Cognitive Shadow: A Policy Capturing Tool to Support ...

Cognitive Shadow: A Policy Capturing Tool to Support Naturalistic Decision Making Daniel Lafond, Sébastien Tremblay, Simon Banbury

Abstract—Policy capturing is an approach to decision analysis using statistical models such as multiple linear regression or machine learning algorithms to approximate the mental models of decision makers. The present work seeks to apply a robust policy capturing technique to functionally mirror expert mental models and create individually-tailored cognitive assistants. The "cognitive shadow" method aims to improve decision quality by recognizing probable errors in cases where the decision maker is diverging from his usual judgmental patterns. The tool actually shadows the decision maker by un-intrusively monitoring the situation and comparing its own decisions to those of the human decision maker, and then provides advisory warnings in case of a mismatch. The support methodology is designed to be minimally intrusive to avoid an increase in cognitive load, either in real-time or off-line dynamic decision making situations. Importantly, user trust is likely to be a key asset since the cognitive shadow is derived from one's own judgments. A use case of the cognitive shadow is described within the context of a maritime threat classification task, using the classic CART decision tree induction algorithm for policy capturing. This approach is deemed applicable to a large variety of domains such as supervisory control, intelligence analysis and surveillance in defence and security, and of particular relevance in high-reliability organizations with low tolerance for error. Index Terms—Adaptive decision support systems, situation assessment, policy capturing, online monitoring, naturalistic decision making.

I. INTRODUCTION

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ormally capturing expert mental models is well-known to be a major challenge for the system engineering of decision support solutions since decision makers are often not fully aware of – or cannot verbally express – the basis for their expert judgments [1]-[2]. However, successful elicitation and formalization of expert mental models have the potential to provide the underlying algorithms for designing cognitive assistants capable of improving decision quality [3]-[4]. Policy capturing is a decision analysis technique using statistical models such as multiple linear regression or machine

Manuscript received October 15, 2012. This work was partly funded by a collaborative R&D grant from Natural Sciences and Engineering Research Council of Canada (NSERC) to S. Tremblay. D, Lafond was supported by a NSERC Industrial R&D Fellowship to Thales Canada. D. Lafond is with Thales Research & Technology Canada, Québec, QC, G1P 4P5, Canada (email: [email protected]). S. Tremblay is with École de psychologie, Université Laval, Québec, QC, G1V 0A6, Canada (e-mail: [email protected]). S. Banbury is with C3 Human Factors Consulting, Québec, QC, G1P 4P5, Canada (email: [email protected]).

learning algorithms to approximate the mental models of decision makers [2]-[3]. A policy or rule is inferred from an individual's decisions as follows. The decision maker must first make judgments on a series of cases with different attributes. Judgment policies are then inferred from the observed decisions using a classification or regression algorithm appropriate for the type of data relevant to the task (e.g., binary, nominal, numerical). For example, when using regression models for policy capturing, the judgments (i.e., the dependent variable) are regressed on the cues. This procedure yields a weighted model describing a judgment policy in terms of relevant cues, weights, and a function (e.g., linear or nonlinear, additive or multiplicative) relating the cues to the judgment). Policy capturing has been successfully applied in a wide variety of domains such as medicine [5], finance [6], education [7], environmental risk assessment [8], judicial decision making [9], and national defense [10]. One major benefit of functionally capturing the “policy” of experts is that the model can actually outperform the decision maker [11]. Indeed, human decision making is inherently variable and susceptible to circumstantial factors that can lead to errors (distraction, fatigue, incomplete processing due to urgency, context-induced cognitive biases, and biases induced by the current emotional state) [12]. Hence, a model that captures the overall correct behavior, and not the irregularities, can actually outperform its human counterpart. Yet, since no model is ever perfect, the goal here is to design joint cognitive systems that actually perform better than purely human or purely automated systems. Classic decision making studies involve using idealized computerized tasks presented in highly-constrained (albeit simplistic) scenarios, which has the benefit of providing a rigorously controlled environment allowing high-precision cognitive modeling due to minimal noise in the data. In contrast, naturalistic decision making (NDM) involves the study of human cognition in realistic contexts that do not isolate a given task from its larger practice settings. In realistic contexts, there are additional constraints that may impact the way experts perform the task [13]. For example, in a dynamic environment involving time-pressure and critical deadlines, the time taken to classify an object leaves less time for classifying the other objects also present in that environment. Furthermore, NDM studies typically involve the presence of simultaneous goals that may interfere with one-another (e.g., threat-evaluation and actually taking defensive measures). In such contexts the adaptive strategies of experts are valuable solutions that can be particularly difficult to model due to the

