), a cohort study of young adults involved in nonmarital sexual relationships in the Los Angeles area, to test whether and how the investment model of commitment processes predicts individuals' self-reported categorizations of their relationships over time. We examined (1) how relationship categorizations are associated with variables outlined by the investment model and (2) whether model variables predict changes in relationship categorization over time. We found that changes in relationship self-categorization were associated with simultaneous changes in investment model variables, and that the model largely predicts the likelihood of future changes in relational self-categorization. These results are the first to examine how the investment model prospectively predicts the progression or regression of relationships beyond relationship dissolution.
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Nicolas, Gandalf, Allison L. Skinner, and Cheryl L. Dickter. "Other Than the Sum: Hispanic and Middle Eastern Categorizations of Black–White Mixed-Race Faces." Social Psychological and Personality Science 10, no. 4 (June 29, 2018): 532–41. http://dx.doi.org/10.1177/1948550618769591.

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The racial categorization literature, reliant on forced-choice tasks, suggests that mixed-race targets are often categorized using the parent faces that created the racially mixed stimuli (e.g., Black or White) or their combination (e.g., Black–White multiracial). In the current studies, we introduce a free-response task that allows for spontaneous categorizations of higher ecological validity. Our results suggest that, when allowed, observers often classify Black–White faces into alternative categories (i.e., responses that are neither the parent races nor their combination), such as Hispanic and Middle Eastern. Furthermore, we find that the stereotypes of the various categories that are mapped to racially mixed faces are distinct, underscoring the importance of understanding how mixed-race targets are spontaneously categorized. Our findings speak to the challenges associated with racial categorization in an increasingly racially diverse population, including discrepancies between target racial identities and their racial categorizations by observers as well as variable stereotype application to mixed-race targets.
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6

Loken, Barbara. "Consumer Psychology: Categorization, Inferences, Affect, and Persuasion." Annual Review of Psychology 57, no. 1 (January 2006): 453–85. http://dx.doi.org/10.1146/annurev.psych.57.102904.190136.

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7

Chen, Jacqueline M., Maria Clara P. de Paula Couto, Airi M. Sacco, and Yarrow Dunham. "To Be or Not to Be (Black or Multiracial or White)." Social Psychological and Personality Science 9, no. 7 (August 28, 2017): 763–72. http://dx.doi.org/10.1177/1948550617725149.

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Culture shapes the meaning of race and, consequently, who is placed into which racial categories. Three experiments conducted in the United States and Brazil illustrated the cultural nature of racial categorization. In Experiment 1, a target’s racial ancestry influenced Americans’ categorizations but had no impact on Brazilians’ categorizations. Experiment 2 showed cultural differences in the reliance on two phenotypic cues to race; Brazilians’ categorizations were more strongly determined by skin tone than were Americans’ categorizations, and Americans’ categorizations were more strongly determined by other facial features compared to Brazilians' categorizations. Experiment 3 demonstrated cultural differences in the motivated use of racial categories. When the racial hierarchy was threatened, only Americans more strictly enforced the Black–White racial boundary. Cultural forces shape the conceptual, perceptual, and ideological construal of racial categories.
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Çoşkun, Filiz, Zeynep Ceyda Sayalı, Emine Gürbüz, and Fuat Balcı. "Optimal time discrimination." Quarterly Journal of Experimental Psychology 68, no. 2 (February 2015): 381–401. http://dx.doi.org/10.1080/17470218.2014.944921.

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In the temporal bisection task, participants categorize experienced stimulus durations as short or long based on their similarity to previously acquired reference durations. Reward maximization in this task requires integrating endogenous timing uncertainty as well as exogenous probabilities of the reference durations into temporal judgements. We tested human participants on the temporal bisection task with different short and long reference duration probabilities (exogenous probability) in two separate test sessions. Incorrect categorizations were not penalized in Experiment 1 but were penalized in Experiment 2, leading to different levels of stringency in the reward functions that participants tried to maximize. We evaluated the judgements within the framework of optimality. Our participants adapted their choice behaviour in a nearly optimal fashion and earned nearly the maximum possible expected gain they could attain given their level of endogenous timing uncertainty and exogenous probabilities in both experiments. These results point to the optimality of human temporal risk assessment in the temporal bisection task. The long categorization response times (RTs) were overall faster than short categorization RTs, and short but not long categorization RTs were modulated by reference duration probability manipulations. These observations suggested an asymmetry between short and long categorizations in the temporal bisection task.
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9

Förster, Jens, Janina Marguc, and Marleen Gillebaart. "Novelty Categorization Theory." Social and Personality Psychology Compass 4, no. 9 (September 2010): 736–55. http://dx.doi.org/10.1111/j.1751-9004.2010.00289.x.

