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A causal illusion occurs when people perceive a causal relationship between two events that are not contingent on each other. How this illusion varies when people reason diagnostically (i.e., in an effect-to-cause direction) is not well known. Thus, this experiment explored how diagnostic reasoning affects causal illusions. Participants learnt about a illusory cause-effect relationship in which the probability of the cause and the probability of the effect were orthogonally manipulated to be either high or low. Participants learnt either predictively (i.e., cause-to-effect) or diagnostically, and at test had to make two causal judgements that encouraged either predictive (cause-to-effect) or diagnostic (effect-to-cause) reasoning. Diagnostic reasoning at test increased the strength of the cause density bias and decreased the strength of the effect density bias. It also decreased causal ratings, but only after predictive learning. These findings highlight the promise of diagnostic reasoning as a tool to debias causal decision making.

CogSci 2023

Question Framing Modulates the Cause Density and Effect Density Biases in Causal Illusions

2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics.

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NAACL 2021

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The 45th Annual Meeting of the Cognitive Science Society will take place in Sydney, Australia as a modified hybrid conference. Presentations from the main-session room will be live-streamed every day for all events. This will include all invited talks and award sessions. Recordings of all CogSci 2023 sessions will be made available for on-demand viewing after the conference.<br>
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The 45th Annual Meeting of the Cognitive Science Society presents the latest research across cognitive science and highlights the theme of Cognition in Context.

Many methods exist to learn causal models from data, as causal relationships form the basis for successful actions. These methods are frequently evaluated based on the completeness of the models they can infer. Yet, there is a gap between the highly complete and potentially complex models algorithms can learn and the types of information people can use successfully to make decisions. To address this we conduct two experiments to understand how the size and features of causal models influence how well they can be used for decision-making. In Experiment 1 we systematically vary model size for a series of topics, finding that there is a negative and linear relationship between causal model size and decision accuracy. In Experiment 2 we examine how model structure influences decisions, varying whether the models include feedback loops, again finding that smaller models lead to better choices, and that feedback loops are also beneficial.

Quantifying the Utility of Causal Models for Decision-Making

Infants must balance interacting with a caregiver and navigating the environment, driven by their curiosity and attachment. This decision involves trading off proximity seeking with the caregiver and novelty seeking in the surroundings. Our hypothesis is that different attachment styles may arise as adaptive reward-seeking strategies based on different frequencies and scales of rewards from both caregiver and environment, including negative rewards due to distressing encounters. We implement a computational model using a reinforcement learning framework to model infants’ attachment and exploratory behaviors as a bandit problem. We study how infants’ exploratory behaviors emerge as they learn from their caregiver and how these learned behaviors transfer to a new caregiver. Our work expands upon existing computational models of attachment and models how social factors can influence curiosity-seeking as infants learn to explore versus exploit.

Reinforcement learning models of tradeoffs between infant attachment and curiosity

Categorical biases in working memory (WM) can impact our recollection of visual stimuli. These are often biased towards or away from category prototypes. Color naming data can be used to predict biases in behavioral and neural data, suggesting sensory WM is affected by the use of verbal labels.

We investigated a possible link between categorical biases and verbal labels for orientation and spatial WM. Forty subjects performed standard orientation and spatial WM tasks, and were later prompted to freely label the stimuli. 

Accuracy is significantly higher when recalling cardinal locations and orientations compared to non-cardinal ones. This contrast is also evidenced by subject-unique neural activity patterns. The behavioral error pattern resembled verbal labeling strategies: Terms for cardinal stimuli were used sparsely and precisely (e.g. vertical, horizontal) while terms describing non-cardinal stimuli were used liberally (e.g. diagonal). Cardinal biases in WM stimuli might result from the use of categorical, verbal representations.

Does visual working memory rely on categorical and verbal neural representations?

High-quality parent language may support preschoolers’ language development by shaping underlying neurodevelopment. Indeed, more interactive language relates to higher frontal brain activity (Romeo et al., 2018). However, it remains to be explored whether other features of parent language may also be related to brain activity. This study examined whether parental linguistic sophistication is related to children’s spontaneous high-frequency neural activity, as indexed by EEG gamma power. Transcripts of parent-child dyads (N=36, children M=4.1 years old) completing a puzzle task were analyzed for mean length of utterance (measure of grammatical complexity) and number of different words (measure of vocabulary sophistication). We found that higher parental mean length of utterance was related to higher regional EEG gamma power (F(2.61,80.78)=2.92, p=.046), suggesting that more grammatically complex parent language is linked with higher child neural maturity. Future studies should explore the directionality of this finding and further measures of parent language quality.

