[Show abstract][Hide abstract] ABSTRACT: Most decisions that we make build upon multiple streams of sensory evidence and control mechanisms are needed to filter out irrelevant information. Sequential sampling models of perceptual decision making have recently been enriched by attentional mechanisms that weight sensory evidence in a dynamic and goal-directed way. However, the framework retains the longstanding hypothesis that motor activity is engaged only once a decision threshold is reached. To probe latent assumptions of these models, neurophysiological indices are needed. Therefore, we collected behavioral and EMG data in the flanker task, a standard paradigm to investigate decisions about relevance. Although the models captured response time distributions and accuracy data, EMG analyses of response agonist muscles challenged the assumption of independence between decision and motor processes. Those analyses revealed covert incorrect EMG activity (“partial error”) in a fraction of trials in which the correct response was finally given, providing intermediate states of evidence accumulation and response activation at the single-trial level. We extended the models by allowing motor activity to occur before a commitment to a choice and demonstrated that the proposed framework captured the rate, latency, and EMG surface of partial errors, along with the speed of the correction process. In return, EMG data provided strong constraints to discriminate between competing models that made similar behavioral predictions. Our study opens new theoretical and methodological avenues for understanding the links among decision making, cognitive control, and motor execution in humans.
The Journal of Neuroscience : The Official Journal of the Society for Neuroscience 07/2015; 35(28):10371-85. DOI:10.1523/JNEUROSCI.0078-15.2015 · 6.34 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: We focus on the descriptive approach to linear discriminant analysis for
matrix-variate data in the binary case. Under a separability assumption on row
and column variability, the most discriminant linear combinations of rows and
columns are determined by the singular value decomposition of the difference of
the class-averages with the Mahalanobis metric in the row and column spaces.
This approach provides data representations of data in two-dimensional or
three-dimensional plots and singles out discriminant components. An application
to electroencephalographic multi-sensor signals illustrates the relevance of
[Show abstract][Hide abstract] ABSTRACT: The main goal of this work is to develop a model for multisensor signals, such as magnetoencephalography or electroencephalography (EEG) signals that account for inter-trial variability, suitable for corresponding binary classification problems. An important constraint is that the model be simple enough to handle small size and unbalanced datasets, as often encountered in BCI-type experiments.
The method involves the linear mixed effects statistical model, wavelet transform, and spatial filtering, and aims at the characterization of localized discriminant features in multisensor signals. After discrete wavelet transform and spatial filtering, a projection onto the relevant wavelet and spatial channels subspaces is used for dimension reduction. The projected signals are then decomposed as the sum of a signal of interest (i.e., discriminant) and background noise, using a very simple Gaussian linear mixed model.
Thanks to the simplicity of the model, the corresponding parameter estimation problem is simplified. Robust estimates of class-covariance matrices are obtained from small sample sizes and an effective Bayes plug-in classifier is derived. The approach is applied to the detection of error potentials in multichannel EEG data in a very unbalanced situation (detection of rare events). Classification results prove the relevance of the proposed approach in such a context.
The combination of the linear mixed model, wavelet transform and spatial filtering for EEG classification is, to the best of our knowledge, an original approach, which is proven to be effective. This paper improves upon earlier results on similar problems, and the three main ingredients all play an important role.
[Show abstract][Hide abstract] ABSTRACT: Analyzing RT distributions in the Simon task reveals that congruency effects decrease for the longest RTs. Four experiments were carried out to examine whether this decrease of the congruency effect with response speed was under a top-down control or due to bottom-up mechanisms. We specifically manipulated the availability of attentional resources by requiring participants to perform a Simon task concurrently to different secondary tasks. RT distribution analysis (in particular delta functions) was performed under both single-task and dual-task conditions. Results show that the reduction of the interference effect with time could be affected when the Simon task was performed concurrently with a secondary task. Nonetheless, the type of the secondary task seems to be a critical factor. Therefore, the data suggest that the mechanisms responsible for the reduction of the interference effect with time are under some attentional control but the exact nature of these mechanisms remains to be explored.
