Learning different light prior distributions for different contexts

Kerrigan, Iona S.; Adams, Wendy J.

doi:10.1016/j.cognition.2012.12.011

Cited by 24 publications

(35 citation statements)

References 12 publications

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“…We propose that this broad generalization across sensory inputs is a default mode that acts to widen the data acquisition "net" for initial prior acquisition, allowing approximations of stimulus distributions to be rapidly learned and modified. This strategy is not fixed, however-in line with previous findings (24,26,27), we find that observers are able to learn stimulus-specific priors with extended training. Together, this work indicates that the structuring of prior knowledge is dynamic and that emphasis shifts from flexibility to specificity as learning progresses.…”

Section: Discussionsupporting

confidence: 73%

“…Our results differ from those of previous studies, where participants have been shown to acquire stimulus-specific priors when provided with more extensive training (24,26,27). To check that multiple priors can be learned in our experimental paradigm, we conducted an additional experiment, in which observers completed repeated testing sessions with alternative presentation of short and long stimulus sets at different spatial locations (Fig.…”

Section: Generalization Across Distributions Paired With Distinct Sencontrasting

confidence: 54%

“…Recent research indicates that participants provided with extended training are able to learn multiple priors for stimuli presented in different contexts. For example, Kerrigan and Adams (26) showed that distinct light position priors can be learned for different colored illumination in a few hours. Similarly, Gekas et al (24) showed that distinct priors for motion direction can be learned for sets of dot stimuli of different colors.…”

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confidence: 99%

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Generalization of prior information for rapid Bayesian time estimation

Roach

McGraw

Whitaker

et al. 2016

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

110

157

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To enable effective interaction with the environment, the brain combines noisy sensory information with expectations based on prior experience. There is ample evidence showing that humans can learn statistical regularities in sensory input and exploit this knowledge to improve perceptual decisions and actions. However, fundamental questions remain regarding how priors are learned and how they generalize to different sensory and behavioral contexts. In principle, maintaining a large set of highly specific priors may be inefficient and restrict the speed at which expectations can be formed and updated in response to changes in the environment. However, priors formed by generalizing across varying contexts may not be accurate. Here, we exploit rapidly induced contextual biases in duration reproduction to reveal how these competing demands are resolved during the early stages of prior acquisition. We show that observers initially form a single prior by generalizing across duration distributions coupled with distinct sensory signals. In contrast, they form multiple priors if distributions are coupled with distinct motor outputs. Together, our findings suggest that rapid prior acquisition is facilitated by generalization across experiences of different sensory inputs but organized according to how that sensory information is acted on.Bayesian inference | time perception | sensorimotor learning L ike all complex animals, humans rely on their senses to extract information about the environment and guide decision making and behavior. Often, however, sensory information is ambiguous. Signals transmitted to the senses can be weak or degraded, such as patterns of reflected light under low illumination or speech sounds in noisy environments. Moreover, sensory representations of even the most high-fidelity signals tend to be variable (1) and are insufficient to completely disambiguate different distal causes (2). Mounting empirical evidence indicates that, when forming decisions and planning actions, the brain combines uncertain sensory information with expectations based on prior knowledge (3-6). For example, a variety of biases in visual perception have been shown to be consistent with reliance on prior knowledge regarding statistical regularities in the environment, such as the distribution of local orientations (7) and speeds (8) in natural scenes and the positioning of light sources (9). In many studies, perception and behavior have been shown to be well-described by near-optimal integration of sensory evidence and prior knowledge according to the principles of statistical decision theory.Prior knowledge can be acquired over a range of different timescales. Priors specifying stable statistical characteristics of the environment are typically thought to be either innate or the consequence of life-long implicit learning (5, 7). However, context-specific priors can also be formed based on recent experiences. Studies using simple sensorimotor tasks suggest that human participants are adept at learning the distribution of set...