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10

Smallman, Rachel, and Neal J. Roese. "Preference Invites Categorization." Psychological Science 19, no. 12 (December 2008): 1228–32. http://dx.doi.org/10.1111/j.1467-9280.2008.02229.x.

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Past research indicates that positive affect (relative to neutral or negative affect) reduces processing and makes categorization less differentiated. The present experiment demonstrated that preference, even though affectively pleasant, invites finer categorization. Expertise is already known to influence categorization; hence, the present experiment used an associative conditioning task (novel symbols paired with positively or negatively valenced photographs) to create new preferences, thereby demonstrating that preference influences categorization independently of preexisting expertise. These findings cast new light on established theory of affect and cognitive processing and suggest new implications for consumer preference and goal pursuit.
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11

Niedenthal, Paula M., Jamin B. Halberstadt, and Åse H. Innes-Ker. "Emotional response categorization." Psychological Review 106, no. 2 (1999): 337–61. http://dx.doi.org/10.1037/0033-295x.106.2.337.

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12

Iankilevitch, Maria, Lindsey A. Cary, Jessica D. Remedios, and Alison L. Chasteen. "How Do Multiracial and Monoracial People Categorize Multiracial Faces?" Social Psychological and Personality Science 11, no. 5 (November 20, 2019): 688–96. http://dx.doi.org/10.1177/1948550619884563.

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Due to their awareness of multiraciality and their perceptions of race categories as fluid, multiracial individuals may be unique in how they racially categorize multiracial faces. Yet race categorization research has largely overlooked how multiracial individuals categorize other mixed-race people. We therefore asked Asian, White, and multiracial individuals to categorize Asian-White faces using an open-ended response format, which more closely mirrors real-world race categorizations than forced-choice response formats. Our results showed that perceivers from all three racial groups tended to categorize Asian-White faces as monoracial Asian, White, or Hispanic. However, multiracial perceivers categorized the Asian-White faces as multiracial more often than monoracial perceivers did. Our findings suggest that multiracial individuals may approach racial categorization differently from either monoracial majority or minority group members. Furthermore, our results illustrate possible difficulties multiracial people may face when trying to identify other multiracial in-group members.
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13

McCauley, Robert N. "Truth, Epistemic Ideals and the Psychology of Categorization." PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association 1986, no. 1 (January 1986): 198–207. http://dx.doi.org/10.1086/psaprocbienmeetp.1986.1.193120.

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14

Wegener, Ingo, and Karl Christoph Klauer. "Social Categorization Without Fit." Zeitschrift für Sozialpsychologie 36, no. 2 (January 2005): 91–101. http://dx.doi.org/10.1024/0044-3514.36.2.91.

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Abstract: Is intercategory fit a prerequisite of social categorization? Intercategory fit is given if more differences are perceived between than within two or more social categories. We created experimental conditions without objective fit, and ruled out subjective perceptions of intercategory fit, such as arise from expectancy-based illusory correlations. In the first experiment, portraits of men and women were presented together with nonsense syllables. Cued by the nonsense syllables, participants were later able to retrieve gender category information, indicating category salience in the presentation phase. The second experiment replicated these results using male and female first names instead of portraits. Inter-category fit thus does not constitute a necessary precondition for categorization. The results bear on the very early stages in the development of stereotypes.
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15

Rule, Nicholas O., Keiko Ishii, Nalini Ambady, Katherine S. Rosen, and Katherine C. Hallett. "Found in Translation." Personality and Social Psychology Bulletin 37, no. 11 (August 1, 2011): 1499–507. http://dx.doi.org/10.1177/0146167211415630.

