An egocentric frame of reference in implicit motor sequence learning

We investigated which frame of reference is evoked during implicit motor sequence learning. Participants completed a typical serial reaction time task. In the first experiment, we isolated egocentric and allocentric frames of reference and found that learning was solely in an egocentric reference frame. In a second experiment, we isolated hand-centered space from other egocentric frames of reference. We found that for a one-handed sequencing task, the sequence was coded in an egocentric reference frame but not a hand-centered reference frame. Our results are restricted to implicit learning of novel sequences in the early stages of learning. These findings are consistent with claims that the neural mechanisms involved in motor skill learning operate in egocentric coordinates.     

Classification as diagnostic reasoning

An ongoing goal in the field of categorization has been to determine how objects’ features provide evidence of membership in one category versus another. Well-known findings include that feature diagnosticity is a function of how often the feature appears in category members versus nonmembers, their perceptual salience, how features are used in support of inferences, and how observable features are related to other observable features. We tested how diagnosticity is affected by causal relations between observable and unobserved features. Consistent with our view of classification as diagnostic reasoning, we found that observable features are more diagnostic to the extent that they are caused by underlying features that define category membership, because the presence of the latter can be (causally) inferred from the former. Implications of these results for current views of conceptual structure and models of categorization are discussed.     

Statistical learning of phonetic categories: insights from a computational approach

Recent evidence ( Maye, Werker & Gerken, 2002 ) suggests that statistical learning may be an important mechanism for the acquisition of phonetic categories in the infant's native language. We examined the sufficiency of this hypothesis and its implications for development by implementing a statistical learning mechanism in a computational model based on a mixture of Gaussians (MOG) architecture. Statistical learning alone was found to be insufficient for phonetic category learning – an additional competition mechanism was required in order for the categories in the input to be successfully learnt. When competition was added to the MOG architecture, this class of models successfully accounted for developmental enhancement and loss of sensitivity to phonetic contrasts. Moreover, the MOG with competition model was used to explore a potentially important distributional property of early speech categories – sparseness – in which portions of the space between phonetic categories are unmapped. Sparseness was found in all successful models and quickly emerged during development even when the initial parameters favoured continuous representations with no gaps. The implications of these models for phonetic category learning in infants are discussed.     

Influence of object size on baseline identification, priming, and explicit memory

We investigated the influence of size on identification, priming, and explicit memory for color photos of common objects. Participants studied objects displayed in small, medium, and large sizes and memory was assessed with both implicit identification and explicit recognition tests. Overall, large objects were easier to identify than small objects and study-to-test changes in object size impeded performance on explicit but not implicit memory tests. In contrast to previous findings with line-drawings of objects but consistent with predictions from the distance-as-filtering hypothesis, we found that study-test size manipulations had large effects on old/new recognition memory test for objects displayed in large size at test but not for objects displayed small or medium at test. Our findings add to the growing body of literature showing that the findings obtained using line-drawings of objects do not necessarily generalize to color photos of common objects. We discuss implications of our findings for theories of object perception, memory, and eyewitness identification accuracy for objects.     

Relative Necessity Reformulated

This paper discusses some serious difficulties for what we shall call the standard account of various kinds of relative necessity, according to which any given kind of relative necessity may be defined by a strict conditional - necessarily, if C then p - where C is a suitable constant proposition, such as a conjunction of physical laws. We argue, with the help of Humberstone (Reports on Mathematical Logic, 31, 33–421, 1981), that the standard account has several unpalatable consequences. We argue that Humberstone’s alternative account has certain disadvantages, and offer another - considerably simpler - solution.     

Time-Soaked: How Trauma Submerges In and Out of Time

This clinical presentation explores how we are submerged in time as patients and analysts, particularly when intergenerational traumas are present. The temporal dimension of this submerged time may involve an engulfing past that is heavy with destructiveness. This paper examines how a patient and analyst emerge from such a past. I suggest a patient may begin to feel a sense of “self in time” (Seligman, 2016) when a foundation for mutual recognition is established through implicit gestures and nonverbal interactions. Once this level of interaction becomes enlivened, time may begin as well as the process of becoming a self with an other.     

Probabilistic constraint satisfaction at the lexical/phonetic interface: evidence for gradient effects of within-category VOT on lexical access

Research in speech perception has been dominated by a search for invariant properties of the signal that correlate with lexical and sublexical categories. We argue that this search for invariance has led researchers to ignore the perceptual consequences of systematic variation within such categories and that sensitivity to this variation may provide an important source of information for integrating information over time in speech perception. Data from a study manipulating VOT continua in words using an eye-movement paradigm indicate that lexical access shows graded sensitivity to within-category variation in VOT and that this sensitivity has a duration sufficient to be useful for information integration. These data support a model in which the perceptual system integrates information from multiple sources and from the surrounding temporal context using probabilistic cue-weighting mechanisms.     

A causal-model theory of conceptual representation and categorization

This article presents a theory of categorization that accounts for the effects of causal knowledge that relates the features of categories. According to causal-model theory, people explicitly represent the probabilistic causal mechanisms that link category features and classify objects by evaluating whether they were likely to have been generated by those mechanisms. In 3 experiments, participants were taught causal knowledge that related the features of a novel category. Causal-model theory provided a good quantitative account of the effect of this knowledge on the importance of both individual features and interfeature correlations to classification. By enabling precise model fits and interpretable parameter estimates, causal-model theory helps place the theory-based approach to conceptual representation on equal footing with the well-known similarity-based approaches.     

A knowledge-resonance (KRES) model of category learning

This article introduces a connectionist model of category learning that takes into account the prior knowledge that people bring to new learning situations. In contrast to connectionist learning models that assume a feedforward network and learn by the delta rule or backpropagation, this model, the knowledge-resonance model, or KRES, employs a recurrent network with bidirectional symmetric connection whose weights are updated according to a contrastive Hebbian learning rule. We demonstrate that when prior knowledge is represented in the network, KRES accounts for a considerable range of empirical results regarding the effects of prior knowledge on category learning, including (1) the accelerated learning that occurs in the presence of knowledge, (2) the better learning in the presence of knowledge of category features that are not related to prior knowledge, (3) the reinterpretation of features with ambiguous interpretations in light of error-corrective feedback, and (4) the unlearning of prior knowledge when that knowledge is inappropriate in the context of a particular category.