Inference in Social Cognition

Inferences are ubiquitous in social cognition, governing everything from first impressions to the communication of meaning itself. Social cognitive inferences are typically varieties of diagnostic reasoning or, more properly, “abductive” reasoning, in which people infer simple but plausible—although not deductively certain—underlying causes for observable social behaviors. Abductive inference and its relationship to inductive and deductive inference are first introduced. A description of how abductive inference operates on a continuum between those that arise rapidly and automatically (and appear like deductions) and those that inspire more deliberative efforts (and thus often recruit more inductive information gathering and testing) is then given. Next, many classic findings in social cognition, and social psychology more broadly, that reveal how widespread this type of inference is explored. Indeed, both judgements under uncertainty and dual-process theories can be illuminated by incorporating the abductive frame. What then follows is a discussion on the work in ecological and evolutionary approaches that suggest that, although these inferences often go beyond the information given and are prone to predictable errors, people are good enough at social inference to qualify as being “ecologically rational.” The conclusion explores emerging themes in social cognition that only heighten the need for this broader understanding of inference processes.

Data-driven abductive inference of library specifications

Programmers often leverage data structure libraries that provide useful and reusable abstractions. Modular verification of programs that make use of these libraries naturally rely on specifications that capture important properties about how the library expects these data structures to be accessed and manipulated. However, these specifications are often missing or incomplete, making it hard for clients to be confident they are using the library safely. When library source code is also unavailable, as is often the case, the challenge to infer meaningful specifications is further exacerbated. In this paper, we present a novel data-driven abductive inference mechanism that infers specifications for library methods sufficient to enable verification of the library's clients. Our technique combines a data-driven learning-based framework to postulate candidate specifications, along with SMT-provided counterexamples to refine these candidates, taking special care to prevent generating specifications that overfit to sampled tests. The resulting specifications form a minimal set of requirements on the behavior of library implementations that ensures safety of a particular client program. Our solution thus provides a new multi-abduction procedure for precise specification inference of data structure libraries guided by client-side verification tasks. Experimental results on a wide range of realistic OCaml data structure programs demonstrate the effectiveness of the approach.

Integrating Theory in Co-design: An Abductive Approach

Co-design empowers people, giving them a voice in social marketing program design; however, approaches have mostly excluded expert knowledge. An abductive approach to co-design allows for inclusion of expert knowledge, providing theoretical guidance while simultaneously investigating user views and ideas extending understanding beyond known effective approaches. We use the seven-step co-design framework and outline how an abductive inference can be applied to co-design. Social cognitive theory constructs were integrated into the seven-step co-design process. The abductive approach to co-design was tested in two co-design sessions involving 40 participants. Findings demonstrate that theory can be successfully integrated into the seven-step co-design process through utilization of theory-mapped activity cards. This article provides guidance on how theory can be incorporated into ideation and insight generation. Limitations and future research recommendations are provided.

Deductive and abductive argumentation based on information graphs

In this paper, we propose an argumentation formalism that allows for both deductive and abductive argumentation, where ‘deduction’ is used as an umbrella term for both defeasible and strict ‘forward’ inference. Our formalism is based on an extended version of our previously proposed information graph (IG) formalism, which provides a precise account of the interplay between deductive and abductive inference and causal and evidential information. In the current version, we consider additional types of information such as abstractions which allow domain experts to be more expressive in stating their knowledge, where we identify and impose constraints on the types of inferences that may be performed with the different types of information. A new notion of attack is defined that captures a crucial aspect of abductive reasoning, namely that of competition between abductively inferred alternative explanations. Our argumentation formalism generates an abstract argumentation framework and thus allows arguments to be formally evaluated. We prove that instantiations of our argumentation formalism satisfy key rationality postulates.