[Show abstract][Hide abstract] ABSTRACT: In a previous article, (RiSs, Legou, Burle, Alario, & Malfait, 2012), we reported that articulatory processes contribute to the well-established finding that response latencies are longer for picture naming than for word reading. We based this conclusion on the observation that picture naming, as compared with word reading, lengthened not only the interval between stimulus onset and the initiation of lip muscle activation (premotor time), but also the interval between lip muscle activation and vocal response onset (motor time). However, on the basis of our subsequent work in this area, we believe that our original definition of premotor time (and, consequently, of motor time) was suboptimal. On a sizable number of trials, this led to the detection of lip muscle activation (as inferred from surface EMG) that was apparently unrelated to the articulation of the vocal response. Therefore, we believe it is preferable to operationalize premotor time as the interval between stimulus onset and the muscle activation that occurred closest in time to vocal response onset. After reestimating premotor times according to this new definition, we no longer found an effect of our task contrast on the motor time interval. The present article explains the caveats regarding our previous analysis.
[Show abstract][Hide abstract] ABSTRACT: Impulsive actions entail (1) capture of the motor system by an action impulse, which is an urge to act and (2) failed suppression of that impulse in order to prevent a response error. Several studies indicate that dopaminergic treatment can induce action impulsivity in patients diagnosed with Parkinson's disease (PD). Whether this effect is due to increased impulse expression or to decreased impulse suppression remains to be deciphered.
We used a novel approach based on electromyographic (EMG) analyses to decipher the effects of the patient's usual dopaminergic therapy on the expression and suppression of subliminal erroneous impulses. To this end, we used a within-subject design and took advantage of the Simon task, that elicits prepotent response tendencies. The patients (N = 15) performed the task on their usual dopaminergic medication and after complete medication withdrawal (for at least 12 h).
The correction rate that measures the ability to suppress subthreshold impulsive muscle activity was lower when the patients were on medication as compared to their off medication state (p < 0.05). The incorrect activation rate that measures the capture of the motor system by action impulses was unaffected by medication.
Dopa therapy affected action impulsivity. Although medication did not influence the incidence of fast action impulses, it significantly reduced patients' ability to abort and suppress muscle activation related to the incorrect response alternative.
[Show abstract][Hide abstract] ABSTRACT: To describe the mental architecture between stimulus and response, cognitive models often divide the stimulus-response (SR) interval into stages or modules. Predictions derived from such models are typically tested by focusing on the moment of response emission, through the analysis of response time (RT) distributions. To go beyond the single response event, we recently proposed a method to fractionate verbal RTs into two physiologically defined intervals that are assumed to reflect different processing stages. The analysis of the durations of these intervals can be used to study the interaction between cognitive and motor processing during speech production. Our method is inspired by studies on decision making that used manual responses, in which RTs were fractionated into a premotor time (PMT), assumed to reflect cognitive processing, and a motor time (MT), assumed to reflect motor processing. In these studies, surface EMG activity was recorded from participants' response fingers. EMG onsets, reflecting the initiation of a motor response, were used as the point of fractionation. We adapted this method to speech-production research by measuring verbal responses in combination with EMG activity from facial muscles involved in articulation. However, in contrast to button-press tasks, the complex task of producing speech often resulted in multiple EMG bursts within the SR interval. This observation forced us to decide how to operationalize the point of fractionation: as the first EMG burst after stimulus onset (the stimulus-locked approach), or as the EMG burst that is coupled to the vocal response (the response-locked approach). The point of fractionation has direct consequences on how much of the overall task effect is captured by either interval. Therefore, the purpose of the current paper was to compare both onset-detection procedures in order to make an informed decision about which of the two is preferable. We concluded in favor or the response-locked approach.
Frontiers in Psychology 10/2014; 5. DOI:10.3389/fpsyg.2014.01213 · 2.80 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: When an on-board system detects a drift of a vehicle to the left or to the right, in what way should the information be delivered to the driver? Car manufacturers have so far neglected relevant results from Experimental Psychology and Cognitive Neuroscience. Here we show that this situation possibly led to the sub-optimal design of a lane departure warning system (AFIL, PSA Peugeot Citroën) implemented in commercially available automobile vehicles. Twenty participants performed a two-choice reaction time task in which they were to respond by clockwise or counter-clockwise wheel-rotations to tactile stimulations of their left or right wrist. They performed poorer when responding counter-clockwise to the right vibration and clockwise to the left vibration (incompatible mapping) than when responding according to the reverse (compatible) mapping. This suggests that AFIL implements the worse (incompatible) mapping for the operators. This effect depended on initial practice with the interface. The present research illustrates how basic approaches in Cognitive Science may benefit to Human Factors Engineering and ultimately improve man-machine interfaces and show how initial learning can affect interference effects.
