Coherence Shifts in Probabilistic Inference Tasks

The fast-and-frugal heuristics approach to probabilistic inference assumes that individuals often employ simple heuristics to integrate cue information that commonly function in a non-reciprocal fashion. Specifically, the subjective validity of a certain cue remains stable during the application of a heuristic and is not changed by the presence or absence of another cue. The parallel-constraint-satisfaction model, in contrast, predicts that information is processed in a reciprocal fashion. Specifically, it assumes that subjective cue validities interactively af-fect each other and are modified to coherently support the favored choice. Corresponding to the model’s simulation, we predicted the direction of such coherence shifts.Cue validities were measured before, after (Exp. 1) and during judgment (Exp. 2 & 3). Coherence shifts were found in environments involving real-world cue knowledge (weather forecasts) and in a domain for which the application of fast-and-frugal heuristics has been demonstrated (city-size tasks). The results indicate that subjective cue validities are not fixed parameters, but that they are interactively changed to form coherent representations of the task.Judgment, Connectionism, Parallel Constraint Satisfaction, Fast-and-Frugal Heuristics, Adaptive Decision Making, Bounded Rationality

The Rumsfeld Effect: The unknown unknown

A set of studies tested whether people can use awareness of ignorance to provide enhanced test consistency over time if they are allowed to place uncertain items into a “don’t know” category. For factual knowledge this did occur, but for a range of other forms of knowledge relating to conceptual knowledge and personal identity, no such effect was seen. Known unknowns would appear to be largely restricted to factual kinds of knowledge

Balancing generalization and lexical conservatism : an artificial language study with child learners

Successful language acquisition involves generalization, but learners must balance this against the acquisition of lexical constraints. Such learning has been considered problematic for theories of acquisition: if learners generalize abstract patterns to new words, how do they learn lexically-based exceptions? One approach claims that learners use distributional statistics to make inferences about when generalization is appropriate, a hypothesis which has recently received support from Artificial Language Learning experiments with adult learners (Wonnacott, Newport, & Tanenhaus, 2008). Since adult and child language learning may be different (Hudson Kam & Newport, 2005), it is essential to extend these results to child learners. In the current work, four groups of children (6 years) were each exposed to one of four semi-artificial languages. The results demonstrate that children are sensitive to linguistic distributions at and above the level of particular lexical items, and that these statistics influence the balance between generalization and lexical conservatism. The data are in line with an approach which models generalization as rational inference and in particular with the predictions of the domain general hierarchical Bayesian model developed in Kemp, Perfors & Tenenbaum, 2006. This suggests that such models have relevance for theories of language acquisition

How Distinct are Intuition and Deliberation? An Eye-Tracking Analysis of Instruction-Induced Decision Modes

In recent years, numerous studies comparing intuition and deliberation have been published. However, until now relatively little is known about the cognitive processes underlying the two decision modes. Therefore, we analyzed processes of information search and integration using eye-tracking technology. We tested hypotheses derived from dual-process models which postulate that intuition and deliberation are completely distinct processes against predictions of interventionist models. The latter assume that intuitive and deliberate decisions are based on the same basic process which is supplemented by additional processes in the deliberate decision mode. We manipulated decision mode between-participants by means of instructions and participants completed simple and complex city-size tasks as well as complex legal inference tasks. Our findings indicate that the instruction to deliberate does not necessarily increase levels of processing. We found no difference in mean fixation duration and the distribution of short, medium and long fixations. Instruction-induced deliberation led to a higher number of fixations, a more complete information search and more repeated information investigations. Overall, the data support interventionist models suggesting that decisions mainly rely on automatic processes which are supplemented by additional operations in the deliberate decision mode.Decision Making, Decision Mode, Intuition, Deliberation, Eye-Tracking

Hashing for Similarity Search: A Survey

Similarity search (nearest neighbor search) is a problem of pursuing the data items whose distances to a query item are the smallest from a large database. Various methods have been developed to address this problem, and recently a lot of efforts have been devoted to approximate search. In this paper, we present a survey on one of the main solutions, hashing, which has been widely studied since the pioneering work locality sensitive hashing. We divide the hashing algorithms two main categories: locality sensitive hashing, which designs hash functions without exploring the data distribution and learning to hash, which learns hash functions according the data distribution, and review them from various aspects, including hash function design and distance measure and search scheme in the hash coding space

A predictive processing theory of sensorimotor contingencies: explaining the puzzle of perceptual presence and its absence in synesthesia

Normal perception involves experiencing objects within perceptual scenes as real, as existing in the world. This property of “perceptual presence” has motivated “sensorimotor theories” which understand perception to involve the mastery of sensorimotor contingencies. However, the mechanistic basis of sensorimotor contingencies and their mastery has remained unclear. Sensorimotor theory also struggles to explain instances of perception, such as synesthesia, that appear to lack perceptual presence and for which relevant sensorimotor contingencies are difficult to identify. On alternative “predictive processing” theories, perceptual content emerges from probabilistic inference on the external causes of sensory signals, however, this view has addressed neither the problem of perceptual presence nor synesthesia. Here, I describe a theory of predictive perception of sensorimotor contingencies which (1) accounts for perceptual presence in normal perception, as well as its absence in synesthesia, and (2) operationalizes the notion of sensorimotor contingencies and their mastery. The core idea is that generative models underlying perception incorporate explicitly counterfactual elements related to how sensory inputs would change on the basis of a broad repertoire of possible actions, even if those actions are not performed. These “counterfactually-rich” generative models encode sensorimotor contingencies related to repertoires of sensorimotor dependencies, with counterfactual richness determining the degree of perceptual presence associated with a stimulus. While the generative models underlying normal perception are typically counterfactually rich (reflecting a large repertoire of possible sensorimotor dependencies), those underlying synesthetic concurrents are hypothesized to be counterfactually poor. In addition to accounting for the phenomenology of synesthesia, the theory naturally accommodates phenomenological differences between a range of experiential states including dreaming, hallucination, and the like. It may also lead to a new view of the (in)determinacy of normal perception