Changes of Mind in an Attractor Network of Decision-Making

Attractor networks successfully account for psychophysical and neurophysiological data in various decision-making tasks. Especially their ability to model persistent activity, a property of many neurons involved in decision-making, distinguishes them from other approaches. Stable decision attractors are, however, counterintuitive to changes of mind. Here we demonstrate that a biophysically-realistic attractor network with spiking neurons, in its itinerant transients towards the choice attractors, can replicate changes of mind observed recently during a two-alternative random-dot motion (RDM) task. Based on the assumption that the brain continues to evaluate available evidence after the initiation of a decision, the network predicts neural activity during changes of mind and accurately simulates reaction times, performance and percentage of changes dependent on difficulty. Moreover, the model suggests a low decision threshold and high incoming activity that drives the brain region involved in the decision-making process into a dynamical regime close to a bifurcation, which up to now lacked evidence for physiological relevance. Thereby, we further affirmed the general conformance of attractor networks with higher level neural processes and offer experimental predictions to distinguish nonlinear attractor from linear diffusion models

Supercritical dynamics at the edge-of-chaos underlies optimal decision-making

Critical dynamics, characterized by scale-free neuronal avalanches, is thought to underlie optimal function in the sensory cortices by maximizing information transmission, capacity, and dynamic range. In contrast, deviations from criticality have not yet been considered to support any cognitive processes. Nonetheless, neocortical areas related to working memory and decision-making seem to rely on long-lasting periods of ignition-like persistent firing. Such firing patterns are reminiscent of supercritical states where runaway excitation dominates the circuit dynamics. In addition, a macroscopic gradient of the relative density of Somatostatin (SST+) and Parvalbumin (PV+) inhibitory interneurons throughout the cortical hierarchy has been suggested to determine the functional specialization of low- versus high-order cortex. These observations thus raise the question of whether persistent activity in high-order areas results from the intrinsic features of the neocortical circuitry. We used an attractor model of the canonical cortical circuit performing a perceptual decision-making task to address this question. Our model reproduces the known saddle-node bifurcation where persistent activity emerges, merely by increasing the SST+/PV+ ratio while keeping the input and recurrent excitation constant. The regime beyond such a phase transition renders the circuit increasingly sensitive to random fluctuations of the inputs -i.e., chaotic-, defining an optimal SST+/PV+ ratio around the edge-of-chaos. Further, we show that both the optimal SST+/PV+ ratio and the region of the phase transition decrease monotonically with increasing input noise. This suggests that cortical circuits regulate their intrinsic dynamics via inhibitory interneurons to attain optimal sensitivity in the face of varying uncertainty. Hence, on the one hand, we link the emergence of supercritical dynamics at the edge-of-chaos to the gradient of the SST+/PV+ ratio along the cortical hierarchy, and, on the other hand, explain the behavioral effects of the differential regulation of SST+ and PV+ interneurons by neuromodulators like acetylcholine in the presence of input uncertainty

Local Dynamics in Decision Making: The Evolution of Preference within and across Decisions

Within decisions, perceived alternatives compete until one is preferred. Across decisions, the playing field on which these alternatives compete evolves to favor certain alternatives. Mouse cursor trajectories provide rich continuous information related to such cognitive processes during decision making. In three experiments, participants learned to choose symbols to earn points in a discrimination learning paradigm and the cursor trajectories of their responses were recorded. Decisions between two choices that earned equally high-point rewards exhibited far less competition than decisions between choices that earned equally low-point rewards. Using positional coordinates in the trajectories, it was possible to infer a potential field in which the choice locations occupied areas of minimal potential. These decision spaces evolved through the experiments, as participants learned which options to choose. This visualisation approach provides a potential framework for the analysis of local dynamics in decision-making that could help mitigate both theoretical disputes and disparate empirical results.Engineering and Applied Science

A neural circuit model of decision uncertainty and change-of-mind

Decision-making is often accompanied by a degree of confidence on whether a choice is correct. Decision uncertainty, or lack in confidence, may lead to change-of-mind. Studies have identified the behavioural characteristics associated with decision confidence or change-of-mind, and their neural correlates. Although several theoretical accounts have been proposed, there is no neural model that can compute decision uncertainty and explain its effects on change-of-mind. We propose a neuronal circuit model that computes decision uncertainty while accounting for a variety of behavioural and neural data of decision confidence and change-of-mind, including testable model predictions. Our theoretical analysis suggests that change-of-mind occurs due to the presence of a transient uncertainty-induced choice-neutral stable steady state and noisy fluctuation within the neuronal network. Our distributed network model indicates that the neural basis of change-of-mind is more distinctively identified in motor-based neurons. Overall, our model provides a framework that unifies decision confidence and change-of-mind

Revisiting the Evidence for Collapsing Boundaries and Urgency Signals in Perceptual Decision-Making

