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The Challenges of Integrating Behavioral and Neural Data: Bridging and Breaking Boundaries Across Levels of Analysis

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Abstract

We describe here two approaches introduced by Abrahamsen (1987) that can be used by behavior analysts to interpret neuroscientific data. The first is a “boundary-bridging” approach aimed at understanding the interdisciplinary interactions between the behavioral and the neural levels of analysis while keeping the two domains independent. When presenting the boundary-bridging approach, we describe neuroplasticity, a perspective that describes how changes at the brain level can be understood by examining behavioral factors. In the second part of the paper, we contrast two “boundary-breaking” perspectives: neuropsychology and behavior analytic neuroscience. In neuropsychology, localized brain activation is used to explain behavior. In behavior analytic neuroscience, brain responses are interpreted as behavior. We discuss the conditions under which brain responses can be considered behavior and propose that including brain responses within a behavioral framework may allow carrying out a more sophisticated and temporally detailed behavior analysis.

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Notes

  1. For a recent and comprehensive treatment of Skinner’s approach to neuroscience, refer to Zilio (2016).

  2. A potential explanation for this approach is the early and extensive reliance of cognitive science on the computer metaphor in which every bit of observed functionality is seen to be the result of software or hardware under the surface. This perspective might have resulted in the subsequent attempt within neuropsychology to map each behavioral regularity to a part of the underlying physiological substrate, i.e., the brain.

  3. So far, three kinds of EEG/ERP brain responses have been used successfully to create a real-time interface with external devices that require a minimal amount of averaging. More specifically, steady-state visually evoked potentials (SSVEPs), the P300 response, and the Mu Rhythm (Wolpaw, Birbaumer, Heetderks, McFarland, Peckham, Schalk, & Vaughan, 2000; Pfurtscheller, Brunner, Schlögl, & Da Silva, 2006a, b; Sepulveda, 2011).

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Authors and Affiliations

  1. Neurobehavioral Laboratory, Department of Behavior analysis, University of North Texas, 1155 Union Circle Box 310919, Denton, TX, 76201, USA

    Daniele Ortu & Manish Vaidya

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  1. Daniele Ortu
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  2. Manish Vaidya
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Correspondence to Daniele Ortu.

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The corresponding author is funded by the Beatrice H. Barrett endowment for research on neuro-operant relations to the University of North Texas.

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This article does not contain any studies with human participants or animals performed by any of the authors.

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Ortu, D., Vaidya, M. The Challenges of Integrating Behavioral and Neural Data: Bridging and Breaking Boundaries Across Levels of Analysis. BEHAV ANALYST 40, 209–224 (2017). https://doi.org/10.1007/s40614-016-0074-5

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