Action Intentions, Predictive Processing, and Mind Reading: Turning Goalkeepers Into Penalty Killers

The key to action control is one’s ability to adequately predict the consequences of one’s actions. Predictive processing theories assume that forward models enable rapid “preplay” to assess the match between predicted and intended action effects. Here we propose the novel hypothesis that “reading” another’s action intentions requires a rich forward model of that agent’s action. Such a forward model can be obtained and enriched through learning by either practice or simulation. Based on this notion, we ran a series of studies on soccer goalkeepers and novices, who predicted the intended direction of penalties being kicked at them in a computerized penalty-reading task. In line with hypotheses, extensive practice in penalty kicking improved performance in penalty reading among goalkeepers who had extensive prior experience in penalty blocking but not in penalty kicking. A robust benefit in penalty reading did not result from practice in kinesthetic motor imagery of penalty kicking in novice participants. To test whether goalkeepers actually use such penalty-kicking imagery in penalty reading, we trained a machine-learning classifier on multivariate fMRI activity patterns to distinguish motor-imagery-related from attention-related strategies during a penalty-imagery training task. We then applied that classifier to fMRI data related to a separate penalty-reading task and showed that 2/3 of all correctly read penalty kicks were classified as engaging the motor-imagery circuit rather than merely the attention circuit. This study provides initial evidence that, in order to read our opponent’s action intention, it helps to observe their action kinematics, and use our own forward model to predict the sensory consequences of “our” penalty kick if we were to produce these action kinematics ourselves. In sum, it takes practice as a penalty kicker to become a penalty killer

Action conflict under control: An integrated perspective on action control and its underlying neurophysiological and computational mechanisms

Item does not contain fulltextUniversiteit van Amsterdam, 1 mei 2015Promotor : Ridderinkhof, K.R. Co-promotor : Wildenberg, W.P.M. van den164 p

TMS over M1 reveals expression and selective suppression of conflicting action impulses

Contains fulltext : 130138-OA.pdf (publisher's version ) (Open Access)Goal-directed action control comes into play when selecting between competing action alternatives. Response capture reflects the susceptibility of the motor system to incitement by task-irrelevant action impulses; the subsequent selective suppression of incorrect action impulses aims to counteract response capture and facilitate the desired response. The goal of this experiment was to clarify physiological mechanisms of response capture and suppression of action impulses during conflict at the level of the motor system. We administered single-pulse TMS at various intervals preceding speeded choice responses. The correct response side was designated by stimulus color, whereas stimulus location (which could match or conflict with response side) was to be ignored. TMS pulses triggered motor evoked potential and silent period, providing sensitive indices of cortico-spinal excitation and inhibition. Motor evoked potential data showed the typical progressive increase in cortico-spinal motor excitability leading up to the imminent (correct) response, which started earlier on nonconflict than on conflict trials. On conflict trials, the irrelevant stimulus location captured the incorrect response, as expressed by an early and transient rise in excitability. Silent period data showed that, already early during the response process, inhibition of the incorrect response was stronger for conflict than for nonconflict trials. Furthermore, inhibition decreased over time for nonconflict trials facilitating the imminent correct response while maintaining higher levels of inhibition on conflict trials. In conclusion, dynamic patterns of cortico-spinal excitability provide unique physiological evidence for the expression and selective suppression of action impulses captured by competing action alternatives.15 p

Correction to: Fast and slow errors: Logistic regression to identify patterns in accuracy-response time relationships

Contains fulltext : 203930.pdf (Publisher’s version ) (Closed access)1 p

Fast and slow errors: Logistic regression to identify patterns in accuracy–response time relationships

Understanding error and response time patterns is essential for making inferences in several domains of cognitive psychology. Crucial insights on cognitive performance and typical behavioral patterns are disclosed by using distributional analyses such as conditional accuracy functions (CAFs) instead of mean statistics. Several common behavioral error patterns revealed by CAFs are frequently described in the literature: response capture (associated with relatively fast errors), time pressure or urgency paradigms (slow errors), or cue-induced speed–accuracy trade-off (evenly distributed errors). Unfortunately, the standard way of computing CAFs is problematic, because accuracy is averaged in RT bins. Here we present a novel way of analyzing accuracy–RT relationships on the basis of nonlinear logistic regression, to handle these problematic aspects of RT binning. First we evaluate the parametric robustness of the logistic regression CAF through parameter recovery. Second, we apply the function to three existing data sets showing that specific parametric changes in the logistic regression CAF can consistently describe common behavioral patterns (such as response capture, time pressure, and speed–accuracy trade-off). Finally, we discuss potential modifications for future research