Two Sides of Appraisal: Implementing Appraisal and Its Consequences within a Cognitive Architecture

نویسنده

  • Eva Hudlicka
چکیده

Appraisal processes provide an affective assessment of an agent’s current situation, in light of its needs and goals. This paper describes a computational model of the appraisal process, implemented within the broader context of a cognitive agent architecture. A particular focus here is on modeling the interacting influences of states and traits on perception and cognition, including their effects on the appraisal process itself. These effects are modeled by manipulating a series of architecture parameters, such as the speed and processing capacity of the individual modules. The paper presents results of an evaluation experiment modeling the behavior of three types of agents: ‘normal’, ‘anxious’, and ‘aggressive’. The appraisal model generated different affective appraisals of the same set of external circumstances for the different agent types, resulting in distinct emotions, and eventually leading to observable differences in behavior. The paper concludes with a brief discussion of some of the issues encountered during the appraisal model development. Introduction and Objectives Appraisal is a core component of cognitive-affective processing. Its objective is to provide an affective assessment of a current situation, internal or external, in light of the organism’s needs and goals. The processes comprising appraisal are subject to a variety of influences, including individual history, personality, and current affective state. The past decade has witnessed a rapid growth in appraisal research, including progress towards resolving the primacy of cognition vs. affect debate (e.g., Scherer, 2003), collection of empirical data (Roseman, 2001), refinement of theories to be increasingly process-oriented (and thus lend themselves to implementation) (e.g., Smith & Kirby, 2001), and, to some extent, the construction of computational models (see reviews in Wehrle & Scherer, 2001; Hudlicka & Fellous, 1996)). Computational models represent an important tool in appraisal research, because the construction of models of hypothesized mechanisms requires a degree of operationalization which often reveals gaps in knowledge or theoretical inconsistencies. Computational modeling thus provides opportunities for validation, and for the generation of alternative hypotheses explaining specific data or phenomena. The objective of this on-going effort is to develop a computational model of the appraisal process, implemented within the broader context of a cognitive agent architecture. The architecture controls the behavior of a simulated agent, within a simulated environment, by processing incoming stimuli and generating responses. Implementing the appraisal process within this context not only provides a dynamic set of stimuli, to which the agent must respond, but also the internal mental constructs necessary for the appraisal process beliefs, goals, expectations. These constructs are dynamically generated in response to changing external and internal conditions, and enable the implementation of a simulated in vivo appraisal functioning. A particular focus here is on modeling the effects of states and traits on perception and cognition, including their effects on the appraisal process itself. Below we discuss the model structure, preliminary results, issues identified during the implementation, and future work. Agent Architecture, Trait / State Modeling, and Affect Appraiser We implemented a staged, multi-level appraisal model, within the context of a cognitive agent architecture (MAMID Hudlicka, 2002). We first describe this architecture, to provide the necessary context for a discussion of the trait / state modeling methodology, and the appraisal model, which follow. Cognitive Architecture The cognitive architecture implements a sequential seethink-do processing sequence (figure 2-1), consisting of the following modules: sensory pre-processing, translating incoming data into task-relevant cues; attention, filtering incoming cues and selecting a subset for processing; situation assessment, integrating individual cues into an overall situation assessment; expectation generation, projecting current situation onto possible future states; affect appraiser, deriving the affective state (both valence and four of the basic emotions) from a variety of external and internal elicitors, both static and dynamic; goal selection, selecting critical goals for achievement; and action selection, selecting the best actions for goal achievement. These modules map the incoming stimuli (cues) onto the outgoing behavior (actions), via a series of intermediate internal representational structures (situations, expectations, and goals), collectively termed mental constructs. This mapping is enabled by long-term memories (LTM) associated with each module, represented in terms of belief nets or rules. Mental constructs are characterized by their attributes (e.g., familiarity, novelty, salience, threat level, valence, etc.), which influence their processing; that is, their rank and the consequent likelihood of being processed within a given architecture cycle; (e.g., cue will be attended, situation derived, goal or action selected). (Note that the availability of the mental constructs from previous frames of the execution cycle allows for dynamic feedback among constructs, and thus departs from a strictly sequential processing sequence.) Figure 2-1: MAMID Cognitive Architecture: Modules & Mental Constructs Modeling State and Trait Effects The effects of states and traits on attention, perception, and decision-making have been extensively documented (e.g., Williams et al., 1997; Isen, 1993; Forgas, 2003). States and traits influence the speed, capacity and accuracy of attention and working memory; long-term memory structure, encoding, and recall; and interpretive, goal-management, and decision-making processes (figure 2-2). Some of the more robust findings regarding state effects include fear and anxiety-induced reduction in attention and working memory capacities, and attentional and interpretive threat-bias. Trait effects can be both structurally-oriented (e.g., higher proportion of selfand threat-oriented memory schemas associated with low emotional stability (Matthews & Deary, 1998)), and functionally-oriented (e.g., traits influence the dynamic characteristics of states such as onset triggers, ramp up and decay rates, and maximum intensities) (Eid, 2001)). We used a previously described methodology for modeling state and trait effects within a cognitive architecture (Hudlicka, 2002; 1998), which consists of mapping particular state / trait profiles onto specific architecture parameter values (figure 2-3). These parameters then control processing within individual architecture modules. Functions implementing these mappings were constructed on the basis of the available empirical data, as outlined above. For example, reduced attentional and working memory (WM) capacity, associated with anxiety and fear, are modeled by dynamically reducing the attentional and WM capacity of the architecture modules, which then reduces the number of constructs processed (fewer cues attended, situations derived, expectations generated, etc.). Attentional threat bias is modeled by higher ranking of threatening cues, thus increasing their likelihood of being attended, and by higher ranking of threatening situations and expectations, thus increasing the chances of a threatening situation / expectation being derived. Trait-linked structural differences in LTM are supported by allowing the flexible selection of alternative LTM clusters, reflecting distinct personality traits. Traits also influence the dynamic characteristics of the emotional responses (ramp up, decay, and maximum intensities). Figure 2-2: State and Trait Effects on Processing Situation Goal

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تاریخ انتشار 2004