To investigate whether spatial learning complies with associative learning theories or with theories of cognitive mapping, rats were trained in three experiments exploring the effect of variations in spatial predictive relationships. In experiment 1, it was found that making one of two landmarks the sole spatial predictor of reward, by varying the spatial relationship between reward and other cues, reduced the control over search exerted by that landmark compared with that observed when the landmark and context cues were both reliable predictors of reward location. This requirement for landmark stability rather than predictive power appears to contradict results obtained in conventional conditioning paradigms. Discrimination learning was unaffected, suggesting a dissociation between discrimination and spatial learning with respect to the influence of geometric stability. Further experiments used arrays of both single and multiple landmarks. Experiment 2 revealed that the stability of a

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Three experiments examined human processing of stimuli as predictors and causes. In Experiments 1A and 1B, two serial events that both preceded a third were assessed as predictors and as causes of the third event. Instructions successfully provided scenarios in which one of the serial (target) stimuli was viewed as a strong predictor but as a weak cause of the third event. In Experiment 2, participants ’ preexperimental knowledge was drawn upon in such a way that two simultaneous antecedent events were processed as predictors or causes, which strongly influenced the occurrence of overshadowing between the antecedent events. Although a tendency toward overshadowing was found between predictors, reliable overshadowing was observed only between causes, and then only when the test question was causal. Together with other evidence in the human learning literature, the present results suggest that predictive and causal learning obey similar laws, but there is a greater susceptibility to cue competition in causal than predictive attribution. This paper examines differences between predictive and causal learning in humans. Events often occur in our environment according to a consistent temporal distribution. Some events occur simultaneously (e.g., the sound and sight of water running out of the tap), whereas other events occur sequentially (e.g., hunger dissipates after the intake of food). When the events repeatedly take place following a sequential distribution in time, the first event (i.e., the antecedent event) can become a signal for the occurrence of the second event (i.e., the subsequent event). Learning to predict the occurrence of an event on O.P. was supported by a postdoctoral fellowship from the Spanish

Biological mechanisms are adapted to the exigencies of the functions they serve. The function of memory is to carry information forward in time. The function of learning is to extract from experience properties of the environment likely to be useful in the determination of future behavior. These different functions lead to different manifestations of the biological principle of adaptive specialization. Adaptive Specialization in the Memory Mechanism We do not know what the neurobiological mechanism of memory is, so we cannot say how it is adapted to its function. We can, however, look at other mechanisms that serve the same function to see what adaptations they suggest we may find in a mechanism with this function. One such mechanism is computer memory; another is DNA, the molecular mechanism for carrying hereditary information from one generation to the next. Both of these “memory ” mechanisms suggest two exigencies and two principles: The exigencies are thermodynamic stability and high density. The principles are that information is information and that information conveyance requires a code. Thermodynamic Stability

This paper considers how fluent language users are rational in their language processing, their unconscious language representation systems optimally prepared for comprehension and production, how language learners are intuitive statisticians, and how acquisition can be understood as contingency learning. But there are important aspects of second language acquisition that do not appear to be rational, where input fails to become intake. The paper describes the types of situation where cognition deviates from rationality and it introduces how the apparent irrationalities of L2 acquisition result from standard phenomena of associative learning as encapsulated in the models of Rescorla and Wagner (1972) and Cheng and Holyoak (1995), which describe how cue salience, outcome importance, and the history of learning from multiple probabilistic cues affect the development of ‘learned selective attention’ and transfer. This article considers how fluent language users are rational in their language processing, rational in the sense that their unconscious language representation