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Across cultures, people converge in some behaviors and diverge in others. As little is known about the accuracy of judgments across cultures outside of the domain of emotion recognition, the present study investigated the influence of culture in another area: the social categorization of men’s sexual orientations. Participants from nations varying in their acceptance of homosexuality (United States, Japan, and Spain) categorized the faces of men from all three cultures significantly better than chance guessing. Moreover, categorizations of individual faces were significantly correlated among the three groups of perceivers. Americans were significantly faster and more accurate than the Japanese and Spanish perceivers. Categorization strategies (i.e., response bias) also varied such that perceivers from cultures less accepting of homosexuality were more likely to categorize targets as straight. Male sexual orientation therefore appears to be legible across cultures.
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16

Wyer, Natalie A. "Selective Self-Categorization: Meaningful Categorization and the In-Group Persuasion Effect." Journal of Social Psychology 150, no. 5 (September 16, 2010): 452–70. http://dx.doi.org/10.1080/00224540903365521.

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17

Mullen, Brian, Michael J. Migdal, and Miles Hewstone. "Crossed categorization versus simple categorization and intergroup evaluations: a meta-analysis." European Journal of Social Psychology 31, no. 6 (2001): 721–36. http://dx.doi.org/10.1002/ejsp.60.

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18

Basu, Kunal. "Consumers' Categorization Processes." Journal of Consumer Psychology 2, no. 2 (April 1993): 97–121. http://dx.doi.org/10.1207/s15327663jcp0202_01.

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19

Rouder, Jeffrey N., and Roger Ratcliff. "Comparing categorization models." Journal of Experimental Psychology: General 133, no. 1 (2004): 63–82. http://dx.doi.org/10.1037/0096-3445.133.1.63.

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20

Gelman, Susan A., and Meredith Meyer. "Child categorization." Wiley Interdisciplinary Reviews: Cognitive Science 2, no. 1 (July 19, 2010): 95–105. http://dx.doi.org/10.1002/wcs.96.

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21

Hoffman, William C. "Group theory and geometric psychology." Behavioral and Brain Sciences 24, no. 4 (August 2001): 674–76. http://dx.doi.org/10.1017/s0140525x01410089.

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The commentary is in general agreement with Roger Shepard's view of evolutionary internalization of certain procedural memories, but advocates the use of Lie groups to express the invariances of motion and color perception involved. For categorization, the dialectical pair is suggested. [Barlow; Hecht; Kubovy & Epstein; Schwartz; Shepard; Todorovič]
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22

Mitchell, Alice, and Fiona M. Jordan. "The Ontogeny of Kinship Categorization." Journal of Cognition and Culture 21, no. 1-2 (June 1, 2021): 152–77. http://dx.doi.org/10.1163/15685373-12340101.

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Abstract Human kinship systems play a central role in social organization, as anthropologists have long demonstrated. Much less is known about how cultural schemas of relatedness are transmitted across generations. How do children learn kinship concepts? To what extent is learning affected by known cross-cultural variation in how humans classify kin? This review draws on research in developmental psychology, linguistics, and anthropology to present our current understanding of the social and cognitive foundations of kinship categorization. Amid growing interest in kinship in the cognitive sciences, the paper aims to stimulate new research on the ontogeny of kinship categorization, a rich domain for studying the nexus of language, culture, and cognition. We introduce an interdisciplinary research toolkit to help streamline future research in this area.
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23

Vandierendonck, André, and Jean-Pierre Thibaut. "Similarity and Categorization: Introduction." Psychologica Belgica 35, no. 2-3 (January 1, 1995): 81. http://dx.doi.org/10.5334/pb.879.

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Dubois, Michaël, and Guy Lories. "Categorization of Autocorrelated Sequences." Psychologica Belgica 37, no. 1-2 (January 1, 1997): 131. http://dx.doi.org/10.5334/pb.912.

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25

Gibbs, Raymond W. "Categorization and metaphor understanding." Psychological Review 99, no. 3 (1992): 572–77. http://dx.doi.org/10.1037/0033-295x.99.3.572.

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Старко, Василь. "Categorization, Fast and Slow." East European Journal of Psycholinguistics 4, no. 1 (June 27, 2017): 205–12. http://dx.doi.org/10.29038/eejpl.2017.4.1.sta.

