4,999 research outputs found

    Dopaminergic and Non-Dopaminergic Value Systems in Conditioning and Outcome-Specific Revaluation

    Full text link
    Animals are motivated to choose environmental options that can best satisfy current needs. To explain such choices, this paper introduces the MOTIVATOR (Matching Objects To Internal Values Triggers Option Revaluations) neural model. MOTIVATOR describes cognitiveemotional interactions between higher-order sensory cortices and an evaluative neuraxis composed of the hypothalamus, amygdala, and orbitofrontal cortex. Given a conditioned stimulus (CS), the model amygdala and lateral hypothalamus interact to calculate the expected current value of the subjective outcome that the CS predicts, constrained by the current state of deprivation or satiation. The amygdala relays the expected value information to orbitofrontal cells that receive inputs from anterior inferotemporal cells, and medial orbitofrontal cells that receive inputs from rhinal cortex. The activations of these orbitofrontal cells code the subjective values of objects. These values guide behavioral choices. The model basal ganglia detect errors in CS-specific predictions of the value and timing of rewards. Excitatory inputs from the pedunculopontine nucleus interact with timed inhibitory inputs from model striosomes in the ventral striatum to regulate dopamine burst and dip responses from cells in the substantia nigra pars compacta and ventral tegmental area. Learning in cortical and striatal regions is strongly modulated by dopamine. The model is used to address tasks that examine food-specific satiety, Pavlovian conditioning, reinforcer devaluation, and simultaneous visual discrimination. Model simulations successfully reproduce discharge dynamics of known cell types, including signals that predict saccadic reaction times and CS-dependent changes in systolic blood pressure.Defense Advanced Research Projects Agency and the Office of Naval Research (N00014-95-1-0409); National Institutes of Health (R29-DC02952, R01-DC007683); National Science Foundation (IIS-97-20333, SBE-0354378); Office of Naval Research (N00014-01-1-0624

    Distinct patterns of outcome valuation and amygdala-prefrontal cortex synaptic remodeling in adolescence and adulthood.

    Get PDF
    Adolescent behavior is typified by increased risk-taking, reward- and novelty-seeking, as well as an augmented need for social and environmental stimulation. This behavioral phenotype may result from alterations in outcome valuation or reward learning. In the present set of experiments, we directly compared adult and adolescent animals on tasks measuring both of these processes. Additionally, we examined developmental differences in dopamine D1-like receptor (D1R), dopamine D2-like receptor (D2R), and polysialylated neural cell adhesion molecule (PSA-NCAM) expression in animals that were trained on an effortful reward valuation task, given that these proteins play an important role in the functional development of the amygdala-prefrontocortical (PFC) circuit and mesocorticolimbic dopamine system. We found that adolescent animals were not different from adults in appetitive associative learning, but exhibited distinct pattern of responses to differences in outcome values, which was paralleled by an enhanced motivation to invest effort to obtain larger rewards. There were no differences in D2 receptor expression, but D1 receptor expression was significantly reduced in the striatum of animals that had experiences with reward learning during adolescence compared to animals that went through the same experiences in adulthood. We observed increased levels of PSA-NCAM expression in both PFC and amygdala of late adolescents compared to adults that were previously trained on an effortful reward valuation task. PSA-NCAM levels in PFC were strongly and positively associated with high effort/reward (HER) choices in adolescents, but not in adult animals. Increased levels of PSA-NCAM expression in adolescents may index increased structural plasticity and represent a neural correlate of a reward sensitive endophenotype

    Initiation and spread of escape waves within animal groups

    Get PDF
    The exceptional reactivity of animal collectives to predatory attacks is thought to be due to rapid, but local, transfer of information between group members. These groups turn together in unison and produce escape waves. However, it is not clear how escape waves are created from local interactions, nor is it understood how these patterns are shaped by natural selection. By startling schools of fish with a simulated attack in an experimental arena, we demonstrate that changes in the direction and speed by a small percentage of individuals that detect the danger initiate an escape wave. This escape wave consists of a densely packed band of individuals that causes other school members to change direction. In the majority of cases this wave passes through the entire group. We use a simulation model to demonstrate that this mechanism can, through local interactions alone, produce arbitrarily large escape waves. In the model, when we set the group density to that seen in real fish schools, we find that the risk to the members at the edge of the group is roughly equal to the risk of those within the group. Our experiments and modelling results provide a plausible explanation for how escape waves propagate in Nature without centralised control

