Alle Publikationen
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2018
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(2018): Individual differences are more than a gene × environment interaction. The role of learning. In: Journal of experimental psychology. Animal learning and cognition 44 (1), S. 36-55. DOI: 10.1037/xan0000157
DOI: http://www.ncbi.nlm.nih.gov/pubmed/29323517 Abstract: Individual differences in behavior are understood generally as arising from an interaction between genes and environment, omitting a crucial component. The literature on animal and human learning suggests the need to posit principles of learning to explain our differences. One of the challenges for the advancement of the field has been to establish how general principles of learning can explain the almost infinite variation in behavior. We present a case that: (a) individual differences in behavior emerge, in part, from principles of learning; (b) associations provide a descriptive mechanism for understanding the contribution of experience to behavior; and (c) learning theories explain dissociable aspects of behavior. We use 4 examples from the field of learning to illustrate the importance of involving psychology, and associative theory in particular, in the analysis of individual differences, these are (a) fear learning; (b) behavior directed to cues for outcomes (i.e., sign- and goal- tracking); (c) stimulus learning related to attention; and (d) human causal learning. (PsycINFO Database Record
2017
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(2017): Social Fear Learning. From Animal Models to Human Function. In: Trends in cognitive sciences 21 (7), S. 546-555. DOI: 10.1016/j.tics.2017.04.010
DOI: http://www.ncbi.nlm.nih.gov/pubmed/28545935 Abstract: Learning about potential threats is critical for survival. Learned fear responses are acquired either through direct experiences or indirectly through social transmission. Social fear learning (SFL), also known as vicarious fear learning, is a paradigm successfully used for studying the transmission of threat information between individuals. Animal and human studies have begun to elucidate the behavioral, neural and molecular mechanisms of SFL. Recent research suggests that social learning mechanisms underlie a wide range of adaptive and maladaptive phenomena, from supporting flexible avoidance in dynamic environments to intergenerational transmission of trauma and anxiety disorders. This review discusses recent advances in SFL studies and their implications for basic, social and clinical sciences.
2015
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(2015): Measuring social complexity. In: Animal Behaviour 103, S. 203-209. DOI: 10.1016/j.anbehav.2015.02.018
DOI: https://doi.org/10.1016/j.anbehav.2015.02.018 Abstract: In one of the first formulations of the social complexity hypothesis, Humphrey (1976, page 316, Growing Points in Ethology, Cambridge University Press) predicts ‘that there should be a positive correlation across species between social complexity and individual intelligence’. However, in the many ensuing tests of the hypothesis, surprisingly little consideration has been given to measures of the independent variable in this evolutionary relationship, that is, social complexity. Here, we seek to encourage more rigorous measures of social complexity. We first review previous definitions of this variable and point to two common flaws; a lack of objectivity and a failure to directly connect sociality to the use of cognition. We argue that, rather than creating circularity, including cognition in the definition of social complexity is necessary for accurately testing the social complexity hypothesis. We propose a new definition of social complexity that is based on the number of differentiated relationships that individuals have. We then demonstrate that the definition is both broadly applicable and flexible, allowing researchers to include more detailed information about the degree of differentiation among individuals when the data are available. While we see this definition of social complexity as one possible way forward, our larger goal is to encourage researchers examining the social complexity hypothesis to carefully consider their measurement of social complexity.
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(2015): Social cognition. In: Animal Behaviour 103, S. 191-202. DOI: 10.1016/j.anbehav.2015.01.030
DOI: https://doi.org/10.1016/j.anbehav.2015.01.030 Abstract: The social intelligence hypothesis argues that competition and cooperation among individuals have shaped the evolution of cognition in animals. What do we mean by social cognition? Here we suggest that the building blocks of social cognition are a suite of skills, ordered roughly according to the cognitive demands they place upon individuals. These skills allow an animal to recognize others by various means; to recognize and remember other animals' relationships; and, perhaps, to attribute mental states to them. Some skills are elementary and virtually ubiquitous in the animal kingdom; others are more limited in their taxonomic distribution. We treat these skills as the targets of selection, and assume that more complex levels of social cognition evolve only when simpler methods are inadequate. As a result, more complex levels of social cognition indicate greater selective pressures in the past. The presence of each skill can be tested directly through field observations and experiments. In addition, the same methods that have been used to compare social cognition across species can also be used to measure individual differences within species and to test the hypothesis that individual differences in social cognition are linked to differences in reproductive success.
2014
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(2014): Profiling nonhuman intelligence. An exercise in developing unbiased tools for describing other “types” of intelligence on earth. In: Acta Astronautica 94 (2), S. 676-680. DOI: 10.1016/j.actaastro.2013.08.007
DOI: https://doi.org/10.1016/j.actaastro.2013.08.007 Abstract: Intelligence has historically been studied by comparing nonhuman cognitive and language abilities with human abilities. Primate-like species, which show human-like anatomy and share evolutionary lineage, have been the most studied. However, when comparing animals of non-primate origins our abilities to profile the potential for intelligence remains inadequate. Historically our measures for nonhuman intelligence have included a variety of tools: (1) physical measurements – brain to body ratio, brain structure/convolution/neural density, presence of artifacts and physical tools, (2) observational and sensory measurements – sensory signals, complexity of signals, cross-modal abilities, social complexity, (3) data mining – information theory, signal/noise, pattern recognition, (4) experimentation – memory, cognition, language comprehension/use, theory of mind, (5) direct interfaces – one way and two way interfaces with primates, dolphins, birds and (6) accidental interactions – human/animal symbiosis, cross-species enculturation. Because humans tend to focus on “human-like” attributes and measures and scientists are often unwilling to consider other “types” of intelligence that may not be human equated, our abilities to profile “types” of intelligence that differ on a variety of scales is weak. Just as biologists stretch their definitions of life to look at extremophiles in unusual conditions, so must we stretch our descriptions of types of minds and begin profiling, rather than equating, other life forms we may encounter. COMPLEX (COmplexity of Markers for Profiling Life in EXobiology) offers a new approach to profile a variety of organisms along multiple dimensions including EQ – Encephalization Quotient, CS – Communication Signal complexity, IC – Individual Complexity, SC – Social Complexity and II – Interspecies Interaction. Because Earth species are found along a variety of continuums, defining an intelligence profile along these different trajectories rather than comparing them only to human intelligence, may give us insight into a potential tool for quickly assessing unknown species. The application of profiling nonhuman species, out of world, will be both observational and potentially interactive in some way. Using profiles and indicators gleaned from Earth species to help us develop profiles and using pattern recognition, modeling and other data mining techniques could help jump start our understanding of other organisms and their potential for certain “types” of intelligence.
2006
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(2006): Conditions for Establishing a System of Fairness. Comment on Brosnan (2006). In: Social Justice Research 19 (2), S. 194-200. DOI: 10.1007/s11211-006-0001-0
DOI: http://www.springerlink.com/content/1573-6725/ Abstract: Comments on an article by S. Brosnan (same issue) on reactions to inequity among nonhuman species, which are assumed to mirror human response tendencies summarized as inequity aversion. Suggestions made by Brosnan concerning the continuity between humans and nonhuman species in cooperative behavior as well as the evolutionary processes that lead to the formation of a sense of equity are elaborated from a social-psychological perspective. The evolutionary process described as a four-step model may also be used to explain intervention process when equity is violated. The remarkable similarity between animals and humans in their sense of fairness is considered a confirmation of the assumed closeness of humans to their evolutionary ancestors.
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