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Section: Discussionsupporting

confidence: 73%

Section: Generalization Across Distributions Paired With Distinct Sencontrasting

confidence: 54%

mentioning

confidence: 99%

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Generalization of prior information for rapid Bayesian time estimation

Roach

McGraw

Whitaker

et al. 2016

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

110

157

View full text Add to dashboard Cite

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“…Observers have a well-documented prior for overhead illumination [1], [2], [25], [26], [34], [48], [51]–[53] that has previously been successfully modelled by a von Mises distribution [51] although the mean of this distribution varies considerably across observers [25]. We employ the von Mises distribution to model observers' prior distribution over illuminant tilt, with the general formwhere is the tilt angle, and are the mean and concentration (inverse variance), and is the modified Bessel function of order 0, required for normalization.…”

Section: Methods and Modelmentioning

confidence: 99%

Effects of Specular Highlights on Perceived Surface Convexity

Adams

Elder

2014

PLoS Comput Biol

Self Cite

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Shading is known to produce vivid perceptions of depth. However, the influence of specular highlights on perceived shape is unclear: some studies have shown that highlights improve quantitative shape perception while others have shown no effect. Here we ask how specular highlights combine with Lambertian shading cues to determine perceived surface curvature, and to what degree this is based upon a coherent model of the scene geometry. Observers viewed ambiguous convex/concave shaded surfaces, with or without highlights. We show that the presence/absence of specular highlights has an effect on qualitative shape, their presence biasing perception toward convex interpretations of ambiguous shaded objects. We also find that the alignment of a highlight with the Lambertian shading modulates its effect on perceived shape; misaligned highlights are less likely to be perceived as specularities, and thus have less effect on shape perception. Increasing the depth of the surface or the slant of the illuminant also modulated the effect of the highlight, increasing the bias toward convexity. The effect of highlights on perceived shape can be understood probabilistically in terms of scene geometry: for deeper objects and/or highly slanted illuminants, highlights will occur on convex but not concave surfaces, due to occlusion of the illuminant. Given uncertainty about the exact object depth and illuminant direction, the presence of a highlight increases the probability that the surface is convex.

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“…This phenomenon has been called cue-recruitment [1], [2], [6]. Since 2006 it has been demonstrated that many signals can be recruited as cues using an associative learning paradigm, including location [2], [4], [6], [11], [15]–[17], translation direction [2], surface-texture [6], vertical disparity [7], color of illumination [18], object shape [5] and motor actions [19].…”

Section: Introductionmentioning

confidence: 99%

Cue-Recruitment for Extrinsic Signals after Training with Low Information Stimuli

2014

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Cue-recruitment occurs when a previously ineffective signal comes to affect the perceptual appearance of a target object, in a manner similar to the trusted cues with which the signal was put into correlation during training [1], [2]. Jain, Fuller and Backus [3] reported that extrinsic signals, those not carried by the target object itself, were not recruited even after extensive training. However, recent studies have shown that training using weakened trusted cues can facilitate recruitment of intrinsic signals [4]–[7]. The current study was designed to examine whether extrinsic signals can be recruited by putting them in correlation with weakened trusted cues. Specifically, we tested whether an extrinsic visual signal, the rotary motion direction of an annulus of random dots, and an extrinsic auditory signal, direction of an auditory pitch glide, can be recruited as cues for the rotation direction of a Necker cube. We found learning, albeit weak, for visual but not for auditory signals. These results extend the generality of the cue-recruitment phenomenon to an extrinsic signal and provide further evidence that the visual system learns to use new signals most quickly when other, long-trusted cues are unavailable or unreliable.

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Learning different light prior distributions for different contexts

Cited by 24 publications

References 12 publications

Generalization of prior information for rapid Bayesian time estimation

Generalization of prior information for rapid Bayesian time estimation

Effects of Specular Highlights on Perceived Surface Convexity

Cue-Recruitment for Extrinsic Signals after Training with Low Information Stimuli

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