Attention to Bright Surfaces Enhances the Pupillary Light Reflex

Binda, Paola; Pereverzeva, Maria; Murray, Scott O.

doi:10.1523/jneurosci.3440-12.2013

Cited by 195 publications

(245 citation statements)

References 25 publications

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“…These stand in contrast to motor reflexes, which are movements in direct response to sensory signals. Note, motor reflexes can be graded in response magnitude and can be modulated by cognitive processes (8,9). One well-known example is the pupillary light reflex, an adjustment in pupil diameter in response to light intensity (10).…”

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confidence: 99%

Sensorimotor integration on a rapid time scale

Luo

Kothari

Moss

2017

Proc. Natl. Acad. Sci. U.S.A.

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Sensing is fundamental to the control of movement: From grasping objects to speech production, sensing guides action. So far, most of our knowledge about sensorimotor integration comes from visually guided reaching and oculomotor integration, in which the time course and trajectories of movements can be measured at a high temporal resolution. By contrast, production of vocalizations by humans and animals involves complex and variable actions, and each syllable often lasts a few hundreds of milliseconds, making it difficult to infer underlying neural processes. Here, we measured and modeled the transfer of sensory information into motor commands for vocal amplitude control in response to background noise, also known as the Lombard effect. We exploited the brief vocalizations of echolocating bats to trace the time course of the Lombard effect on a millisecond time scale. Empirical studies revealed that the Lombard effect features a response latency of a mere 30 ms and provided the foundation for the quantitative audiomotor model of the Lombard effect. We show that the Lombard effect operates by continuously integrating the sound pressure level of background noise through temporal summation to guide the extremely rapid vocal-motor adjustments. These findings can now be extended to models and measures of audiomotor integration in other animals, including humans.S ensing plays a critical role in the control of movement. In humans, natural behaviors, from grasping a coffee mug to producing intelligible speech, all rely on the guidance of sensing. Similarly, sensory signals lie at the core of most animal behaviors. However, the brain mechanisms underlying sensorimotor integration are not well understood. This is particularly true regarding the control of vocalizations, which are used by a wide range of animal species for communication. At present, a dominant model for motor control is derived from the state feedback control (SFC) theory, which successfully accounts for a wide range of motor behaviors, such as visually guided arm movement (1) and speech production (2). SFC models posit that motor control is based on a comparison of sensory prediction generated from an internal forward model with actual sensory feedback, and sensory feedback is used to train and update the internal forward model. According to this SFC model, sensory feedback is not directly used to guide motor commands, due to its noisy and delayed characteristics, and this notion has been supported by a large body of experimental and theoretical work (1-7). These stand in contrast to motor reflexes, which are movements in direct response to sensory signals. Note, motor reflexes can be graded in response magnitude and can be modulated by cognitive processes (8, 9). One well-known example is the pupillary light reflex, an adjustment in pupil diameter in response to light intensity (10).The Lombard effect refers to an animal's increase in vocal signal amplitude in response to an increase in background noise (11). Evidence of the Lombard effect comes from s...

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confidence: 99%

Sensorimotor integration on a rapid time scale

Luo

Kothari

Moss

2017

Proc. Natl. Acad. Sci. U.S.A.

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“…voluntary (Binda & Murray, 2015;Binda et al, 2013;Mathôt et al, 2013;Naber et al, 2013), involuntary (Mathôt et al, 2014), presaccadic (Mathôt, van der Linden, Grainger, & Vitu, 2015; see also Ebitz, Pearson, & Platt, 2014), and feature-based attention (Binda, Pereverzeva, & Murray, 2014). With respect to reliability, we have previously shown that manual responses and the pupillary light response are about equally sensitive measures of attention .…”