II. METHOD A. Policy Capturing The classification and regression tree (CART) induction algorithm [14] was selected for policy capturing because it is a general machine learning method suitable for two general types of decision making situations: • Classification tasks, where the dependent variable is a category label (i.e., a choice amongst several alternatives); • Regression tasks, where the dependent variable is a numerical value (i.e., a decision about a quantitative value). CART produces trees with any number of categories, based on binary or continuous attributes, which can handle missing data and are robust to noise. Additionally, decision trees are readily interpretable as logical rules that can then be provided to operators and trainees as “cognitive feedback” [5]. The graphical representation of decision trees is also very intuitive compared to abstract rule sets or equations. Alternatively, neural networks may provide even greater flexibility – at the possible expense of transparency (i.e., producing black-box models) and with a greater risk of overfitting. In decision trees, each leaf represents a class label or decision, and branches represent conjunctions of attributes that lead to a given decision. A particularly unique aspect of decision trees is that they correspond to a dynamic information acquisition process in which the outcomes of earlier attribute assessments alter the nature and extent of attribute assessments prior to making a decision. This type of process can in principle save time and effort for many decisions that require less information (i.e., some leaves are closer to the root than others). Conversely, compensatory or exhaustive-search

models (such as linear regression) assume that the whole set of relevant attributes is considered prior to each decision [4]. The CART algorithm, as implemented in the free RapidMiner Software (Rapid-i.com), thus constitutes the policy capturing algorithm of choice for configuring the Cognitive Shadow tool for a specific user. B. Mode of operation The planned mode of operation of the Cognitive Shadow is as follows. In Step 1, a cognitive engineering specialist creates a list of decisions in an Excel spreadsheet with the contextual attributes associated with each decision (i.e., one decision per line, one column per attribute) based on previous data or a targeted exercise. In Step 2, this data is imported into RapidMiner and the CART algorithm is used to derive a decision tree. In Step 3, the decision tree model is entered into the Java-based Cognitive Shadow policy input module. In Step 4, the Cognitive Shadow’s situation monitoring module is configured to read the decisions of the user in real-time as well as the current parameters of the decision context available in the information system. Specifically, this means connecting the Cognitive Shadow application to the operator’s information system in order to monitor in real-time: 1) the current focus of the operator’s decision making, 2) the current value of the relevant parameters, and 3) the operator’s decisions. Finally, Step 5 involves configuring the Response Module to specify how to alert the operator of a possible error when a mismatch is observed (visual on-screen cue, or auditory signal). Figure 1 shows the key design components of the Cognitive Shadow.

HUMAN DECISIONS (Example Set)

POLICY CAPTURING ALGORITHM

POLICY INPUT MODULE

SITUATION MONITORING MODULE

RESPONSE MODULE

New data used to further improve the model

inherent noise in data from naturalistic settings. Hence, policy capturing in naturalistic settings poses a serious challenge. Traditional statistical approaches to policy capturing (e.g., linear multiple regression and logistic regression) are not adequate for handling missing data and non-linear or non-compensatory decision rules that may better represent the adaptive strategies of experts [13]. The goal of this work is therefore to apply a robust policy capturing technique based on a relevant machine learning algorithm to formally capture expert mental models and create individuallytailored cognitive assistants capable of improving decision quality by recognizing probable errors in cases where the decision maker is diverging from his usual judgment patterns. The "cognitive shadow" method proposed herein embodies this novel decision support capability. The present paper is organized as follows. After this introduction, Section II describes the Cognitive Shadow methodology for creating a user-customized decision support capability. Section III presents a use case to illustrate one possible implementation of the Cognitive Shadow and its mode of operation. Section IV critically analyzes potential risks and benefits of this approach, discusses a planned validation experiment, and outlines envisioned extensions to the current design.

