Commentaries

Kaznatcheev, A., & Shultz, T. (2018). Moral externalization may precede, not follow, subjective preferences. Behavioral and Brain Sciences, 41, E107. doi:10.1017/S0140525X18000122 We offer four counterarguments against Stanford’s dismissal of moral externalization as an ancestral condition, based on requirements for ancestral states, mismatch between theoretical and empirical games, passively correlated interactions, and social interfaces that prevent agents’ knowing game payoffs. The fact that children’s externalized phenomenology precedes their discovery of subjectivized phenomenology also suggests that externalized phenomenology is an ancestral condition.
Shultz, T. R. (2014). Let’s not forget undergraduate interdisciplinary education. IEEE CIS Autonomous Mental Development Newsletter, 11(2), 11. http://www.cse.msu.edu/amdtc/amdnl/AMDNL-V11-N2.pdf - The dialogue initiated by Rohlfing et al. (2014) raises interesting and important points about how to train future contributors to the emerging interdisciplinary fields concerned with psychological development and learning. I argue here that the interdisciplinary education required in developmental robotics and psychology can usefully begin at the undergraduate level.
Ruths, D., & Shultz, T. R. (2014). Understanding social networks requires more than two dimensions. Behavioral and Brain Sciences, 37(1), 99. http://dx.doi:10.1017/S0140525X13001878. - Bentley et al. proposed framework is insufficient to categorize and understand current evidence on decision making. There are some ambiguities in the questions asked that require additional distinctions between correctness and accuracy, decision making and learning, accuracy and confidence, and social influence and empowerment. Social learning techniques are not all the same: behavior copying is quite different from theory passing. Sigmoidal acquisition curves are not unique to social learning and are often mistaken for other accelerating curves.
Kaznatcheev, A., & Shultz, T. R. (2013). Limitations of the Dirac formalism as a descriptive framework for cognition. Behavioral and Brain Sciences, 36(3), 292-293. http://dx.doi.org/10.1017/S0140525X12003007 - We highlight methodological and theoretical limitations of Pothos and Busemeyer's Dirac formalism and suggest the von Neumann open systems approach as a resolution. The open systems framework is a generalization of classical probability and we hope it will allow cognitive scientists to extend quantum probability from perception, categorization, memory, decision making, and similarity judgments to phenomena in learning and development.
Dandurand, F., & Shultz, T. R. (2002). Modeling consciousness. Behavioral and Brain Sciences, 25, 334. - Perruchet & Vinter do not fully resolve issues about the role of consciousness and the unconscious in cognition and learning, and it is doubtful that consciousness has been computationally implemented. The cascade-correlation (CC) connectionist model develops high-order feature detectors as it learns a problem. We describe an extension, knowledge-based cascade-correlation (KBCC), that uses knowledge to learn in a hierarchical fashion.
Shultz, T. R. (2000). Prototypes and portability in artificial neural network models. Behavioral and Brain Sciences, 23, 493-494. - The Page target article is interesting because of apparent coverage of many psychological phenomena with simple, unified neural techniques. However, prototype phenomena cannot be covered because the strongest response would be to the first-learned stimulus in each category rather than to a prototype stimulus or most frequently presented stimuli. Alternative methods using distributed coding can also achieve portability of network knowledge.
Mareschal, D., & Shultz, T. R. (1997). From neural constructivism to children's cognitive development: Bridging the gap. Behavioral and Brain Sciences, 20, 571-572. - Missing from Quartz & Sejnowski's unique and valuable effort to relate cognitive development to neural constructivism is an examination of the global emergent properties of adding new neural circuits. Such emergent properties can be studied with computational models. Modeling with constructivist connectionist networks shows that synaptogenic mechanisms can account for progressive increases in children's representational power.
Szilas, N, & Shultz, T. R. (1997). Prospects for automatic recoding of inputs in connectionist learning. Behavioral and Brain Sciences, 20, 81-82. - Clark & Thornton present the well-established principle that recoding inputs can make learning easier. A useful goal would be to make such recoding automatic. We discuss some ways in which incrementality and transfer in connectionist networks could attain this goal.
Shultz, T. R. (1994). The challenge of representational redescription. Behavioral and Brain Sciences, 17, 728-729. - Representational redescription poses a significant challenge to cognitive science, but Karmiloff-Smith underestimates the extent to which some current computational models already engage in it. Moreover, a large part of the existing challenge is to produce convincing psychological evidence that deserves to be modeled. Finally, task constraints are essential for success in both psychological theorizing and modeling.
Shultz, T. R. (1992). Choosing a unifying theory for cognitive development. Behavioral and Brain Sciences, 15, 456-457. - A commentary on Newell’s proposal of Soar for a unified theory of cognition, including cognitive development.
Shultz, T. R. (1991). The rationality of causal inference. Behavioral and Brain Sciences, 14, 503-504. - A commentary on John Anderson’s target article Is human cognition adaptive?