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

Right heart ischemia in cases of sepsis.

Data from the literature suggest that cases of sepsis complicated by right ventricular (RV) dysfunction have poorer prognosis. In these cases progressive hypoperfusion associated to increasing, injury-related, pulmonary vascular resistance account for RV ischemia. In the present analysis, we wanted to evaluate whether prevalent RV cardiac ischemic damage could be detected in a series of fatal sepsis cases. We retrospectively investigated 20 cases of sepsis that underwent forensic autopsy (study group-11♀, 9♂, mean age 57 years) and compared them to a group of 20 cases of hanging (hanging group-4 ♀, 16 ♂, mean age 44 years) as well as to a group of 20 cases of myocardial infarction (MI group-9 ♀, 11 ♂, mean age 65 years), as examples of cardiac damage due to global hypoxia during agony and ischemic damage, respectively. We performed immunohistochemistry with the antibodies anti-fibronectin and C5b-9. The reactions were semiquantitively classified and the groups were compared. In 30% of the cases of sepsis prevalent RV ischemic damage could be detected with the antibody anti-fibronectin. This expression was significantly different from that observed in cases of MI (p=0.028) and hanging (p<0.001). Our study showed that, in cases of fatal sepsis, prevalent RV ischemic damage occurred in a substantial minority of cases

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

Model-independent characterization of integration timescales.

<p>Threshold vs. duration functions from all conditions. Circles represent psychophysical thresholds for each stimulus duration. Solid lines: best fitting bilinear function, with the slopes constrained to -0.5 (first branch) and 0 (second branch; see text for details). Error bars, 60% confidence intervals (bootstrap). Inset: bar graphs of the joint point estimates of the best fitting bilinear function. Error bars, 95% confidence intervals (bootstrap).</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

Experimental design.

<p>On each trial, the observer was required to discriminate the direction (upward or downward) of the random dot kinematogram (RDK), while fixating the central crosshair. The RDK was presented for one of a number of different durations and levels of motion strength <b>(A)</b> Variable “Pre” DR-mapping condition. The DR-mapping cue (two arrows mapping up/down motion directions onto left/right hand button presses) was presented before the RDK (separated by a variable delay), and it varied randomly from trial to trial. After another variable delay, a color switch of the fixation cross prompted the observer to indicate the choice with a button press. <b>(B)</b> Variable”Post” DR-mapping condition. Identical to “Pre”, except that the DR-mapping cue was presented after the RDK (“Post”). <b>(C)</b> “Fixed” DR-mapping condition. Identical to “Pre”, except that the DR-mapping cue was kept constant throughout the experiment, enabling long-term learning of sensory-motor associations.</p