For nearly 50 years, the dominant account of decision-making holds that noisy information is accumulated until a fixed threshold is crossed. This account has been tested extensively against behavioral and neurophysiological data for decisions about consumer goods, perceptual stimuli, eyewitness testimony, memories, and dozens of other paradigms, with no systematic misfit between model and data. Recently, the standard model has been challenged by alternative accounts that assume that less evidence is required to trigger a decision as time passes. Such "collapsing boundaries" or "urgency signals" have gained popularity in some theoretical accounts of neurophysiology. Nevertheless, evidence in favor of these models is mixed, with support coming from only a narrow range of decision paradigms compared with a long history of support from dozens of paradigms for the standard theory. We conducted the first large-scale analysis of data from humans and nonhuman primates across three distinct paradigms using powerful model-selection methods to compare evidence for fixed versus collapsing bounds. Overall, we identified evidence in favor of the standard model with fixed decision boundaries. We further found that evidence for static or dynamic response boundaries may depend on specific paradigms or procedures, such as the extent of task practice. We conclude that the difficulty of selecting between collapsing and fixed bounds models has received insufficient attention in previous research, calling into question some previous results

The Dynamics of Ethical Decision-Making in Business Settings: Some Implications for the Teaching of Ethics in Business Education

This study investigated ethical decision-making in the context of business settings and the teaching of ethics in business education. Over the past century or so, there has been a general acceptance of the need to teach business ethics in business education. However, close scrutiny shows that it has been patchy, and where it has been taught it is often only as an elective. Nevertheless, ethics is important in the business world and unethical behaviour can exact severe punishment. This suggests it should be taught, but that raises questions of how, and on what theoretical basis? For the past half-century, Kohlberg’s developmental theory of moral development has provided the main model in business education, but in the last decade criticisms have come from the social-intuitionist approach and from dynamic systems theory (DST). A dynamic systems approach was used as the main theoretical framework for this study. The adoption of this approach impacted the research methodology of this study and the interpretation of the data. In terms of method, whereas Kohlbergian and similar cognitivist approaches used hypothetical dilemmas in conducting research, this study employed five scenarios that attempt to mirror real-life ethical dilemmas that arise within a business context. Each scenario contained an ethical dilemma, but it was up to the participant to identify it. In terms of the analysis of data, whereas previous approaches applied supposition-laden scoring schemes based on assumed stabilities (e.g., stages and levels) in development, this study attempted to identify emerging and shifting patterns of decision-making in different contexts. Research was undertaken with 16 MBA students enrolled in a business school in a university in Sydney. The findings of this study appear to provide important clues about ethical decision-making, which could be used in future to design alternative pedagogies and teaching materials, when teaching business ethics in the context of business education

The Dynamics of Ethical Decision-Making in Business Settings: Some Implications for the Teaching of Ethics in Business Education

This study investigated ethical decision-making in the context of business settings and the teaching of ethics in business education. Over the past century or so, there has been a general acceptance of the need to teach business ethics in business education. However, close scrutiny shows that it has been patchy, and where it has been taught it is often only as an elective. Nevertheless, ethics is important in the business world and unethical behaviour can exact severe punishment. This suggests it should be taught, but that raises questions of how, and on what theoretical basis? For the past half-century, Kohlberg’s developmental theory of moral development has provided the main model in business education, but in the last decade criticisms have come from the social-intuitionist approach and from dynamic systems theory (DST). A dynamic systems approach was used as the main theoretical framework for this study. The adoption of this approach impacted the research methodology of this study and the interpretation of the data. In terms of method, whereas Kohlbergian and similar cognitivist approaches used hypothetical dilemmas in conducting research, this study employed five scenarios that attempt to mirror real-life ethical dilemmas that arise within a business context. Each scenario contained an ethical dilemma, but it was up to the participant to identify it. In terms of the analysis of data, whereas previous approaches applied supposition-laden scoring schemes based on assumed stabilities (e.g., stages and levels) in development, this study attempted to identify emerging and shifting patterns of decision-making in different contexts. Research was undertaken with 16 MBA students enrolled in a business school in a university in Sydney. The findings of this study appear to provide important clues about ethical decision-making, which could be used in future to design alternative pedagogies and teaching materials, when teaching business ethics in the context of business education

Metacognitive Decisions on Decision Accuracy: Confidence Judgment and Changes of Mind

Even in the absence of external feedback, humans are capable of subjectively estimating the accuracy of their own decisions, resulting in a sense of confidence that a decision is correct. While decision confidence has been proposed to be closely related to other metacognitive judgments, including error awareness (i.e., awareness that a decisions error has occurred) and changes of mind (i.e., reversal of previously made decisions), their relationships so far remain unclear. The current project aimed to investigate how confidence could be related to metacognitive judgments from two perspectives. First, Studies 1 and 2 investigated how confidence and changes of mind were affected by changes in different stimulus properties, particularly absolute evidence strength. In a brightness judgment task, participants were presented with two flickering, grayscale squares and required to select the square that appeared brighter. After each trial, participants reported their subjective accuracy on a rating scale ranging from “surely incorrect” to “surely correct”. Results showed that with stronger absolute evidence (i.e., increased overall luminance across both squares), confidence was increased and the proportion of changes of mind trials was reduced. These consistent changes support the hypothesis that higher confidence could contribute to less frequent changes of mind. Second, Study 3 investigated the relationships between confidence and the event-related potential (ERP) components of the centro-parietal potential (CPP) and the error positivity (Pe), which have been respectively proposed to be indexes of pre- and post-decisional evidence accumulation processes. In the same brightness judgment task, it was found that the relationships between confidence and these two ERP components depended on decision accuracy: Confidence was positively related to CPP amplitudes in correct trials, but negatively related to Pe amplitudes in error trials. These findings suggest that confidence in correct and error decisions involve different pre- and post- decisional processes. Overall, the current findings suggest that (a) confidence could serve as a basis of changes of mind, and (b), confidence in correct and erroneous decisions was differentially related to pre- and post-decisional ERP indexes of evidence accumulation. Taken together, they suggest that confidence might emerge during decision formation and could, with the contribution from post-decisional processes, serve as a basis of changes of mind