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The title of this study is inspired by Daniel Kahneman’s best-selling book Thinking, Fast and Slow. In it, the Nobel Prize winner explains in great detail the working of two systems of human reasoning: System 1, which is fast, automatic, associative, subconscious, involuntary and (nearly) effortless, and System 2, which is slow, intentional, logical, conscious, effortful and requires executive control, attention, and concentration. This distinction applies to human categorization as well. Each of the two labels refers, in fact, to a set of systems, which is why the designations Type 1 and Type 2 processes are preferable. The default-interventionist architecture presupposes the constant automatic activation of categories by Type 1 processes and interventions of Type 2 processes if necessary. Type 1 categorization relies on the ‘shallow’ linguistic representation of the world, while Type 2 uses ‘deep’ extralinguistic knowledge. A series of linguistic examples are analyzed to illustrate the differences between Type 1 and Type 2 categorization. A conclusion is drawn about the need to take this distinction into account in psycholinguistic and linguistic research on categorization. References Barrett, F., Tugade, M. M., & Engle, R. (2004). Individual differences in working memorycapacity in dual-process theories of the mind. Psychological Bulletin, 130(4), 553–573. Chaiken, S., & Trope, Y. (Eds.). (1999). Dual-process theories in social psychology. NewYork, NY: Guilford Press. Devine, P. G. (1989). Stereotypes and prejudice: Their automatic and controlledcomponents. Journal of Personality and Social Psychology, 56, 5–18. Evans, J. St. B. T., & Stanovich, K. (2013) Dual-process theories of higher cognition:Advancing the debate. Perspectives on Psychological Science, 8(3), 223–241. Geeraerts, D. (1993). Vagueness’s puzzles, polysemy’s vagaries. Cognitive Linguistics,4(3), 223–272. Heider, Eleanor Rosch (1973). On the internal structure of perceptual and semanticcategories. In: Cognitive Development and the Acquisition of Language, (pp. 111–144).T. E. Moore, (ed.). New York: Academic Press Kahneman, D. (2003). A perspective on judgement and choice. American Psychologist, 58,697–720. Kahneman, D. (2015). Thinking, Fast and Slow. New York: Farrar, Straus and Giroux. Kahneman, D., & Frederick, S. (2002). Representativeness revisited: Attribute substitutionin intuitive judgement. In: Heuristics and Biases: The Psychology of Intuitive Judgment,(pp. 49–81). T. Gilovich, D. Griffin, & D. Kahneman, (eds.). Cambridge, MA: CambridgeUniversity Press. Lakoff, G. (1973). Hedges: A study in meaning criteria and the logic of fuzzy concepts.Journal of Philosophical Logic, 2, 458–508. Lakoff, G. (1987). Women, Fire, and Dangerous Things. Chicago, London: University ofChicago Press. Reber, A. S. (1993). Implicit Learning and Tacit Knowledge. Oxford, England: OxfordUniversity Press. Stanovich, K. E. (1999). Who is Rational? Studies of Individual Differences in Reasoning.Mahwah, NJ: Erlbaum. Stanovich, K. E., & West, R F. (2000). Individual difference in reasoning: implications forthe rationality debate? Behavioural and Brain Sciences, 23, 645–726. Старко В. Категоризаційні кваліфікатори// Проблеми зіставної семантики. 2013,№ 11. С. 132–138.Starko, V. (2013). Katehoryzatsiini kvalifikatory. Problemy Zistavnoyi Semantyky, 11,132–138. Sun, R., Slusarz, P., & Terry, C. (2005). The interaction of the explicit and the implicit inskill learning: A dual-process approach. Psychological Review, 112, 159–192. Teasdale, J. D. (1999). Multi-level theories of cognition–emotion relations. In: Handbookof Cognition and Emotion, (pp. 665–681). T. Dalgleish & M. J. Power, (eds.). Chichester,England: Wiley. Wason, P. C., & Evans, J. St. B. T. (1975). Dual processes in reasoning? Cognition, 3,141–154. Whorf, B. L. (1956). The relation of habitual thought and behavior to language. In:Language, Thought, and Reality. Selected Writings of Benjamin Lee Whorf, (pp. 134–159). Cambridge, Massachusetts: The M.I.T. Press. (originally published in 1941) Wierzbicka, A. (1996). Semantic Primes and Universals. Oxford: Oxford UniversityPress.
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27

Mareschal, Denis, and Paul C. Quinn. "Categorization in infancy." Trends in Cognitive Sciences 5, no. 10 (October 2001): 443–50. http://dx.doi.org/10.1016/s1364-6613(00)01752-6.

