text:
Experimental Data (8)
feedback loop (5)
choice task (3)
computational models (3)
Simulation results (3)
PLOS Comp. Biol.
Apr '15
Embodied Choice: How Action Influences Perceptual Decision Making
Nathan F. Lepora, Giovanni Pezzulo
Published in PLOS Comp. Biol., April 2015
The central statement of embodied choice is the existence of bidirectional influences between action and decisions. This implies that for a decision expressed by an action, the action dynamics and its constraints (e.g. current trajectory and kinematics) influence the decision making process. Here we use a perceptual decision making task to compare three types of model: a serial decision-then-action model, a parallel decision-and-action model, and an embodied choice model where the action feeds back into the decision making. The embodied model incorporates two key mechanisms that together are lacking in the other models: action preparation and commitment. First, action preparation strategies alleviate delays in enacting a choice but also modify decision termination. Second, action dynamics change the prospects and create a commitment effect to the initially preferred choice. Our results show that these two mechanisms make embodied choice models better suited to combine decision and action appropriately to achieve suitably fast and accurate responses, as usually required in ecologically valid situations. Moreover, embodied choice models with these mechanisms give a better account of trajectory tracking experiments during decision making. In conclusion, the embodied choice framework offers a combined theory of decision and action that gives a clear case that embodied phenomena such as the dynamics of actions can have a causal influence on central cognition.
However, this evidence accumulation model neglects the contribution of action and motor processes to the choice that is made. Recent novel studies that track the changing dynamics of actions during perceptual decisions are increasingly revealing the contribution of the actions we make to our perceptual choices. Thus, the action dynamics of our bodies causally influences our central cognition, which is a core assumption of embodied theories of mind. This paper presents an explanation of how actions, encompassing behavioral strategies such as preparation and commitment, can bias decision making processes in ways that optimize the ecological choices of animals behaving in natural environments. It thus combines two disconnected research streams, decision-making and action control in a manner consistent With theoretical and psychological arguments for embodied cognition.
This idea has been formalized using various methods, which include signal detection theory, neural race models, attractor dynamics and Bayesian inference [1—8]. However, although this diffusion-to-bound framework has received strong support from the neuroscience literature [9] , it has been limited by only addressing decisions in isolation without considering if and how decision and action systems interact.
To make comparison with previous work on decision making, we discuss three different ways to link decisions to action: the serial model, the parallel model, and the embodied choice model. We describe their theoretical underpinning first, before successively analyzing and comparing them from a computational perspective. The results of this comparison show that the serial model has a poorer speed-accuracy tradeoff than the parallel model; however, the parallel model is limited in that it cannot trade accuracy for speed to achieve the quickest decision times, which requires the full embodied choice model. We first clarify the differences between these three types of decision making model in the remainder of the introduction, before giving results on how these models compare on a simple perceptual choice task involving action on synthetic data, and then give empirical support for embodied choice models from motion tracking experiments during decision making. Finally, in the discussion, we discuss the broader implications of an embodied choice framework and how it relates to recent developments in the neuroscience and psychology of decision making.
Serial models: segregating decision and action
According to serial models (Fig. 1 (A)), decision and action are neural-ly/ computationally segregated and arranged in a pipeline [10]: the decision is made first and then the action (e. g. pressing a button to report the choice) is executed at the time of decision completion; the costs of action are not considered.
Parallel decision and action: the continuous flow model
That response regions in the brain show effects of decision signals before committing to the final outcome was known 25 years ago; for example, an EMG study found activation of the incorrect response hand in a flanker task, even on trials Where subjects eventually gave the correct response [11]. Successive EEG and neurophysiological studies of decision-making consistently found a covert preparation of multiple motor plans in parallel, providing a strong support for parallel views of human information processing [12—15]. More recently, several studies tracking response dynamics (e. g. mouse movements of subjects Who must click buttons to report choices) showed a similar effect for the overt phases of (A) Serial models process Action continuous forward flow Preparation & process Action (C) Embodied choice models continuous fonNard flow Preparation & process Action continuous feedback
Schematic illustration of alternative ways to link decision and action systems: (A) serial models, (B) parallel models, and (C) embodied choice models. Note that here decision, choice, and action are shown as separate and modular systems for illustrative purposes only.
A consistent finding across several paradigms tracking mouse movements, from simple perceptual categorizations to more compleX lexical and numerical decisions, is that overt action does not simply start when decision is complete. Instead, the action begins early in the decision process and is continuously revised, with action dynamics (e. g. mouse trajectories, acceleration peaks) reflecting the choice uncertainty [16—22].
For example, in the continuous flow model [14], the partial results of the ongoing decision computations are continuously transferred to the action component (see Fig. 1 (B)). In this way action can start before completion of the decision and be revised during the decision process. See Table 1 for some differences between serial and parallel models of decision making.
According to a recent proposal the continuous flow model can eXplain also overt revisions of actions during decision making and in particular so-called "changes of mind", or the fact that an initially preferred choice is changed during the course of the decision making [23]. In this model, a change of mind happens when the drift-diffusion process first reaches a threshold that triggers action initiation, but then later (due to accumulated evidence not initially considered) passes an opposition threshold that triggers execution of an initially non-preferred action. Another way the continuous flow model can eXplain overt response competition is to implement an action preparation strategy: the partial results of the competition are used to approach Table 1. Differences between serial and parallel models of decision-making. Serial models Parallel models Subjects accumulate evidence until they make a Subjects start moving before they make a final decision final decision (reach a bound) then act and can revise the initial choice (change mind) successively Decision is completed before starting the action Decision is not completed before starting the action Uncertainty in the decision is reflected in errors Uncertainty in the decision is also reflected in the and response time movement trajectories the more likely option (e. g. the more likely response button to be clicked with a mouse) or to move in between the alternatives if these are uncertain [24]. This can be realized, for example, by allowing the action system to generate two simultaneous motor commands and to average them over time with changing weights [25], producing a 'continuous competition' between response alternatives [20]. This strategy can eXplain why, during perceptual choices, participants' movements (e. g. mouse trajectories) start almost immediately and can be attracted to the unselected alternative.
Embodied choice
However, in ecologically valid scenarios, the situated aspects of choice (e. g. the action dynamics and the achievability of the action alternatives) should be worth considering as part of the decision making process. Ecological psychologists have long recognized that agents do not passively receive sensory stimuli, but contribute with their actions to shaping the sensory flux [26—28]. Not only sensations determine (choice and) action but actions determine the next sensations in a continuous perception-action loop. Within this loop, several aspects of action and its dynamics can influence the choice, as we now discuss.
For example, reaching a far object is energetically more costly than reaching a close object, and takes more time. Hence, living organisms typically trade off the benefits of an outcome from the cost of obtaining it. In accordance, the biomechanical costs of movement and target distance influence choice [29—3 1] , motivating recent computational studies that begin to incorporate action costs in the decision process [24, 32]. In principle, even serial and parallel models can incorporate action cost in the decision process; for example, they can trade off the benefits of pressing a button and the required effort. However, these models can only include costs that are known a priori and not those that emerge (or change) due to action dynamics, because they do not consider the feedback process from action to decision. In ecologically valid situations, costs cannot be completely specified a priori because they change in an action-dependent way. For example, the biomechani-cal costs of changing a decision (e. g. the choice of whether a lion should continue pursuing a gazelle or give up) will vary depending on the relative trajectory and the distance from the target.
Imagine, for example, a lion given the choice between capturing two gazelles: if the lion waits until its decision is complete, it risks missing an opportunity because one or both gazelles may run away. The lion faces a decision problem that is not stable but dynamic. In dynamic, real-world environments, costs and benefits cannot be completely specified in advance but are defined by various situated factors such as the relative distance between the lion and the gazelles, which change over time as a function of the geometry of the environment (e. g. a gazelle jumping over an obstacle can follow a new escape path) and the decision maker's actions (e. g. if the lion approaches one gazelle the other can escape) [33]. It seems that the brain solves this problem by preparing multiple actions in parallel and selecting between them through biased competition in the sensorimotor system [34, 35]. Furthermore, choice experiments conducted to assess the relative contribution of perceptual evaluation and motor execution to response time (using a 'compelled response' paradigm where participants need to initiate a saccade towards a target before the stimulus signalling the correct target is fully revealed) provide strong evidence in favor of the idea that action plans can be prepared, and even launched, prior to the completion of the decision process [36]. The continuous flow model can partially capture these processes by allowing the preparation of actions through the influence of the ongoing decision; however, as we noted before, it has principally focused on the covert components of preparation (e. g. allocation of neuronal resources) rather than also its overt components (e. g. initiation of movements towards or between alternative goal locations).
What these computational studies suggest is that action dynamics in all their aspects (i. e. both their covert planning and their overt execution) have a backwards influence on the decision process by changing the prospects (the value and costs of the action alternatives). For example, when the lion starts tracking one of the gazelles, undoing that action can be too costly and thus the overall benefit of continuing to track the same gazelle increases. This produces a commitment effect to the initial choice that reflects both the situated nature of the choice and the cognitive effort required for changing mind at later stages of the decision [38].
