Multiple time-scales of decision making in the hippocampus and prefrontal cortex

ABSTRACTThe prefrontal cortex and hippocampus are crucial for memory-guided decision-making. Neural activity in the hippocampus exhibits place-cell sequences at multiple timescales, including slow behavioral sequences (∼seconds) and fast theta sequences (∼100-200 ms) within theta oscillation cycles. How prefrontal ensembles interact with hippocampal sequences to support decision-making is unclear. Here, we examined simultaneous hippocampal and prefrontal ensemble activity during learning of a spatial working-memory decision task. We found clear theta sequences in prefrontal cortex, nested within its behavioral sequences. In both regions, behavioral sequences maintained representations of current goals during navigation. In contrast, hippocampal theta sequences encoded alternatives for deliberation, and were coordinated with prefrontal theta sequences that predicted upcoming choices. During error trials, these representations were preserved to guide ongoing behavior, whereas replay sequences during inter-trial periods were impaired prior to navigation. These results establish cooperative interaction between hippocampal and prefrontal sequences at multiple timescales for memory-guided decision making.

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Multiple time-scales of decision making in the hippocampus and prefrontal cortex

eLife ◽

10.7554/elife.66227 ◽

2021 ◽

Vol 10 ◽

Cited By ~ 1

Author(s):

Wenbo Tang ◽

Justin D Shin ◽

Shantanu P Jadhav

Keyword(s):

Decision Making ◽

Prefrontal Cortex ◽

Spatial Working Memory ◽

Cooperative Interaction ◽

Multiple Time Scales ◽

Decision Task ◽

Multiple Time ◽

Multiple Timescales ◽

Ongoing Behavior ◽

Behavioral Sequences

The prefrontal cortex and hippocampus are crucial for memory-guided decision-making. Neural activity in the hippocampus exhibits place-cell sequences at multiple timescales, including slow behavioral sequences (~seconds) and fast theta sequences (~100-200 ms) within theta oscillation cycles. How prefrontal ensembles interact with hippocampal sequences to support decision-making is unclear. Here, we examined simultaneous hippocampal and prefrontal ensemble activity in rats during learning of a spatial working-memory decision task. We found clear theta sequences in prefrontal cortex, nested within its behavioral sequences. In both regions, behavioral sequences maintained representations of current choices during navigation. In contrast, hippocampal theta sequences encoded alternatives for deliberation, and were coordinated with prefrontal theta sequences that predicted upcoming choices. During error trials, these representations were preserved to guide ongoing behavior, whereas replay sequences during inter-trial periods were impaired prior to navigation. These results establish cooperative interaction between hippocampal and prefrontal sequences at multiple timescales for memory-guided decision-making.

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Predictability and hierarchy in Drosophila behavior

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1607601113 ◽

2016 ◽

Vol 113 (42) ◽

pp. 11943-11948 ◽

Cited By ~ 67

Author(s):

Gordon J. Berman ◽

William Bialek ◽

Joshua W. Shaevitz

Keyword(s):

Temporal Dynamics ◽

Neural Circuitry ◽

Multiple Time Scales ◽

Multiple Time ◽

Behavioral Repertoire ◽

Temporal Complexity ◽

Stereotyped Behaviors ◽

Internal States ◽

Behavioral Sequences ◽

Organizing Principles

Even the simplest of animals exhibit behavioral sequences with complex temporal dynamics. Prominent among the proposed organizing principles for these dynamics has been the idea of a hierarchy, wherein the movements an animal makes can be understood as a set of nested subclusters. Although this type of organization holds potential advantages in terms of motion control and neural circuitry, measurements demonstrating this for an animal’s entire behavioral repertoire have been limited in scope and temporal complexity. Here, we use a recently developed unsupervised technique to discover and track the occurrence of all stereotyped behaviors performed by fruit flies moving in a shallow arena. Calculating the optimally predictive representation of the fly’s future behaviors, we show that fly behavior exhibits multiple time scales and is organized into a hierarchical structure that is indicative of its underlying behavioral programs and its changing internal states.

