This paper describes a theory that explains both the creativity and the efficiency of people's conceptual combination. In the constraint theory, conceptual combination is controlled by three constraints of diagnosticity, plausibility, and informativeness. The constraints derive from the pragmatics of communication as applied to compound phrases. The creativity of combination arises because the constraints can be satisfied in many different ways. The constraint theory yields an algorithmic model of the efficiency of combination. The C3 model admits the full creativity of (...) combination and yet efficiently settles on the best interpretation for a given phrase. The constraint theory explains many empirical regularities in conceptual combination, and makes various empirically verified predictions. In computer simulations of compound phrase interpretation, the C3 model has produced results in general agreement with people's responses to the same phrases. (shrink)

This special issue presents developments in research on the cognitive mechanisms and consequences of surprise. Amidst much progress, surprise research has often been siloed, so, as editors, we have sought to juxtapose insights, theories, and findings, to support cross‐fertilization in future research. The present paper sets the stage by presenting a historical summary, highlighting contrasts in definitions, and tracing major threads running through this issue and the larger surprise literature.

Surprise has been explored as a cognitive-emotional phenomenon that impacts many aspects of mental life from creativity to learning to decision-making. In this paper, we specifically address the role of surprise in learning and memory. Although surprise has been cast as a basic emotion since Darwin's (1872) The Expression of the Emotions in Man and Animals, recently more emphasis has been placed on its cognitive aspects. One such view casts surprise as a process of “sense making” or “explanation finding”: metacognitive (...) explanation-based theory proposes that people's perception of surprise is a metacognitive assessment of the cognitive work done to explain a surprising outcome. Or, to put it more simply, surprise increases with the explanatory work required to resolve it. This theory predicts that some surprises should be more surprising than others because they are harder to explain. In the current paper, this theory is extended to consider the role of surprise in learning as evidenced by memorability. This theory is tested to determine how scenarios with differentially surprising outcomes impact the memorability of those outcomes. The results show that surprising outcomes (less-known outcomes) that are more difficult to explain are recalled more accurately than less-surprising outcomes that require little (known outcomes) or no explanation (normal). (shrink)

Much research has linked surprise to violation of expectations, but it has been less clear how one can be surprised when one has no particular expectation. This paper discusses a computational theory based on Algorithmic Information Theory, which can account for surprises in which one initially expects randomness but then notices a pattern in stimuli. The authors present evidence that a “randomness deficiency” heuristic leads to surprise in such cases.

Though we often “fear the worst”, worrying that unexpectedly bad things will happen, there are times when we “hope for the best”, imagining that unexpectedly good things will happen, too. The paper explores how the valence of the current situation influences people's imagining of unexpected future events when participants were instructed to think of “something unexpected”. In Experiment 1, participants (N = 127) were asked to report unexpected events to everyday scenarios under different instructional conditions (e.g., asked for “good” or (...) “bad” unexpected events), and manifested a strong negativity bias in response to non-valenced instructions (i.e., being asked to “think of the unexpected” with no valence given). They mainly reported quite “predictable” unexpected outcomes that were negative; however, a post-test (N = 31) showed that the scenarios used were predominantly positive. In Experiment 2 (N = 257), when participants were instructed to think of “something unexpected and bizarre”, under the same instructional manipulations as Experiment 1, this negativity bias was replicated. In Experiment 3, using a design in which positive/negative materials were matched (verified by a pre-test, N = 60), it was found that when participants (N = 102) were given negative scenarios, they reported more positive events than they do when they are given positive scenarios. Though responding still retained an overwhelming negative bias, this result provided some evidence for a weaker valence-countering strategy; that is, where a negative scenario can lead to positive unexpected events being mentioned, and a positive scenario leads to negative unexpected events being reported. The implications of these results for people's projections of unexpected futures in their everyday lives is discussed. (shrink)

This paper advances a framework for modeling the component interactions between cognitive and social aspects of scientific creativity and technological innovation. Specifically, it aims to characterize Innovation Networks; those networks that involve the interplay of people, ideas and organizations to create new, technologically feasible, commercially-realizable products, processes and organizational structures. The tri-partite framework captures networks of ideas (Concept Level), people (Individual Level) and social structures (Social-Organizational Level) and the interactions between these levels. At the concept level, new ideas are the (...) nodes that are created and linked, kept open for further investigation or closed if solved by actors at the individual or organizational levels. At the individual level, the nodes are actors linked by shared worldviews (based on shared professional, educational, experiential backgrounds) who are the builders of the concept level. At the social-organizational level, the nodes are organizations linked by common efforts on a given project (e.g., a company–university collaboration) that by virtue of their intellectual property or rules of governance constrain the actions of individuals (at the Individual Level) or ideas (at the Concept Level). After describing this framework and its implications we paint a number of scenarios to flesh out how it can be applied. (shrink)