To achieve this aim, we introduce an Embodied Choice framework in which action and its dynamics are considered an integral part of the decision making process, rather than merely a way to report an already made choice. The brain considers both the decision part (i. e. making the right choice) and the action part (i. e. minimizing movement costs) simultaneously. A consequence of this view is that actions and their constraints influence decisions. Fig. 1 (C) illustrates this aspect by including a feedback loop from the action to decision systems that is missing from both serial and parallel models. Table 2 further clarifies the differences between embodied choice and continuous flow models of decision making.
In this article we propose a computational characterization of Embodied Choice and compare it with alternative proposals made in the literature. We keep the model minimalistic (e. g. by using simplified movement dynamics) to better highlight the essential characteristics of embodied choice in comparison with alternative models. Table 2. Differences between parallel (continuous flow) and embodied choice models of decision-making. Parallel (continuous flow) models Embodied choice models Action and its dynamics do not influence decision Action and its dynamics directly influence decision making optimization making optimization The partial results of the choice computation flow In addition, action dynamics influence (e.g. fonNard to the action system provide evidence for) the decision making process The forward influence from decision to action system The backward influence from action to decision permits changes of mind and motor preparation produces commitment The deadline for a decision is a threshold (or bound) The natural deadline for a decision is the within the decision system completion of the situated action
Here we incorporate these assumptions in four computational models and test them in a simulation of a simple perceptual choice task involving action. This test aids understanding of the differences between the alternative theoretical models while elucidating their predictions.
2). For concreteness, we consider a task based around a computer mouse eXperiment in which the subject moves a cursor onto a target while making a perceptual choice (e. g. a leXical decision [16, 39]). However, we emphasize that this model is considered here as more broadly representative of any decision making task using action to make the choice.
In the first study, we simulate the decision trajectories for four computational models with increasingly sophisticated interaction between the decision making and action components. Model 1 serially initiates action after decision completion; Model 2 is parallel by allowing changes of mind in the decision system after the action initiates; Model 3 has action preparation operating in parallel with decision system, with also some aspects of embodiment by using action completion for decision completion; and Model 4 is fully embodied with a feedback loop from the action to decision systems, encompassing both action preparation and commitment. The trajectories from these four models are then compared with motion tracking eXperiments during decision making to provide empirical support for the models. Then in Study 2 we compute the speed-accuracy tradeoff curves resulting from these four models, and compare their performance to assess the overall effectiveness of serial, parallel and embodied choice models.
Study 1: Decision trajectories during embodied choice
Continuous measures of processing are more informative about the dynamics of choice than response time experiments. Thus, we model an experimental task in which participants performed the decision by moving the mouse to indicate their response. Experimental data was collected using a MouseTracker apparatus [17] during a visual-lexical decision task to observe the graded effects of competing items attracting the trajectory of the mouse [16]. By tracking continuous reaching movements, the technique enables study of the dynamics of choice between multiple competing hypotheses and can reveal graded processing and uncertainty throughout the response. Some example of mouse-tracking trajectories are shown in Fig. 2. In our model of this task, we consider a square arena within which the mouse pointer follows a two-dimensional trajectory (x(t), y(t)). The choice is indicated when the mouse pointer reaches one of two targets: T1 at position (x1, y1) and T2 at (x2, yz).
All computational models that we compare below are built on the drift-diffusion model [1], which is a model of the cognitive processes involved in making simple two-choice decisions. Decisions are made by a noisy process that accumulates information z(t) over time t (Fig. 4) from a starting point (assumed at 2(0) 2 0) towards one of two decision boundaries that initiate a response (assumed equal and opposite at z = ib). Mathematically, the information accumulation can be defined as Where AZ is the increment of sensory information over a time increment At. The rate of accumulation of information is called the drift rate, and is determined by the quality of the
Experiment: Mouse—tracking data
target T1 target T2 Vertical position, y location
Horizontal position, x
Mouse-tracking data. An action is made to eithertarget T1 ortarget T2 to indicate the decision. This action has a trajectory beginning at a start position equidistant from both targets. The red line indicated the mean trajectory. Experiment: 10 trajectories randomly chosen from the data in ref. [16].
Within-trial variability (noise) in the accumulation of information from the starting point toward the boundaries results in processes with the same mean drift rate terminating at different times (giving response time distributions) and sometimes at the wrong boundary (producing errors). Speed-accuracy tradeoffs are modeled by changing the distance between the decision boundaries: wider boundaries (larger (9) require more information to make a decision, leading to more accurate but slower responses. For further details of the model, see Ratcliff and McKoon [40].
3). Mathematically, we represent the action focus as a point (x(z), y(z)) focus that is a function of the accumulated information z(t). At each instance of time, the action is a move (Ax, Ay) from the present location towards the action focus which for simplicity we assume is at constant speed 1/ taken at discrete time-steps of At. The action focus provides a way in which the decision and action systems can interact, resulting in a continuum of models from fully non-embodied (i. e. where decision and action are two serial stages and only the former can influence the latter) to fully embodied (i. e. where decision and action systems interact bidirectionally and continuously). Below we consider four models in order of increasing sophistication and adherence to the characteristics of embodied models as defined in Table 2.
The simplest way to augment the drift diffusion model with an action system is to consider a serial model with two stages: first the decision is made at time tdec (when the decision variable first passes a boundary), which then initiates the appropriate movement to enact that decision. For simplicity, we assume a
Model: Move towards focus
Vertical position, y
Simulated 2AFC task with action dynamics (trajectory from Model 3). For simplicity, movement along the trajectory is at constant velocity. The trajectory depends on the interaction between the decision and action systems, driven by information forthe alternatives. An action focus is used to linkthe decision and action systems, which here drifts from target T2 to T1 with the accumulated information for each alternative. basic action model With constant movement speed, With then the time-optimal action to move in a straight line towards the chosen target. This action model can be represented by having an action focus that is coincident on the chosen target for times t2 tdec
An example time-history of the action focus is shown in Fig. 4 as information is accumulated for the two choices during a decision process.
Next, we consider a model that also initiates movement after passing a decision boundary, as in Model 1, but updates the decision variable with new sensory information during the movement. Consequently, if the opposing decision barrier is passed before the trajectory reaches a target, then the action is revised to move towards the other target. The possibility to revise an initial decision is a characteristic of parallel models, where the decision system does not stop functioning after a deliberation. Note that this model does not need a 'continuous flow' of information from decision to action systems [14]; it is sufficient that the decision system instructs the action system when the action is revised. This Model 2 is similar to a model of 'changes of mind' by Resulaj et al [23], although that model used distinct decision and change-boundaries whereas the present model just has a change boundary with action completion triggering the final decision, to consistently represent the situated demands of the choice. Such changes of mind can improve decision accuracy by allowing choices to be revised after receiving initially misleading sensory data, but are penalized by longer response times due to suboptimal 'kinked' trajectories to reach a target (e. g. that of Fig. 3). This action model can be represented by having an action focus that is coincident on the currently chosen target
Again, an example time-history of the action focus is shown in Fig. 4.
Next, we consider a parallel model of preparatory motor response from the start of the decision process, rather than waiting for a decision to trigger action initiation. In general, proactive action preparation consists of readying oneself so that successive actions can be executed better [24]: in the two-target 2AFC task considered here, we consider a preparatory move towards an action focus between the two targets, to approach the most likely target prior to accumulating sufficient information to make a decision. Note that calculating the action focus as a function of the ongoing decision (e. g. as a function of the target likelihood) requires a 'continuous flow' of information from decision to action systems. Mathematically, we define the focus as collinear With the two targets With distance from each in the proportion |b + z| : |b — z| for —b g z s b:
Hence, the decision bound no longer defines decision termination directly, but rather the choice follows indirectly from action completion (upon reaching a target). Models 2 and 3 incorporate one element of embodiment because they use action completion as a deadline for the decision. In other words, in these models the decision is terminated when reaching a target, in contrast to the non-embodied method of having the decision complete upon reaching a decision bound. However, they are not a fully embodied models (as defined in Table 2) because the interaction between the decision and action systems is not bidirectional.
Our final model implements embodied choice with both a preparatory motor response and commitment to an action when sufficiently engaged in moving towards a target. This model is built on the same mechanism as Model 3, with a preparatory move towards an action focus between the two targets, but also has commitment: movement towards a target biases the focus to be closer to that target. The dependence of the action focus upon both the accumulated information and the current movement and location is characteristic of embodied choice models, because such models require a feedback loop from action to decision systems. Mathematically, we implement commitment by including a position-dependent term in the accumulated information with d1, d2 the distances to the two targets and g is a gain that determines the degree of commitment (here set to g = 4b). The model is then fully embodied, because there is direct feedback from the action system (represented by the location (x, y)) into the decision system as a component of the evidence (see Fig. 1C). In this case, this creates a commitment efiect: once you start moving towards a target, it is more difficult to change your mind because the sensory information 2: must outweigh the commitment from having initiated an action.
