Purchase this article with an account.
Andrew B. Leber, Nicholas B. Turk-Browne, Marvin M. Chun; Pre-trial fMRI activity predicts behavioral success. Journal of Vision 2007;7(9):811. doi: 10.1167/7.9.811.
Download citation file:
© ARVO (1962-2015); The Authors (2016-present)
Attentional control affords great flexibility in adapting to new goals and task challenges in the visual environment. However, this process of adapting can carry significant costs, revealing that the exertion of control is not always fully successful. How can the degree of success be predicted? In this study, we examined the possibility that pre-trial fMRI activity might reflect the current capability to implement control. Subjects were scanned while switching between two tasks in which they categorized visual stimuli. On each trial, a cue informed them to make either a parity or magnitude judgment on a subsequent target digit presented 1000 ms later. This procedure yielded robust behavioral costs of switching between tasks: trials in which the current task was switched from the previous task were associated with slower responses than those in which the task was repeated. A fronto-parietal “attentional control network” was identified by contrasting the BOLD responses associated with switch and repeat trials. Within this network, greater BOLD activity at the single timepoint immediately preceding the task cue predicted smaller costs of switching tasks; specifically, greater pre-trial activity led to a selective decrease in response time on switch trials. Using a data-driven approach, we then searched the whole brain for regions exhibiting this behavioral interaction with pre-trial activity, again finding only regions in the control network. Further analyses ruled out contributions of recent trial history, instead linking the result to gradual shifts in tonic states. We conclude that one's moment-to-moment capacity to mobilize attentional control is directly measurable in the neural activity within parietal and frontal regions, and this activity serves as a key predictor of control success.
This PDF is available to Subscribers Only