Individual differences in attention strategies during detection, fine discrimination, and coarse discrimination

Abstract

Interacting with the environment requires the ability to flexibly direct attention to relevant features. We examined the degree to which individuals attend to visual features within and across Detection, Fine Discrimination, and Coarse Discrimination tasks. Electroencephalographic (EEG) responses were measured to an unattended peripheral flickering (4 or 6 Hz) grating while individuals (n = 33) attended to orientations that were offset by 0°, 10°, 20°, 30°, 40°, and 90° from the orientation of the unattended flicker. These unattended responses may be sensitive to attentional gain at the attended spatial location, since attention to features enhances early visual responses throughout the visual field. We found no significant differences in tuning curves across the three tasks in part due to individual differences in strategies. We sought to characterize individual attention strategies using hierarchical Bayesian modeling, which grouped individuals into families of curves that reflect attention to the physical target orientation ("on-channel") or away from the target orientation ("off-channel") or a uniform distribution of attention. The different curves were related to behavioral performance; individuals with "on-channel" curves had lower thresholds than individuals with uniform curves. Individuals with "off-channel" curves during Fine Discrimination additionally had lower thresholds than those assigned to uniform curves, highlighting the perceptual benefits of attending away from the physical target orientation during fine discriminations. Finally, we showed that a subset of individuals with optimal curves ("on-channel") during Detection also demonstrated optimal curves ("off-channel") during Fine Discrimination, indicating that a subset of individuals can modulate tuning optimally for detection and discrimination.

Feature-based attentional tuning during biological motion detection measured with SSVEP

“…Features and object categories can also be decoded from the neural activity in parietal cortex, an important brain region for cortical control of attention (Erez & Duncan, 2015;Liu & Hou, 2013). This finding is also consistent with early steady-state visual-evoked potential (SSVEP) studies that find parietal responses to be modulated by feature-based attention (Bridwell & Srinivasan, 2012;Bridwell et al, 2013).…”

“…In this study, we use a novel neurophysiological approach to isolate the motion features observers selectively attend that promote point-light biological motion detection. In tasks with known targets, selective attention filters are employed in anticipation of the attended items, to bias perceptual encoding in favor of those features (Bridwell, Hecker, Serences, & Srinivasan, 2013;Bridwell & Srinivasan, 2012). Feature-based attention is also not spatially specific, with attentionmediated gain observed in neural populations in visual cortex tuned to the attended feature across visual field (Saenz, Buracas, & Boynton, 2002;Serences & Boynton, 2007).…”

Changing the Spatial Scope of Attention Alters Patterns of Neural Gain in Human Cortex

“…The signal-tonoise ratio (SNR) of the SSVEP response was calculated on a trial-by-trial basis by dividing the power at the frequency bin centered on the stimulus frequency by the mean power in the two frequency bins 0.69 Hz above and below the center frequency of 21.25 Hz (corresponding to two bins on either side of the center frequency) and 0.68 Hz above and below the center frequency of 28.33 Hz. This SNR metric has been used in previous SSVEP studies (Bridwell and Srinivasan, 2012;Kim and Verghese, 2012;Bridwell et al, 2013;, and we focused on analyzing the SNR rather than the raw power/amplitude of the SSVEP to ensure that the modulations of the SSVEP were not confounded by any changes in broadband power at ␤ frequencies.…”

How attention influences perceptual decision making: Single-trial EEG correlates of drift-diffusion model parameters

“…During visual tasks individuals may deploy different attention strategies such as: enhancing the signal, suppressing external noise (distractors), or suppressing internal noise (Lu and Dosher, 1998; Dosher and Lu, 2000). These strategies are thought to change based on the signal to noise ratio of the stimulus, such that individuals will enhance sensory gain to both signal and noise during periods of low noise and sharpen attention to only signal during periods of high noise (Lu and Dosher, 1998), although specific strategies have been shown to differ across subjects (Bridwell et al, 2013; Krishnan et al, 2013; Nunez et al, 2015). Multiple groups have proposed models of visual attention and decision making that yield diverse reaction time and choice distributions dependent upon attentional load (Spieler et al, 2000; Smith and Ratcliff, 2009).…”