Broadening Plausibility: A Sensitivity Analysis of PAM

Broadening Plausibility: A Sensitivity Analysis of PAM Louise Connell (louise.connell@northumbria.ac.uk) Division of Psychology, Northumbria University, Newcastle upon Tyne, NE1 8ST, UK Mark T. Keane (mark.keane@ucd.ie) Department of Computer Science, University College Dublin, Belfield, Dublin 4, Ireland produced (Costello & Keane, 2000; Lynott, Tagalakis & Keane, 2004). Many of these tasks have broad implications for models of cognition and underscore the centrality of plausibility. In this paper, we explore the computational aspects of our research program on plausibility. Specifically, we outline a computational model of plausibility and demonstrate its robustness as a model with an extensive sensitivity analysis. Abstract The judgement of plausibility is severely under-specified in cognitive science despite its diverse uses in many cognitive tasks. Recently, a model of human plausibility judgement, called the Plausibility Analysis Model (PAM), has been proposed and has been shown to closely model human plausibility ratings of event scenarios. In the present study, we present a sensitivity analysis to explore PAM’s robustness with a view to assessing its broader implications in cognitive science and cognitive modelling. Overall, this analysis shows that PAM is consistent with its underlying theory and is robust in a wide range of operational contexts, thus indicating that the model is well grounded in its characterisation of plausibility effects. Plausibility and the Knowledge-Fitting Theory In the Knowledge-Fitting Theory of Plausibility, Connell and Keane (2003, in prep.) define plausibility judgements as being about assessing how well a scenario fits with prior knowledge. They show that the plausibility rating of a scenario depends upon its concept-coherence (i.e., the inference and prior knowledge used to connect the scenario’s events). In addition, Connell and Keane (2004) have shown that the type of connection between a scenario’s events influences its plausibility (see Table 1 for examples). People consider events linked by causal connections (e.g., event Y was caused by event X) to be the most plausible, followed by events linked by the assertion of a previous entity’s attribute (e.g., proposition Y adds an attribute to entity X), followed by events linked by temporal connections (e.g., event Y follows event X in time). Lastly, and perhaps more obviously, people consider scenarios containing unrelated events to be the least plausible of all. In the Knowledge-Fitting Theory, plausibility judgement spans two stages: comprehension (where a representation of the scenario is formed) and assessment (where this representation is analysed to ascertain its concept- coherence). The Knowledge-Fitting Theory holds that three key aspects of the representation interact to determine a scenario’s concept-coherence: complexity, corroboration and conjecture. Briefly stated, as complexity increases, plausibility decreases. This, however, is tempered by the corroboration of the scenario, as even a very complex scenario will be plausible if it is corroborated by prior knowledge. In addition, the interaction of complexity and corroboration is affected by conjecture, as conjecture will make even the simplest, best-supported scenario seem less plausible. In essence, the most plausible scenarios are those with high concept-coherence. Introduction People make consistent and constant use of plausibility judgements in everyday life for a variety of reasons, from assessing the quality of a movie plot, to determining guilt in a tabloid murder trial, to considering a child’s excuse for a broken dish. Yet, plausibility remains poorly understood or explored in cognitive science. Recently, Connell and Keane (2003, in prep.) have advanced the Plausibility Analysis Model (PAM) as the first cognitive model of human plausibility judgements. In this paper, we consider the implications of this model in a broader context and illustrate the robustness of its performance with sensitivity analyses. We know of very few cognitive models that make explicit use of plausibility to guide, for example, decision-making, problem solving or natural language understanding. Yet, people constantly seem to use plausibility judgements to guide diverse cognitive tasks. For example, people often use plausibility judgements in place of costly retrieval from long-term memory, especially when verbatim memory has faded (Lemaire & Fayol, 1995; Reder, Wible & Martin, 1986). Plausibility is also used as a kind of cognitive shortcut in reading, to speed parsing and resolve ambiguities (Pickering & Traxler, 1998; Speer & Clifton, 1998). In everyday thinking, plausible reasoning that uses prior knowledge appears to be commonplace (Collins & Michalski, 1989), and can even aid people in making inductive inferences about familiar topics (Smith, Shafir & Osherson, 1993). It has also been argued that plausibility plays a fundamental role in understanding novel word combinations by helping to constrain the interpretations