The embodied choice framework assumes that choice is biased by the constraints of action, Which are directly considered in the decision process. Not only is this true at the beginning of a choice but also during the unfolding of the action (e. g. While a subject is moving the mouse to press a button). The framework thus makes a number of predictions, some of Which are supported by available data While others require future investigation. Here we focus on studies based on tracking mouse movements (see [16, 18, 21]).
5, Models 3 and 4) With experimental data (Fig. 2). Notably, While the trajectories of Model 2 head towards one choice then undergo a distinct change of mind to the other, the trajectories of Models 3 and 4 can occasionally produce discrete revisions but also graded shifts between choices (Fig. 5) that are interpreted as reflecting a process of graded, continuous competition among the alternatives [41]. Both patterns are observed in the human studies [16, 18, 21, 42]. Interestingly, both discrete and gradual revision processes have been reported in the same study and even in the same experimental condition, pointing to the necessity of a model that accounts for both [39].
The larger this average area, the more the trajectories deviate from heading directly towards the final choice. The minimum area is for Model 1 which always heads directly towards the chosen target (mean area: 0.75), followed by Model 2 (mean area: 0.91), then Model 4 (mean area: 1.06) and the largest is Model 3 (mean area 1.25). These values compare with a mean area of 1.03 for the Experimental Data, which is closest to that of Model 4 (consistent with a visual comparison of Fig. 5). Note that these values do depend on the noise levels (I assumed in the model, as is apparent in the 'curvature' of mouse movements discussed below. The sharp kink with the change of mind in Model 2 does not occur in human studies, although the modeled trajectories can be smoothed with minor model amendments such as limiting the angular velocity; such smoothing would also occur naturally with a more realistic model of the action kinematics [43]. However, the sharp kink does serve to illustrate that there is a distinct change of mind in this model, whereas human studies show both distinct changes of mind and more gradual shifts from one choice to the other as in Models 3 and 4.
Fig. 5 shows individual trajectories from the sample task introduced earlier: while in the serial Models 1 and 2 the initial parts of the trajectories already point towards one of the two buttons, in Models 3 and 4 this is not necessarily the case, as observed empirically in the Experimental Data and in a variety of studies [16, 18, 21, 42]. This finding can be quantified with the average trajectories (Fig. 5; red curves), which are initially almost vertical for the Experimental Data (initial angle from vertical: 6 deg) and Models 3 and 4 (angle: 2 deg, 7 deg), but off-vertical for Models 1 and 2 (angle: 34 deg, 21 deg). It is interesting that embodied choice Model 4 (action preparation and commitment) more closely resembles the Experimental Data than the parallel Model 3 (action preparation only);
Another similarity is that the changes of mind are earlier in the trajectory when commitment is modeled (Fig. 5), as also occurs in the experimental situation. This happens because the later trajectories are more likely to be close to a target, which with commitment makes it more difficult to have a change of mind. Again, this finding can be quantified with the average trajectories, with now the final approach to target across the diagonal for the Experimental Data (final angle from vertical: 55 deg) and Model 4 (angle: 55 deg) but almost horizontal for Model 3 (angle: 90 deg). In general, we expect that the effect of the embodied motor feedback upon the trajectories may depend on task characteristics such as the amount of sensory uncertainty [44]. Thus there may be some experimental situations with trajectories that better resemble Model 4 (as here) and others more like Model 3, depending upon the relative contribution, or gain, of the motor feedback and sensory information to the decision making process.
Fig. 6 shows the average trajectories produced by the Models 3 and 4 for the stimuli used so far and with stimuli having fourfold higher (4x) levels of noise (1'. e. a more difficult choice). These two conditions closely resemble the comparison between high and low-frequency words in [16] and the comparison between low and highly-ambiguous stick figures in [42]. In Model 3, the curvature of mouse movements (as measured for example by its 'area under the curve' [17]) is greater in more difficult choices, as is consistent with the empirical result that, even without changes of mind, higher levels of uncertainty 'curve' the mouse trajectories. Meanwhile, in Model 4 with commitment, the curvature is still affected but to a lesser degree; this gives an experimental prediction that with commitment, the trajectory will be less sensitive to changes in stimulus noise. The trajectories of both Model 3 and Model 4 are compatible with the results of an experiment that explicitly manipulated decision uncertainty. The study compared two choice conditions having different levels of certainty (100% vs. 75%) and found a small but significant difference in mouse movement curvature [45]. The currently available data cannot clearly adjudicate between the models. We expect these similarities and normal 4x noise
Trajectory dependence on noise. The 'normal' average trajectories are taken from Fig. 5. A second average trajectory was computed with increased noise a = 4 (red curve). differences can be controlled to quantify further With specific experimental procedures for eXamining commitment, such as by manipulating task urgency [46].
Study 2: Decision speed and accuracy from embodied choice
In the Baseline Model, we consider just the drift-diffusion model With an action made instantaneously When the decision variable z(t) passes a decision boundary. The speed-accuracy tradeoff for this model is represented by a plot of mean response time against the mean error rate While varying the decision boundary (Fig. 7, dashed black line). Because no action is necessary to make a decision, unlike the following models, this baseline represents the best possible speed-accuracy tradeoff With this simulated sensory information; all of the other considered models Will have speed-ac-curacy tradeoffs above baseline (1'. e. worse mean errors for a given mean RT), With closeness to the baseline indicating their overall performance.
Because both targets are equidistant from the start location, the effect of action initiation after decision completion is to increase the response times by a constant delay Ttarget (time to reach a target; here 0.9 sec). Therefore, the speed-accuracy tradeoff curve for this model of 'action initiation after decision completion' has the same shape as the baseline drift-diffusion model, but shifted to longer response times (Fig. 7, black curve). Clearly, no decisions can be made quicker than the time-to-target Ttarget, Which now defines a lower bound that the speed-accuracy curve approaches With increasing mean decision error. Because mean errors decrease With increasing mean response time, the rightwards shift of Model 1 compared With the Baseline Model implies its speed-accuracy curve always lies above baseline.
Comparison of 4 decision making with action models and a Baseline Model without action. Each model is characterized by a speed-accuracy curve obtained from simulating (100000 samples) over a range of decision boundaries, with mean decision error plotted against mean response time. The minimal time-to-target Ttarget for action completion is also shown (dotted line). Note that curves for Model 3 (green line) and Model 4 (red line) do not overlap, but span different response times. Model 2. Simulation results for this model of 'action initiation and changes of mind'
7; blue plot) reveal a better speed-accuracy tradeoff than that of Model 1 (action initiation after decision; no changes of mind) but poorer than the (unattainable) Baseline Model of instantaneous action to target. The better performance with changes of mind indicates that the improvements in decision accuracy compensate for the resulting slower response times in the speed-accuracy tradeoff, consistent with the extra information during making an action now being used rather than ignored. A second implication of permitting changes of mind is that the least mean response time for Model 2 (~ 1.2 sec) is farther above the time-to-target Ttarget than Model 1; in accordance, the maximal mean errors in Model 2 (~ 12%) are smaller than those permitted in Model 1. These constraints can actually be a disadvantage in some ecological scenarios, because it is not possible to sacrifice decision accuracy to achieve faster response times in contexts where response speed is critical.
Simulation results for this model of 'action preparation' (Fig. 7; green plot) show that the speed-accuracy tradeoff for Model 3 resembles but performs better than that for Model 2 (action initiation and changes of mind), and is only slightly poorer than that of the Baseline Model (instantaneous action). The resemblance to the performance of Model 2 is not unexpected, because the above model of action preparation naturally allows changes of mind when the focus point moves from one target to nearby the other. The performance benefits over Model 2 are expected from the response time savings of the preparatory motor response. Note that the minimum possible response time (0.9 sec) is attainable only by making random choices and heading directly to a target; such random choices are not possible in Model 3, and consequently the response times has a minimum bound (1.2 sec) and minimum accuracy (12%). Thus, similarly to Model 2, the present model of action preparation also suffers from not being able to sacrifice accuracy for faster response times, which could be crucial in situations relying on response speed.
Simulation results for this model of action preparation and commitment (Fig. 7; red plot) show that the speed-accuracy tradeoff is again between the Baseline Model (instantaneous selection) and Model 1 (action initiation after decision), like Models 2 and 3 (changes of mind and preparation). However, the commitment shifts the mean response time range to faster than that of Models 2 and 3 (~ 1—1.2 sec) at the expense of poorer accuracy (~ 12—25%); in particular, the speed-accuracy curve for Model 4 looks like continuation of Model 3 to quicker response times. Hence, commitment solves the problem that changes of mind (in preparation or otherwise) lead to relatively slow but accurate decision making, whereas some ecological contexts may demand faster response times. The commitment enforces a quicker (but more inaccurate) decision than would be possible otherwise by heading towards the final target earlier in the decision process, but consequently disregarding the later information. Thus, with commitment the accuracy is sacrificed to achieve greater decision speed, appropriate to situations where time is of the essence.
Our results show that the serial decision-then-action system has the poorest speed-accuracy tradeoff of all considered models, being the slowest to reach decisions for comparable accuracy. This speaks against its application to ecological scenarios where taking action rapidly can be crucial. Parallel decision-and-action models permit faster responses than the serial model for the same accuracy. Our first parallel model (Model 2), similar to the one suggested by [23], permits the revision of initial decisions as changes of mind. A second parallel model (Model 3) includes an action preparation mechanism that permits action initiation before the accumulated information passes a decision bound. This latter model is more accurate than the first parallel model for comparable response times, suggesting an advantage of acting upon partial information. Still, parallel models do not include mechanisms for changing the ongoing decision based on the current action, and thus do not assign action a role in influencing decisions. Embodied choice models, here exemplified by Model 4, include both action preparation and a feedback loop from action to decision systems that, while allowing changes of mind, also produce a commitment effect: changes of mind become less likely as the action nears completion. A principal advantage of commitment is that decision accuracy can then be traded for speed, allowing quicker response times than possible in parallel or serial models. Embodied choice could thus yield particular advantage in ecological situations where decision speed is at a premium.