Changes of Mind in voluntary action – Flexibility vs. stability of intentions

Changes of Mind’ can provide insights into the dynamic and continuous processes underlying decision making and action selection. Previous studies on Changes of Mind have exclusively focused on either perceptual or value-based choice. This thesis investigates the flexible neurocognitive mechanisms that shape voluntary actions, which require integration of internally-generated (endogenous) intentions and externally-cued (exogenous) sensory or value-based information. When information is noisy or changes dynamically, agents sometimes change their voluntary intentions and/or change the movements that are required to implement intentions into action. Continuous movement trajectories were used to capture both types of Change of Mind during ongoing action execution, revealing that ‘Changes of Intention’ are more frequent when intentions are weak or when the cost of pursuing an intention is high. These findings could be qualitatively reproduced by an attractor network model that continuously integrates endogenous and exogenous information over time, occasionally switching from one attractor state to a different one later on. In an fMRI study, the neural dynamics of intention reversals were investigated, providing evidence that neural patterns in a fronto-parietal network change dynamically to incorporate new decision- and action-relevant evidence after action onset. Finally, while behavioural flexibility is advantageous in many situations, an important hallmark of voluntary control is intention pursuit despite external changes or challenges. For example, people often need to persevere in the face of effort. Patients with post-stroke fatigue showed reduced perseverance compared to healthy controls when goal pursuit required continuous effort, which may cause adverse health-related outcomes. In conclusion, this thesis provides new insights into the continuous neurocognitive mechanisms that shape voluntary actions as they unfold. Reversibility of intentions allows agents to adjust their own actions to the current context, while stability of intentions is necessary for successful goal pursuit. Hence, volition requires balanced integration of endogenous intentions with dynamically-changing exogenous information

Population analysis of neural data -- developments in statistical methods and related computational models

A key goal of neuroscience is to understand how the remarkable computational abilities of our brain emerge as a result of interconnected neuronal populations. Recently, advances in technologies for recording neural activity have increased the number of simultaneously recorded neurons by orders of magnitude, and these technologies are becoming more widely adopted. At the same time, massive increases in computational power and improved algorithms have enabled advanced statistical analyses of neural population activity and promoted our understanding of population coding. Nevertheless, there are many unanswered emerging questions, when it comes to analyzing and interpreting neural recordings. There are two major parts to this study. First, we consider an issue of increasing importance: that many in vivo recordings are now made by calcium-dependent fluorescent imaging, which only indirectly reports neural activity. We compare measurements of extracellular single units with fluorescence changes extracted from single neurons (often used as a proxy for spike rates), both recorded from cortical neural populations of behaving mice. We perform identical analyses at the single cell level and population level, and compare the results, uncovering a number of differences, or biases. We propose a phenomenological model to transform spike trains into synthetic imaging data and test whether the transformation explains the biases found. We discover that the slow temporal dynamics of calcium imaging obscure rapid changes in neuronal selectivity and disperse dynamic features in time. As a result, spike rate modulation that is locked to temporally localized events can appear as a more sequence-like pattern of activity in the imaging data. In addition, calcium imaging is more sensitive to increases rather than decreases in spike rate, leading to biased estimates of neural selectivity. These biases need to be considered when interpreting calcium imaging data. The second part of this work embarks on a challenging yet fruitful study of latent variable analysis of simultaneously recorded neural activity in a decision-making task. To connect the neural dynamics in different stages of a decision-making task, we developed a time-varying latent dynamics system model that uncovers neural dynamics shared by neurons in a local decision-making circuit. The shared neural activity supports the dynamics of choice generation and memory in a fashion akin to drift diffusion models, and robustly maintains a decision signal in the post-decision period. Importantly, we find that error trials follow similar dynamics to those of correct trials, but their dynamics are separated in shared neural activity space, proving a more correct early decoding estimation of an animal's success or failure at a given trial. Overall, the shared neural activity dynamics can predict multiple measures of behavioral variability including performance, reaction time, and trial correctness, and therefore are a useful summary of the neural representation. Such an approach can be readily applied to study complex dynamics in other neural systems. In summary, this dissertation represents an important step towards developing model-based analysis of neuronal dynamics and understanding population codes in large-scale neural data