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Spatial and temporal attention modulate the early stages of face processing: behavioural evidence from a reaching paradigm.

Quek GL, Finkbeiner M - PLoS ONE (2013)

Bottom Line: In the present study, we reconcile this divide by using a continuous behavioural response measure that indexes face processing at a temporal resolution not available in discrete behavioural measures (e.g. button press).Using reaching trajectories as our response measure, we observed that although participants were able to process faces both when attended and unattended (as others have found), face processing was not impervious to attentional modulation.Attending to the face conferred clear benefits on sex-classification processes at less than 350ms of stimulus processing time.

View Article: PubMed Central - PubMed

Affiliation: Department of Cognitive Science, ARC Centre of Excellence in Cognition and its Disorders, Macquarie University, Sydney, New South Wales, Australia. genevieve.quek@mq.edu.au

ABSTRACT
A presently unresolved question within the face perception literature is whether attending to the location of a face modulates face processing (i.e. spatial attention). Opinions on this matter diverge along methodological lines - where neuroimaging studies have observed that the allocation of spatial attention serves to enhance the neural response to a face, findings from behavioural paradigms suggest face processing is carried out independently of spatial attention. In the present study, we reconcile this divide by using a continuous behavioural response measure that indexes face processing at a temporal resolution not available in discrete behavioural measures (e.g. button press). Using reaching trajectories as our response measure, we observed that although participants were able to process faces both when attended and unattended (as others have found), face processing was not impervious to attentional modulation. Attending to the face conferred clear benefits on sex-classification processes at less than 350ms of stimulus processing time. These findings constitute the first reliable demonstration of the modulatory effects of both spatial and temporal attention on face processing within a behavioural paradigm.

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Grouping trajectories by Target-Viewing Time.(A) Analysis begins with the distribution of LiftOff Latencies (i.e. Target-Viewing times), estimated relative to target onset. A modified version of OPTA is used to fit a polynomial regression model to the x-velocity profile for each trial. The model includes LiftOff Latency percentile as a covariate (see text). (B) Predicted x-velocity profiles are grouped into semi-decile intervals. Red colours indicate trials with short LiftOff Latencies (beginning at the 1st semi-decile); white-colours correspond to longest LiftOff Latencies (20th semi-decile). Note the clear effect of LiftOff Latency: the longer subjects wait to begin moving, the faster the finger moves in the correct direction.
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pone-0057365-g003: Grouping trajectories by Target-Viewing Time.(A) Analysis begins with the distribution of LiftOff Latencies (i.e. Target-Viewing times), estimated relative to target onset. A modified version of OPTA is used to fit a polynomial regression model to the x-velocity profile for each trial. The model includes LiftOff Latency percentile as a covariate (see text). (B) Predicted x-velocity profiles are grouped into semi-decile intervals. Red colours indicate trials with short LiftOff Latencies (beginning at the 1st semi-decile); white-colours correspond to longest LiftOff Latencies (20th semi-decile). Note the clear effect of LiftOff Latency: the longer subjects wait to begin moving, the faster the finger moves in the correct direction.

Mentions: In the present experiments, the OPTA procedure described below was implemented using custom-software written in R (www.r-project.org). Trials with correct responses in each experimental design cell (i.e. Subject, level of Cue Location, level of Prime Type) were ranked according to their LiftOff Latency, from the shortest (ranked 1st) to longest (ranked nth, where n is the number of trials for that subject in this design cell). A polynomial regression model was then fitted to the x-velocities using LiftOff Latency Rank as the covariate and polynomial terms up to the 6th order. Polynomial terms that did not account for a significant proportion of variance were removed, and the remaining coefficients used to generate predicted x-velocity values (one per trial for all subjects). To visualise the effect of Target-Viewing Time on reaching responses predicted trajectories were averaged into semi-decile intervals, resulting in 20 predicted trajectories per experimental condition, per subject (see Figure 3),. The first of these Quantiles represents those trials corresponding to the fastest 5% of LiftOff Latencies; the second represents the next fastest 5% of LiftOffs, and so on. Because we were interested in the participants’ classification responses at the time of movement initiation, we restricted our analysis to the initial 30 samples of the predicted trajectories (i.e. first 30% of the trajectory). We computed the mean x-velocity across this initial portion of the trajectory, resulting in a single value for each trial, which was then submitted to a linear mixed-effects model (LMM) with LiftOff Latency semi-decile included as a fixed effect. Note that although the duration of the initial 30% of trajectories is not uniform across trials (see Figure S2 & S4 in the supplementary materials), we have found that x-velocity is better predicted by the information available at the point of movement initiation (i.e. LiftOff Latency) than total duration (i.e. LiftOff Latency plus the duration of the initial movement). Further details regarding this appear in the supplementary materials.