Frontiers in Psychology 10/2014; 5:1045. DOI:10.3389/fpsyg.2014.01045 · 2.80 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: This paper proposes a new method for constructing and selecting of discriminant space-time-scale features for electroencephalogram (EEG) signal classification, suitable for Error Related Potentials (ErrP)detection in brain-computer interface (BCI). The method rests on a new variant of matrix-variate Linear Discriminant Analysis (LDA), and differs from previously proposed approaches in mainly three ways. First, a discrete wavelet expansion is introduced for mapping time-courses to time-scale coefficients, yielding time-scale localized features. Second, the matrix-variate LDA is modified in such a way that it yields an interesting duality property, that makes interpretation easier. Third, a space penalization is introduced using a surface Laplacian, so as to enforce spatial smoothness. The pro-posed approaches, termed D-MLDA and D-MPDA are tested on EEG signals, with the goal of detecting ErrP. Numerical results show that D-MPDA outperforms D-MLDA and other matrix-variate LDA techniques. In addition this method produces relevant features for interpretation in ErrP signals.
[Show abstract][Hide abstract] ABSTRACT: Formal models of decision-making have traditionally focused on simple, two-choice perceptual decisions. To date, one of the most influential account of this process is Ratcliff's drift diffusion model (DDM). However, the extension of the model to more complex decisions is not straightforward. In particular, conflicting situations, such as the Eriksen, Stroop, or Simon tasks, require control mechanisms that shield the cognitive system against distracting information. We adopted a novel strategy to constrain response time (RT) models by concurrently investigating two well-known empirical laws in conflict tasks, both at experimental and modeling levels. The two laws, predicted by the DDM, describe the relationship between mean RT and (i) target intensity (Piéron's law), (ii) standard deviation of RT (Wagenmakers-Brown's law). Pioneering work has shown that Piéron's law holds in the Stroop task, and has highlighted an additive relationship between target intensity and compatibility. We found similar results in both Eriksen and Simon tasks. Compatibility also violated Wagenmakers-Brown's law in a very similar and particular fashion in the two tasks, suggesting a common model framework. To investigate the nature of this commonality, predictions of two recent extensions of the DDM that incorporate selective attention mechanisms were simulated and compared to the experimental results. Both models predict Piéron's law and the violation of Wagenmakers-Brown's law by compatibility. Fits of the models to the RT distributions and accuracy data allowed us to further reveal their relative strengths and deficiencies. Combining experimental and computational results, this study sets the groundwork for a unified model of decision-making in conflicting environments.
[Show abstract][Hide abstract] ABSTRACT: The capacity to evaluate the outcomes of our actions is fundamental for adapting and optimizing behavior and depends on an action-monitoring system that assesses ongoing actions and detects errors. The neuronal network underlying this executive function, classically attributed to the rostral cingulate zone, is poorly characterized in humans, owing to the limited number of direct neurophysiological data. Using intracerebral recordings, we show that the leading role is played by the supplementary motor area (SMA), which rapidly evaluates successful and erroneous actions. The rostral part of medial prefrontal cortex, driven by the SMA, was activated later and exclusively in the case of errors. This suggests a hierarchical organization of the different frontal regions involved in implementation of action monitoring and error processing.