Without action, the drift-diffusion model implements optimal decision making, because for discrete time it is formally equivalent to the sequential probability ratio test (SPRT) [5] , which minimizes a linear cost function of response time and decision errors averaged over many decision trials [47]. With action, the speed-accu-racy tradeoff curve of the original drift diffusion model is unattainable, because actions have a cost in delaying response time. Moreover, the minimum time to complete an action gives a lower bound on decision speed that can only be attained with random choice. The optimal speed-accuracy curve for decisions with action would thus extend from 50% accuracy at the minimum action time down towards the curve of the original DDM with increasing decision time. Here we found that a parallel model of action preparation (Model 3) early in the decision process approached the performance of the original DDM for relatively long response times; meanwhile, a feedback loop from action to decision gave a commitment effect necessary to achieve response speeds close to the minimum action time (Model 4). It thus appears that urgent situations are aided by a greater contribution of the motor feedback to the decision process, whereas tasks demanding greater accuracy may demand a greater contribution from the sensory information.
The results in this section reinforce that eXpectation because fast response times were only attained for Model 4, with a large contribution of the motor feedback relative to the sensory information; meanwhile, superior accuracies but with slower response times were attained in Model 3 with no motor feedback but a mechanism for preparation. Taken together, both preparation and commitment within a framework of embodied choice are necessary for covering the full speed-accuracy range.
Traditional decision making models were developed mainly to account for laboratory tasks with simplified action choices, which we believe has hindered a full understanding of the importance of action for ecologically relevant decision making. Indeed, living organisms evolved to deal with ecological decisions and the selection of life-or-death actions (such as fight, flight and foraging) rather than laboratory tasks. Given this evolutionary background, it is plausible that decision making and sensorimotor control systems are highly integrated in the brain [48, 49]. Embodied choice is a framework for situated decisions that have similar characteristics to those found in ecologically valid environments, and in doing so makes apparent the importance of action, its dynamics and its constraints within the decision making process.
Here we presented a general framework highlighting the importance of action for decision; within this framework, specific models can be designed and tested that include mechanisms such as action preparation and commitment that are currently not considered or considered only partially in current theories of decision-making. Indeed, although we have constructed the present proposal around the drift-diffusion model [1] , we expect the overall formalism should be similar for any model based on an evolving decision variable, whether that variable relates to leaky competing accumulators [2], estimated probability [6, 7, 50—52] or an urgency signal [46]. Some empirical support for embodied models already exists but clearly much remain to be done to test current embodied models of choice and develop better ones. The ultimate ambition of this framework is to provide reference models to study ecologically valid choice, in the same way as drift-diffu-sion and related models have provided (and still provide) a theoretical support to understand perceptual decisions in the laboratory.
In a speeded reaching task where subjects knew the target only probabilistically, their initial movement trajectory approached the mean of the goal selection distribution and then veered toward the goal when revealed [53]; see also [54]. These findings cannot be explained by current parallel models (not even the "changes of mind" model of [23]) because they lack a mechanism for action preparation and use the evolving sensory representation only for decision-making and not also for movement preparation and planning. Action preparation dynamics could also explain why the bio-mechanical costs of movement and target distance influence choice, as reported in [29, 30].
Such a mechanism might reflect both the situated aspects of the choice (e. g. biomechanical constraints) and the cognitive cost of changes of mind. A recent neurophysiological study of a two-choice reaching task reported evidence of a commitment effect at both behavioral and neuronal levels, with suppression of M1 activity tuned to the unselected target [38]. Other studies that did not directly test the commitment hypothesis reported evidence that is congruent with this idea. For example, it has been consistently reported that early evidence can have greater influence on the final perceptual choice: a primacy effect that can be explained within the leaky competing accumulator framework by adding an inhibition dominance mechanism [55] or in terms of hysteresis mechanisms in dynamical systems models [3]. Yet this same evidence could, in principle, also support the commitment hypothesis discussed here. That being said, the two phenomena are different: inhibition dominance and hysteresis depend on processes that are internal to the decision system (e. g. attractor dynamics in neuronal populations), whereas commitment would be due to the embodied and situated nature of the choice. In situated cognition theories, the current movement trajectory can be considered an external memory of the ongoing decision that both biases and facilitates the underlying choice computations by offloading them onto the environment [56]. Overall, the exact relations between inhibition dominance and hysteresis (due to neuronal dynamics) versus commitment (due to the embodiment of choice) remain to be studied, and experiments that distinguish between them are required. For example, a possible way to test the commitment hypothesis is to influence the initial stages of action movements without directly changing the evidence accumulation process, such as by studying whether (irrelevant) visuomotor priming can bias a perceptual decision.
For the sake of simplicity, we reduced the complexity of action dynamics (assuming, for example, constant velocity). A more realistic model of action and its biomechanics is required to fully formalize a fully embodied model, by directly considering action costs (see [32]). For example, the present embodied choice models have action initiation at the start of the trial, whereas relevant data (e. g. [29]) show a short deliberation period. Quantifying in more detail the action dynamics and the costs of action in the model of embodied choice should in principle allow the movement initiation to depend on the evolving decision, because a movement that is initiated too prematurely might be too costly to reverse afterwards. A comprehensive formalization should also consider the situated characteristics of the task, such as the fact that different tasks can differ in their intrinsic geometries for movement. For example, if two targets are at different distances, the trajectory that makes them equiprobable is not straight but a curve ending at their center-point.
Different experimental setups with different action constraints (e. g. buttons at various distances; the presence or absence of a deadline and its length; differing kinematic costs for making a movement; using a keyboard, mousetracker, tablet, etc.) can induce different choices or even different choice strategies with the same sensory information. For example, the framework predicts that in setups with buttons at different distance, the more uncertain choices should be biased towards the closest button, which requires less biomechanical costs at least in conditions of high uncertainty. It also predicts that once initiated an action should be harder to revise in the light of new evidence as an effect of commitment. Recent studies provide preliminary empirical support for these predictions (see [57—59]) although much remains to be investigated. Furthermore, experiments with different deadlines and time constraints will change the way actions are executed; for example, an empirical prediction of embodied choice that remains to be tested is that in urgent situations trajectories tend to be straighter. These considerations do not imply that decision making is a volatile phenomenon or cannot be studied empirically, but rather that its full understanding at the conceptual, computational and neuronal level cannot omit the action dynamics.
This view is shared by recent theories suggesting that motor control is essentially a decision making process that can be modeled with Bayesian decision theory [61, 62]. However, the proposal made here takes a complementary approach in suggesting that action optimization is also part of the decision making. In this way, it should balance the benefits of making the correct decision (e. g. clicking the proper button) with its costs (time and biomechanical constraints to reach it). In general, it is an open question how to attain decision optimality if action is required to make a choice—or whether our brains do actually optimize the choice process or just use heuristics that are 'good enough'. Whatever the underlying mechanism, our results here indicate that elements of embodied choice should be included to successfully model decisions in ecologically-relevant scenarios.
The proposal made here is largely consistent with recent decision making theories that recognize the close connections between decision and action systems. For example, according to the intentional framework, choice recruits sensorimotor circuits that are ultimately responsible for initiating actions or action plans [35]. Along similar lines, perceptual decision making has also been proposed to be action selection [63]. Another recent proposal is that choice is implemented as a distributed consensus between multiple brain circuits calculating outcome-relat-ed and action-related aspects of a decision [64]. These ideas are convergent and largely complementary to the proposed embodied choice framework and might offer testable proposals on the putative neuronal substrate underlying embodied choice. For example, in this article, we have suggested that commitment effects could be implemented by biasing the fixation point to be closer to the currently closest target, but the neuronal underpinnings of this mechanism remains unspecified. One possibility is that such biases are directly incorporated in a 'biased competition' process between choice alternatives, as also suggested by the distributed consensus model [64]. Another candidate mechanism is the use of corollary discharge and/or the action trajectory as evidence for decision making (in addition to sensory evidence). These hypotheses remain for empirical study.
Indeed, it is a clear example that embodied phenomena (here action movements) can have causal influence on so-called central cognition (here perceptual choice). We make the case that as cognitive processes are studied in ever more ecological contexts, the importance of the cognitive embodiment Will become increasingly apparent [65—67].
All models are built on the drift-dif-fusion model of Ratcliff and colleagues [1, 40]. A decision variable z(t) represents the sensory information accumulated to time 1' from unbiased starting information 2(0) 2 O. For convenience, we discretize time in uniform steps At, so that the update equation is where AZ is the increment of sensory information received at time t, which is conventionally assumed drawn from a (stationary) normal distribution N([,i, 02) of mean [,4 and variance 02. Here we assume [,4 = 1/3 and o = 1, with step-size At=50 ms. We then employ various criteria to make decisions from this accumulated sensory information.
Here we consider values of (9 between 0.01 and 10 (considered in 31 increments of boundary value). We consider crossing the boundary +19 to represent a correct decision of error zero and crossing the boundary —b an incorrect decision of error one. Results of the model simulations are averaged over 10000 runs, to decrease the variance in estimating the mean decision error and mean response time for each boundary value.
