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Greg Jensen

Publications and source records attributed to Greg Jensen.

2 recordsLinked to original sources

Transitive inference in humans and rhesus macaques after massed training of the last two list items

Transitive inference (TI) is a classic learning paradigm for which the relative contributions of experienced rewards and representation-driven inference have been vigorously debated, particularly with regard to the notion that animals are capable of logic and reasoning. Rhesus macaque subjects and human participants performed a TI task in which, prior to learning a seven-item list ABCDEFG, a block of trials presented exclusively the pair FG. Contrary to the expectation of associative models, the high prior rate of reward for F did not disrupt learning of the entire list. Monkeys (who each completed many sessions) learned to anticipate that novel stimuli should be preferred over F. We interpret this as evidence of a general task representation of TI that generalizes beyond learning about specific stimuli. Humans (who were task-naive) showed a transitory bias to F when it was paired with novel stimuli, but very rapidly unlearned that bias. Performance with respect to the remaining stimuli was consistent with past reports of TI in both species. These results are difficult to reconcile with any account that seeks to assign the strength of association between individual stimuli and rewards. Instead, they support both sophisticated cognitive processes in both species, albeit with some species differences.

Animal Behavior and Cognition

Learning In Spike Trains: Estimating Within-Session Changes In Firing Rate Using Weighted Interpolation

The electrophysiological study of learning is hampered by modern procedures for estimating firing rates: Such procedures usually require large datasets, and also require that included trials be functionally identical. Unless a method can track the real-time dynamics of how firing rates evolve, learning can only be examined in the past tense. We propose a quantitative procedure, called ARRIS, that can uncover trial-by-trial firing dynamics. ARRIS provides reliable estimates of firing rates based on small samples using the reversible-jump Markov chain Monte Carlo algorithm. Using weighted interpolation, ARRIS can also provide estimates that evolve over time. As a result, both real-time estimates of changing activity, and of task-dependent tuning, can be obtained during the initial stages of learning.

Neuroscience