New data used to further

Fig. 1. The Cognitive Shadow methodology.

Once configured, the tool continuously shadows the operator in his task, performing its own assessments in parallel with the operator and actively comparing the observed and anticipated decision, and reacting only when detecting a disparity. The Cognitive Shadow is thus designed to be minimally intrusive and requires no interaction to avoid

increasing cognitive load; only making its presence aware to the user at critical moments. Furthermore, this approach keeps the operator fully in control and minimizes the risk creating a dependency on the tool (or cognitive crutch effect). Since the tool's accuracy can be improved as feedback on decisions in different situations (i.e., attribute combinations) accumulates, a periodical process of model re-calibration may progressively increase the tool’s effectiveness and robustness.

which is less than half that of the decision rule prescribed in the initial training (complexity of 63). Note that decision trees with a complexity below 29 are also possible, but come with a cost in classification accuracy. So, not only can this policy capturing method be used to model an operator’s adaptive decision making strategy, it can also help identify the simplest possible strategy available without incurring loss in accuracy; an attribute that has significant pedagogical value.

III. USE CASE A. Naval Air-Defence Context The present use case depicts an operator performing a computerized naval air-defence task in the Simulated Combat Control System (S-CCS) microworld [15]. This environment will actually serve as the initial testing context for objectively assessing of the effectiveness and viability of the prototype tool. In S-CCS, the operator plays the role of a tactical coordinator, in charge of monitoring the operational space, assessing whether unidentified radar contacts are hostile, nonhostile or uncertain, and coordinating responses to threats. Figure 2 shows the S-CCS interface. 1

2

3 Fig. 2. S-CCS microworld interface. 1 = Track attributes, 2 = Geospatial display, 3 = Response buttons for the main classification task (hostility assessment), for a secondary task (threat immediacy assessment), and for taking defensive measures (launch anti-missile to currently hooked target). Weapon = Weapon system detected, IFF = Identify friend or foe signal, Origin: Region of origin, Emissions: military electronic emissions.

B. Key Scenario Challenges The S-CCS microworld creates cognitively challenging scenarios involving successive bursts of multiple unknown radar contacts that must be classified within a short time-frame and occasionally re-classified following critical changes in behavior. The task also includes interruptions and auditory distractions. Contacts classified as hostile must also be neutralized using anti-missiles – failure to do so inevitably leads to the ownship being hit. While operators are initially trained to use a perfect classification rule under nominal conditions, several practice blocks allow the users to develop their own adaptive heuristics to cope with the severe time pressure and cognitive stressors inherent in this task. Figure 3 shows an example decision tree actually derived from an operator performing this task, correctly classifying all radar contacts encountered. The complexity of this decision tree (i.e., the number of leaves and nodes in the tree) is 29 –

Fig. 3. Example (high-level view) of a decision tree derived from an operator following an S-CCS exercise. Square decision leafs: 1 = Non-hostile, 2 = Uncertain, 3 = Hostile. Circular nodes are attributes whose value can be either potentially threatening (=1) or non-threatening (=0).

C. Performance Episode Following an initial exercise to collect policy capturing data, the Cognitive Shadow is then configured by entering the decision rule derived using CART. The tool is also configured to read current track parameters in the S-CCS console and the classification decisions of the operator. Finally, the S-CCS console is modified to make the screen flash red for two seconds when it receives an alert signal from the Cognitive Shadow. Once turned on, the Cognitive Shadow actually performs the task in parallel to the operator, unobtrusively shadowing his or her moves until the operator makes a decision that does not match the expected decision pattern – at which time the screen with instantly start flashing in red for two seconds to signal a potential error. The tactical coordinator begins a burst with eight radar contacts to classify and monitor. As he selects a contact and looks at the relevant parameters for judging its allegiance (non-hostile, uncertain, hostile), the Cognitive Shadow instantly reads the track parameters and applies the decision tree model previously derived from this operator’s example set, to predict the operator’s answer, and the anticipated answer is “Non-Hostile”. The operator makes his decision and hits the “Non-Hostile” button. Observing a match between the anticipated and the observed answer, the Cognitive Shadow takes no action. Several classifications later, there is still no intervention from the cognitive shadow since each of the user’s decisions matched those predicted by the decision tree model. However, the operator progressively finds the situation harder to manage because not only have new radar contacts entered the area, but three previously classified contacts have significantly changed speed and are heading toward the vicinity of the ownship. The tactical coordinator naturally assesses the threat level of the closest contact and (appropriately) identifies it as hostile, followed by a threat immediacy rating and the launch of an anti-missile. The Cognitive Shadow still does not disturb the operator nor requires from him any attentional resources. Sensing that the three closest contacts might be

