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This article reviews the research literature on the differences between word reading and picture naming. A theory for the visual and cognitive processing of pictures and words is then introduced. The theory accounts for slower naming of pictures than reading of words. Reading aloud involves a fast, grapheme-to-phoneme transformation process, whereas picture naming involves two additional processes: (a) determining the meaning of the pictorial stimulus and (b) finding a name for the pictorial stimulus. We conducted a reading-naming experiment, and the time to achieve (a) and (b) was determined to be approximately 160 ms. On the basis of data from a second experiment, we demonstrated that there is no significant difference in time to visually compare two pictures or two words when size of the stimuli is equated. There is no difference in time to make the two types of cross-modality conceptual comparisons (picture first, then word, or word first, then picture). The symmetry of the visual and conceptual comparison results supports the hypothesis that the coding of the mind is neither intrinsically linguistic nor imagistic, but rather it is abstract. There is a potent stimulus size effect, equal for both pictorial and lexical stimuli. Small stimuli take longer to be visually processed than do larger stimuli. For optimal processing, stimuli should not only be equated for size, but should subtend a visual angle of at least 3 degrees. The article ends with the presentation of a mathematical theory that jointly accounts for the data from word-reading, picture-naming visual comparison, and conceptual-comparison experiments.

Argues that there is a confounding between stimulus presentation probability and memory set size in S. Sternberg's classic study of character recognition. 2 experiments were conducted, with 20 undergraduates in each, in which stimulus probability and memory set size were varied independently. Using Sternberg's analysis, results essentially replicate his. However, a pronounced and systematic stimulus probability effect was found on reaction time to both positive and negative stimuli. The data are consistent with a self-terminating process which searches a stochastically ordered memory stack containing representations of both positive and negative stimuli which are stored with their appropriate response codes as paired associates. (20 ref.)

72 rats were run in a 2 X 3 factorial experiment combining 2 conditioning procedures (1-way and shuttle responding) and 3 shock intensities (1.0, 1.5, and 2.5 ma.). Conditioning was much faster with the 1-way procedure than with the shuttle procedure. Rate of conditioning decreased with shock intensity with the shuttle procedure, but not with the 1-way procedure. Escape latencies did not vary systematically with shock intensity. The data indicate that the slower learning rates with high-intensity shock in the shuttle condition are not due to the disruption of the escape response by high-intensity shock. Rather, it appears that with the shuttle procedure the decreased rate of conditioning is primarily due to the fact that high-intensity shock increases the resistance to extinction of a response which is incompatible with the avoidance response. The results of a 2nd experiment indicated that handling of S during the intertrial interval contributed a small amount to the difference between the 1-way and shuttle conditions.

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Rats were trained to avoid shock with either a oneway procedure, a shuttle procedure, or both. One-way conditioning was found to be consistent with a two-step all-or-none process and was a much simpler task then shuttle conditioning which could not be accounted for by that process or by a linear operator process. ProblemThe use of a shuttle procedure in avoidance conditioning has become popular due to the convenience it allows in automating the experiment. In spite ofthe automation, many investigators have had difficulty in training rats to a level of 100% avoidance in a reasonable length of time. For example, with optimumshockintensities the rats of Moyer & Korn (1964) took an average of about 90 trials to reach a level of 90% avoidance responses.By contrast, Theios (1963) reported it took only about 11 trials to reach a level of 90% avoidance responses when the rats were run in one direction rather than having them shuttle. In view ofthese differences, it is of interest whether the one-way procedure yields a different conditioning process than does the shuttle procedure.The purpose of this research was to compare one-way and shuttle procedures under as nearly identical conditions as possible. Specifically, it was asked whether both these procedures would yield grossly discrete behavior changes such as predicted by a two-stage all-or-none learning model (Bower & Theios, 1964) or gradual changes predicted by a two-operator linear model (Bush & Mosteller, 1955). MethodThe Ss were 89 male albino rats between 120 and 190 days of age. The apparatus was an automated shuttle box divided into two 14 x 8 x 6 inch compartments by a guillotine door in the center. The CS consi. sted of the simultaneous raising of the door, the onset of a light, and the presentation of 89 db white noise. The US was a 2.5 rna scrambled shock delivered to the grids. The CS-US interval was 5 seconds and the intertrial interval was 30 seconds. Crossing responses with a latency of 5 seconds or less were scored as avoidance responses (CRs). Responses with latencies longer than 5 seconds were scored as errors. Following a pretesting period with no shock, each S was given training trials until he met a criterion of 10 consecutive CRs. The 75 Ss in the one-way (OW) condition were allowed to remain in the side responded to for 15 seconds and then were replaced in the original side. At the end of the criterion run, 57 of these Ss (Group OW-S) were given shuttle training until the criterion was met again. The 14 Ss in Group S received only shuttle training.

A menu-driven software system was developed to implement schemata organization in a constraintsatisfaction neural network. The current neural-net model presents several advantages and modifications over previous related models. On the one hand, the schemata behavior shown by the present model is based on conceptual definitions and ratings obtained empirically from human subjects rather than on the idiosyncratic knowledge database of a single programmer. On the other hand, this property is most valuable to experimental research in which the current knowledge status of the subjects is critical for the experiments (e.g., word-recognition experiments).The present paper describes a menu-driven software system called Semantic Analyzer of Schemata Organization (SASO). This program was developed to implement schemata organization in a constraint-satisfaction neural network similar to the connectionist model for schemata postulated by Rumelhart, Smolensky, McClelland, and Hinton in 1986. However, the current neural-net model presents several advantages and modifications over previous related models. First, the schemata behavior shown by the present neural net is based on conceptual definitions and ratings obtained empirically from human subjects rather than on the idiosyncratic knowledge database of a single programmer. This property is especially well suited to experimental research in which the knowledge status of the subjects is critical for the experiments (e.g. , word-recognition experiments). Second, SASO provides the user with a set of utilities to process raw data generated by the subjects. It also automatically sets up the weight-connectivity matrix among concepts and generates the interface data files needed to run schemata simulations on the constraint-satisfaction neural-net program provided by McClelland and Rumelhart in 1988. A psychological experiment and computer neural-net simulations were carried out to test schemata organization in the current neural-net model. Furthermore, some statistical analysis was performed on the weight-eonnectivity matrix of the neural net. The advantages of using the current model as a computational tool for psychological experiments will be argued elsewhere in this paper, ANALYSIS AND SCALING OF CONCEPTUAL DEF1NITIONSThe first part of the study obtained the empirical data that were used in the neural-network simulations. The schema of room and related schemata were chosen for this experiment in order to work with some of the same schemata that Rumelhart et al. (1986) used in their simulations and thus be able to compare results. Rumelhart et al. (1986) chose a priori the descriptor concepts implemented in their network. By contrast, in the present study, a technique to obtain semantic networks from subjects was used to obtain the concepts of the neural network. Thus, the current schemata simulations are based on the information generated from the memories of a sample of 24 undergraduate students. The technique to obtain conceptual definitions is illustrated in the Method sec...