Implicit memory influences the allocation of attention in visual cortex

Johnson, Jeffrey S.; Woodman, Geoffrey F.; Braun, Elsie L.; Luck, Steven J.

doi:10.3758/bf03194108

Cited by 86 publications

(111 citation statements)

References 24 publications

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“…Although this view is compatible with our data, it has difficulty explaining previous eyetracking and ERP/MEG data in which repeated displays were associated with significantly fewer eye movements before the target was found (Peterson & Kramer, 2001;Tseng & Li, 2004;Zhao et al, 2012). In addition, the N2pc component of the ERP is greater for old than for new displays (Johnson et al, 2007;Schankin & Schubö, 2009). This component is known to index spatial attention rather than postperceptual decision and response (Luck & Hillyard, 1994).…”

Section: Discussionsupporting

confidence: 44%

“…Similarly, scalp event-related potential (ERP) studies have reported a significantly greater N2pc component for repeated than for unrepeated displays approximately 200 ms after display onset (Johnson, Woodman, Braun, & Luck, 2007;Schankin & Schubö, 2009). Because N2pc is an index of spatial attention (Luck & Hillyard, 1994), these differences suggest that contextual cueing affects spatial attention relatively early.…”

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confidence: 99%

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The time course of attentional deployment in contextual cueing

2012

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The time course of attention is a major characteristic on which different types of attention diverge. In addition to explicit goals and salient stimuli, spatial attention is influenced by past experience. In contextual cueing, behaviorally relevant stimuli are more quickly found when they appear in a spatial context that has previously been encountered than when they appear in a new context. In this study, we investigated the time that it takes for contextual cueing to develop following the onset of search layout cues. In three experiments, participants searched for a T target in an array of Ls. Each array was consistently associated with a single target location. In a testing phase, we manipulated the stimulus onset asynchrony (SOA) between the repeated spatial layout and the search display. Contextual cueing was equivalent for a wide range of SOAs between 0 and 1,000 ms. The lack of an increase in contextual cueing with increasing cue durations suggests that as an implicit learning mechanism, contextual cueing cannot be effectively used until search begins.Keywords Spatial attention . Contextual cueing . Visual search . Implicit learningThe modern world presents us with a myriad of sensory inputs, only a subset of which are behaviorally relevant. Selective attention depends partially on one's previous experience. For example, in studies on contextual cueing, participants become faster searching for a target on displays that occasionally repeat (Chun & Jiang, 1998). Several neurophysiological studies have investigated the time course of contextual cueing, demonstrating relatively rapid emergence of the cueing effect. However, these findings have not been corroborated in behavioral work. The goal of the present study was to characterize the time course of contextual cueing in behavior.In an intracranial electroencephalography (EEG) study, Olson, Chun, and Allison (2001) observed a significantly greater N210 wave (in V1/V2) approximately 200 ms after viewing of a previously encountered display rather than a new display. Similarly, scalp event-related potential (ERP) studies have reported a significantly greater N2pc component for repeated than for unrepeated displays approximately 200 ms after display onset (Johnson, Woodman, Braun, & Luck, 2007;Schankin & Schubö, 2009). Because N2pc is an index of spatial attention (Luck & Hillyard, 1994), these differences suggest that contextual cueing affects spatial attention relatively early. Earlier time differences were observed with magnetoencephalography (MEG). In one MEG study, participants were presented with repeated search layouts, some with consistent and others with random target locations. After search onset, greater gamma activity occurred 100-300 ms earlier in the consistent than in the random condition (Chaumon, Drouet, & Tallon-Baudry, 2008). Predictable search layouts appear to affect brain activity shortly after search onset.Does the early time course that has been observed with ERPs and MEG translate into an early behavioral gain? This question ha...

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Section: Discussionsupporting

confidence: 44%

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confidence: 99%

The time course of attentional deployment in contextual cueing

2012

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“…Primary visual cortex (V1 and V3 within the medial occipital lobes) as well as higher-level multimodal association areas receive inputs from both visual and auditory sensory systems. This neuroanatomic substrate provides the functional connectivity that supports implicit auditory-visual interactions, outside of conscious awareness, while at the same time supporting cognitive control (Banich & Compton, 2011;Johnson, Woodman, Braun, & Luck, 2007;Naatanen, Kujala, & Winkler, 2011;Salmi, Rinne, Degerman, & Alho, 2007). In fact, during the administration of the ''auditory'' digit span task, and most other ''auditory attention'' tasks, the medial occipital cortex is robustly activated (Gerton et al, 2004).…”

Section: Integrating Network Auditory-visual Interactions and Actimentioning

confidence: 94%

Structure and Function of Large-Scale Brain Systems

Koziol¹,

Barker

Joyce³

et al. 2014

Applied Neuropsychology: Child

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This article introduces the functional neuroanatomy of large-scale brain systems. Both the structure and functions of these brain networks are presented. All human behavior is the result of interactions within and between these brain systems. This system of brain function completely changes our understanding of how cognition and behavior are organized within the brain, replacing the traditional lesion model. Understanding behavior within the context of brain network interactions has profound implications for modifying abstract constructs such as attention, learning, and memory. These constructs also must be understood within the framework of a paradigm shift, which emphasizes ongoing interactions within a dynamically changing environment.

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“…Targets are identified more effectively when they appear within repeated spatial configurations of distractors compared with novel contexts. Electrophysiological recordings during contextual cueing experiments have revealed modulation of targetselection mechanisms (Johnson et al, 2007;Chaumon et al, 2008Chaumon et al, , 2009Schubö, 2009, 2010;Telling et al, 2010), although findings have not been entirely consistent and studies have not always controlled adequately for eye movements. Furthermore, because the target appears embedded within distractors, it is not possible to test for perceptual modulations unambiguously.…”

Section: Introductionmentioning

confidence: 99%

Biasing Perception by Spatial Long-Term Memory

Summerfield¹,

Rao²,

Garside³

et al. 2011

J. Neurosci.

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Human perception is highly flexible and adaptive. Selective processing is tuned dynamically according to current task goals and expectations to optimize behavior. Arguably, the major source of our expectations about events yet to unfold is our past experience; however, the ability of long-term memories to bias early perceptual analysis has remained untested. We used a noninvasive method with high temporal resolution to record neural activity while human participants detected visual targets that appeared at remembered versus novel locations within naturalistic visual scenes. Upon viewing a familiar scene, spatial memories changed oscillatory brain activity in anticipation of the target location. Memory also enhanced neural activity during early stages of visual analysis of the target and improved behavioral performance. Both measures correlated with subsequent target-detection performance. We therefore demonstrated that memory can directly enhance perceptual functions in the human brain.

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Implicit memory influences the allocation of attention in visual cortex

Cited by 86 publications

References 24 publications

The time course of attentional deployment in contextual cueing

The time course of attentional deployment in contextual cueing

Structure and Function of Large-Scale Brain Systems

Biasing Perception by Spatial Long-Term Memory

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