Zoltan Dienes

A theory of implicit and explicit knowledge

The implicit-explicit distinction is applied to knowledge representations. Knowledge is taken to be an attitude towards a proposition which is true. The proposition itself predicates a property to some entity. A number of ways in which knowledge can be implicit or explicit emerge. If a higher aspect is known explicitly then each lower one must also be known explicitly. This partial hierarchy reduces the number of ways in which knowledge can be explicit. In the most important type of implicit knowledge, representations merely reflect the property of objects or events without predicating them of any particular entity. The clearest cases of explicit knowledge of a fact are representations of ones own attitude of knowing that fact. These distinctions are discussed in their relationship to similar distinctions such as procedural-declarative, conscious-unconscious, verbalizable-nonverbalizable, direct-indirect tests, and automatic-voluntary control. This is followed by an outline of how these distinctions can be used to integrate and relate the often divergent uses of the implicit-explicit distinction in different research areas. We illustrate this for visual perception, memory, cognitive development, and artificial grammar learning.

Bayesian Versus Orthodox Statistics: Which Side Are You On?

Researchers are often confused about what can be inferred from significance tests. One problem occurs when people apply Bayesian intuitions to significance testing—two approaches that must be firmly separated. This article presents some common situations in which the approaches come to different conclusions; you can see where your intuitions initially lie. The situations include multiple testing, deciding when to stop running participants, and when a theory was thought of relative to finding out results. The interpretation of nonsignificant results has also been persistently problematic in a way that Bayesian inference can clarify. The Bayesian and orthodox approaches are placed in the context of different notions of rationality, and I accuse myself and others as having been irrational in the way we have been using statistics on a key notion of rationality. The reader is shown how to apply Bayesian inference in practice, using free online software, to allow more coherent inferences from data.

Using Bayes to get the most out of non-significant results

No scientific conclusion follows automatically from a statistically non-significant result, yet people routinely use non-significant results to guide conclusions about the status of theories (or the effectiveness of practices). To know whether a non-significant result counts against a theory, or if it just indicates data insensitivity, researchers must use one of: power, intervals (such as confidence or credibility intervals), or else an indicator of the relative evidence for one theory over another, such as a Bayes factor. I argue Bayes factors allow theory to be linked to data in a way that overcomes the weaknesses of the other approaches. Specifically, Bayes factors use the data themselves to determine their sensitivity in distinguishing theories (unlike power), and they make use of those aspects of a theory’s predictions that are often easiest to specify (unlike power and intervals, which require specifying the minimal interesting value in order to address theory). Bayes factors provide a coherent approach to determining whether non-significant results support a null hypothesis over a theory, or whether the data are just insensitive. They allow accepting and rejecting the null hypothesis to be put on an equal footing. Concrete examples are provided to indicate the range of application of a simple online Bayes calculator, which reveal both the strengths and weaknesses of Bayes factors.

Implicit learning: Below the subjective threshold

In this review, we consider three possible criteria by which knowledge might be regarded as implicit or inaccessible: It might be implicit only in the sense that it is difficult to articulate freely, or it might be implicit according to either an objective threshold or a subjective threshold. We evaluate evidence for these criteria in relation to artificial grammar learning, the control of complex systems, and sequence learning, respectively. We argue that the convincing evidence is not yet in, but construing the implicit nature of implicit learning in terms of a subjective threshold is most likely to prove fruitful for future research. Furthermore, the subjective threshold criterion may demarcate qualitatively different types of knowledge. We argue that (1) implicit, rather than explicit, knowledge is often relatively inflexible in transfer to different domains, (2) implicit, rather than explicit, learning occurs when attention is focused on specific items and not underlying rules, and (3) implicit learning and the resulting knowledge are often relatively robust.

Measuring consciousness: relating behavioural and neurophysiological approaches

The resurgent science of consciousness has been accompanied by a recent emphasis on the problem of measurement. Having dependable measures of consciousness is essential both for mapping experimental evidence to theory and for designing perspicuous experiments. Here, we review a series of behavioural and brain-based measures, assessing their ability to track graded consciousness and clarifying how they relate to each other by showing what theories are presupposed by each. We identify possible and actual conflicts among measures that can stimulate new experiments, and we conclude that measures must prove themselves by iteratively building knowledge in the context of theoretical frameworks. Advances in measuring consciousness have implications for basic cognitive neuroscience, for comparative studies of consciousness and for clinical applications.

Redefine Statistical Significance

We propose to change the default P-value threshold for statistical significance from 0.05 to 0.005 for claims of new discoveries.

Connectionist and Memory-Array Models of Artificial Grammar Learning

Subjects exposed to strings of letters generated by a finite state grammar can later classify grammatical and nongrammatical test strings, even though they cannot adequately say what the rules of the grammar are (e.g., Reber, 1989). The MINERVA 2 (Hintzman, 1986) and Medin and Schaffer (1978) memory-array models and a number of connectionist outoassociator models are tested against experimental data by deriving mainly parameter-free predictions from the models of the rank order of classification difficulty of test strings. The importance of different assumptions regarding the coding of features (How should the absence of a feature be coded? Should single letters or digrams be coded?), the learning rule used (Hebb rule vs. delta rule), and the connectivity (Should features be predicted only by previous features in the string, or by all features simultaneously?) is investigated by determining the performance of the models with and without each assumption. Only one class of connectionist model (the simultaneous delta rule) passes all the tests. It is shown that this class of model can be regarded by abstracting a set of representative but incomplete rules of the grammar.

Do Fielders Know Where to Go to Catch the Ball or Only How to Get There

Skilled fielders were filmed as they ran backward or forward to catch balls projected toward them from a bowling machine 45 m away. They ran at a speed that kept the acceleration of the tangent of the angle of elevation of gaze to the ball at 0. This algorithm does not tell fielders where or when the ball will land, but it ensures that they run through the place where the ball drops to catch height at the precise moment that the ball arrives there. The algorithm leads to interception of the ball irrespective of the effect of wind resistance on the trajectory of the ball.

Filtering by movement in visual search

Search for a target defined by a conjunction of movement and form (e.g., an X moving up in a display of intermingled Os moving up and stationary Xs) is parallel. This result is also found if (a) the moving Os and target X move in unpredictable directions so that the moving stimuli do not form a clear perceptual group or (b) the nontarget Xs also move but in a known, different direction from the Os and target X. In contrast, search is slow and serial if the target may be unpredictably among either moving or stationary stimuli. These results suggest that a component of the visual system operates as a movement filter that can direct attention to stimuli with a common movement characteristic. The filtering cue can be moving (vs. stationary), or movement in 1 particular direction. The results do not support the view that attention can only be directed to groups defined by common fate.

Mapping across domains without feedback: A neural network model of transfer of implicit knowledge

This paper shows how a neural network can model the way people who have acquired knowledge of an artificial grammar in one perceptual domain (e.g., sequences of tones differing in pitch) can apply the knowledge to a quite different perceptual domain (e.g., sequences of letters). It is shown that a version of the Simple Recurrent Network (SRN) can transfer its knowledge of artificial grammars across domains without feedback. The performance of the model is sensitive to at least some of the same variables that affect subjects’ performance-for example, the model is responsive to both the grammaticality of test sequences and their similarity to training sequences, to the cover task used during training, and to whether training is on bigrams or larger sequences.