Spatial and temporal attention modulate the early stages of face processing: behavioural evidence from a reaching paradigm.

Quek GL, Finkbeiner M - PLoS ONE (2013)

Grouping trajectories by Target-Viewing Time.(A) Analysis begins with the distribution of LiftOff Latencies (i.e. Target-Viewing times), estimated relative to target onset. A modified version of OPTA is used to fit a polynomial regression model to the x-velocity profile for each trial. The model includes LiftOff Latency percentile as a covariate (see text). (B) Predicted x-velocity profiles are grouped into semi-decile intervals. Red colours indicate trials with short LiftOff Latencies (beginning at the 1st semi-decile); white-colours correspond to longest LiftOff Latencies (20th semi-decile). Note the clear effect of LiftOff Latency: the longer subjects wait to begin moving, the faster the finger moves in the correct direction.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3585364&req=5

pone-0057365-g003: Grouping trajectories by Target-Viewing Time.(A) Analysis begins with the distribution of LiftOff Latencies (i.e. Target-Viewing times), estimated relative to target onset. A modified version of OPTA is used to fit a polynomial regression model to the x-velocity profile for each trial. The model includes LiftOff Latency percentile as a covariate (see text). (B) Predicted x-velocity profiles are grouped into semi-decile intervals. Red colours indicate trials with short LiftOff Latencies (beginning at the 1st semi-decile); white-colours correspond to longest LiftOff Latencies (20th semi-decile). Note the clear effect of LiftOff Latency: the longer subjects wait to begin moving, the faster the finger moves in the correct direction.
Mentions: In the present experiments, the OPTA procedure described below was implemented using custom-software written in R (www.r-project.org). Trials with correct responses in each experimental design cell (i.e. Subject, level of Cue Location, level of Prime Type) were ranked according to their LiftOff Latency, from the shortest (ranked 1st) to longest (ranked nth, where n is the number of trials for that subject in this design cell). A polynomial regression model was then fitted to the x-velocities using LiftOff Latency Rank as the covariate and polynomial terms up to the 6th order. Polynomial terms that did not account for a significant proportion of variance were removed, and the remaining coefficients used to generate predicted x-velocity values (one per trial for all subjects). To visualise the effect of Target-Viewing Time on reaching responses predicted trajectories were averaged into semi-decile intervals, resulting in 20 predicted trajectories per experimental condition, per subject (see Figure 3),. The first of these Quantiles represents those trials corresponding to the fastest 5% of LiftOff Latencies; the second represents the next fastest 5% of LiftOffs, and so on. Because we were interested in the participants’ classification responses at the time of movement initiation, we restricted our analysis to the initial 30 samples of the predicted trajectories (i.e. first 30% of the trajectory). We computed the mean x-velocity across this initial portion of the trajectory, resulting in a single value for each trial, which was then submitted to a linear mixed-effects model (LMM) with LiftOff Latency semi-decile included as a fixed effect. Note that although the duration of the initial 30% of trajectories is not uniform across trials (see Figure S2 & S4 in the supplementary materials), we have found that x-velocity is better predicted by the information available at the point of movement initiation (i.e. LiftOff Latency) than total duration (i.e. LiftOff Latency plus the duration of the initial movement). Further details regarding this appear in the supplementary materials.

Bottom Line: In the present study, we reconcile this divide by using a continuous behavioural response measure that indexes face processing at a temporal resolution not available in discrete behavioural measures (e.g. button press).Using reaching trajectories as our response measure, we observed that although participants were able to process faces both when attended and unattended (as others have found), face processing was not impervious to attentional modulation.Attending to the face conferred clear benefits on sex-classification processes at less than 350ms of stimulus processing time.

View Article: PubMed Central - PubMed

Affiliation: Department of Cognitive Science, ARC Centre of Excellence in Cognition and its Disorders, Macquarie University, Sydney, New South Wales, Australia. genevieve.quek@mq.edu.au

ABSTRACT
A presently unresolved question within the face perception literature is whether attending to the location of a face modulates face processing (i.e. spatial attention). Opinions on this matter diverge along methodological lines - where neuroimaging studies have observed that the allocation of spatial attention serves to enhance the neural response to a face, findings from behavioural paradigms suggest face processing is carried out independently of spatial attention. In the present study, we reconcile this divide by using a continuous behavioural response measure that indexes face processing at a temporal resolution not available in discrete behavioural measures (e.g. button press). Using reaching trajectories as our response measure, we observed that although participants were able to process faces both when attended and unattended (as others have found), face processing was not impervious to attentional modulation. Attending to the face conferred clear benefits on sex-classification processes at less than 350ms of stimulus processing time. These findings constitute the first reliable demonstration of the modulatory effects of both spatial and temporal attention on face processing within a behavioural paradigm.

Show MeSH
Related in: MedlinePlus