For simplicity, we assume a basic action model that moves a point (x, y) along a trajectory with constant speed v = 2 units/ sec, in a two-dimensional arena from starting point (0, 0) with targets 1,2 at positions (IF 1, 1.5). In this first model, we assume that the movement is initiated when the decision variable passes a decision boundary and the trajectory is a straight line to the target. This action model can be represented by having an action focus that is coincident on the chosen target for times t 2 tdec, as represented in equation (3). Prior to the decision time, there is no choice and the agent cannot move (equivalently, the focus is at the start location). Then the effect of action initiation after decision completion is to increase decision times by a constant target (ii) Model 2: Action initiation and changes of mind: We now consider a decision model in which an action must be made to indicate the choice of a target, as in Model 1, but augmented with a mechanism for 'changes of mind' during the movement to the target [23]. This is implemented by updating the decision variable with new sensory information during the movement, with the alternative target selected if the accumulated information z( t) passes the opposing decision barrier before the trajectory reaches a target. This action model can be represented by having an action focus that is coincident on the currently chosen target, as represented in equation (4). Similarly to Model 1, if the accumulated information has not passed threshold, then there is no choice and the agent cannot move.
Here we consider a preparatory move towards a focus point between the two targets, to approach the most likely target prior to accumulating sufficient information to make a decision. Mathematically, this focus point is defined as collinear between the two targets with distance from each in the proportion lb + zl : lb — zl for —b g z s +b, and at target 1 for z s —b or target 2 for z 2 b. Mathematically, we define the focus as collinear with the two targets with distance from each in the proportion lb + zl : lb — zl for —b g z s b, as represented in equation (5). This range is bounded such that the focus is coincident with a target if the decision bound is passed. Hence, the decision bound no longer defines decision termination directly, but rather the embodied choice follows indirectly from action completion (upon reaching a target).
Mathematically, we implement commitment by including an additional position-dependent term g(d1 — d2)/(d1 + d2) in the update equation (7), with d1, d2 the distances to the two targets, as represented by equation (6) and g a gain for the degree of commitment (here set to g = 4b). All other details are unchanged from Model 3.
We thank Roger Ratcliff for useful comments on an earlier version of this paper, and Marius Usher and the anonymous reviewers for their advice in revising the paper. The GEFORCE Titan used for this research was donated by the NVIDIA Corporation.
Performed the experiments: NFL GP. Analyzed the data: NFL GP. Contributed reagents/materials/analysis tools: NFL GP. Wrote the paper: NFL GP.
Appears in 40 sentences as: decision maker's (1) decision making (43)
In Embodied Choice: How Action Influences Perceptual Decision Making
Embodied Choice considers action performance as a proper part of the decision making process rather than merely as a means to report the decision.
Page 1, "Abstract"
current trajectory and kinematics) influence the decision making process.
Here we use a perceptual decision making task to compare three types of model: a serial decision-then-action model, a parallel decision-and-action model, and an embodied choice model where the action feeds back into the decision making .
Moreover, embodied choice models with these mechanisms give a better account of trajectory tracking experiments during decision making .
This paper presents an explanation of how actions, encompassing behavioral strategies such as preparation and commitment, can bias decision making processes in ways that optimize the ecological choices of animals behaving in natural environments.
Page 1, "Author Summary"
According to a widely held view, perceptual decision making is a competitive process based on the accumulation of sensory evidence up to a threshold.
Page 2, "Introduction"
In this article, we propose a framework of Embodied Choice, in which action performance is considered as a proper part of the decision making process.
To make comparison with previous work on decision making , we discuss three different ways to link decisions to action: the serial model, the parallel model, and the embodied choice model.
We first clarify the differences between these three types of decision making model in the remainder of the introduction, before giving results on how these models compare on a simple perceptual choice task involving action on synthetic data, and then give empirical support for embodied choice models from motion tracking experiments during decision making .
Finally, in the discussion, we discuss the broader implications of an embodied choice framework and how it relates to recent developments in the neuroscience and psychology of decision making .
Most studies using the diffusion-to-bound framework implicitly assume that decision making is a serial process (although this is not a necessary feature of the diffusion-to-bound framework—see later).
Page 2, "Serial models: segregating decision and action"
See all papers in April 2015 that mention decision making.
See all papers in PLOS Comp. Biol. that mention decision making.
Appears in 8 sentences as: Experimental Data (6) Experimental data (1) experimental data (1)
Experimental data was collected using a MouseTracker apparatus [17] during a visual-lexical decision task to observe the graded effects of competing items attracting the trajectory of the mouse [16].
Page 6, "Study 1: Decision trajectories during embodied choice"
5, Models 3 and 4) With experimental data (Fig.
Page 10, "Horizontal position, x"
These values compare with a mean area of 1.03 for the Experimental Data , which is closest to that of Model 4 (consistent with a visual comparison of Fig.
5 shows individual trajectories from the sample task introduced earlier: while in the serial Models 1 and 2 the initial parts of the trajectories already point towards one of the two buttons, in Models 3 and 4 this is not necessarily the case, as observed empirically in the Experimental Data and in a variety of studies [16, 18, 21, 42].
5; red curves), which are initially almost vertical for the Experimental Data (initial angle from vertical: 6 deg) and Models 3 and 4 (angle: 2 deg, 7 deg), but off-vertical for Models 1 and 2 (angle: 34 deg, 21 deg).
It is interesting that embodied choice Model 4 (action preparation and commitment) more closely resembles the Experimental Data than the parallel Model 3 (action preparation only);
for example, the initial trajectories have a similar angular bias towards the target in the Experimental Data (6 deg) and for Model 4 (7 deg).
Again, this finding can be quantified with the average trajectories, with now the final approach to target across the diagonal for the Experimental Data (final angle from vertical: 55 deg) and Model 4 (angle: 55 deg) but almost horizontal for Model 3 (angle: 90 deg).
See all papers in April 2015 that mention Experimental Data.
See all papers in PLOS Comp. Biol. that mention Experimental Data.
Appears in 6 sentences as: decision-making (6)
It thus combines two disconnected research streams, decision-making and action control in a manner consistent With theoretical and psychological arguments for embodied cognition.
Successive EEG and neurophysiological studies of decision-making consistently found a covert preparation of multiple motor plans in parallel, providing a strong support for parallel views of human information processing [12—15].
Page 2, "Parallel decision and action: the continuous flow model"
Differences between serial and parallel models of decision-making .
Differences between parallel (continuous flow) and embodied choice models of decision-making .
Page 5, "Embodied choice"
Here we presented a general framework highlighting the importance of action for decision; within this framework, specific models can be designed and tested that include mechanisms such as action preparation and commitment that are currently not considered or considered only partially in current theories of decision-making .
Page 16, "Discussion"
These findings cannot be explained by current parallel models (not even the "changes of mind" model of [23]) because they lack a mechanism for action preparation and use the evolving sensory representation only for decision-making and not also for movement preparation and planning.
See all papers in April 2015 that mention decision-making.
See all papers in PLOS Comp. Biol. that mention decision-making.
Appears in 5 sentences as: feedback loop (5)
1 (C) illustrates this aspect by including a feedback loop from the action to decision systems that is missing from both serial and parallel models.
Model 1 serially initiates action after decision completion; Model 2 is parallel by allowing changes of mind in the decision system after the action initiates; Model 3 has action preparation operating in parallel with decision system, with also some aspects of embodiment by using action completion for decision completion; and Model 4 is fully embodied with a feedback loop from the action to decision systems, encompassing both action preparation and commitment.
Page 6, "Results"
The dependence of the action focus upon both the accumulated information and the current movement and location is characteristic of embodied choice models, because such models require a feedback loop from action to decision systems.
Embodied choice models, here exemplified by Model 4, include both action preparation and a feedback loop from action to decision systems that, while allowing changes of mind, also produce a commitment effect: changes of mind become less likely as the action nears completion.
Page 15, "Study 2: Decision speed and accuracy from embodied choice"
Here we found that a parallel model of action preparation (Model 3) early in the decision process approached the performance of the original DDM for relatively long response times; meanwhile, a feedback loop from action to decision gave a commitment effect necessary to achieve response speeds close to the minimum action time (Model 4).
See all papers in April 2015 that mention feedback loop.
See all papers in PLOS Comp. Biol. that mention feedback loop.
choice task
Appears in 3 sentences as: choice task (3)
We first clarify the differences between these three types of decision making model in the remainder of the introduction, before giving results on how these models compare on a simple perceptual choice task involving action on synthetic data, and then give empirical support for embodied choice models from motion tracking experiments during decision making.
Here we incorporate these assumptions in four computational models and test them in a simulation of a simple perceptual choice task involving action.
All simulations represent a two-alternative forced choice task (2AFC) in which an action must be made to one of two targets to indicate the decision (Fig.
See all papers in April 2015 that mention choice task.
See all papers in PLOS Comp. Biol. that mention choice task.
computational models
Appears in 3 sentences as: computational models (3)
In the first study, we simulate the decision trajectories for four computational models with increasingly sophisticated interaction between the decision making and action components.
All computational models that we compare below are built on the drift-diffusion model [1], which is a model of the cognitive processes involved in making simple two-choice decisions.
See all papers in April 2015 that mention computational models.
See all papers in PLOS Comp. Biol. that mention computational models.
Simulation results
Appears in 3 sentences as: Simulation results (3)
Simulation results for this model of 'action initiation and changes of mind'
Simulation results for this model of 'action preparation' (Fig.
Simulation results for this model of action preparation and commitment (Fig.
See all papers in April 2015 that mention Simulation results.
See all papers in PLOS Comp. Biol. that mention Simulation results.
PDF available from www.ploscompbiol.org.


