Mouse-tracking evidence for parallel anticipatory option evaluation.

In fast-paced, dynamic tasks, the ability to anticipate the future outcome of a sequence of events is crucial to quickly selecting an appropriate course of action among multiple alternative options. There are two classes of theories that describe how anticipation occurs. Serial theories assume options are generated and evaluated one at a time, in order of quality, whereas parallel theories assume simultaneous generation and evaluation. The present research examined the option evaluation process during a task designed to be analogous to prior anticipation tasks, but within the domain of narrative text comprehension. Prior research has relied on indirect, off-line measurement of the option evaluation process during anticipation tasks. Because the movement of the hand can provide a window into underlying cognitive processes, online metrics such as continuous mouse tracking provide more fine-grained measurements of cognitive processing as it occurs in real time. In this study, participants listened to three-sentence stories and predicted the protagonists' final action by moving a mouse toward one of three possible options. Each story was presented with either one (control condition) or two (distractor condition) plausible ending options. Results seem most consistent with a parallel option evaluation process because initial mouse trajectories deviated further from the best option in the distractor condition compared to the control condition. It is difficult to completely rule out all possible serial processing accounts, although the results do place constraints on the time frame in which a serial processing explanation must operate.

Towards a Cognitive Theory of Cyber Deception.

This work is an initial step toward developing a cognitive theory of cyber deception. While widely studied, the psychology of deception has largely focused on physical cues of deception. Given that present-day communication among humans is largely electronic, we focus on the cyber domain where physical cues are unavailable and for which there is less psychological research. To improve cyber defense, researchers have used signaling theory to extended algorithms developed for the optimal allocation of limited defense resources by using deceptive signals to trick the human mind. However, the algorithms are designed to protect against adversaries that make perfectly rational decisions. In behavioral experiments using an abstract cybersecurity game (i.e., Insider Attack Game), we examined human decision-making when paired against the defense algorithm. We developed an instance-based learning (IBL) model of an attacker using the Adaptive Control of Thought-Rational (ACT-R) cognitive architecture to investigate how humans make decisions under deception in cyber-attack scenarios. Our results show that the defense algorithm is more effective at reducing the probability of attack and protecting assets when using deceptive signaling, compared to no signaling, but is less effective than predicted against a perfectly rational adversary. Also, the IBL model replicates human attack decisions accurately. The IBL model shows how human decisions arise from experience, and how memory retrieval dynamics can give rise to cognitive biases, such as confirmation bias. The implications of these findings are discussed in the perspective of informing theories of deception and designing more effective signaling schemes that consider human bounded rationality.

Toward Personalized Deceptive Signaling for Cyber Defense Using Cognitive Models.

Recent research in cybersecurity has begun to develop active defense strategies using game-theoretic optimization of the allocation of limited defenses combined with deceptive signaling. These algorithms assume rational human behavior. However, human behavior in an online game designed to simulate an insider attack scenario shows that humans, playing the role of attackers, attack far more often than predicted under perfect rationality. We describe an instance-based learning cognitive model, built in ACT-R, that accurately predicts human performance and biases in the game. To improve defenses, we propose an adaptive method of signaling that uses the cognitive model to trace an individual's experience in real time. We discuss the results and implications of this adaptive signaling method for personalized defense.