orchestrating a concerted attack, the tactical coordinator hurries to follow on with reclassifying the second and third closest ones. In his hurry, the operator misperceives the contact as hostile and after entering that response in the system the screen immediately starts flashing in red for two seconds. The operator instantly re-visits the track’s attributes and corrects his mistake. Time being excessively short to classify the third contact, the operator selects and immediately labels it as hostile after a very quick glance at the track parameters, hoping that the cognitive shadow will concur with his rapid assessment. No red flash followed the assessment giving the tactical coordinator enough confidence to go through with his decision to defend against this third contact. Over several such bursts, the cognitive shadow reacted six times. Five of these times were appropriate error alerts that were rapidly corrected by the operator. However, on one occasion, the operator did not agree with the model’s alert and persisted with his initial (correct) assessment. Had the operator or the model performed the classification task autonomously, performance would not have been as high as with the two entities working together as a joint cognitive system. IV. DISCUSSION We presented a prototype decision support capability called the Cognitive Shadow. The method allows creating tailored cognitive assistants capable of improving decision quality by recognizing probable errors in cases where the decision maker is diverging from his usual judgmental patterns. A key strength of this approach is the use of a systematic and tractable approach to capturing individual expertise. The ‘shadowing’ principle that characterizes the approach also minimizes the operator’s cognitive load both in term of interface requirements or tool interaction requirements. Importantly, user trust is likely to be a key asset since the cognitive shadow is derived from one's own judgments. This approach has one critical limitation however. The decision support model inferred from the example set may capture an operator’s imperfect decision heuristic and therefore will not correct any systematic errors that may result from that adaptive strategy. One way to resolve this type of issue is to support learning by providing the operator with his own decision model as cognitive feedback and pinpointing nodes that lead to errors. Another approach may be to configure the Cognitive Shadow with the decision model of another operator with a superior decision making performance. In addition to decision support by means of error detection, the Cognitive Shadow method can also support more generic adaptive technologies. In adaptive automation, the division of labour between human and machine is not fixed, but fluid dependent on the context; whether that is environmental (the situation), cognitive (the operator), or both. The goal is to balance the task allocation between the human and machine in a way to optimise performance. An adaptive automation model may explicitly transfer response selection responsibility to the Cognitive Shadow under specific circumstances that are precursor to task errors or breakdowns (i.e., general overload)

– unless overridden by the user. In addition, by focusing on specific steps of the operator’s decision making process, the adaptivity of the system will be more focused and quicker to respond to rapidly evolving situations. Another area of application for the Cognitive Shadow method is the development of adaptive learning technologies. In a similar manner to adaptive automation, adaptive learning technologies (e.g., intelligent tutoring systems) continuously adjust the student’s computer-based learning environment to optimise their learning experience. These systems depend on an accurate representation of expert behavior to evaluate the difference between the student’s current state of knowledge and the desired learning outcome. Both the student and expert models could thus be extracted and represented using the Cognitive Shadow approach. In this case, the student will be, in effect, shadowing the cognitive shadow of an expert. Finally, one promising extension to the Cognitive Shadow would be to include eye movement analysis to further constrain the modeling process, and to provide a more detailed feedback capability when used for intelligent tutoring. Upcoming work will experimentally assess the effectiveness of the Cognitive Shadow using the S-CCS testbed by comparing classification accuracy (and overall task performance) of operators with or and without this form of support. REFERENCES [1] [2]

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