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What is causing “inhibition of return” in spatial cueing tasks? Temporally disentangling multiple cue-triggered effects on multiple time scales using response history and conditional accuracy analyses.

10.31234/osf.io/udpvs ◽

2020 ◽

Author(s):

Sven Panis ◽

Thomas Schmidt

Keyword(s):

Working Memory ◽

Inhibition Of Return ◽

Spatial Working Memory ◽

Direct Result ◽

Multiple Time Scales ◽

Multiple Time ◽

Target Onset ◽

Channel Activation ◽

Spatial Cueing ◽

Response Channel

Research on spatial cueing has shown that uninformative cues often facilitate mean response time (RT) performance in valid- compared to invalid-cueing conditions at short cue-target stimulus-onset-asynchronies (SOAs), and robustly generate a reversed or inhibitory cueing effect at longer SOAs that is widely known as inhibition-of-return (IOR). To study the within-trial time course of IOR we employ discrete-time hazard and conditional accuracy analyses to describe and model the shapes of the RT and accuracy distributions measured in two experimental tasks. In contrast to the mean performance measures, our distributional analyses show that (a) the uninformative cue generates response channel activation, (b) which continues during the cue-target interval so that the cue location must be stored in spatial working memory, (c) the premature cue-triggered response is selectively inhibited before target onset, (d) the IOR effect (valid versus invalid cueing) emerges around 160 ms after target onset in the hazard functions when cue-target SOA exceeds ~200 ms, quickly increases and decreases in size, and is gone within 120 ms, (e) the inhibitory component does not diminish over the course of the experiment, and (f) the location of an additional central cue relative to the current focus of spatial attention can generate response channel activation as well. These distributional data show that mean performance patterns conceal crucial information about behavioral dynamics, and suggest that sensory IOR is the direct result of encoding the cue location in spatial working memory to promote change detection, instead of attention leaving an inhibitory tag to promote visual search.

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Predictability and hierarchy in Drosophila behavior

10.1101/052928 ◽

2016 ◽

Cited By ~ 2

Author(s):

Gordon J. Berman ◽

William Bialek ◽

Joshua W. Shaevitz

Keyword(s):

Temporal Dynamics ◽

Neural Circuitry ◽

Multiple Time Scales ◽

Multiple Time ◽

Behavioral Repertoire ◽

Temporal Complexity ◽

Stereotyped Behaviors ◽

Internal States ◽

Behavioral Sequences ◽

Organizing Principles

Even the simplest of animals exhibit behavioral sequences with complex temporal dynamics. Prominent amongst the proposed organizing principles for these dynamics has been the idea of a hierarchy, wherein the movements an animal makes can be understood as a set of nested sub-clusters. Although this type of organization holds potential advantages in terms of motion control and neural circuitry, measurements demonstrating this for an animal’s entire behavioral repertoire have been limited in scope and temporal complexity. Here, we use a recently developed unsupervised technique to discover and track the occurrence of all stereotyped behaviors performed by fruit flies moving in a shallow arena. Calculating the optimally predictive representation of the fly’s future behaviors, we show that fly behavior exhibits multiple time scales and is organized into a hierarchical structure that is indicative of its underlying behavioral programs and its changing internal states.

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Rule-dependent Prefrontal Cortex Activity across Episodic and Perceptual Decisions: An fMRI Investigation of the Criterial Classification Account

Journal of Cognitive Neuroscience ◽

10.1162/jocn.2009.21060 ◽

2009 ◽

Vol 21 (5) ◽

pp. 922-937 ◽

Cited By ~ 12

Author(s):

Sanghoon Han ◽

Scott A. Huettel ◽

Ian G. Dobbins

Keyword(s):

