Periodic Reporting for period 4 - INFOSAMPLE (Information Sampling in Multiattribute Choice)
Periodo di rendicontazione: 2023-03-01 al 2024-05-31
Understanding how humans make decisions is a central theme in the behavioural sciences, with several interdisciplinary ramifications. Recent studies in the fields of psychology and neuroscience have shed light on the neural and computational mechanisms via which humans and other animals make sensorimotor decisions in simple laboratory tasks. However, little is known about the mechanisms underlying more complex multialternative, multiattribute decisions. For instance, what are the mechanisms that the brain employs when we try to choose between a small flat with a short commute and a large house with a long commute?
Existing models of sensorimotor decisions do not readily apply to multiattribute decisions. This is because human choices in the latter often exhibit patterns that defy rational explanations and challenge the models designed for simpler decisions. For instance, an initial preference for a small city flat can shift to favour a large suburban house once a medium-sized house in the outskirts (i.e. inferior and thus irrelevant) becomes available.
What distinguishes simple perceptual decisions—like determining motion direction in a cloud of moving dots—from more complex choices involving multiple alternatives and attributes? In this project, we suggest that in complex scenarios, the brain cannot process all relevant information in parallel. Instead, it serially samples subsets of information as the decision progresses. To understand the mechanisms behind complex decisions and why preferences can irrationally reverse, we posit that we must explore how people sample information during these complex choices.
We developed novel approaches towards uncovering the mechanisms of information sampling during complex decisions. We used magnetoencephalography (MEG) recordings of cortical population dynamics of human participants who performed novel multialternative, multialternative choice tasks. Using neural decoding techniques, we continuously traced the locus and strength of attention and uncovered patterns of information sampling that conventional techniques (such as eye-tracking) cannot capture. This approach provided a new window into the natural interplay between selective attention and decision-making.
Our goal is to develop a neurophysiologically detailed theory of multiattribute choice equipped with computational mechanisms that dynamically guide attention towards different aspects of a choice problem. Achieving this goal could significantly impact applied behavioural science by informing the design of more precise interventions and the development of consumer protection tools. Additionally, the emerging framework could have clinical implications by offering insights into how information sampling changes in neuropsychiatric disorders.
Our work clarified how information sampling adjusts to complex decisions. We revealed consistent sampling patterns in 3-alternative choices, that first focus on the worst alternative and subsequently us it as an anchor (or reference point) to infer the desirability of the two remaining high-valued alternatives. This “anchoring” effectively leads to irrational preference reversals. At the neural level, we found that information sampling fluctuates rhythmically at 11 HZ and uncovered neural mechanisms that distinguish between focused processing of a single alternative vs. comparing across alternatives. Pharmacologically boosting cortical GABA-A slowed-down the 11 HZ rhythmicity and enhanced the elimination of the worst alternative, and at the behavioural level leads to weaker contextual preference reversals.
Existing models of sensorimotor decisions do not readily apply to multiattribute decisions. This is because human choices in the latter often exhibit patterns that defy rational explanations and challenge the models designed for simpler decisions. For instance, an initial preference for a small city flat can shift to favour a large suburban house once a medium-sized house in the outskirts (i.e. inferior and thus irrelevant) becomes available.
What distinguishes simple perceptual decisions—like determining motion direction in a cloud of moving dots—from more complex choices involving multiple alternatives and attributes? In this project, we suggest that in complex scenarios, the brain cannot process all relevant information in parallel. Instead, it serially samples subsets of information as the decision progresses. To understand the mechanisms behind complex decisions and why preferences can irrationally reverse, we posit that we must explore how people sample information during these complex choices.
We developed novel approaches towards uncovering the mechanisms of information sampling during complex decisions. We used magnetoencephalography (MEG) recordings of cortical population dynamics of human participants who performed novel multialternative, multialternative choice tasks. Using neural decoding techniques, we continuously traced the locus and strength of attention and uncovered patterns of information sampling that conventional techniques (such as eye-tracking) cannot capture. This approach provided a new window into the natural interplay between selective attention and decision-making.
Our goal is to develop a neurophysiologically detailed theory of multiattribute choice equipped with computational mechanisms that dynamically guide attention towards different aspects of a choice problem. Achieving this goal could significantly impact applied behavioural science by informing the design of more precise interventions and the development of consumer protection tools. Additionally, the emerging framework could have clinical implications by offering insights into how information sampling changes in neuropsychiatric disorders.
Our work clarified how information sampling adjusts to complex decisions. We revealed consistent sampling patterns in 3-alternative choices, that first focus on the worst alternative and subsequently us it as an anchor (or reference point) to infer the desirability of the two remaining high-valued alternatives. This “anchoring” effectively leads to irrational preference reversals. At the neural level, we found that information sampling fluctuates rhythmically at 11 HZ and uncovered neural mechanisms that distinguish between focused processing of a single alternative vs. comparing across alternatives. Pharmacologically boosting cortical GABA-A slowed-down the 11 HZ rhythmicity and enhanced the elimination of the worst alternative, and at the behavioural level leads to weaker contextual preference reversals.
