Analytical reasoning task reveals limits of social learning in networks

Social learning—by observing and copying others—is a highly successful cultural mechanism for adaptation, outperforming individual information acquisition and experience. Here, we investigate social learning in the context of the uniquely human capacity for reflective, analytical reasoning. A hallmark of the human mind is its ability to engage analytical reasoning, and suppress false associative intuitions. Through a set of laboratory-based network experiments, we find that social learning fails to propagate this cognitive strategy. When people make false intuitive conclusions and are exposed to the analytic output of their peers, they recognize and adopt this correct output. But they fail to engage analytical reasoning in similar subsequent tasks. Thus, humans exhibit an ‘unreflective copying bias’, which limits their social learning to the output, rather than the process, of their peers’ reasoning—even when doing so requires minimal effort and no technical skill. In contrast to much recent work on observation-based social learning, which emphasizes the propagation of successful behaviour through copying, our findings identify a limit on the power of social networks in situations that require analytical reasoning.     

Qualitative and quantitative conditions for the transitivity of perceived causation:

If A caused B and B caused C, did A cause C? Although laypersons commonly perceive causality as being transitive, some philosophers have questioned this assumption, and models of causality in artificial intelligence are often agnostic with respect to transitivity. We consider two formal models of causation that differ in the way they represent uncertainty. The quantitative model uses a crude probabilistic definition, arguably the common core of more sophisticated quantitative definitions; the qualitative model uses a definition based on nonmonotonic consequence relations. Different sufficient conditions for the transitivity of causation are laid bare by the two models: The Markov condition on events for the quantitative model, and a so-called saliency condition (A is perceived as a typical cause of B) for the qualitative model. We explore the formal and empirical relations between these sufficient conditions, and between the underlying definitions of perceived causation. These connections shed light on the range of applicability of each model, contrasting commonsense causal reasoning (supposedly qualitative) and scientific causation (more naturally quantitative). These speculations are supported by a series of three behavioral experiments.     

Two routes for bipolar information processing,and a blind spot in between

This position paper addresses the question of why, whilst the human brain is apparently geared to process information of mixed polarities, human reasoners sometimes fail to deal appropriately with simple instances of mixed evidence or mixed prospects. From a dual‐process perspective of thinking, two mental routes for bipolar information processing are identified. One is biologically acquired and evolution tied, and the other is the product of a cultural elaboration of rational norms. In between the two routes, a blind spot accounts for failures of bipolar information processing. © 2008 Wiley Periodicals, Inc.     

An Empirical Test of Patterns for Nonmonotonic Inference

Human inference can be used to test the inference patterns a reliable nonmonotonic consequence should satisfy, because it appears to be nonmonotonic, it is adaptive and it generally achieves efficiency. In this study, an experiment is conducted to investigate whether human inference tends to be consistent with rationality postulates (System P plus Rational Monotony), especially when it no longer satisfies the Monotony property. The experimental protocol uses a possibilistic semantics for plausible rules. Our results appear to be consistent with all the studied properties. Exceptions are the Cut property (with one kind of content out of two) and Left Logical Equivalence which could not be tested. Moreover, when Monotony was not satisfied by participants' inferences, Cut, Cautious Monotony and And properties were corroborated (Rational Monotony was only plausibly supported and the other properties were not tested). Our results emphasize the psychological plausibility of rationality postulates and support the working hypothesis in Artificial Intelligence that System P plus Rational Monotony offer a plausible basic set of properties for nonmonotonic logics.     

Putting Ifs to Work: Goal-Based Relevance in Conditional Directives.

Conditional directives are used by speakers to instruct hearers which actions are to be taken should certain events occur. The authors demonstrate that conditional directives are distinct from indicative conditionals in which speakers predict what is likely to be observed should certain events occur. The 1st set of experiments shows that goal structure determines what information speakers will select to test whether conditional directives have been followed but that these selections do not reflect their interpretations of the deontic necessity and sufficiency of the conditional relation. The 2nd set of experiments shows that formulations of conditional directives differ in how clearly speakers consider them to express their situation-specific intentions and that hearers accurately perceive what speakers intend them to do as a result of these formulations. The authors' findings illustrate a form of social rationality common in everyday interaction, which broadens normative conceptions of conditionals. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Computer-mediated trust in self-interested expert recommendations

Important decisions are often based on a distributed process of information processing, from a knowledge base that is itself distributed among agents. The simplest such situation is that where a decision-maker seeks the recommendations of experts. Because experts may have vested interests in the consequences of their recommendations, decision-makers usually seek the advice of experts they trust. Trust, however, is a commodity that is usually built through repeated face time and social interaction and thus cannot easily be built in a global world where we have immediate internet access to a vast pool of experts. In this article, we integrate findings from experimental psychology and formal tools from Artificial Intelligence to offer a preliminary roadmap for solving the problem of trust in this computer-mediated environment. We conclude the article by considering a diverse array of extended applications of such a solution.     

Consequential Conditionals: Invited and Suppressed Inferences From Valued Outcomes.

Consequential conditionals are defined as "if P then Q" statements, where P is an action, and Q a predicted outcome of this action, which is either desirable or undesirable to the agent. Experiment 1 shows that desirable (viz. undesirable) outcomes invite an inference to the truth (viz. falsity) of their antecedent. Experiment 2 shows that the more extreme the outcome is, the stronger the invited inference is. Experiment 3 shows that modus ponens from premises "If A then C, A" can be suppressed with the introduction of a consequential conditional, "If C then Q," where Q is an undesirable outcome. Experiment 4 shows that the more undesirable Q is, the larger the suppression is. The authors discuss how these results can enrich current approaches of conditional inference on the basis of mental models, complementary necessary conditions, and conditional probabilities. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Politeness and conditional reasoning: Interpersonal cues to the indirect suppression of deductive inferences.

A successful theory of conditional reasoning requires an account of how reasoners recognize the pragmatic function a conditional statement is meant to perform. Situations in which it is ambiguous whether a conditional statement was meant to add information or to correct a mistake are discussed in this article. This ambiguity has direct consequences on the way reasoners update their beliefs and derive conclusions. In an analysis of ambiguity from the perspective of politeness theory, the authors suggest that any contextual factor that increases the face threat of a correction will encourage reasoners to construe the ambiguous conditional as a correction. This construal will impact their beliefs about the piece of information that is ambiguously corrected, and their beliefs will affect the deductive conclusions they are willing to draw. This nested mediation structure was observed in 2 experiments. The first experiment manipulated the threat level of a correction through the portrayed personality of the person being corrected; the second experiment manipulated the affective distance between the corrector and the corrected. (PsycINFO Database Record (c) 2010 APA, all rights reserved)