One of the oldest questions in psychology and neuroscience is whether associations of stimuli and responses are sufficient to explain learning. Or, in addition, are there conditions that require knowledge of rules and representations? We propose a new approach to the study of serial learning by investigating how humans and monkeys infer ordinal knowledge implicitly during training on a Transitive Inference (TI) task. In its simplest form, TI is the ability to conclude that A > C, if A > B and B > C, but here we extend the same logic to longer series composed of 7 items. TI has been shown to exist in species as diverse as pigeons, monkeys, and humans and has been used to explain complex social relationships such as dominance hierarchies. TI is critical for understanding ordinal knowledge, which, by definition, obeys transitivity, and which is believed to give rise to an internal representation of serial order. To investigate this theory, we plan to study learning and representation of ordinal knowledge during and following TI training in monkeys and human subjects. The logic of our experiments is to show (1) how manipulations of expected value do not alter the representation of ordinal knowledge in studies on overtraining of particular pairs during TI acquisition and in studies in which there is a reversal of reward magnitude during TI training, (2) overtraining of a particular stimulus-response contingency does not impair learning, and (3) the inability of association theory to account for accurate performance on derived lists on which knowledge of associations learned on the original list are irrelevant. Our monkey experiments are the first to investigate implicit inference at the behavioral level that is synchronized to simultaneous measurement of the activity of individual neurons in prefrontal cortex (PFC) and posterior parietal cortex (PPC) throughout TI learning (including acquisition). Our experiments aim to show that 1) TI training leads to a representation of serial order of novel stimulus pairs and 2) ordinal position and symbolic distance are represented in PFC and LIP and that those representations arise de novo each time an animal learns a new list. Health Relatedness: These experiments are relevant to Schizophrenia, Autism, Alzheimer?s disease, and other conditions whose patient populations have deficits in learning and reasoning that manifest in the performance of TI problems.
The goal of this project is to investigate how humans and non-human primates learn and represent information about serial order using inferential reasoning. We propose that serial order representations arise from training on a transitive inference task that involves cognitive rather than associative learning. Health Relatedness: These experiments are relevant to Schizophrenia, Autism, Alzheimer?s disease, and other conditions whose patient populations have cognitive deficits, specifically those related to reasoning.
