Action planning and the timescale of evidence accumulation

Perceptual decisions are based on the temporal integration of sensory evidence for different states of the outside world. The timescale of this integration process varies widely across behavioral contexts and individuals, and it is diagnostic for the underlying neural mechanisms. In many situations, the decision-maker knows the required mapping between perceptual evidence and motor response (henceforth termed “sensory-motor contingency”) before decision formation. Here, the integrated evidence can be directly translated into a motor plan and, indeed, neural signatures of the integration process are evident as build-up activity in premotor brain regions. In other situations, however, the sensory-motor contingencies are unknown at the time of decision formation. We used behavioral psychophysics and computational modeling to test if knowledge about sensory-motor contingencies affects the timescale of perceptual evidence integration. We asked human observers to perform the same motion discrimination task, with or without trial-to-trial variations of the mapping between perceptual choice and motor response. When the mapping varied, it was either instructed before or after the stimulus presentation. We quantified the timescale of evidence integration under these different sensory-motor mapping conditions by means of two approaches. First, we analyzed subjects’ discrimination threshold as a function of stimulus duration. Second, we fitted a dynamical decision-making model to subjects’ choice behavior. The results from both approaches indicated that observers (i) integrated motion information for several hundred ms, (ii) used a shorter than optimal integration timescale, and (iii) used the same integration timescale under all sensory-motor mappings. We conclude that the mechanisms limiting the timescale of perceptual decisions are largely independent from long-term learning (under fixed mapping) or rapid acquisition (under variable mapping) of sensory-motor contingencies. This conclusion has implications for neurophysiological and neuroimaging studies of perceptual decision-making

Psychophysiological response patterns to affective film stimuli.

Psychophysiological research on emotion utilizes various physiological response measures to index activation of the defense system. Here we tested 1) whether acoustic startle reflex (ASR), skin conductance response (SCR) and heart rate (HR) elicited by highly arousing stimuli specifically reflect a defensive state and 2) the relation between resting heart rate variability (HRV) and affective responding. In a within-subject design, participants viewed film clips with a positive, negative and neutral content. In contrast to SCR and HR, we show that ASR differentiated between negative, neutral and positive states and can therefore be considered as a reliable index of activation of the defense system. Furthermore, resting HRV was associated with affect-modulated characteristics of ASR, but not with SCR or HR. Interestingly, individuals with low-HRV showed less differentiation in ASR between affective states. We discuss the important value of ASR in psychophysiological research on emotion and speculate on HRV as a potential biological marker for demarcating adaptive from maladaptive responding

Psychophysiological response patterns to affective film stimuli

Psychophysiological research on emotion utilizes various physiological response measures to index activation of the defense system. Here we tested 1) whether acoustic startle reflex (ASR), skin conductance response (SCR) and heart rate (HR) elicited by highly arousing stimuli specifically reflect a defensive state and 2) the relation between resting heart rate variability (HRV) and affective responding. In a within-subject design, participants viewed film clips with a positive, negative and neutral content. In contrast to SCR and HR, we show that ASR differentiated between negative, neutral and positive states and can therefore be considered as a reliable index of activation of the defense system. Furthermore, resting HRV was associated with affect-modulated characteristics of ASR, but not with SCR or HR. Interestingly, individuals with low-HRV showed less differentiation in ASR between affective states. We discuss the important value of ASR in psychophysiological research on emotion and speculate on HRV as a potential biological marker for demarcating adaptive from maladaptive responding.status: publishe

Lapse rates.

<p>Numbers are estimates of lapse rate (<i>λ</i>) and by 95% confidence intervals.</p><p>Lapse rates.</p

Model-based time constant estimates.

<p>Numbers are estimates of time constant (<i>τ</i>) and 95% confidence intervals.</p><p>Model-based time constant estimates.</p

Example psychometric functions of one observer in all conditions.

<p>Solid curves are maximum likelihood fits of cumulative Weibull functions to the proportion correct data. Vertical dashed lines represent estimated threshold parameters (solid horizontal lines at bottom, 95% confidence intervals). The horizontal dashed line represents the lapse rate. <b>(A)</b> “Pre”- condition. The performance data and psychometric functions are shown separately for all stimulus durations. <b>(B)</b> As in A, but for “Post”. <b>(C)</b> As in A, but for “Fixed”.</p

Sensitivity of the model-based timescale estimation.

<p>Sensitivity of the model-based timescale estimation. Three distributions of estimated time constants obtained by simulating leaky integrator models with three different time constants (0.2, 0.4, 0.6 s). Vertical lines indicate the 2.5% and 97.5% quantiles. The overlap between the distributions is small (<5%).</p

Summary of integration times using model-independent (A) and model-based (B) characterization of time-constants.

<p>Summary of integration times under the three DR-mappings tested. The gray horizontal line marks the median, the upper and lower edges of the box mark the 25^th and 75^th percentiles, and the whiskers extend to the most extreme data points, excluding outliers. <b>(A)</b> Joint points of the bilinear fit to threshold vs. duration functions. <b>(B)</b> Time constants derived from LCA model fits.</p