Abstract—In both Pavlovian conditioning and human causal judgment, competition between cues is well known to occur when multiple cues are presented in compound and followed by an outcome. More questionable is the occurrence of competition between outcomes when a single cue is followed by multiple outcomes presented in compound. In the experiment reported here, we demonstrated blocking (a type of stimulus competition) between outcomes. When the cue predicted one outcome, its ability to predict a second outcome that was presented in compound with the first outcome was reduced. The procedure minimized the likelihood that the observed competition between outcomes arose from selective attention. The competition between outcomes that we observed is problematic for contemporary theories of learning. When a cue (i.e., an antecedent event) is followed by an outcome (i.e., a subsequent event), the association that is ordinarily formed may be assessed predictively (i.e., by presenting the cue and assessing whether participants predict the outcome) or diagnostically (i.e., by presenting the outcome and assessing whether participants diagnose the cue). Moreover, when two cues are presented together prior to an outcome, the cue with the higher predictive value ordinarily competes with the other cue for predicting the outcome. Examples of cue competition include overshadowing (Pavlov, 1927), blocking (Kamin, 1968), and the relative stimulus-validity effect (Wagner, Logan, Haberlandt, & Price, 1968; Wasserman, 1974). Cue competition occurs in both humans and animals (e.g., Balaz, Gutsin, Cacheiro, & Miller, 1982; Kamin, 1968; Matute, Arcediano, & Miller, 1996; Shanks, 1985; Wasserman, 1990) and is now an established phenomenon that is addressed by many models developed in areas as diverse as neural networks, animal conditioning, causal attribution, and categorization

The effects of altering the contingency between the conditioned stimulus (CS) and the unconditioned stimulus (US) on the acquisition of autoshaped responding was investigated by changing the frequency of unsignaled USs during the intertrial interval. The addition of the unsignaled USs had an effect on acquisition speed comparable with that of massing trials. The effects of these manipulations can be understood in terms of their effect on the amount of information (number of bits) that the average CS conveys to the subject about the timing of the next US. The number of reinforced CSs prior to acquisition is inversely related to the information content of the CS.

A growing number of studies involving nonlinear discrimination problems suggests that stimuli in human associative learning are represented configurally with narrow generalization, such that presentation of stimuli that are even slightly dissimilar to stored configurations weakly activate these configurations. We note, however, that another well-known set of findings in human associative learning, cue-interaction phenomena, suggest relatively broad generalization. Three experiments show that current models of human associative learning, which try to model both non-linear discrimination and cue interaction as the result of one process, fail because they cannot simultaneously account for narrow and broad generalization. The results suggest that human associative learning involves (a) an exemplar-based process with configural stimulus representation and narrow generalization and (b) an adaptive learning process characterized by broad generalization and cue interaction. 2 Stimulus Generalization

We draw together and develop previous timing models for a broad range of conditioning phenomena to reveal their common conceptual foundations: First, conditioning depends on the learning of the temporal intervals between events and the reciprocals of these intervals, the rates of event occurrence. Second, remembered intervals and rates translate into observed behavior through decision processes whose structure is adapted to noise in the decision variables. The noise and the uncertainties consequent upon it have both subjective and objective origins. A third feature of these models is their time-scale invariance, which we argue is a deeply important property evident in the available experimental data. This conceptual framework is similar to the psychophysical conceptual framework in which contemporary models of sensory processing are rooted. We contrast it with the associative conceptual framework.

Inspired by recent work in ethology and animal training, we integrate representations for time and rate into a behavior-based architecture for autonomous virtual creatures. The resulting computational model of affect and action selection allows these creatures to discover and refine their understanding of apparent temporal causality relationships which may or may not involve self-action. The fundamental action selection choice that a creature must make in order to satisfy its internal needs is whether to explore, react or exploit. In this architecture, that choice is informed by an understanding of apparent temporal causality, the representation for which is integrated into the representation for action. The ability to accommodate changing ideas about causality allows the creature to exist in and adapt to a dynamic world. Not only is such a model suitable for computational systems, but its derivation from biological models suggests that it may also be useful for gaining a new perspective on learning in biological systems. The implementation of a complete character built using this architecture is able to reproduce a variety of conditioning phenomena, as well as learn using a training technique used with live animals.