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Sundararajan, Louise. "Hegemonic categorization of the other contributes to epistemological violence." Theory & Psychology 30, no. 3 (June 2020): 377–83. http://dx.doi.org/10.1177/0959354320915977.

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Held (2020) missed one central concern of Indigenous psychology (IP), namely that hegemony of knowledge production in mainstream psychology (MP) is to be resisted. In this commentary, I identify two prevalent assumptions in MP that warrant resistance: first, to gain knowledge of the other is to categorize them; second, the use of neutral categories can reduce epistemic violence against the culturally different other. My critique is based on a cultural analysis of strong-ties versus weak-ties rationalities.
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29

Krueger, Joachim I. "The Categorization Heuristic." American Journal of Psychology 135, no. 2 (July 1, 2022): 237–39. http://dx.doi.org/10.5406/19398298.135.2.09.

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30

Hugenberg, Kurt, and Donald F. Sacco. "Social Categorization and Stereotyping: How Social Categorization Biases Person Perception and Face Memory." Social and Personality Psychology Compass 2, no. 2 (March 2008): 1052–72. http://dx.doi.org/10.1111/j.1751-9004.2008.00090.x.

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31

Lamberts, Koen. "Categorization under time pressure." Journal of Experimental Psychology: General 124, no. 2 (June 1995): 161–80. http://dx.doi.org/10.1037/0096-3445.124.2.161.

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Maddox, W. Todd, Michelle R. Molis, and Randy L. Diehl. "Generalizing a neuropsychological model of visual categorization to auditory categorization of vowels." Perception & Psychophysics 64, no. 4 (May 2002): 584–97. http://dx.doi.org/10.3758/bf03194728.

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33

Hayes, Brett K., and John E. Taplin. "Developmental changes in categorization processes: Knowledge and similarity-based modes of categorization." Journal of Experimental Child Psychology 54, no. 2 (October 1992): 188–212. http://dx.doi.org/10.1016/0022-0965(92)90035-5.

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34

Cho, Jeung-Ryeul, and Robert C. Mathews. "Interactions between Mental Models Used in Categorization and Experiential Knowledge of Specific Cases." Quarterly Journal of Experimental Psychology Section A 49, no. 3 (August 1996): 572–95. http://dx.doi.org/10.1080/713755638.

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Three experiments investigated the relation between recognition of specific cases and categorization in a double-task paradigm that requires both types of information (Estes, 1986b). Results indicated that recognition and categorization were often affected differently by experimental variables. However, mental models used in categorization sometimes hindered development of experiential (case-based) knowledge, leading to lower levels of case recognition and suboptimal categorization performance. When mental models were complex or difficult to discover (non-salient), subjects often used experiential knowledge to classify into categories, resulting in dependence between categorization and recognition. A model of interactions between the two tasks is proposed that postulates two separate but interacting types of knowledge.
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Totaro, Paolo, and Thais Alves Marinho. "The duality of social self-categorization in consumption." Journal of Consumer Culture 19, no. 2 (July 6, 2017): 189–212. http://dx.doi.org/10.1177/1469540517717774.

