Greg Jensen

Conditioned [corrected] stimulus informativeness governs conditioned stimulus-unconditioned stimulus associability.

In a conditioning protocol, the onset of the conditioned stimulus ([CS]) provides information about when to expect reinforcement (unconditioned stimulus [US]). There are two sources of information from the CS in a delay conditioning paradigm in which the CS-US interval is fixed. The first depends on the informativeness, the degree to which CS onset reduces the average expected time to onset of the next US. The second depends only on how precisely a subject can represent a fixed-duration interval (the temporal Weber fraction). In three experiments with mice, we tested the differential impact of these two sources of information on rate of acquisition of conditioned responding (CS-US associability). In Experiment 1, we showed that associability (the inverse of trials to acquisition) increased in proportion to informativeness. In Experiment 2, we showed that fixing the duration of the US-US interval or the CS-US interval or both had no effect on associability. In Experiment 3, we equated the increase in information produced by varying the C/T ratio with the increase produced by fixing the duration of the CS-US interval. Associability increased with increased informativeness, but, as in Experiment 2, fixing the CS-US duration had no effect on associability. These results are consistent with the view that CS-US associability depends on the increased rate of reward signaled by CS onset. The results also provide further evidence that conditioned responding is temporally controlled when it emerges.

Transfer of a serial representation between two distinct tasks by rhesus macaques.

Do animals form task-specific representations, or do those representations take a general form that can be applied to qualitatively different tasks? Rhesus monkeys (Macaca mulatta) learned the ordering of stimulus lists using two different serial tasks, in order to test whether prior experience in each task could be transfered to the other, enhancing performance. The simultaneous chaining paradigm delivered rewards only after subjects responded in the correct order to all stimuli displayed on a touch sensitive video monitor. The transitive inference paradigm presented pairs of items and delivered rewards when subjects selected the item with the lower ordinal rank. After learning a list in one paradigm, subjects’ knowledge of that list was tested using the other paradigm. Performance was enhanced from the very start of transfer training. Transitive inference performance was characterized by ‘symbolic distance effects,’ whereby the ordinal distance between stimuli in the implied list ordering was strongly predictive of the probability of a correct response. The patterns of error displayed by subjects in both tasks were best explained by a spatially coded representation of list items, regardless of which task was used to learn the list. Our analysis permits properties of this representation to be investigated without the confound of verbal reasoning.

Improving temporal cognition by enhancing motivation.

Increasing motivation can positively impact cognitive performance. Here we employed a cognitive timing task that allows us to detect changes in cognitive performance that are not influenced by general activity or arousal factors such as the speed or persistence of responding. This approach allowed us to manipulate motivation using three different methods; molecular/genetic, behavioral and pharmacological. Increased striatal D2Rs resulted in deficits in temporal discrimination. Switching off the transgene improved motivation in earlier studies, and here partially rescued the temporal discrimination deficit. To manipulate motivation behaviorally, we altered reward magnitude and found that increasing reward magnitude improved timing in control mice and partially rescued timing in the transgenic mice. Lastly, we manipulated motivation pharmacologically using a functionally selective 5-HT2C receptor ligand, SB242084, which we previously found to increase incentive motivation. SB242084 improved temporal discrimination in both control and transgenic mice. Thus, while there is a general intuitive belief that motivation can affect cognition, we here provide a direct demonstration that enhancing motivation, in a variety of ways, can be an effective strategy for enhancing temporal cognition. Understanding the interaction of motivation and cognition is of clinical significance since many psychiatric disorders are characterized by deficits in both domains.

Rapid cognitive flexibility of rhesus macaques performing psychophysical task-switching

Three rhesus monkeys (Macaca mulatta) performed a simultaneous chaining task in which stimuli had to be sorted according to their visual properties. Each stimulus could vary independently along two dimensions (luminosity and radius), and a cue indicating which dimension to sort by was random trial to trial. These rapid and unpredictable changes constitute a task-switching paradigm, in which subjects must encode task demands and shift to whichever task-set is presently activated. In contrast to the widely reported task-switching delay observed in human studies, our subjects show no appreciable reduction in reaction times following a switch in the task requirements. Also, in contrast to the results of studies on human subjects, monkeys experienced enduring interference from trial-irrelevant stimulus features, even after exhaustive training. These results are consistent with a small but growing body of evidence that task-switching in rhesus macaques differs in basic ways from the pattern of behavior reported in studies of human cognition. Given the importance of task-switching paradigms in cognitive and clinical assessment, and the frequency with which corresponding animal models rely on non-human primates, understanding these differences in behavior is essential to the comparative study of cognitive impairment.

Information: Theory, brain, and behavior

In the 65 years since its formal specification, information theory has become an established statistical paradigm, providing powerful tools for quantifying probabilistic relationships. Behavior analysis has begun to adopt these tools as a novel means of measuring the interrelations between behavior, stimuli, and contingent outcomes. This approach holds great promise for making more precise determinations about the causes of behavior and the forms in which conditioning may be encoded by organisms. In addition to providing an introduction to the basics of information theory, we review some of the ways that information theory has informed the studies of Pavlovian conditioning, operant conditioning, and behavioral neuroscience. In addition to enriching each of these empirical domains, information theory has the potential to act as a common statistical framework by which results from different domains may be integrated, compared, and ultimately unified.

