This article presents a theory in which automatization is construed as the acquisition of a domain-specific knowledge base, formed of separate representations, instances, of each exposure to the task. Processing is considered automatic if it relies on retrieval of stored instances, which will occur only after practice in a consistent environment. Practice is important because it increases the amount retrieved and the speed of retrieval; consistency is important because it ensures that the retrieved instances will be useful. The theory accounts quantitatively for the power-function speed-up and predicts a power-function reduction in the standard deviation that is constrained to have the same exponent as the power function for the speed-up. The theory accounts for qualitative properties as well, explaining how some may disappear and others appear with practice. More generally, it provides an alternative to the modal view of automaticity, arguing that novice performance is limited by a lack of knowledge rather than a scarcity of resources. The focus on learning avoids many problems with the modal view that stem from its focus on resource limitations. Automaticity is an important phenomenon in everyday men-tal life. Most of us recognize that we perform routine activities quickly and effortlessly, with little thought and conscious aware-ness--in short, automatically (James, 1890). As a result, we of-ten perform those activities on "automatic pilot " and turn our minds to other things. For example, we can drive to dinner while conversing in depth with a visiting scholar, or we can make coffee while planning dessert. However, these benefits may be offset by costs. The automatic pilot can lead us astray, caus-ing errors and sometimes catastrophes (Reason & Myceilska, 1982). If the conversation is deep enough, we may find ourselves and the scholar arriving at the office rather than the restaurant, or we may discover that we aren't sure whether we put two or three scoops of coffee into the pot. Automaticity is also an important phenomenon in skill acqui-sition (e.g., Bryan & Harter, 1899). Skills are thought to consist largely of collections of automatic processes and procedures

Many researchers interested in connectionist models accept that such models are "neurally inspired " but do not worry too much about whether their models are biologically realistic. While such a position may be perfectly justifiable, the present paper attempts to illustrate how biological information can be used to constrain connectionist models. Two particular areas are discussed. The first section deals with visual information processing in the primate and human visual system. It is argued that speed with which visual information is processed imposes major constraints on the architecture and operation of the visual system. In particular, it seems that a great deal of processing must depend on a single bottum-up pass. The second section deals with biological aspects of learning algorithms. It is argued that although there is good evidence for certain coactivation related synaptic modification schemes, other learning mechanisms, including back-propagation, are not currently supported by experimental data.

Efficient categorizations of complex visual stimuli require effective encodings of their distinctive properties. However, the question remains of how processes of object and scene categorization use the information associated with different perceptual spatial scales. The psychophysics of scale perception suggests that recognition uses coarse blobs before fine scale edges, because the former is perceptually available before the latter. Although possible, this perceptually determined scenario neglects the nature of the task the recognition system must solve. If different spatial scales transmit different information about the input, an identical scene might be flexibly encoded and perceived at the scale that optimizes information for the considered task—i.e., the diagnostic scale. This paper tests the hypothesis that scale diagnosticity can determine scale selection for recognition. Experiment 1 tested whether coarse and fine spatial scales were both available at the onset of scene categorization. The second experiment tested that the selection of one scale could change depending on the diagnostic information present at this scale. The third and fourth experiments investigated whether scalespecific cues were independently processed, or whether they perceptually cooperated in the recognition of the input scene. Results suggest that a mandatory low-level registration of multiple spatial scales promotes flexible scene encodings, perceptions, and categorizations.

This paper presents a learning theory pertinent to dynamic decision making (DDM) called instancebased learning theory (IBLT). IBLT proposes five learning mechanisms in the context of a decision-making process: instance-based knowledge, recognition-based retrieval, adaptive strategies, necessity-based choice, and feedback updates. IBLT suggests in DDM people learn with the accumulation and refinement of instances, containing the decision-making situation, action, and utility of decisions. As decision makers interact with a dynamic task, they recognize a situation according to its similarity to past instances, adapt their judgment strategies from heuristic-based to instance-based, and refine the accumulated knowledge according to feedback on the result of their actions. The IBLT’s learning mechanisms have been implemented in an ACT-R cognitive model. Through a series of experiments, this paper shows how the IBLT’s learning mechanisms closely approximate the relative trend magnitude and performance of human data. Although the cognitive model is bounded within the context of a dynamic task, the IBLT is a general theory of decision making applicable to other dynamic environments.