Decision Making ◽

Prefrontal Cortex ◽

Forced Choice ◽

Decision Task ◽

Overt Response ◽

Lateral Prefrontal Cortex ◽

Competing Stimuli

Although lateral prefrontal cortex (LPFC) is clearly involved in decision-making, competing functional characterizations exist. One characterization posits that activation reflects the need to select among competing representations. In contrast, recent fMRI research suggests that activation is driven by the criterial classification of representations, even with minimal competition. To adjudicate between these hypotheses, we used event-related fMRI and contrasted tasks that required different numbers of criterial classifications prior to response in both perceptual and memory domains. Additionally, we manipulated the level of interstimulus competition by increasing the number of probes. Experiment 1 demonstrated that LPFC activation tracked the number of intermediate classifications during trials yet was insensitive to the number of competing probes and the behavioral decline accompanying competition. Furthermore, Experiment 2 demonstrated equivalent increases in LPFC activation for a task requiring two overt criterial classifications (independent classification) and one requiring two covert criterial classifications prior to the single overt response (same–different judgment). As found in Experiment 1, both tasks showed greater activation than a judgment requiring only one classification act (forced choice). These data indicate that LPFC responses reflect the number of executed criterial classifications or judgments, independent of the number of competing stimuli and the overt response demands of the decision task.

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Information Structures for Causally Explainable Decisions

Entropy ◽

10.3390/e23050601 ◽

2021 ◽

Vol 23 (5) ◽

pp. 601

Author(s):

Louis Anthony Cox

Keyword(s):

Decision Making ◽

Expected Utility Theory ◽

Causal Models ◽

Multiple Time Scales ◽

Multiple Time ◽

Information Structures ◽

Uncertain Environments ◽

Human Psychology ◽

Trade Offs ◽

Acceptable Risks

For an AI agent to make trustworthy decision recommendations under uncertainty on behalf of human principals, it should be able to explain why its recommended decisions make preferred outcomes more likely and what risks they entail. Such rationales use causal models to link potential courses of action to resulting outcome probabilities. They reflect an understanding of possible actions, preferred outcomes, the effects of action on outcome probabilities, and acceptable risks and trade-offs—the standard ingredients of normative theories of decision-making under uncertainty, such as expected utility theory. Competent AI advisory systems should also notice changes that might affect a user’s plans and goals. In response, they should apply both learned patterns for quick response (analogous to fast, intuitive “System 1” decision-making in human psychology) and also slower causal inference and simulation, decision optimization, and planning algorithms (analogous to deliberative “System 2” decision-making in human psychology) to decide how best to respond to changing conditions. Concepts of conditional independence, conditional probability tables (CPTs) or models, causality, heuristic search for optimal plans, uncertainty reduction, and value of information (VoI) provide a rich, principled framework for recognizing and responding to relevant changes and features of decision problems via both learned and calculated responses. This paper reviews how these and related concepts can be used to identify probabilistic causal dependencies among variables, detect changes that matter for achieving goals, represent them efficiently to support responses on multiple time scales, and evaluate and update causal models and plans in light of new data. The resulting causally explainable decisions make efficient use of available information to achieve goals in uncertain environments.

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A Bird in the Hand Isn’t Good for Long

Experimental Psychology (formerly Zeitschrift für Experimentelle Psychologie) ◽

10.1027/1618-3169/a000385 ◽

2018 ◽

Vol 65 (1) ◽

pp. 23-31 ◽

Cited By ~ 2

Author(s):

Stefan Scherbaum ◽

Simon Frisch ◽

Maja Dshemuchadse

Keyword(s):

Decision Making ◽

Cognitive Control ◽

The Other ◽

Decision Task ◽

Short Term ◽

Additional Time ◽

Control Processes ◽

Folk Wisdom ◽

Study Participants ◽

Insight Into

Abstract. Folk wisdom tells us that additional time to make a decision helps us to refrain from the first impulse to take the bird in the hand. However, the question why the time to decide plays an important role is still unanswered. Here we distinguish two explanations, one based on a bias in value accumulation that has to be overcome with time, the other based on cognitive control processes that need time to set in. In an intertemporal decision task, we use mouse tracking to study participants’ responses to options’ values and delays which were presented sequentially. We find that the information about options’ delays does indeed lead to an immediate bias that is controlled afterwards, matching the prediction of control processes needed to counter initial impulses. Hence, by using a dynamic measure, we provide insight into the processes underlying short-term oriented choices in intertemporal decision making.

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