Attention selects which aspects of sensory input receive cognitive processing and thereby influence behavior. Drug addiction alters the attentional system, resulting in prominent attentional biases towards drug cues. Such drug-related attentional biases are related to the broader phenomenology of addiction, including craving and relapse. There has been long-standing interest in implementing attentional bias measures in clinical settings, either as a predictive measure to inform treatment decisions or as a target of treatment. However, a major barrier to the realization of this goal is that current means of assessing these biases are not sufficiently precise to support clinical utility, which has stifled progress in this area. Mirroring this complexity, and underscoring the need for clarity, debate has arisen concerning the role of learning history in the guidance of attention more broadly. Persistent attentional biases have been linked to reward history, learning from aversive outcomes, and outcome-independent selection history (e.g., familiarity). Emerging accounts of such experience-dependent attentional biases disagree about the nature of the underlying mechanism(s) involved. If we do not understand the variety of influences of learning history on attention at a fundamental level, how can we understand how these influences contribute to addiction-related attentional biases? The proposed research directly addresses this need by identifying, isolating, and measuring multiple hypothesized components of the attentional biases that characterize addiction, providing the precision necessary for more accurate predictions of patient outcomes and more targeted efforts to improve these outcomes through attentional bias modification.
Specific Aim 1 will distinguish between common and distinct attentional priority signals arising from reward learning and reward-independent selection history, probing both the cognitive and neural mechanisms underlying each of these sources of priority.
Specific Aim 2 will identify the cognitive profile and neural mechanisms underlying attentional biases attributable to aversive conditioning, which together with Specific Aim 1 will provide a comprehensive picture of the multifaceted nature of experience-dependent attention. The overarching goal of the proposed research is to characterize multiple distinct components of experience-dependent attentional bias that contribute to attentional biases evident in drug-dependent individuals. These fundamental components of attentional bias will provide a much more precise window into the attentional processes that are relevant to our understanding of addiction than existing measures can offer. It is anticipated that the knowledge gained from the proposed research with provide a foundation for overcoming fundamental limitations in the clinical utility of attentional bias measures, allowing for fruitful exploration of this aspect of addiction in the context of improving assessment and treatment.

Public Health Relevance

The proposed project seeks to better understand how what we pay attention to can be biased by past experience. Difficulty ignoring previously rewarding stimuli that conflict with current goals can be maladaptive and lead to undesired behaviors, as in relapses into addiction. Understanding how and why these disruptive attention patterns develop and persist holds promise in providing insights that will lead to more effective treatments for addiction and strategies aimed at preventing relapse.

Agency
National Institute of Health (NIH)
Institute
National Institute on Drug Abuse (NIDA)
Type
Research Project (R01)
Project #
1R01DA046410-01A1
Application #
9684044
Study Section
Cognition and Perception Study Section (CP)
Program Officer
Grant, Steven J
Project Start
2019-02-01
Project End
2023-12-31
Budget Start
2019-02-01
Budget End
2019-12-31
Support Year
1
Fiscal Year
2019
Total Cost
Indirect Cost
Name
Texas A&M University
Department
Psychology
Type
Schools of Arts and Sciences
DUNS #
020271826
City
College Station
State
TX
Country
United States
Zip Code
77845