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Abstract

When perceiving rich sensory information, some people may integrate its various aspects, whereas other people may selectively focus on its most salient aspects. We propose that neural gain modulates the trade-off between breadth and selectivity, such that high gain focuses perception on those aspects of the information that have the strongest, most immediate influence, whereas low gain allows broader integration of different aspects. We illustrate our hypothesis using a neural-network model of ambiguous-letter perception. We then report an experiment demonstrating that, as predicted by the model, pupil-diameter indices of higher gain are associated with letter perception that is more selectively focused on the letter’s shape or, if primed, its semantic content. Finally, we report a recognition-memory experiment showing that the relationship between gain and selective processing also applies when the influence of different stimulus features is voluntarily modulated by task demands.

Keywords

neural gain perception attention memory neural network pupillometry

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References

Arthur

Jr. Doverspike

(1992). Locus of control and auditory selective attention as predictors of driving accident involvement: A comparative longitudinal investigation. Journal of Safety Research, 23, 73–80.

Aston-Jones

Cohen

J. D.

(2005). An integrative theory of locus coeruleus-norepinephrine function: Adaptive gain and optimal performance. Annual Review of Neuroscience, 28, 403–450.

Baddeley

A. D.

(1972). Selective attention and performance in dangerous environments. British Journal of Psychology, 63, 537–546.

Cohen

J. D.

Dunbar

McClelland

J. L.

(1990). On the control of automatic processes: A parallel distributed processing account of the Stroop effect. Psychological Review, 97, 332–361.

Coltheart

(1981). The MRC psycholinguistic database. The Quarterly Journal of Experimental Psychology Section A, 33, 497–505.

Coultrip

Granger

Lynch

(1992). A cortical model of winner-take-all competition via lateral inhibition. Neural Networks, 5, 47–54.

Easterbrook

J. A.

(1959). The effect of emotion on cue utilization and the organization of behavior. Psychological Review, 66, 183–201.

Einhäuser

Stout

Koch

Carter

(2008). Pupil dilation reflects perceptual selection and predicts subsequent stability in perceptual rivalry. Proceedings of the National Academy of Sciences, USA, 105, 1704–1709.

Eldar

Cohen

J. D.

Niv

(2013). The effects of neural gain on attention and learning. Nature Neuroscience, 16, 1146–1153.

10.

Gilzenrat

M. S.

Nieuwenhuis

Jepma

Cohen

J. D.

(2010). Pupil diameter tracks changes in control state predicted by the adaptive gain theory of locus coeruleus function. Cognitive, Affective, & Behavioral Neuroscience, 10, 252–269.

11.

Graf

Ryan

(1990). Transfer-appropriate processing for implicit and explicit memory. Journal of Experimental Psychology: Learning, Memory, and Cognition, 16, 978–992.

12.

Green

C. S.

Bavelier

(2003). Action video game modifies visual selective attention. Nature, 423, 534–537.

13.

Happé

F. G.

(1996). Studying weak central coherence at low levels: Children with autism do not succumb to visual illusions. A research note. Journal of Child Psychology and Psychiatry, 37, 873–877.

14.

Hoeks

Levelt

W. J.

(1993). Pupillary dilation as a measure of attention: A quantitative system analysis. Behavior Research Methods, Instruments, & Computers, 25, 16–26.

15.

Holland

P. W.

Welsch

R. E.

(1977). Robust regression using iteratively reweighted least-squares. Communication in Statistics: Theory and Methods, 6, 813–827.

16.

Jepma

Nieuwenhuis

(2011). Pupil diameter predicts changes in the exploration–exploitation trade-off: Evidence for the adaptive gain theory. Journal of Cognitive Neuroscience, 23, 1587–1596.

17.

Joshi

Kalwani

R. M.

Gold

J. I.

(2016). Relationships between pupil diameter and neuronal activity in the locus coeruleus, colliculi, and cingulate cortex. Neuron, 89, 221–234.

18.

Kucˇera

Francis

W. N.

(1967). Computational analysis of present-day American English. Providence, RI: Dartmouth Publishing Group.

19.

Lambert

Wells

Kean

(2003). Do isoluminant color changes capture attention? Perception & Psychophysics, 65, 495–507.

20.

Lucas

(2000). Semantic priming without association: A meta-analytic review. Psychonomic Bulletin & Review, 7, 618–630.

21.

McClelland

J. L.

Rumelhart

D. E.

(1981). An interactive activation model of context effects in letter perception: I. An account of basic findings. Psychological Review, 88, 375–407.

22.

Morris

C. D.

Bransford

J. D.

Franks

J. J.

(1977). Levels of processing versus transfer appropriate processing. Journal of Verbal Learning and Verbal Behavior, 16, 519–533.

23.

Murphy

P. R.

Robertson

I. H.

Balsters

J. H.

O’connell

R. G.

(2011). Pupillometry and P3 index the locus coeruleus–noradrenergic arousal function in humans. Psychophysiology, 48, 1532–1543.

24.

Reas

Fry

(2007). Processing: A programming handbook for visual designers and artists. Cambridge, MA: MIT Press.

25.

Richards

G. P.

Samuels

S. J.

Turnure

J. E.

Ysseldyke

J. E.

(1990). Sustained and selective attention in children with learning disabilities. Journal of Learning Disabilities, 23, 129–136.

26.

Richler

J. J.

Cheung

O. S.

Gauthier

(2011). Holistic processing predicts face recognition. Psychological Science, 22, 464–471.

27.

Rousseeuw

P. J.

Leroy

A. M.

(2005). Robust regression and outlier detection. Hoboken, NJ: John Wiley & Sons.

28.

Servan-Schreiber

Printz

Cohen

J. D.

(1990). A network model of catecholamine effects: Gain, signal-to-noise ratio, and behavior. Science, 249, 892–895.

29.

Tarver

S. G.

Hallahan

D. P.

Kauffman

J. M.

Ball

D. W.

(1976). Verbal rehearsal and selective attention in children with learning disabilities: A developmental lag. Journal of Experimental Child Psychology, 22, 375–385.

30.

Usher

Davelaar

E. J.

(2002). Neuromodulation of decision and response selection. Neural Networks, 15, 635–645.

31.

Waterhouse

B. D.

Moises

H. C.

Woodward

D. J.

(1980). Noradrenergic modulation of somatosensory cortical neuronal responses to iontophoretically applied putative neurotransmitters. Experimental Neurology, 69, 30–49.

32.

Waterhouse

B. D.

Moises

H. C.

Yeh

H. H.

Geller

H. M.

Woodward

D. J.

(1984). Comparison of norepinephrine- and benzodiazepine-induced augmentation of Purkinje cell responses to gamma-aminobutyric acid (GABA). Journal of Pharmacology and Experimental Therapeutics, 228, 257–267.

33.

Waterhouse

B. D.

Woodward

D. J.

(1980). Interaction of norepinephrine with cerebrocortical activity evoked by stimulation of somatosensory afferent pathways in the rat. Experimental Neurology, 67, 11–34.

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Do You See the Forest or the Tree? Neural Gain and Breadth Versus Focus in Perceptual Processing

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