At the start of the project, we focused on developing tools for precisely tracking information sampling during complex choice tasks. These explorations led to our first MEG experiment (N=20, two 5-hour sessions per participant), where participants made decisions among three alternatives differing on a single perceptual feature. We examined the "distractor" effect, which suggests that as the value of the worst alternative increases, it becomes harder to distinguish between the two high-value options. Although we failed to detect this effect at the level of choice probabilities (Fig.1) we found that higher values of the worst alternative slowed decisions. This finding replicated in a behavioural study (N=30).
Decoding attention from MEG signals revealed that participants first focused on the worst alternative to eliminate it (Fig. 2), then used it as a reference for comparing the remaining options. This "anchoring to the worst" strategy enhances the perceived value of the best alternative, counteracting the negative distractor effect anticipated by divisive normalisation. These results have been drafted in a manuscript. In independent analyses we focused on the micromechanisms of information sampling discovering striking rhythmicity (at 11 HZ) in attentional fluctuations and distinct neural signatures that drive the sampling of unexplored information (Siems et al., 2023). We also reanalysed open-access data and conducted online experiments, finding that some reported distractor effects in reward learning could be explained by sampling asymmetries. Overall, we conclude that information sampling plays a crucial role in decision-making, overriding the effects of hardwired distortions like divisive normalisation. These results have been presented in international conferences (e.g. SfN, SBDM meetings) and in invited talks.
In a second pharmacological MEG study (N= 24, three 2-hour sessions, involving the administration of placebo, lorazepam and donepezil) we focused on classical multiattribute preference reversals. This cohort was complemented by N=12 participants performing the three pharmacological sessions without MEG. We devised a novel experimental paradigm (Fig. 3), which we refined using an online cohort of participants, and replicated classical preference reversals (Fig. 4). Lorazepam but not donepezil decreased the magnitude of a specific reversal(the attraction effect). Decoding the locus of attention, we found that participants alternated their sampling across attributes, and within-attribute, followed an “eliminate and anchor” strategy like our first study. The 11 HZ attentional rhythmicity was also detectable in the placebo session, with the rhythm slowing down in the lorazepam session. Due to pandemic delays, data collection concluded towards the end of the funding period. The dataset is still being analysed by the PI, and results are being disseminated at conferences and drafted in manuscripts.
Decoding attention from MEG signals revealed that participants first focused on the worst alternative to eliminate it (Fig. 2), then used it as a reference for comparing the remaining options. This "anchoring to the worst" strategy enhances the perceived value of the best alternative, counteracting the negative distractor effect anticipated by divisive normalisation. These results have been drafted in a manuscript. In independent analyses we focused on the micromechanisms of information sampling discovering striking rhythmicity (at 11 HZ) in attentional fluctuations and distinct neural signatures that drive the sampling of unexplored information (Siems et al., 2023). We also reanalysed open-access data and conducted online experiments, finding that some reported distractor effects in reward learning could be explained by sampling asymmetries. Overall, we conclude that information sampling plays a crucial role in decision-making, overriding the effects of hardwired distortions like divisive normalisation. These results have been presented in international conferences (e.g. SfN, SBDM meetings) and in invited talks.
In a second pharmacological MEG study (N= 24, three 2-hour sessions, involving the administration of placebo, lorazepam and donepezil) we focused on classical multiattribute preference reversals. This cohort was complemented by N=12 participants performing the three pharmacological sessions without MEG. We devised a novel experimental paradigm (Fig. 3), which we refined using an online cohort of participants, and replicated classical preference reversals (Fig. 4). Lorazepam but not donepezil decreased the magnitude of a specific reversal(the attraction effect). Decoding the locus of attention, we found that participants alternated their sampling across attributes, and within-attribute, followed an “eliminate and anchor” strategy like our first study. The 11 HZ attentional rhythmicity was also detectable in the placebo session, with the rhythm slowing down in the lorazepam session. Due to pandemic delays, data collection concluded towards the end of the funding period. The dataset is still being analysed by the PI, and results are being disseminated at conferences and drafted in manuscripts.
Our work revealed the pivotal role of information sampling in influencing choices. Ascribing century-old behavioural puzzles, such as irrational preference reversals, to regularities in information sampling represents a significant decision-theoretic advancement. Our pharmacological multiattribute study is the first of its kind, linking behavioural puzzles to large-scale neural dynamics and to the function of neurotransmitters. At the methodological level our work showcased how non-invasive neural recordings can be harnessed to continuously trace the locus of covert attention during decision-making. These techniques are superior to conventional methods and are expected to inspire researchers interested in probing information processing during complex behaviours.