In three experiments, the subjects ' task was to decide whether each of a series of words con-noted something good (e.g., fame, comedy, rescue) or bad (stress, detest, malaria). One-half second before the presentation of each such target word, an evaluatively polarized priming word was presented briefly to the nondominant eye and was masked dichoptically by either the rapidly following (Experiment 1) or simultaneous (Experiments 2 and 3) presentation of a random letter-fragment pattern to the dominant eye. (The effectiveness of the masking procedure was demon-strated by the subjects ' inability to discriminate the left vs. right position of a test series of words.) In all experiments, significant masked priming effects were obtained; evaluative decisions to con-gruent masked prime-target combinations (such as a positive masked prime followed by a posi-tive target) were significantly faster than those to incongruent (e.g., negative primelpositive tar-get) or noncongruent (e.g., neutral primelpositive target) combinations. Also, in two of the three experiments, when subjects were at chance accuracy in discriminating word position, their posi-tion judgments were nevertheless significantly influenced by the irrelevant semantic content (LEFT vs. RIGHT) of the masked position-varying words. The series of experiments demonstrated that two very different tasks-speeded judgment of evaluative meaning and nonspeeded judg-

of objects

VISOR is a large connectionist system that shows how visual schemas can be learned, represented, and used through mechanisms natural to neural networks. Processing in VISOR is based on cooperation, competition, and parallel bottom-up and top-down activation of schema representations. Simulations show that VISOR is robust against noise and variations in the inputs and parameters. It can indicate the confidence of its analysis, pay attention to important minor differences, and use context to recognize ambiguous objects. Experiments also suggest that the representation and learning are stable, and its behavior is consistent with human processes such as priming, perceptual reversal, and circular reaction in learning. The schema mechanisms of VISOR can serve as a starting point for building robust high-level vision systems, and perhaps for schema-based motor control and natural language processing systems as well. 1 Introduction Neural networks have been successfully applied to problems su...

Acoustic Distortion 2 This study explored the role of attentional and perceptual factors in lexical access by examining the effects of acoustic distortion on semantic priming of spoken words by a sentence context. The acoustic manipulations included low-pass filtering, which was intended to interfere with the sensory encoding of the acoustic signal by reducing intelligibility, and time compression, which was intended to disrupt central language processing by reducing processing time. These distortions were applied to the sentence context to explore how the contribution of contextual information to lexical access is affected by acoustic degradation. Low-pass filtering significantly reduced semantic facilitation. In contrast, temporal compression significantly reduced semantic inhibition without affecting facilitation. These qualitative differences between two forms of acoustic distortion are discussed in terms of the activation, selection, and integration of lexical-semantic information in models of lexical access. Filtering may have its primary effect on a relatively early, automatic process (reflected in facilitation effects), while compression has its primary effect on a later, more demanding process (reflected in inhibition

VISOR is a neural network system for object recognition and scene analysis that learns visual schemas from examples. Processing in VISOR is based on cooperation, competition, and parallel bottom-up and top-down activation of schema representations. Similar principles appear to underlie much of human visual processing, and VISOR can therefore be used to model various perceptual phenomena. This paper focuses on analyzing three phenomena through simulation with VISOR: (1) priming and mental imagery, (2) perceptual reversal, and (3) circular reaction. The results illustrate similarity and subtle differences between the mechanisms mediating priming and mental imagery, show how the two opposing accounts of perceptual reversal (neural satiation and cognitive factors) may both contribute to the phenomenon, and demonstrate how intentional actions can be gradually learned from reflex actions. Successful simulation of such effects suggests that similar mechanisms may govern human visual perceptio...

Why anticipatory processes correspond to cognitive abilities of living systems? To be adapted to an environment, behaviors need at least i) internal representations of events occurring in the external environment; and ii) internal anticipations of possible events to occur in the external environment. Interactions of these two opposite but complementary cognitive properties lead to various patterns of experimental data on semantic processing. How to investigate dynamic semantic processes? Experimental studies in cognitive psychology offer several interests such as: i) the control of the semantic environment such as words embedded in sentences; ii) the methodological tools allowing the observation of anticipations and adapted oculomotor behavior during reading; and iii) the analyze of different anticipatory processes within the theoretical framework of semantic processing. What are the different types of semantic anticipations? Experimental data show that semantic anticipatory processes involve i) the coding in memory of sequences of