[Show abstract][Hide abstract] ABSTRACT: Formal models of decision-making have traditionally focused on simple, two-choice perceptual decisions. To date, one of the most influential account of this process is Ratcliff’s drift diffusion model (DDM). However, the extension of the model to more complex decisions is not straightforward. In particular, conflicting situations, such as the Eriksen, Stroop, or Simon tasks, require control mechanisms that shield the cognitive system against distracting information. We adopted a novel strategy to constrain response time (RT) models by concurrently investigating two well-known empirical laws in conflict tasks, both at experimental and modeling levels. The two laws, predicted by the DDM, describe the relationship between mean RT and (i) target intensity (Piéron’s law), (ii) standard deviation of RT (Wagenmakers–Brown’s law). Pioneering work has shown that Piéron’s law holds in the Stroop task, and has highlighted an additive relationship between target intensity and compatibility. We found similar results in both Eriksen and Simon tasks. Compatibility also violated Wagenmakers–Brown’s law in a very similar and particular fashion in the two tasks, suggesting a common model framework. To investigate the nature of this commonality, predictions of two recent extensions of the DDM that incorporate selective attention mechanisms were simulated and compared to the experimental results. Both models predict Piéron’s law and the violation of Wagenmakers–Brown’s law by compatibility. Fits of the models to the RT distributions and accuracy data allowed us to further reveal their relative strengths and deficiencies. Combining experimental and computational results, this study sets the groundwork for a unified model of decision-making in conflicting environments.
[Show abstract][Hide abstract] ABSTRACT: Appropriate reactions to erroneous actions are essential to keeping behavior adaptive. Erring, however, is not an all-or-none process: electromyographic (EMG) recordings of the responding muscles have revealed that covert incorrect response activations (termed "partial errors") occur on a proportion of overtly correct trials. The occurrence of such "partial errors" shows that incorrect response activations could be corrected online, before turning into overt errors. In the present study, we showed that, unlike overt errors, such "partial errors" are poorly consciously detected by participants, who could report only one third of their partial errors. Two parameters of the partial errors were found to predict detection: the surface of the incorrect EMG burst (larger for detected) and the correction time (between the incorrect and correct EMG onsets; longer for detected). These two parameters provided independent information. The correct(ive) responses associated with detected partial errors were larger than the "pure-correct" ones, and this increase was likely a consequence, rather than a cause, of the detection. The respective impacts of the two parameters predicting detection (incorrect surface and correction time), along with the underlying physiological processes subtending partial-error detection, are discussed.
[Show abstract][Hide abstract] ABSTRACT: In conflict tasks, the irrelevant stimulus attribute needs to be suppressed for the correct response to be produced. In the Simon task, earlier researchers have proposed that this suppression is the reason that, after an initial increase, the interference effect decreases for longer RTs, as reflected by late, negative-going delta plots. This view has been challenged by observations of positive-going delta plots, even for long RTs, in other conflict tasks, despite a similar necessity for suppression. For late negative-going delta plots to be interpreted as reflecting suppression, a necessary, although maybe not sufficient, condition is that similar patterns should be observed for other conflict tasks. We reasoned that a similar suppression could be present, but hidden, in the Eriksen flanker task. By recording and analyzing electromyograms of the muscles involved in response execution, we could compute delta plots separately for trials that elicited a subthreshold incorrect response activation (partial error). Late negative-going delta plots were observable on partial-error trials, although they were weaker than for the Simon task, reducing the impact of this inversion on the overall distribution. We further showed that this pattern is modulated by time pressure. Those results indicate that mechanisms leading to negative-going delta plots, similar to those observed in the Simon task, are also at play in the Eriksen task. The link between negative-going delta plots and executive online control is discussed.
[Show abstract][Hide abstract] ABSTRACT: We studied the impact of sleep deprivation on action monitoring. Each participant performed a Simon task after a normal night of sleep and after 26 h of awakening. Reaction time (RT) distributions were analyzed and the sensitivity of the error negativity (Ne/Ne like) to response correctness was examined.Results showed that (1) the Simon effect persisted for the longest RTs only after sleep deprivation and (2) the sensitivity of the Ne/Ne like to correctness decreased after sleep deprivation, especially on incongruent trials. This suggests that after sleep deprivation (1) the ability to inhibit prepotent response tendencies is impaired and (2) the sensitivity of a response monitoring system as revealed by the error negativity is less sensitive to performance.In conclusion, action monitoring was affected by sleep deprivation as revealed by distributional analyses and the sensitivity of the Ne/Ne like to performance, which may be attributed to the fragility of prefrontal structures to sleep deprivation.