    Preventing Keystroke Based Identification in Open Data Sets

    Get PDF
    Large-scale courses such as Massive Online Open Courses (MOOCs) can be a great data source for researchers. Ideally, the data gathered on such courses should be openly available to all researchers. Studies could be easily replicated and novel studies on existing data could be conducted. However, very fine-grained data such as source code snapshots can contain hidden identifiers. For example, distinct typing patterns that identify individuals can be extracted from such data. Hence, simply removing explicit identifiers such as names and student numbers is not sufficient to protect the privacy of the users who have supplied the data. At the same time, removing all keystroke information would decrease the value of the shared data significantly. In this work, we study how keystroke data from a programming context could be modified to prevent keystroke latency based identification whilst still retaining information that can be used to e.g. infer programming experience. We investigate the degree of anonymization required to render identification of students based on their typing patterns unreliable. Then, we study whether the modified keystroke data can still be used to infer the programming experience of the students as a case study of whether the anonymized typing patterns have retained at least some informative value. We show that it is possible to modify data so that keystroke latency based identification is no longer accurate, but the programming experience of the students can still be inferred, i.e. the data still has value to researchers. In a broader context, our results indicate that information and anonymity are not necessarily mutually exclusive.Peer reviewe

    Privacy versus Information in Keystroke Latency Data

    Get PDF
    The computer science education research field studies how students learn computer science related concepts such as programming and algorithms. One of the major goals of the field is to help students learn CS concepts that are often difficult to grasp because students rarely encounter them in primary or secondary education. In order to help struggling students, information on the learning process of students has to be collected. In many introductory programming courses process data is automatically collected in the form of source code snapshots. Source code snapshots usually include at least the source code of the student's program and a timestamp. Studies ranging from identifying at-risk students to inferring programming experience and topic knowledge have been conducted using source code snapshots. However, replicating source code snapshot -based studies is currently hard as data is rarely shared due to privacy concerns. Source code snapshot data often includes many attributes that can be used for identification, for example the name of the student or the student number. There can even be hidden identifiers in the data that can be used for identification even if obvious identifiers are removed. For example, keystroke data from source code snapshots can be used for identification based on the distinct typing profiles of students. Hence, simply removing explicit identifiers such as names and student numbers is not enough to protect the privacy of the users who have supplied the data. At the same time, removing all keystroke data would decrease the value of the data significantly and possibly preclude replication studies. In this work, we investigate how keystroke data from a programming context could be modified to prevent keystroke latency -based identification whilst still retaining valuable information in the data. This study is the first step in enabling the sharing of anonymized source code snapshots. We investigate the degree of anonymization required to make identification of students based on their typing patterns unreliable. Then, we study whether the modified keystroke data can still be used to infer the programming experience of the students as a case study of whether the anonymized typing patterns have retained at least some informative value. We show that it is possible to modify data so that keystroke latency -based identification is no longer accurate, but the programming experience of the students can still be inferred, i.e. the data still has value to researchers

    Growth and splitting of neural sequences in songbird vocal development

    Get PDF
    Neural sequences are a fundamental feature of brain dynamics underlying diverse behaviours, but the mechanisms by which they develop during learning remain unknown. Songbirds learn vocalizations composed of syllables; in adult birds, each syllable is produced by a different sequence of action potential bursts in the premotor cortical area HVC. Here we carried out recordings of large populations of HVC neurons in singing juvenile birds throughout learning to examine the emergence of neural sequences. Early in vocal development, HVC neurons begin producing rhythmic bursts, temporally locked to a prototype syllable. Different neurons are active at different latencies relative to syllable onset to form a continuous sequence. Through development, as new syllables emerge from the prototype syllable, initially highly overlapping burst sequences become increasingly distinct. We propose a mechanistic model in which multiple neural sequences can emerge from the growth and splitting of a commo n precursor sequence.National Institutes of Health (U.S.) (Grant R01DC009183)National Science Foundation (U.S.) (Grant DGE-114747
    • …
    corecore