Unexpected downshifts in reward magnitude induce variation in human behavior

We investigated how changes in outcome magnitude affect behavioral variation in human volunteers. Our participants entered strings of characters using a computer keyboard, receiving feedback (gaining a number of points) for any string at least ten characters long. During a “surprise” phase in which the number of points awarded was changed, participants only increased their behavioral variability when the reward value was downshifted to a lower amount, and only when such a shift was novel. Upshifts in reward did not have a systematic effect on variability.

Implicit Value Updating Explains Transitive Inference Performance: The Betasort Model.

Transitive inference (the ability to infer that B > D given that B > C and C > D) is a widespread characteristic of serial learning, observed in dozens of species. Despite these robust behavioral effects, reinforcement learning models reliant on reward prediction error or associative strength routinely fail to perform these inferences. We propose an algorithm called betasort, inspired by cognitive processes, which performs transitive inference at low computational cost. This is accomplished by (1) representing stimulus positions along a unit span using beta distributions, (2) treating positive and negative feedback asymmetrically, and (3) updating the position of every stimulus during every trial, whether that stimulus was visible or not. Performance was compared for rhesus macaques, humans, and the betasort algorithm, as well as Q-learning, an established reward-prediction error (RPE) model. Of these, only Q-learning failed to respond above chance during critical test trials. Betasort’s success (when compared to RPE models) and its computational efficiency (when compared to full Markov decision process implementations) suggests that the study of reinforcement learning in organisms will be best served by a feature-driven approach to comparing formal models.

Transitive inference in humans (Homo sapiens) and rhesus macaques (Macaca mulatta) 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-based inference have been debated vigorously, particularly regarding 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 7-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 subsequent learning of the entire list. Monkeys (who each completed many sessions with novel stimuli) learned to anticipate that novel stimuli should be preferred over F. We interpret this as evidence of a task representation of TI that generalizes beyond learning about specific stimuli. Humans (who were task-naïve) 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 assigns the strength of association between individual stimuli and rewards. Instead, they support sophisticated cognitive processes in both species, albeit with some species differences.

Inferential Learning of Serial Order of Perceptual Categories by Rhesus Monkeys ( Macaca mulatta )

Category learning in animals is typically trained explicitly, in most instances by varying the exemplars of a single category in a matching-to-sample task. Here, we show that male rhesus macaques can learn categories by a transitive inference paradigm in which novel exemplars of five categories were presented throughout training. Instead of requiring decisions about a constant set of repetitively presented stimuli, we studied the macaques ability to determine the relative order of multiple exemplars of particular stimuli that were rarely repeated. Ordinal decisions generalized both to novel stimuli and, as a consequence, to novel pairings. Thus, we showed that rhesus monkeys could learn to categorize on the basis of implied ordinal position, without prior matching-to-sample training, and that they could then make inferences about category order. Our results challenge the plausibility of association models of category learning and broaden the scope of the transitive inference paradigm. SIGNIFICANCE STATEMENT The cognitive abilities of nonhuman animals are of enduring interest to scientists and the general public because they blur the dividing line between human and nonhuman intelligence. Categorization and sequence learning are highly abstract cognitive abilities each in their own right. This study is the first to provide evidence that visual categories can be ordered serially by macaque monkeys using a behavioral paradigm that provides no explicit feedback about category or serial order. These results strongly challenge accounts of learning based on stimulus–response associations.

Perceptual category learning of photographic and painterly stimuli in rhesus macaques (Macaca mulatta) and humans

Humans are highly adept at categorizing visual stimuli, but studies of human categorization are typically validated by verbal reports. This makes it difficult to perform comparative studies of categorization using non-human animals. Interpretation of comparative studies is further complicated by the possibility that animal performance may merely reflect reinforcement learning, whereby discrete features act as discriminative cues for categorization. To assess and compare how humans and monkeys classified visual stimuli, we trained 7 rhesus macaques and 41 human volunteers to respond, in a specific order, to four simultaneously presented stimuli at a time, each belonging to a different perceptual category. These exemplars were drawn at random from large banks of images, such that the stimuli presented changed on every trial. Subjects nevertheless identified and ordered these changing stimuli correctly. Three monkeys learned to order naturalistic photographs; four others, close-up sections of paintings with distinctive styles. Humans learned to order both types of stimuli. All subjects classified stimuli at levels substantially greater than that predicted by chance or by feature-driven learning alone, even when stimuli changed on every trial. However, humans more closely resembled monkeys when classifying the more abstract painting stimuli than the photographic stimuli. This points to a common classification strategy in both species, one that humans can rely on in the absence of linguistic labels for categories.