text:
HomeThe Chefs' LineSeason 1, Episode 12 - Turkish
The Chefs' Line - Season 1, Episode 12 - Turkish
Station chef Inal from Turkish restaurant Anason goes up against three passionate home cooks in a showcase of culinary excellence.
The Chefs' Line was broadcast on SBS ONE at Tuesday 16 November 2021, 07:00.
Season 1, Episode 12 - Turkish
Season 1Episode 12
Season 2, Episode 50 - Spanish
Season 2, Episode 45 - Greek
The Chefs' Line
The Chef's Line is an Australian cooking contest series written for television and createdby the producers of The Voice Australia and Behave Yourself. Each episode of the show revolves around a different type of cuisine, from Mexican to British to Japanese, and much more. The concept is based on a team from a restaurant kitchen, from assistents to the head chef, taking on a group of home cooks who are specialised in the cuisine of their home country. The home cooks take on the members of the other team one by one in hierarchical order, until the home cooks that are left face the restaurant's head chef. What makes The Chef's Line different from many other cooking shows, such as My Kitchen Rules, is not only the format of the competition, but also the fact that there is no prize money to be won. Due to the multicultural approach of the show each episode is about representing a family, a culture and a heritage, which gives it a whole different and unique feel.
The Chefs' Line catch up

redpajama_set_name: RedPajamaCommonCrawl


text:
Edit count
Statistic of Wikimedia contributors
For the Wikipedia essay on edit count, see Wikipedia:Edit count.
An edit count, in Wikipedia and other projects within the Wikimedia movement, and in similar wikis, is a record of the number of edits performed by a particular editor, or by all editors on a particular page. An edit, in this context, is an individually recorded change to the content of a page. Within Wikimedia projects, a number of tools exist to determine and compare edit counts, resulting in their usage for various purposes, with both positive and negative effects.[1]
Use and misuse of edit counts[edit]
Pie chart of all edits to the English Wikipedia, with a slice for each thousand of the 10,000 most active Wikipedians, and everyone else in brown (as of July 3, 2019)[original research?]
An editor's edit count is often used as a shorthand for determining their level of activity within a wiki, to the extent that it has been said that "one's edit count is a sort of coin of the realm".[2] Tools exist on Wikipedia "to compare contributors by the number of edits they have made",[3] and a Wikipedia editor's edit count "may bring suffrage in elections where only community members may participate".[1] Edit counts are also often weighed in community determinations of whether to grant editors extended rights.[1]
It has been noted, however, that the measure fails to reflect the quality of contributions, with minor improvements and even acts of vandalism potentially registering the same number of edits as substantial additions and improvements of content.[1][4][5] Vandalism can similarly increase the edit count associated with a specific article without indicating improvements to the quality of the article.[6] The edit count recorded for a particular article tends to reflect the general level of interest in the article, with relatively obscure or unfamiliar topics sustaining low overall edit counts over time.[7]
At the extreme end, editors who are more focused on increasing their edit count than on improving the quality of work being performed may suffer social consequences due to excessive time invested in making edits.[1] Historically, it has been asserted that some Wikipedia administrators "engage in 'drive-by reversing' so as to drive up their edit count", with such edits sometimes being made too quickly for the editor to have actually read the content being reverted.[8] The development of "an unhealthy obsession with the notion of edit count" has locally been deemed "editcountitis".[1][9][10]
The total edit count for all Wikipedians combined was reported as having reached one billion in mid-January 2021, with the billionth edit being registered by the most prolific non-bot Wikipedia editor, Steven Pruitt, though it was also noted that several hundred thousand early Wikipedia edits were lost in a software upgrade.[11] Notably, some of the highest edit counts in Wikipedia are achieved by bots running automated tasks, rather than by human editors.[12] Among human editors, it has been observed that a small percentage of editors are responsible for a substantial proportion of the total number of edits that have been made.[3]
Academic studies[edit]
One of the earliest significant academic studies about Wikipedia was a 2007 peer-reviewed paper,[13] also mentioned in The Guardian,[14] a team of six researchers from the University of Minnesota measured the relationship between editors' edit count and the editors' ability to convey their writings to Wikipedia readers, measured in terms of persistent word views (PWV)—the number of times a word introduced by an edit is viewed. The accounting method is best described using the author's own words: "each time an article is viewed, each of its words is also viewed. When a word written by editor X is viewed, he or she is credited with one PWV".[13] The number of times an article was viewed was estimated from the web server logs.[13]
The researchers analyzed 25 trillion PWVs attributable to registered users in the interval September 1, 2002 − October 31, 2006. At the end of this period, the top 10% of editors (by edit count) were credited with 86% of PWVs, the top 1% about 70%, and the top 0.1% (4200 users) were attributed 44% of PWVs, i.e. nearly half of Wikipedia's "value" as measured in this study. The top 10 editors (by PWV) contributed only 2.6% of PWVs, and only three of them were in top 50 by edit count. From the data, the study authors concluded that "[g]rowth of PWV share increases super-exponentially by edit count rank; in other words, elite editors (those who edit the most times) account for more value than they would [be attributed] given a power-law relationship".[13] The study also analyzed the impact of bots on content. By edit count, bots dominate Wikipedia; 9 of the top 10 and 20 of the top 50 are bots. In contrast, in the PWV ranking only two bots appear in the top 50, and none in the top 10. Based on the steady growth of the influence on those top 0.1% editors by PWV, the study concluded that the editors with the highest edit counts "dominate what people see when they visit Wikipedia".[13]
A 2008 study of English Wikipedia editors who had successfully passed the peer review process to become admins by researchers from Carnegie Mellon University devised a probit model, which found that the number of edits was a factor in the success of such efforts.[15] Edit counts in certain Wikipedia namespaces were more valuable to this end.
^ a b c d e f Phoebe Ayers, Charles Matthews, and Ben Yates, How Wikipedia Works: And How You Can be a Part of it (2008), p. 323–24.
^ Dariusz Jemielniak, Common Knowledge?: An Ethnography of Wikipedia (2014), p. 39, quoting Joseph M. Reagle Jr. (2010), p. 157.
^ a b Schiff, Stacy (July 31, 2006). "Know It All". The New Yorker. Vol. 82 no. 23. p. 40. Retrieved March 6, 2021.
^ Claudia Müller-Birn, Janette Lehmann, and Sabina Jeschke, "A Composite Calculation for Author Activity in Wikis: Accuracy Needed", Sabina Jeschke, Ingrid Isenhardt, and Klaus Henning, eds., Automation, Communication and Cybernetics in Science and Engineering 2009/2010 (2010), p. 138.
^ Adele Godoy Vrana, Anasuya Sengupta, and Siko Bouterse, "Toward a Wikipedia For and From Us All", in Joseph Reagle, Jackie Koerner, Wikipedia @ 20: Stories of an Incomplete Revolution (2020), p. 253.
^ Andrew Lih, "Wikipedia as Participatory Journalism: Reliable Sources? Metrics for evaluating collaborative media as a news resource", Journalism and Media Studies Centre, University of Hong Kong; Paper for the 5th International Symposium on Online Journalism (April 16–17, 2004), p. 20.
^ Ehmann, Katherine; Large, Andrew; Beheshti, Jamshid (October 6, 2008). "Collaboration in context: Comparing article evolution among subject disciplines in Wikipedia". First Monday. doi:10.5210/fm.v13i10.2217.
^ O'Neil, Mathieu (2009). Cyberchiefs: Autonomy and Authority in Online Tribes. London; New York: Pluto Press. p. 163. doi:10.2307/j.ctt183pc3c. ISBN 9781849644037. OCLC 656841493.
^ Dariusz Jemielniak, Common Knowledge?: An Ethnography of Wikipedia (2014), p. 40.
^ Jane Klobas, Wikis: Tools for information Work and Collaboration (2006), p. 199.
^ Gault, Matthew (January 15, 2021). "The English Language Wikipedia Just Had Its Billionth Edit". Motherboard.
^ Priedhorsky, Reid; Chen, Jilin; Lam, Shyong (Tony) K.; Panciera, Katherine; Terveen, Loren; Riedl, John (November 2007). "Creating, destroying, and restoring value in Wikipedia". GROUP '07: Proceedings of the 2007 International ACM Conference on Supporting Group Work: 259. doi:10.1145/1316624.1316663. ISBN 9781595938459. S2CID 15350808.
^ a b c d e Priedhorsky, Reid; Chen, Jilin; Lam, Snider (Tony); Panciera, Katherine; Terveen, Loren; Austin, Shane (2007). "Creating, Destroying, and Restoring Value in Wikipedia". Proceedings of the 2007 international ACM conference on Supporting group work. Conference on Supporting Group Work. ACM Press. pp. 259–268. doi:10.1145/1316624.1316663. ISBN 978-1-59593-845-9.
^ Baker, Nicholson (April 10, 2008). "How I fell in love with Wikipedia". The Guardian. Retrieved November 29, 2010.
^ Burke, Moira; Kraut, Robert (2008). Taking up the mop. Proceedings of the Twenty-sixth Annual CHI Conference Extended Abstracts on Human Factors in Computing Systems - CHI '08. p. 3441. doi:10.1145/1358628.1358871. ISBN 978-1-60558-012-8.
(outline)
Conflict-of-interest editing
Biases
ideological
Deletion of articles
Deletionism and inclusionism
"Ignore all rules"
Predictions of the project's end
AfroCrowd
Arbitration Committee
Art+Feminism
Lsjbot
List of Wikipedias
The Signpost
Wiki Indaba
Wiki Loves Monuments
Wiki Loves Pride
WikiConference India
WikiConference North America
Wikipedian in residence
Women in Red
Florence Devouard
Sue Gardner
James Heilman
Maryana Iskander
Katherine Maher
Magnus Manske
Erik Möller
Ira Brad Matetsky
María Sefidari
Lila Tretikov
Jimmy Wales
Wikipedians
Wikimedian of the Year
Bomis
U.S. Congressional staff edits
Essjay controversy
Seigenthaler biography incident
Henryk Batuta hoax
Jar'Edo Wens hoax
Croatian Wikipedia administration
First edit
Internet Watch Foundation
CAMERA campaign
Italian Wikipedia blackout
English Wikipedia blackout
Hillsborough disaster Wikipedia posts
MyWikiBiz
VisualEditor
#1Lib1Ref
Coverage of American politics
Coverage of the COVID-19 pandemic
2021 WMF actions on the Chinese Wikipedia
2008 Quadriga award
Wikipedia Monument
2015 Erasmus Prize
2015 Princess of Asturias Award
and analysis
Listen to Wikipedia
Wiki-Watch
Wikipediocracy
WikiScanner
Wikipedia Review
QRpedia
Wapedia
WikiNodes
Content use
Books LLC
Boundless (company)
DBpedia
Deletionpedia
Science information
Interpedia
Nupedia
Citizendium
Enciclopedia Libre Universal en Español
Veropedia
List of online encyclopedias
The Iraq War: A Historiography of Wikipedia Changelogs
Print Wikipedia
Magna Carta (An Embroidery)
Wiki rabbit hole
Wikiracing
274301 Wikipedia
Wikipedia for World Heritage
Retrieved from "https://en.wikipedia.org/w/index.php?title=Edit_count&oldid=1061341321"
Wiki concepts
Use mdy dates from March 2021
All articles that may contain original research
Articles that may contain original research from March 2021
Articles containing Spanish-language text