Sophisticated parent language is linked to high-frequency neural activity in preschoolers

Spatial navigation is supported by the combined activity of brain regions in the human navigation network (HNN) that integrates environmental information across complementary egocentric and allocentric reference frames. Despite humans mainly relying on vision to navigate space, the impact of early visual deprivation on the HNN is mostly unknown. In our experiment sighted and early blind participants were tested in two tasks of imagined navigation while undergoing fMRI. Results show, on the one hand, that both sighted and blind activated regions of the HNN during navigation, with no differences between groups. On the other hand, quadrature filter analysis in the entorhinal cortex, a crucial region for the creation of cognitive maps, revealed an alteration of grid-like coding in early blind individuals, which might be correlated to the adoption of egocentric-like strategies to navigate in space.

Altered grid-like coding in early blind people during imagined navigation

World-wide, millions experience mild traumatic brain injury (mTBI) annually, which may lead to persistent neurobehavioral symptoms (NBS). Post-mTBI speed of processing studies have shown mixed results, but few have examined NBS. A novel ERP task examined the relationship between NBS, mTBI, and target detection performance. P300 latency and amplitude, response time (RTs) and accuracy were measured from community-dwelling adults with (+mTBI n=18; Female 12) and without mTBI (-mTBI n=21; Female 11). The Neurobehavioral Symptom Inventory (NSI) measured self-reported NBS. We expected +mTBI to show increased P300 latencies and RTs. No differences were observed between +mTBI and -mTBI on RTs, P300 amplitude or latency. However, increased symptomatology, regardless of mTBI status, predicted slower target RTs, R2 = 0.125, F (1, 37) = 5.296, p = 0.027, β = 0.354. Our findings suggest that researchers and clinicians should consider NBS when assessing processing speed complications in mTBI.

Neurobehavioral symptomatology predicts response times during target detection but not the P300: a multi-method study of mild traumatic brain injury

ASD children are characterised by their refusal to change and difficulty in changing their responses to different social situations, which make it difficult for them to communicate with others, including their parents. We applied the comprehensive care technique Humanitude™ to mothers of children with ASD to support them from a multimodal communication perspective.
Changes in mother-child communication were analysed in terms of mother-child gaze and the time the mother and child spent playing together. The results indicate that after the Humanitude™ intervention, there were significant differences in eye contact, mothers looking at their children, and children looking at their mothers. There was also a significant decrease in time children spent playing alone. There was a high correlation between pre and post-Humanitude™ intervention in the amount of time both mothers and children spent looking at each other, which suggests that incorporating multimodal communication can affect communication between parents and children.

Behavioural changes in the interaction between child with autism spectrum disorder and mother through the Comprehensive Care Humanitude™ intervention

While communication is an essential part of life, it is not always easy and effortless. This is especially true when talking with someone in a noisy environment. Although communicating in noise is often the rule rather than the exception, very little research has investigated the behavioral processes individuals might use to minimize miscommunication when listening becomes challenged. Here we explored synergistic speech and movement processes that 22 pairs of young, normal hearing adults used to hear and be heard when (mis)communicating in noise. The results revealed intricate dynamics both with respect to acoustic optimization of the speech produced and heard, as well as how individuals modulate interpersonal distance and behavioral coordination patterns.

Behavioral dynamics of conversation and (mis)communication in noisy environments

We investigated how verbal communication with a robot differs from talking to a human in terms of brain activity by analysing an open-source fMRI dataset. We focused on modeling conversational dynamics rather than conversation as a whole, by analysing fine-grained events, in particular turn initiation. The results indicate that turn initiation in a conversation with a human involves higher activation in auditory and visual cortex than turn initiation with a robot. Conversely, listening to the robot showed higher engagement of auditory cortex than listening to a human. We suggest that verbal and non-verbal turn-taking cues provided by the human agent engage more cognitive processing for picking up the turn. On the other hand, listening to a robot agent requires more processing than listening to a human. Both findings suggest that the accurate simulation of appropriate turn-taking cues and behaviors will help robots to establish more natural conversation dynamics and that the use of brain imaging can provide valuable objective measurements for assessing user states in human-robot interaction.

Investigating Conversational Dynamics in Human-Robot Interaction with fMRI

It has been argued that patterns of cross-linguistic variation in the semantic categories labelled by individual words are a result of a trade-off between cognitive pressures (so as to be simple to learn and use) and communicative pressures (so as to be efficient in communication). However, the question of what exact mechanisms control this trade-off has been left largely unanswered. We argue that one factor could be the extent to which referential contexts at the level of local interactions are similar or different across users of a category system. To test this hypothesis we propose a hierarchical Bayesian model for communication in a multidimensional meaning space, in which agents actively consider spatial similarity relations during interaction. Our models predict that less variability in contexts across interactions induces categories with lower communicative cost, while more variable contexts across partners are more strongly associated with category systems with lower cognitive cost.

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Quantifying the Utility of Causal Models for Decision-Making

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