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“Consumer culture” theories frequently claim that people use symbols of consumption to socially self-categorize and satisfy their needs for assimilation or differentiation. Through two empirical quantitative studies, we argue that self-categorization operates according to a duality overlooked by these theories. On one hand, self-categorization can be understood as the assimilation of the “I” into a normatively well-characterized group, a self-categorization that we define as “ontological” in this article. On the other hand, it can be understood as the identification with socially standardized and impersonal models, a self-categorization that we term “formal.” In the two studies, we investigated whether the psychologically perceived distance between the in-group and out-group (metacontrast) is greater in ontological than in formal self-categorization and, second, whether these two forms of self-categorizations operate independently (not correlated) in consumption. The results support the two hypotheses. However, further studies should be developed in order to give a more definitive character to the theory. If the findings of this work were to be confirmed by other ecological contexts and sampling techniques, there might be consequences for “self-brand connection” analyses and in general for consumption studies where social self-categorization theory can be applied.
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Ho, Arnold K., Nour S. Kteily, and Jacqueline M. Chen. "Introducing the Sociopolitical Motive × Intergroup Threat Model to Understand How Monoracial Perceivers’ Sociopolitical Motives Influence Their Categorization of Multiracial People." Personality and Social Psychology Review 24, no. 3 (May 25, 2020): 260–86. http://dx.doi.org/10.1177/1088868320917051.

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Researchers have used social dominance, system justification, authoritarianism, and social identity theories to understand how monoracial perceivers’ sociopolitical motives influence their categorization of multiracial people. The result has been a growing understanding of how particular sociopolitical motives and contexts affect categorization, without a unifying perspective to integrate these insights. We review evidence supporting each theory’s predictions concerning how monoracial perceivers categorize multiracial people who combine their ingroup with an outgroup, with attention to the moderating role of perceiver group status. We find most studies cannot arbitrate between theories of categorization and reveal additional gaps in the literature. To advance this research area, we introduce the sociopolitical motive × intergroup threat model of racial categorization that (a) clarifies which sociopolitical motives interact with which intergroup threats to predict categorization and (b) highlights the role of perceiver group status. Furthermore, we consider how our model can help understand phenomena beyond multiracial categorization.
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Bar-haim, Eyal. "DISCIPLINES AND THE CATEGORIZATION OF SCIENTIFIC TRUTH." Journal of Sociocybernetics 15, no. 2 (December 26, 2018): 20–37. http://dx.doi.org/10.26754/ojs_jos/jos.201822619.

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For the general audience, Wikipedia is considered the source of “truth,” especially for scientific knowledge. While studies of Wikipedia usually focus around the accuracy of the knowledge within it, few studies have explored its hierarchy and categorization. This study aims to describe how scientific information is organized into disciplines in Wikipedia. I take as a case study the Hebrew Wikipedia () and examine the representation and interrelations of five social sciences: sociology, anthropology, economics, political science, and psychology. I gather data from Wikipedia entries categorized under each of these disciplines and create a network that contains categories and subcategories derived from these entries. Using network analysis techniques, I estimate the strength of the relations between the disciplines. I find that while sociology, anthropology, and political science are strongly linked to each other, psychology and economics are relatively isolated. An interesting case is the distance between economics and sociology, since under the subcategory “Inequality,” the entries are uniquely categorized under sociology or economics but rarely under both categories. I claim this is an example of a fractal walk in the distinction between the two disciplines. As there is a hierarchical difference between these disciplines, the end result is a hierarchical value of the scientific knowledge presented in these Wikipedia entries.
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Toscano, Joseph C., Bob McMurray, Joel Dennhardt, and Steven J. Luck. "Continuous Perception and Graded Categorization." Psychological Science 21, no. 10 (October 2010): 1532–40. http://dx.doi.org/10.1177/0956797610384142.

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39

McGarty, Craig, and R. E. C. Penny. "Categorization, accentuation and social judgement." British Journal of Social Psychology 27, no. 2 (June 1988): 147–57. http://dx.doi.org/10.1111/j.2044-8309.1988.tb00813.x.

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Van Knippenberg, Ad, and Henk Wilke. "Social categorization and attitude change." European Journal of Social Psychology 18, no. 5 (October 1988): 395–406. http://dx.doi.org/10.1002/ejsp.2420180503.

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41

Volpato, Chiara, Anne Maass, Angelica Mucchi-Faina, and Elisa Vitti. "Minority influence and social categorization." European Journal of Social Psychology 20, no. 2 (March 1990): 119–32. http://dx.doi.org/10.1002/ejsp.2420200204.