Get down on one knee.
Ask her to marry me.
He doesn't understand that planning a wedding is a big deal. I'm not a Bridezilla—give me a break. I just know what I want, and everything has to be perfect. After all, I am marrying my Mr. Right.
From roomies to lovers, it was an easy transition for Zoey and Drew. It's only natural that marriage was the next step. Between Drew's hectic work schedule and Zoey's obsession with planning the perfect wedding, neither of them expected it to be this hard.
And the only way they knew how to communicate: fight until they've started a war.
Will Zoey and Drew manage to set aside their differences and make it to the altar, or is this the end for the perfectly mismatched couple?


text:
Home Cryptocurrency News India on the verge of total cryptocurrency ban
Cryptocurrency NewsRegulations
India on the verge of total cryptocurrency ban
By Alex Vet
Read us in Google News
According to CNBC-TV18, a research group set up by the government of India has concluded that all virtual currencies in the country should be outlawed. The task assigned to the employees of the group by the authorities was to create a bill regulating the circulation of cryptocurrencies.
The group, headed by Subhash Chandra Garg, Secretary of the Department of Economics under the Ministry of Finance of India, was established in November of this year. After completing its mission, the group handed the conclusion to Finance Minister Arun Jaitley, who must make a final decision.
Members of the group believe that the Reserve Bank of India needs a new law to implement the ban on cryptocurrencies: "The law should prescribe punitive measures that governments and law enforcement officers will apply to persons or structures that sell cryptocurrencies and/or own them."
If the authorities agree with the conclusion of the group members, a crushing blow will be dealt to the cryptocurrency sector in India. Theoretically, this may also affect the global cryptocurrency market.
It is noteworthy that, advocating a total ban on cryptocurrency, the research group suggested that the government "in the future" consider the possibility of creating a sovereign national digital currency.
Although we do not have comprehensive data on the number of cryptocurrency users in India, according to some estimates, the number of active traders reaches (or has reached this year) five to six million. According to Reuters, which conducted a survey in January of this year, for a period of 17 months, Indian citizens conducted trades and other cryptocurrency transactions in the amount of $ 3.5 billion.
However, it should be taken into account that this is data at the beginning of the year, while in April the Reserve Bank of India instructed financial institutions to cut off all ties with cryptocurrency exchanges and investors (five million traders, according to Ajit Khurana, heads of The blockchain committee and cryptocurrency at the Internet and Mobile Association, were engaged in trading through regulated banking channels). The ban, despite attempts by the public to challenge it in the Supreme Court, entered into force in July.
As a result of this step, in September, one of the country's largest cryptocurrency exchanges, Zebpay, was forced to stop working. Restrictive measures by the Central Bank led to the fact that Zebpay users could no longer introduce funds to the platform in the Indian national currency, as well as carry out their withdrawal. In October, representatives of the exchange reported that they were transferring operations to Malta. Zebpay serviced up to half of all bitcoin transactions in India. The users of the platform were more than 5 million people, and the Zebpay mobile application made it possible to conduct operations with 20 digital currencies.
national cryptocurrency


text:
Q: Simplify and solve an N-term exponential equation How does one express this sum into Mathematica:
$$10^{k-1} + 10^{k-2} +....+ 10 + 1$$
I think I have to use the Sum function? Then run it over N=K..1? But then I want to substitute and evaluate it for various k's.
Ok once I get that input correctly, my ultimate goal is to Simplify this for P, Q, R, and k positive integers:
$$P(10^{2k-1} + 10^{2k-2} + 10^{2k-3} +....+ 10^k + 10^{k-1} + ... +10 + 1) - Q(10^{k-1} + 10^{k-2} +....+ 10 + 1) = R^2(10^{k-1} + 10^{k-2} +....+ 10 + 1)^2 $$
I need to have Mathematica Simplify that, then run it over some large values of the 4 variables and try and see which ones solve the equality.
P.S. in a Nutshell what that is saying is this: Find me a series of 2k-length repeated digits then subtract another series of k-length digits from it, that will result in a new set of k-length digits squared - the multiplier of which is also squared.
So say for a fake example: $$444444-222=2^2(333)^2$$  I just made that up to show you what it would look like. That equality is 666 off! Just notice how the first number has 6 digits and the next 2 have half (3 digits), etc. In this case P=4, Q=2, R=2, and k=3 . I want Mathematical to go over all k's (say for k from 1 to 10000) and give me exact solutions and tell me for what values of P, Q, and R, the equality is satisfied over that range of k.

A: First notice that Sum[10^n, {n, 0, k - 1}] is 1/9 (-1 + 10^k), so you can make a closed form.
This generates at most 20 solutions for each $k$ from 1, 100. Change those numbers if you have more time:
eqn = FullSimplify[p (-1 + 10^(2 k))/9 - q (-1 + 10^k)/9 == r^2 ((-1 + 10^k)/9)^2]

Table[{i, FindInstance[(eqn /. k -> i) && 1 <= p <= 9 && 1 <= q <= 9 &&
    1 <= r <= 9, {p, q, r}, PositiveIntegers, 20]}
 , {i, 1, 100}]

The equation is always satisfied by:
eqn /. {p -> 1, q -> 2, r -> 3}
eqn /. {p -> 4, q -> 8, r -> 6}

... for any $k\ge1$. For example with $k=3$ we have: $$444444 - 888 = 6^2 (111)^2 \\
111111 - 222 = 3^2 (111)^2$$

You could also have done this in a much more 'brute-force' way without the closed form simplifications, which is possible because there are only a small number of tuples for $p,q,r$, but I don't know why you'd ever want to do this:
(* create all possible assignments of p,q,r *)
assignments = Thread[{p, q, r} -> #] & /@ Tuples[Range[9], {3}];

eqn = (p*Sum[10^n, {n, 0, 2 k - 1}] - q*Sum[10^n, {n, 0, k - 1}] ==
    r^2*Sum[10^n, {n, 0, k - 1}]^2);

