scholarly journals Cognitive Science as an Interface Between Rational and Mechanistic Explanation

2014 ◽  
Vol 6 (2) ◽  
pp. 331-337 ◽  
Author(s):  
Nick Chater
2019 ◽  
Vol 29 (5) ◽  
pp. 676-696 ◽  
Author(s):  
Sabrina Golonka ◽  
Andrew D. Wilson

In 2010, Bechtel and Abrahamsen defined and described what it means to be a dynamic causal mechanistic explanatory model. They discussed the development of a mechanistic explanation of circadian rhythms as an exemplar of the process and challenged cognitive science to follow this example. This article takes on that challenge. A mechanistic model is one that accurately represents the real parts and operations of the mechanism being studied. These real components must be identified by an empirical programme that decomposes the system at the correct scale and localises the components in space and time. Psychological behaviour emerges from the nature of our real-time interaction with our environments—here we show that the correct scale to guide decomposition is picked out by the ecological perceptual information that enables that interaction. As proof of concept, we show that a simple model of coordinated rhythmic movement, grounded in information, is a genuine dynamical mechanistic explanation of many key coordination phenomena.


2018 ◽  
Author(s):  
sabrina golonka ◽  
Andrew D Wilson

Bechtel & Abrahamsen (2010) defined and described what it means to be a dynamic causal mechanistic explanatory model. They discussed the development of a mechanistic explanation of circadian rhythms as an exemplar of the process, and they challenged cognitive science to follow this example. This paper takes on this challenge. A mechanistic model is one that accurately represents the real parts and operations of the mechanism being studies. These real components must be identified by an empirical programme that decomposes the system at the correct level and localises the components in space and time. Psychological behaviour emerges from the nature of our real time interaction with our environments, and so here we show that the correct level for decomposition is the ecological perceptual information that enables that interaction. As proof of concept, we show that a simple model of coordinated rhythmic movement, grounded in information, is a genuine dynamical mechanistic explanation of many key coordination phenomena.


Author(s):  
Daniel D. Hutto ◽  
Erik Myin

This chapter introduces the E-turn in cognitive science –the move to embrace enactive, embodied, extended and ecological views of cognition–and the empirical and theoretical considerations that spurred it on. It explains how E-approaches differ from classical forms of cognitivism: in particular the degree to which different E-approaches move away from the cognitivist commitments to representationalism, computationalism and mechanistic explanation. Against this backdrop, it becomes clear in which ways REC’s proposal is not just radically revisionary but revolutionary in spirit. The chapter also sets out the basic rules of naturalistic play, reminding the reader why attempts to dismiss REC by appeal to a priori intuitions about what is essential to cognition violate the methodological scruples of naturalism.


Author(s):  
Marcin Miłkowski ◽  
Przemysław Nowakowski

Abstract In this paper, we defend a novel, multidimensional account of representational unification, which we distinguish from integration. The dimensions of unity are simplicity, generality and scope, non-monstrosity, and systematization. In our account, unification is a graded property. The account is used to investigate the issue of how research traditions contribute to representational unification, focusing on embodied cognition in cognitive science. Embodied cognition contributes to unification even if it fails to offer a grand unification of cognitive science. The study of this failure shows that unification, contrary to what defenders of mechanistic explanation claim, is an important mechanistic virtue of research traditions.


2019 ◽  
Vol 29 (5) ◽  
pp. 719-735 ◽  
Author(s):  
Lawrence Shapiro

The development of a theory of mechanistic explanation has been a welcome advance over previous theories of explanation, such as deductive nomological explanation. However, despite the claims of some supporters of mechanistic explanation, not all explanation in cognitive science is or should be mechanistic. I defend the claim that functional analysis remains a distinct and legitimate form of explanation within cognitive science.


Author(s):  
Stephen H. Adamo ◽  
Brian J. Gereke ◽  
Sarah Shomstein ◽  
Joseph Schmidt

AbstractFor over 50 years, the satisfaction of search effect has been studied within the field of radiology. Defined as a decrease in detection rates for a subsequent target when an initial target is found within the image, these multiple target errors are known to underlie errors of omission (e.g., a radiologist is more likely to miss an abnormality if another abnormality is identified). More recently, they have also been found to underlie lab-based search errors in cognitive science experiments (e.g., an observer is more likely to miss a target ‘T’ if a different target ‘T’ was detected). This phenomenon was renamed the subsequent search miss (SSM) effect in cognitive science. Here we review the SSM literature in both radiology and cognitive science and discuss: (1) the current SSM theories (i.e., satisfaction, perceptual set, and resource depletion theories), (2) the eye movement errors that underlie the SSM effect, (3) the existing efforts tested to alleviate SSM errors, and (4) the evolution of methodologies and analyses used when calculating the SSM effect. Finally, we present the attentional template theory, a novel mechanistic explanation for SSM errors, which ties together our current understanding of SSM errors and the attentional template literature.


2020 ◽  
Vol 43 ◽  
Author(s):  
Charles P. Davis ◽  
Gerry T. M. Altmann ◽  
Eiling Yee

Abstract Gilead et al.'s approach to human cognition places abstraction and prediction at the heart of “mental travel” under a “representational diversity” perspective that embraces foundational concepts in cognitive science. But, it gives insufficient credit to the possibility that the process of abstraction produces a gradient, and underestimates the importance of a highly influential domain in predictive cognition: language, and related, the emergence of experientially based structure through time.


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