Section: Discussionmentioning

confidence: 98%

“…This technique exploits that the pupillary light response is driven mostly by attended stimuli; that is, if eye movements and visual input are controlled, your pupil is smaller when you attend to a bright, compared to a dark, stimulus (e.g., Binda, Pereverzeva, & Murray, 2013;Mathôt et al, 2013;Naber, Alvarez, & Nakayama, 2013;reviewed in Mathôt & Van der Stigchel, 2015). We conducted two experiments with the same overall design.…”

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Attending and inhibiting stimuli that match the contents of visual working memory: Evidence from eye movements and pupillometry

Mathôt

Heusden

Stigchel

2015

Preprint

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When you keep a red apple in working memory, your attention is usually—but not always—attracted by other red objects. The conditions under which the contents of visual working memory guide visual attention are still unclear. Here we tested whether attention is indeed biased toward memory-match stimuli, and, if so, whether this bias is transient or sustained. We used a new pupillometric technique, which exploits that the pupil is larger when you covertly attend to dark, compared to bright, stimuli. This allowed us to test whether and when attention was biased toward memory-match stimuli, by placing them either on a dark or a bright background, while measuring pupil size over time. We found that the eyes were captured by memory-match probes early on in the trial. However, the pupillary data suggested that there was no sustained attention bias toward memory-match probes later in time; rather, attention appeared to be biased away from them. Together, our results suggest that: (1) memory-match stimuli automatically capture attention; but (2) they do so only briefly; and (3) they may even become inhibited later in time.

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“…Based on our experience, most researchers now apply some form of baseline correction (but see e.g. Gamlin et al, 2007), and variations of subtractive baseline correction (Binda, Pereverzeva, & Murray, 2013;Hupé, Lamirel, & Lorenceau, 2009;Jainta, Vernet, Yang, & Kapoula, 2011;Knapen et al, 2016;Koelewijn, Zekveld, Festen, & Kramer, 2012;e.g. Laeng & Sulutvedt, 2014;Murphy, Moort, & Nieuwenhuis, 2016;Porter, Troscianko, & Gilchrist, 2007;Privitera, Renninger, Carney, Klein, & Aguilar, 2010) seem somewhat more common than variations of divisive baseline correction (Bonmati-Carrion et al, 2016;Herbst, Sander, Milea, LundAndersen, & Kawasaki, 2011;Mathôt, van der Linden, Grainger, & Vitu, 2013;H.…”

Section: Of 25mentioning

confidence: 99%

Safe and sensible baseline correction of pupil-size data

Mathôt

Fabius

Heusden

et al. 2017

Preprint

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Measurement of pupil size (pupillometry) has recently gained renewed interest from psychologists, but there is little agreement on how pupil-size data is best analyzed. Here we focus on one aspect of pupillometric analyses: baseline correction, that is, analyzing changes in pupil size relative to a baseline period. Baseline correction is useful in experiments that investigate the effect of some experimental manipulation on pupil size. In such experiments, baseline correction improves statistical power by taking into account random fluctuations in pupil size over time. However, we show that baseline correction can also distort data if unrealistically small pupil sizes are recorded during the baseline period, which can easily occur due to eye blinks, data loss, or other distortions. Divisive baseline correction (corrected pupil size = pupil size / baseline) is affected more strongly by such distortions than subtractive baseline correction (corrected pupil size = pupil size - baseline). We make four recommendations for safe and sensible baseline correction of pupil-size data: 1) use subtractive baseline correction; 2) visually compare your corrected and uncorrected data; 3) be wary of pupil-size effects that emerge faster than the latency of the pupillary response allows (within ±220 ms after the manipulation that induces the effect); and 4) remove trials on which baseline pupil size is unrealistically small (indicative of blinks and other distortions).

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Attention to Bright Surfaces Enhances the Pupillary Light Reflex

Cited by 195 publications

References 25 publications

Sensorimotor integration on a rapid time scale

Sensorimotor integration on a rapid time scale

Attending and inhibiting stimuli that match the contents of visual working memory: Evidence from eye movements and pupillometry

Safe and sensible baseline correction of pupil-size data

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