(* for every i, 1..10, replace k with i and perform each assignment, selecting the assignments that satisfy the equation *)
ParallelTable[
 DeleteDuplicates[
   Select[assignments,
     ReplaceAll[eqn /. k -> i, #] &
   ], {i, 1, 10}]

redpajama_set_name: RedPajamaStackExchange


text:
Q: How to change central meridian of world map json file? I've loaded a json file of a world map and am using d3. However, part of Russia is on the opposite side of the map, next to Alaska.
The json file was created from a shapefile that I exported as unprojected (WGS84)
Is there a way to change the central meridian so that Russia isn't split?
I've tried changing the center in d3, but it hasn't worked.
Code for reference:
d3.json("data/world.json", function (error, myMap) {

    if (error) return console.error(errorMap);
    var myMapFeatures = topojson.feature(myMap, myMap.objects.world);

    var projection = d3.geo.mercator()
        .center([30,39])
        .scale(width*.10)
        .translate([width/2, height/2]);
    var geoProjectionPath = d3.geo.path()
        .projection(projection);

svg.selectAll("path")
    .data(myMapFeatures.features)
    .enter()
    .append("path")
    .attr("class", function(d) {
        return "country " + d.properties.Terr_Name;
        })
    .attr("d", geoProjectionPath);

})

A: You can handle that with the help of rotate on projection
var projection = d3.geo.equirectangular()
 .center([30,39])
.scale(100)
.rotate([-90,0]);//this will make Russia unsplit

working code here


Upcoming senior Brooklynn Byford is a Tennessee native who's fascination with the ocean brought her to the East Coast to study ocean engineering. Her positive and go-getter attitude helps her overcome the stigma of women in STEM fields and encourages the younger generation to do the same.
Byford team's remotely operated sea crawler won Best in Show in Ocean Engineering at the 2016 Northrop Grumman Engineering & Science Student Design Showcase.
I am a junior majoring in ocean engineering and I am from a very small town in Tennessee. Most people look at me funny when they hear this and ask me how I even heard of ocean engineering when there is no "ocean" near me. I have a deep fascination with the underwater world and all the exciting adventures and possibilities that it holds. I also love to take things apart and see how they work. Putting those two things together lead me to ocean engineering! When I found Florida Tech, it was as if I was finding a home away from home. So I strapped on my boots, headed down to the Sunshine State and here I am now. During my time here, I've been able to be explore the wonderful areas of my major and have been able to get an understanding on what direction I want to steer my career. My dream is to be able to help innovate more sustainable ways of harnessing the ocean's energy along with helping design sufficient means of underwater exploration. During the summer between my sophomore and junior year, I completed my senior design project with three teammates on the enhancement of the fifth generation of the remotely operated sea crawler (ROSCoV) that is designed for unmanned, underwater exploration and our project won 2016's Best in Show in Ocean Engineering at Florida Tech's student design showcase. I loved working on this project because it involved every aspect of my major and helped me narrow my field of interest. It made me realize the need for further advancements in exploration of the underwater world as little is known about our oceans.
I'm also actively involved in other parts of Florida Tech. During my time here at Florida Tech, I have served as Secretary and President of the Marine Technology Society and the Society of Naval Architects and Marine Engineers, Vice President of Campus Affairs and am the current President for Alpha Phi Sorority. I am an Orientation Panther Prep Leader and twice running Orientation Lead Staff Student Coordinator. I am also AAUS dive certified.
I had a vision and a dream of what I wanted to do with my life. I knew that I was passionate about the ocean. At the time, I didn't know the extent of what I wanted to do with my career, but I have always been mesmerized by the mysteriously unknown underwater world and I've always been passionate about finding ways to preserve its beauty.
I feel that the largest and most common challenge women in STEM come across is being treated equally. There are often times when a women's voice, opinion, or idea is the last to be heard and considered. When this happens to women, they recognize it and see that they aren't held to the same standards as their male counterparts and this can dramatically decrease all levels of confidence. We often feel as if we aren't being taken seriously and this can be extremely frustrating. We often limit ourselves and our abilities in fear of this. I find that there is a fine line between being a strong, empowering woman and being labeled as too aggressive. Women have to strive harder to find this balance and are constantly strategically altering themselves to maintain it. We work five times harder only to get to the same level.
The obstacles that women face in STEM are real and at some point, every woman will face them. How you react to these obstacles is what really matters! I am a very competitive person and I take these obstacles as a challenge to better myself and as motivation to become the best that I can be. This often means going the extra mile, putting in more effort, and being the most prepared, but I realize that I will become a better version of myself because of this. I strive to jump at every opportunity that is thrown my way and I always look for the positive in all situations I encounter. I also try to remember that standing out isn't a bad thing and I can work it to my advantage. I understand that everyone I come across has something to give so I give everyone I meet the same respect that I seek.
Go for it! Don't let the biases and expectations of society limit you in your journey to achieving your dreams. Yes, it will be hard at times but you will experience empowering things and learn valuable key lessons that will push you to that next level. Set your own standards and break those expectations. Throw yourself into everything with your head held high.
If there is something you want to do, do it! Give it your all every single time. Don't be discouraged or afraid to go after your dreams and aspirations. The road can be rough at times, but it makes reaching your dreams that much more rewarding. There are so many wonderful things to experience and learn so I encourage you to go out there and give it 100%.
For the first time, I felt the stigma of being a woman in a male-dominated environment. I was a minority in a very competitive school and questioned my ability to rise up to the challenge. However, I was surprised to find the amount of supportive and empowering influences I would meet along the way. There is an entire network of hardworking, diligent, and inspiring women in the STEM community who truly believe in you and understand the challenges that are associated with being a woman in STEM.
More STEM related programs offered at a younger age will help introduce the point that there is a vast amount of opportunities available to them. I feel the issue is the lack of knowledge about STEM related fields. By reaching out to them at a young age, they can have a better idea of what is out there and how to direct their talents. Also, leading by example can send a powerful message. I know this because I have two younger sisters that look to me in everything they do and I understand the importance of serving as a positive, empowering influence for them. Both men and women need to work on breaking the stigma associated with women in STEM. This shows younger girls that these subjects and fields are easily within reach. The younger generation is looking to us and we need to be the role models they deserve.


text:
First Play & Reviews
Now Loading Forum
By Ginja, February 11, 2012 in First Play & Reviews
360 demo tried
RetroBob 0
So whilst others were looking forward to new 'massive game' Red Dead Redemption, 2 I've been back to replay the Mass Effect series over the last few months - maybe 40-50 hours in each game. Originally got in to it after Alex & Vinny stated playing though this for Mass Alex on GiantBomb and like many others was inspired to play along - I soon found myself playing way past where they were up to, partly to avoid spoilers but also because it's just so damn good.
This is the first time I've been back to play this since finishing ME3 (April 10th 2012 according to this thread). Playing them back to back has been an amazing ride. It's been so long that I'd forgotten the majority of the story, so it was like playing through them as new games and this time I just bought ALL of the DLC so there was plenty of genuinely new stuff too. I used to prefer ME2, but I think the passage of time and playing them together gives a bit more objectivity (we are years past the ending controversy)... so now I think 3 is the best, mainly due to the refined combat and also some of the incredible moments in the game.
I was enjoying each game so much that I tried to do and see as MUCH as possible, not because to hit a completion number but because it's just so fucking good - trying to absorb as much of it as possible. This time I went with a female Shepard and holy cow, Jennifer Hale's performance is incredible. As I came towards finishing 2 and same with 3, I was genuinely feeling sad that this would be the last time for several more years that I'd play this (I know I can play anytime, but realistically). Maybe I'm just a soft shite now I have a kid but I shed a tear on two occasions.
Endings wise, ultimately I ended up going with Synthesis rather than Control or Destroy. I think I went with destroy originally and wanted to try something different. Synthesis is a genuinely happy ending, resolving the reaper (and synthetics vs organics) problem altogether and doesn't result in lots of death. Also, the changes made improve the ending were definitely good.
So what they hell do I play now? There's nothing really like this... I Iove the futuristic science fiction but not interested in medieval fantasy style. A really nicely improved version of Phantasy Star just came out on the Switch for the cost of a couple of beers so maybe I try that, they made an auto-mapping system and a nuber of other quality of life improvements!
Somerset_Bumkin 0
It's all in the reflexes
Location:Zummerzet
Andromeda?
I read our thread yesterday and listened to podcasts at the time. It can only be let down to go so quickly in to even a mediocre game.
It's great and references the trilogy a lot. Which made me wish I could remember the story as I was playing it!
The future is tricklenet
Interests:Games, music, books, boobies
I tried to play ME1 on BC, but it was horrible. Nasty framtrate, even worse during firefights.
Would love to do it all again, but time is short for games atm.
I didnt notice anything, was this on standard Bone or an X?
Yeah my games time is severely curtailed. I've been getting the odd session when my 19 month old is asleep in the day, but it's mainly in evening, sometimes until late. Man, I wonder what my 2012 self earlier in this thread would say...
Oh game time.... How I miss thee
just the regular Xbox @RetroBob
El_Patriot 0
Location:A Hive of Scum and Villainy
3 hours ago, Al said:
When did you attempt it? When bc launched it was poor, but they fixed it later.
Also, ME1 had a pretty choppy frame rate anyway.
Ginja 0
Mehathon!!!
Location:Inside my head.
I'm wearing my Mass Effect 3 t shirt. It's one of my I'm home and not leaving the house again today items of clothing.
Not that I ever wore it when it was new because it's Mass Effect 3. If it had been 2 I'd have proudly worn it outside the house
I'm sold mate. Going to make this trilogy my replay this year instead of Halo!
Edited January 10 by Somerset_Bumkin
Copyright NowLoading 2018 ©
Mind (Default)