Monitoring simultaneous auditory messages

If a S is asked to monitor two simultaneous auditory speech messages and to report only on the occurrence of target words appearing at random in either message, then it is shown that he witt fail to detect all of them but will detect significantly more than half. The targets used in these experiments were immediate repeats of text words. The results reject theories that part of the sensory input is blocked or that all is recognized. Detection performance was a function of rate of speech and of intertarget interval; there was a small, not significant, effect of instruction to recognize message content.     

Variables underlying the recognition of random shapes

Speed of sorting decks of 32 cards with two alternative stimuli was measured. The stimuli were pairs of dots with attributes of distance between dots, angle of orientation of the pair, and position of the pair to the right or left of center. The attributes varied in relative discriminability and were used in all possible pair- and triple-correlated (redundant) combinations. Results showed an increase in speed of sorting with either an increase in discriminability or an increase in number of redundant attributes. It is argued that the increased speed with addition of redundant attributes is due both to a selective serial processing of attributes (requiring attribute separability) and to an increased discriminability with combined attributes (requiring attribute integrality).     

Verbal interference in a perceptual comparison task

When Ss had to indicate if the colors of pairs of items from the Stroop test were the same or different, the usual interference effect was completely eliminated. However, when the verbal information consisted of SAME and DIFF rather than color names, interference was reestablished. This indicates that the perceptual comparison task does not eliminate interference simply by rendering Ss insensitive to the meanings of words.     

The uniqueness and significance of simple structure demonstrated by contrasting organic “natural structure” and “random structure” data

The study compares the extent to which naturally structured data and artificial, relatively random data (both with the same basic parameters) produce simple structure factors which are uniquely determined. Two examples of unstructured matrices were compared with the ball problem matrix. The results show that an oblique position of maximum hyperplane count in the structured data differs from that in the unstructured by reaching a significantly more unique position in terms of the exactitude with which it is re-discoverable when starting from different positions, and by reaching (at the maximum) a significantly higher hyperplane count.     

Effects of deprivation and reinforcement-magnitude on response variability

Animals were trained to displace any one of five response keys in order to put themselves in a stimulus condition in which reinforcement could be obtained by depressing a response lever. Decreased deprivation and magnitude of reinforcement were found to increase the variability of the distribution of key responses. The relevance of these findings to other experiments in which deprivation, reinforcement magnitude, and intermittent reinforcement were studied is discussed.     

Simulation of human problem-solving behavior

In the simulation of human behavior on a digital computer, one first attempts to discover the manner in which subjects (Ss) internally represent the environment and the rules that they employ for acting upon this representation. The interaction between the rules and the environmental representation over a period of time constitutes a set of processes. Processes can be expressed as flow charts which, in turn, are stated formally in terms of a computer program. The program serves as a theory which is tested by executing the program on a computer and comparing the machine's performance with S's behavior.     

A note on mathematical models for learning

It is shown that a rational learning curve developed by Estes for paired associate learning is a special case of a more general function. The latter is the product of two functions and assumes that the discovery and fixation aspects of learning are independent. The indications are that the form of one of the functions has not been tested sharply by Estes in the paired associate learning setting.