Sensitivity of implicit evaluations to accurate and erroneous propositional inferences

Advanced testing of the LoT hypothesis by social reasoning

I elaborate on Quilty-Dunn et al.'s integration of the language-of-thought hypothesis in social reasoning by outlining two discrepancies between the experimental paradigms referred to by the authors and the social world: Self-referential projection and deliberate thinking in experiments. Robust tests of the hypothesis in social reasoning should include observational, natural, and cross-cultural approaches.

Attribution Impacts Implicit (And Explicit) Evaluations

Two studies examined whether the attributional framing of negative events affected explicit evaluations, as measured by differences in liking, and implicit evaluations, as measured by the Implicit Association Test (Greenwald et al., 1998). In Study 1, participants read negative information about a novel target ordered to elicit internal or external attributions. Participants in the internal attribution condition evaluated the target more negatively than did those in the external attribution condition on both implicit and explicit measures. Study 2 replicated these results and tested the effects of attribution on recently formed negative evaluations. Participants who evaluated targets before and after receiving additional negative information about them demonstrated implicit and explicit evaluative change consistent with the attributional framing of that information. This research provides clear evidence that attribution influences the formation and change of implicit evaluations; however, these effects were weaker than those on explicit measures of evaluation.

The best game in town: The reemergence of the language-of-thought hypothesis across the cognitive sciences

Mental representations remain the central posits of psychology after many decades of scrutiny. However, there is no consensus about the representational format(s) of biological cognition. This paper provides a survey of evidence from computational cognitive psychology, perceptual psychology, developmental psychology, comparative psychology, and social psychology, and concludes that one type of format that routinely crops up is the language-of-thought (LoT). We outline six core properties of LoTs: (i) discrete constituents; (ii) role-filler independence; (iii) predicate-argument structure; (iv) logical operators; (v) inferential promiscuity; and (vi) abstract content. These properties cluster together throughout cognitive science. Bayesian computational modeling, compositional features of object perception, complex infant and animal reasoning, and automatic, intuitive cognition in adults all implicate LoT-like structures. Instead of regarding LoT as a relic of the previous century, researchers in cognitive science and philosophy-of-mind must take seriously the explanatory breadth of LoT-based architectures. We grant that the mind may harbor many formats and architectures, including iconic and associative structures as well as deep-neural-network-like architectures. However, as computational/representational approaches to the mind continue to advance, classical compositional symbolic structures - that is, LoTs - only prove more flexible and well-supported over time.

Problems and Mysteries of the Many Languages of Thought

"What is the structure of thought?" is as central a question as any in cognitive science. A classic answer to this question has appealed to a Language of Thought (LoT). We point to emerging research from disparate branches of the field that supports the LoT hypothesis, but also uncovers diversity in LoTs across cognitive systems, stages of development, and species. Our letter formulates open research questions for cognitive science concerning the varieties of rules and representations that underwrite various LoT-based systems and how these variations can help researchers taxonomize cognitive systems.

The role of causal structure in implicit evaluation

Causal relationships, unlike mere co-occurrence, allow humans to obtain rewards and avoid punishments by intervening on their environment. Accordingly, explicit (controlled) evaluations of stimuli encountered in the environment are known to be sensitive to causal relationships above and beyond mere co-occurrence. In this project, we conduct stringent tests of whether implicit (automatic) evaluation also reflects causal relationships and begin to probe the representational mechanisms underlying such sensitivity. Participants (N = 4836) observed causal events during which two stimuli were equally contingent with positive or negative outcomes but only one of them was causally responsible for these outcomes. Across 6 studies, varying in design and amount of verbal scaffolding provided, differences in causal status consistently guided not only explicit measures of evaluation (Likert and slider scales; Bayes Factor meta-analysis: Cohen's d = 0.28, BF₁₀ > 10⁴⁶) but also their implicit counterparts (Implicit Association Tests; Bayes Factor meta-analysis: Cohen's d = 0.22, BF₁₀ > 10²⁹). However, unlike their explicit counterparts, implicit evaluations were not sensitive to causal relationships that had to be flexibly derived by combining disparate past experiences. Taken together, these studies suggest that implicit evaluations are sensitive to causal information. Such sensitivity seems to be mediated via precompiled, causally informed value representations rather than online computations over a causal model.