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42

Hugenberg, Kurt, and Galen V. Bodenhausen. "Ambiguity in Social Categorization. The Role of Prejudice and Facial Affect in Race Categorization." Psychological Science 15, no. 5 (May 2004): 342–45. http://dx.doi.org/10.1111/j.0956-7976.2004.00680.x.

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43

Rosseel, Yves. "Mixture Models of Categorization." Journal of Mathematical Psychology 46, no. 2 (April 2002): 178–210. http://dx.doi.org/10.1006/jmps.2001.1379.

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44

Baillargeon, Renée, and Su-hua Wang. "Event categorization in infancy." Trends in Cognitive Sciences 6, no. 2 (February 2002): 85–93. http://dx.doi.org/10.1016/s1364-6613(00)01836-2.

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Herry, Caroline, Célia Maintenant, Isabelle Blanchette, Elodie Tricard, Guillaume Gimenes, and Valérie Pennequin. "Emotional Response Categorization in Adolescents and Young Adults." Psychological Reports 122, no. 4 (June 29, 2018): 1349–71. http://dx.doi.org/10.1177/0033294118784864.

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Categorization is based on cognitive mechanisms allowing the development of internal representations of the environment that guide behavior. This study tests the influence of emotions on categorization in adolescents and young adults. After a mood induction (negative, positive, or neutral), we compared how 68 adolescents aged 13 to 15 and 57 young adults aged 21 to 29 categorized emotional concepts using a lexical emotional categorization task. Participants had to choose which of three associates, of different emotional valence (positive, negative or neutral), was more similar to a target concept. The aim of this study was to determine if adolescents rely on the emotional dimension in categorization more than adults. The results show that the emotional state can influence the cognitive process of categorization in adolescence, particularly in the negative mood.
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Ellemers, Naomi, and Manuela Barreto. "Categorization in everyday life: the effects of positive and negative categorizations on emotions and self-views." European Journal of Social Psychology 36, no. 6 (2006): 931–42. http://dx.doi.org/10.1002/ejsp.333.

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Morsella, Ezequiel, Carlos Montemayor, Jason Hubbard, and Pareezad Zarolia. "Conceptual knowledge: Grounded in sensorimotor states, or a disembodied deus ex machina?" Behavioral and Brain Sciences 33, no. 6 (December 2010): 455–56. http://dx.doi.org/10.1017/s0140525x10001573.

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AbstractIf embodied models no longer address thesymbol grounding problemand a “disembodied” conceptual system can step in and resolve categorizations when embodied simulations fail, then perhaps the next step in theory-building is to isolate the unique contributions of embodied simulation. What is a disembodied conceptual system incapable of doing with respect to semantic processing or the categorization of smiles?
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Peery, Destiny, and Galen V. Bodenhausen. "Black + White = Black." Psychological Science 19, no. 10 (October 2008): 973–77. http://dx.doi.org/10.1111/j.1467-9280.2008.02185.x.

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Historically, the principle of hypodescent specified that individuals with one Black and one White parent should be considered Black. Two experiments examined whether categorizations of racially ambiguous targets reflect this principle. Participants studied ambiguous target faces accompanied by profiles that either did or did not identify the targets as having multiracial backgrounds (biological, cultural, or both biological and cultural). Participants then completed a speeded dualcategorization task requiring Black/not Black and White/not White judgments (Experiments 1 and 2) and deliberate categorization tasks requiring participants to describe the races (Experiment 2) of target faces. When a target was known to have mixed-race ancestry, participants were more likely to rapidly categorize the target as Black (and not White); however, the same cues also increased deliberate categorizations of the targets as “multiracial.” These findings suggest that hypodescent still characterizes the automatic racial categorizations of many perceivers, although more complex racial identities may be acknowledged upon more thoughtful reflection.
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Dellantonio, Sara, Claudio Mulatti, and Remo Job. "Artifact and Tool Categorization." Review of Philosophy and Psychology 4, no. 3 (April 20, 2013): 407–18. http://dx.doi.org/10.1007/s13164-013-0140-9.

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Esterman, Michael, William Prinzmetal, and Lynn Robertson. "Categorization influences illusory conjunctions." Psychonomic Bulletin & Review 11, no. 4 (August 2004): 681–86. http://dx.doi.org/10.3758/bf03196620.

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